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Artemis Regression Bands🟦 Artemis Regression Bands is a kernel-driven volatility envelope indicator built on the KernelLens Nadaraya–Watson regression library (a_jabbaroff/KernelLens/1). A single kernel estimate — selectable from eight classical kernel families — anchors the Fair Value line. Around it, three residual-standard-deviation bands (±1σ, ±2σ, ±3σ) fan outward with either Linear or Exponential spacing, producing a statistically grounded envelope far cleaner than the classical close-stdev approach used by legacy Bollinger-style indicators. A four-gate Romb signal engine overlays buy / sell diamond markers when price pokes through the outermost enabled σ boundary and reverses back inside. 🟦 HOW IT WORKS Artemis calls the KernelLens library's unified dispatcher once per bar to build the Fair Value line, then queries three additional library exports to derive the band widths, slope direction, and residual σ: ``` fair = kl.estimate (type, src, ℓ, α, period, phase, filter) sigma = kl.confidenceBand(src, fair, window) slopeVal = kl.slope (fair, 1) trendSt = kl.trendState (fair, 1) dev = baseMult · sigma upper1 = fair + 1·dev lower1 = fair − 1·dev upper2 = fair + 2·dev lower2 = fair − 2·dev upper3 = fair + k3·dev lower3 = fair − k3·dev (k3 = 3 Linear | 4 Exp) ``` The library handles all weighted-sum computation, loop-depth selection, NA-safe iteration, division-by-zero guards, and input validation internally. Artemis contains zero kernel math — every bug fix or optimization in the library automatically propagates to this indicator. 🟦 KERNEL LIBRARY INTEGRATION Artemis imports the published KernelLens library and uses the following exports: | Library Export | Used For | |---|---| | `kl.estimate()` | Unified dispatcher — routes to the correct kernel based on the user's Kernel Type dropdown. Called once per bar to produce the Fair Value line. | | `kl.confidenceBand()` | Rolling standard deviation of the (source − Fair Value) residual. Drives the band half-widths on every bar. | | `kl.slope()` | Discrete first derivative of the Fair Value line. Feeds trend flip alerts. | | `kl.trendState()` | Ternary classifier (+1 rising / −1 falling / 0 flat) of the Fair Value line. Drives the slope-adaptive color, the kernel trend confluence filter, and the dashboard Trend row. | Every regression computation — kernel weight evaluation, NA-safe summation, bandwidth-aware loop termination, residual stdev, finite-difference slope — is delegated to the library. The indicator itself only orchestrates the four library calls and layers the visual pipeline on top. 🟦 EIGHT KERNEL FAMILIES A single Kernel Type dropdown selects any of the eight kernels shipped with the KernelLens library. Each is a different mathematical smoother with its own statistical character: | Kernel | Formula | Best For | |---|---|---| | Rational Quadratic | (1 + d² / (2·α·ℓ²))^(−α) | Multi-scale mixer; α controls stretch. Recommended default. | | Gaussian / RBF | exp(−d² / (2·ℓ²)) | Canonical smoother; infinitely differentiable. | | Periodic | exp(−2·sin²(π·d/p) / ℓ²) | Resonates with a known repetition distance p. | | Locally Periodic | Periodic × Gaussian | Seasonal patterns with slow trend drift. | | Epanechnikov | (3/4)·(1 − u²), \|u\| ≤ 1 | MSE-optimal; compact support, no tail contamination. | | Tricube | (70/81)·(1 − \|u\|³)³, \|u\| ≤ 1 | LOWESS standard; near-Gaussian compact profile. | | Triangular | (1 − \|u\|), \|u\| ≤ 1 | Simplest compact kernel; cheapest to compute. | | Cosine | (π/4)·cos(π·u/2), \|u\| ≤ 1 | Raised-cosine; smooth boundary transition. | Because the dropdown feeds the library's `kl.estimate()` dispatcher directly, every kernel inherits the same three-mode filter layer (No Filter / Smooth / Zero Lag) and the same non-repainting guarantees — there is no special case per kernel in Artemis. 🟦 FILTER LAYER A second dropdown applies an optional post-processing layer on top of the raw Nadaraya–Watson estimate: | Filter | Formula | Trade-off | |---|---|---| | No Filter | ŷ = ŷ_raw | Single-pass kernel. Rawest output, most reactive. | | Smooth | ŷ = K(ŷ_raw) | Double-pass — kernel applied to its own output. Cleaner line, slightly more lag. | | Zero Lag | ŷ = 2·ŷ_raw − K(ŷ_raw) | Ehlers de-lagging identity — sharpens edges without adding lag. | The filter is resolved entirely inside `kl.estimate()`, so switching modes incurs no runtime cost beyond the extra kernel pass. 🟦 RESIDUAL-σ BAND ENGINE Artemis bands are statistically grounded on the residual standard deviation — not on raw close stdev as in classical Bollinger indicators. The residual is computed as: ``` residual = src − fair sigma = ta.stdev(residual, window) // via kl.confidenceBand() ``` Because Fair Value is already an unbiased local estimate of the source, the residual is a zero-mean noise series and its stdev captures **only the portion of price variance that the kernel could not explain**. This produces three benefits over the classical approach: 1. **Tighter bands in trending regimes** — close-stdev widens during strong trends because the trend itself inflates the variance; residual-σ does not, because the kernel absorbs the trend. 2. **Faster reaction to volatility regime changes** — residual-σ tightens as soon as the kernel fits well, and widens the instant the market breaks out of the kernel's neighborhood. 3. **True statistical interpretation** — under the assumption of locally Gaussian residuals, ±1σ / ±2σ / ±3σ enclose approximately 68 % / 95 % / 99.7 % of near-term price variation. The traditional close-stdev envelope carries no such interpretation. A dedicated Residual σ Window input controls the lookback; typical values range from 50 (reactive, scalping) to 300 (stable, position trading). 🟦 BAND SPACING MODES Two spacing presets shape the outward fan of the three σ bands: | Mode | Multipliers | Character | |---|---|---| | Linear | 1·, 2·, 3· | Classical Bollinger-style uniform steps. Predictable, symmetric. | | Exponential | 1·, 2·, 4· | Fibonacci-flavored — outer band (4σ) is reserved for genuine blow-off excursions. | Base Multiplier scales all three bands uniformly (default 1.0). The formula is: ``` band_level = fair ± (baseMult · k · sigma) k ∈ {1, 2, k3} ``` where k3 resolves to 3 in Linear mode and 4 in Exponential mode. Every band has an independent visibility toggle, so minimalist users can run ±1σ only, swing traders ±3σ only, or any combination. 🟦 FOUR-GATE ROMB SIGNAL ENGINE The Romb engine prints buy / sell diamond markers when price pokes through the outermost enabled σ band and reverses back inside. Four sequential gates protect against false entries: | Gate | Logic | Purpose | |---|---|---| | 1 — Crossover | `ta.crossunder(high, triggerUp)` / `ta.crossover(low, triggerDn)` | Detects the reversal back through the outer band. | | 2 — Warm-up | Residual σ computable for N consecutive bars | Blocks signals during the early kernel-settlement window. | | 3 — Confluence | Fair Value slope aligns with the reversal direction | Optional PRO filter — Sell Romb requires falling kernel, Buy Romb requires rising kernel. | | 4 — Cooldown | Minimum bar gap since the last same-side Romb | Prevents signal clustering on a single extended poke-and-reverse sequence. | A Signal Mode toggle layers on top: - **Confirmed** — signals fire only on `barstate.isconfirmed`; zero repaint on closed bars. - **Realtime** — signals fire live on the current open bar; faster reaction, may vanish if price reverses before close. Each confirmed signal is rendered as a two-layer neon diamond: - **Halo** — `size.small`, 40 % transparent theme hue (glow layer). - **Core** — `size.tiny`, fully opaque theme hue (bright center). The halo renders first so the core sits cleanly on top, producing a sharp luminous marker that reads instantly even on dense price charts. 🟦 ADAPTIVE OUTER-BAND TRIGGER The Romb engine does not hard-code the ±3σ band as the signal trigger. Instead, it resolves the outermost currently-enabled band on every bar: ``` triggerUp = show3 ? upper3 : show2 ? upper2 : show1 ? upper1 : na triggerDn = show3 ? lower3 : show2 ? lower2 : show1 ? lower1 : na ``` The result is an envelope that respects the user's visibility choices: | Visible Bands | Romb Fires At | |---|---| | ±1σ + ±2σ + ±3σ | ±3σ (default) | | ±1σ + ±2σ | ±2σ | | ±1σ only | ±1σ | | All off | no signals | Diamond positioning follows the same trigger, so the glyph always floats ~0.3σ outside whatever envelope is actually drawn on the chart. The behavior matches user intent: the band I can see is the band that fires signals. 🟦 NON-REPAINTING BEHAVIOR Artemis inherits non-repainting behavior directly from the KernelLens library's `_phase` parameter. A single Phase input (default 2) shifts the kernel center into the past by that many bars: - **Phase = 0** — live estimate, flickers on the current bar (real-time only; history is immutable). - **Phase = 1** — 1-bar lag, non-repainting once the bar is confirmed. - **Phase = 2** — recommended balance between freshness and stability (default). - **Phase = 3+** — extra margin against erratic ticks, higher lag. Historical repainting never occurs at any phase value. The library contains no `request.security` calls, no lookahead, and no array rotation that could leak future data. Every historical bar's plotted Fair Value, band, and Romb signal is final once confirmed. 🟦 VISUAL PIPELINE **σ Band Outlines** — Three upper bands (±1σ / ±2σ / ±3σ) in progressively lighter `thBear` hues, three lower bands in progressively lighter `thBull` hues. Hidden bands collapse to na via their individual visibility toggles; the outline widths share a single Band Line Width input. **Tapered Gradient Fills** — Six fills drawn between the Fair Value line and each σ band. Opacity scales progressively from ±1σ (densest, most opaque) to ±3σ (lightest, most transparent), creating a halo that mirrors the statistical density of price residuals under normality. Master Fill Opacity input (0 = invisible, 100 = fully opaque) scales all three fills uniformly. **Fair Value Line** — Slope-adaptive color resolver swaps between `thBull` (rising kernel) and `thBear` (falling kernel). Flat bars retain the previous color so the line never flashes neutral on a perfectly horizontal tick. Width is user-controlled (1–5 px). **Romb Diamonds** — Two-layer neon glow at the adaptive trigger band; halo + core rendering described above. **Bar Coloring** — Optional theme-aware candle coloring driven by the Fair Value slope. Off by default; when enabled it paints every bar with the active theme's bull / bear hue based on the current trend state. 🟦 THEME SYSTEM Twelve cohesive color palettes drive every visual component — Fair Value line, σ band outlines, gradient fills, Romb diamonds, bar coloring, and dashboard accents — all sharing the same four color axes (`thBull`, `thBear`, `thNeutral`, `thSignal`): | Theme | Bull | Bear | |---|---|---| | Tropic | Cyan steel | Deep orange | | Amber | Warm amber | Indigo blue | | Pastel | Sky blue | Soft lavender | | Cyber | Neon lime | Hot crimson | | Helios | Bright gold | Scarlet | | Electric | Electric aqua | Magenta | | Candy | Neon green | Hot pink | | Bloomberg | Terminal orange | Cyan | | Solar | Solarized olive | Crimson | | Royal | Imperial gold | Deep purple | | Midnight | Deep navy | Dark crimson | | Graphite | Near-black | Silver grey | A separate Display Mode toggle (Dark / Light) controls the dashboard palette independently of the chart theme — so a Bloomberg chart theme with a Light dashboard is a valid configuration, as is Midnight chart + Dark dashboard. 🟦 DASHBOARD A 2-column, 12-row theme-aware status panel that updates only on the last bar (zero historical overhead). Supports Dark and Light display modes, six docking positions, and four text sizes. Renders via `force_overlay = true` on the main price chart. | Row | Label | Content | |---|---|---| | Header | ARTEMIS | DARK / LIGHT | | Theme | Theme | Active palette name | | Kernel | Kernel | Selected kernel type | | Divider | REGRESSION | — | | Bandwidth | Bandwidth ℓ | Bandwidth value / Phase offset φ | | Filter | Filter | No Filter / Smooth / Zero Lag | | Fair Value | Fair Value | Current Fair Value in chart mintick format | | Divider | BANDS | — | | Spacing | Spacing | Linear 1·/2·/3· or Exp 1·/2·/4· | | Residual σ | Band σ | Rolling residual standard deviation | | Trend | Trend | ▲ BULL / ▼ BEAR / ━ FLAT (bull/bear colored) | | Last Romb | Last Romb | ▲ BUY (N ago) / ▼ SELL (N ago) — bull/bear colored | **Zebra-stripe layout** — alternating `dashBg` / `dashBgAlt` row backgrounds improve scan-ability on narrow cells. Section dividers (REGRESSION, BANDS) use a third background tone (`dashSection`) with the theme's bull accent as the header color — preserving brand identity across both Display Modes. 🟦 ALERT CONDITIONS Six opt-in alert conditions, each gated by its own toggle: | Alert | Fires When | |---|---| | Bullish Trend Flip | Fair Value slope crosses from ≤ 0 into positive territory | | Bearish Trend Flip | Fair Value slope crosses from ≥ 0 into negative territory | | Buy Romb | Confirmed Buy Romb fires — all four signal gates passing | | Sell Romb | Confirmed Sell Romb fires — all four signal gates passing | | Upper Band Touch | Price touches or exceeds the outermost enabled upper band | | Lower Band Touch | Price touches or falls below the outermost enabled lower band | All alerts use `alertcondition()` for maximum compatibility with TradingView's alert system including webhooks. Messages are structured as `"Artemis Regression Bands: "` for easy parsing in downstream automation. Touch alerts are off by default (can be noisy in trending markets); the four core alerts are on by default. 🟦 RECOMMENDED PRESETS | Style | Bandwidth ℓ | Filter | Phase | Spacing | σ Window | Chart | |---|---|---|---|---|---|---| | Scalper | 10–20 | No Filter | 1 | Linear | 50–80 | 1m–5m | | Day Trader | 20–40 | Smooth | 2 | Linear | 80–120 | 15m–1h | | Swing | 30–60 | Smooth | 2 | Linear or Exp | 100–200 | 4h–1D | | Position | 60–120 | Smooth or Zero Lag | 3 | Exp | 200–300 | 1D–1W | **Kernel type tuning** - **Trending instruments** — Rational Quadratic (α = 1–3) or Gaussian. Smooth multi-scale response. - **Mean-reverting instruments** — Epanechnikov or Tricube. Compact support keeps the band envelope tight. - **Session-cyclic patterns** — Periodic (with p = session length in bars) or Locally Periodic. Resonates with known cycles. **Romb filter tuning** — Keep Kernel Trend Confluence ON for high-conviction setups only. Switch OFF on range-bound instruments to capture both sides of the oscillation. 🟦 COMPATIBILITY - Pine Script v6 - All exchanges, all asset classes (crypto, forex, equities, commodities, indices) - All timeframes (1 minute through Monthly) - Both Dark and Light chart themes — the Display Mode toggle controls dashboard palette independently - No exchange-specific logic — fully deterministic 🟦 TECHNICAL NOTES - **Library dependency** — `import a_jabbaroff/KernelLens/1` — all kernel regression, residual σ, slope, and trend-state math is delegated to the published library. - **Plot budget** — 6 band plots + 1 Fair Value anchor + 1 Fair Value visible + 6 gradient fills + 4 Romb plotshapes + 1 barcolor = well under Pine's plot limits. - **Table** — Single `var table` rebuilt on `barstate.islast` with `force_overlay = true`; zero historical overhead. - **Signal state** — Two `var int` cooldown anchors (`lastSellBar`, `lastBuyBar`) seeded at −10000 so the very first bar always passes the gap test. A `var int stabCount` warm-up counter blocks signals during early kernel settlement. - **No persistent drawing objects** — no `box.new`, `line.new`, no array rotations; every visual is either a plot or a single-bar plotshape. - **Adaptive trigger resolver** — Romb crossover detection, touch alerts, and diamond positioning all read from the same `triggerUp` / `triggerDn` resolver, so band visibility toggles stay semantically coherent across every layer of the indicator. - **Non-repainting** — inherits from the library's `_phase` parameter; no `request.security`, no lookahead, no future-bar leakage at any phase value. 🟦 DISCLAIMER Artemis Regression Bands is a technical analysis indicator built on the KernelLens Nadaraya–Watson regression library. It is provided solely for educational and research purposes and does not constitute financial, investment, or trading advice. Kernel regression is a local smoothing technique. It estimates the mean of a source series in the neighborhood of the current bar based on historical data, but it does not predict future prices, does not generate trading signals on its own, and does not guarantee the profitability of any strategy built on top of its output. The residual-σ envelope describes past dispersion around the kernel estimate — not a forecast of future range — and should always be combined with broader context: higher-timeframe structure, volatility regime, liquidity, news, and risk management. Past performance of any model does not guarantee future results. Markets contain systemic risks that cannot be eliminated by any amount of mathematical rigor. Responsibility for any trading decisions rests entirely with the user. Always apply sound capital management, conduct your own independent analysis, and never risk capital you are not prepared to lose. The author assumes no liability for direct or indirect losses incurred through the use of Artemis Regression Bands or the underlying KernelLens library.
Penunjuk Pine Script®
oleh a_jabbaroff
Prism Channel Architecture [JOAT]Prism Channel Architecture Introduction Prism Channel Architecture is a dual-channel overlay indicator that layers two mathematically distinct structural frameworks onto your price chart simultaneously: a best-fit Pivot Channel derived from actual price pivot points, and a Linear Regression Channel built from statistical least-squares fitting. Together they create a structural prism through which trend direction, channel quality, and breakout momentum can be evaluated from multiple angles at once. Most channel tools force you to choose between objectivity and responsiveness. Pivot channels adapt to real market structure but can lag. Regression channels are statistically rigorous but ignore actual swing highs and lows. PCA runs both engines in parallel and highlights the moments when they agree — bull alignment and bear alignment states — as the highest-conviction reads in the system. Core Concepts Pivot Channel Fitting The indicator collects up to a configurable maximum of confirmed pivot highs and pivot lows using TradingView's built-in pivot functions: float pivHigh = ta.pivothigh(high, pivLeft, pivRight) float pivLow = ta.pivotlow( low, pivLeft, pivRight) From those stored pivot arrays, it searches for the best pair of recent pivot highs to fit the upper channel boundary, and the best pair of recent pivot lows to fit the lower channel boundary. The quality score for each candidate pair is computed by checking how many of the recent bars were actually contained below the upper line (or above the lower line) within an ATR tolerance: for k = 0 to checks - 1 float lineY = linePrice(x2, y2, x1, y1, bar_index - k) if high <= lineY + atrVal * 0.3 contained += 1 float q = safeDiv(float(contained), float(checks), 0.0) The pair with the highest containment ratio wins and becomes the drawn channel. This means the upper channel line is always the tightest valid resistance line through recent pivot highs, not an arbitrary parallel projection. Linear Regression Channel The regression channel computes a full manual least-squares fit over the lookback window, producing slope, intercept, and residual standard deviation: float slope = safeDiv(n * sumXY - sumX * sumY, n * sumXSq - sumX * sumX, 0.0) float intc = safeDiv(sumY - slope * sumX, n, close) float stdDev = math.sqrt(safeDiv(ssRes, n, 0.0)) The upper and lower bands are drawn at `stdDev × Deviation Multiplier` distance from the regression midline, giving bands that are statistically calibrated to the actual spread of price around the trend. Color shifts from bull to bear when slope changes sign. Channel Alignment Confluence The system declares a Bull Alignment when both channels simultaneously agree price is in a bullish position — the regression slope is rising AND price is above the regression midline, AND price is in the upper half of the pivot channel (between the midline and the upper band): bool lrBull = close > midNow and slope > 0.0 bool pivBull = close > uMid and close < uNow bool alignBull = lrBull and pivBull This confluence state is highlighted with a subtle background color — a quiet but meaningful signal that two independent structural frameworks are pointing in the same direction. ATR-Based Breakout Detection Breakout signals fire when price moves more than a configurable ATR multiple beyond the prior bar, provided the regression slope confirms direction: bool brkUp = ta.crossover(close, close + crossTol * atrVal) and lrSlope > 0.0 bool brkDn = ta.crossunder(close, close - crossTol * atrVal) and lrSlope < 0.0 Breakout labels (▲ BRK / ▼ BRK) appear above or below the breakout bar and are alert-enabled. Features Pivot Channel — best-fit upper/lower boundaries through recent pivot highs/lows, quality-scored by containment ratio Regression Channel — least-squares midline with statistically calibrated deviation bands, auto-colored by slope direction Channel midline — dashed neutral midline bisecting the pivot channel for zone positioning Bull and Bear Alignment detection — background highlight when both channels agree on direction ATR-normalized breakout labels — ▲ BRK and ▼ BRK when price breaks out with trend confirmation Channel Quality score — displayed in dashboard as percentage of recent bars contained Pivot position classification — Bull Zone (upper half) or Bear Zone (lower half) Up to 40 pivot highs and 40 pivot lows stored and evaluated 10-bar channel projection extended to the right of the last bar Dashboard: LR direction, deviation mult, pivot quality, pivot position, alignment, breakout, ATR, pivot count Alerts for bullish breakout, bearish breakout, bull alignment, and bear alignment Webhook JSON alert format Watermark Input Parameters Pivot Channel Pivot Lookback Left — bars to the left required to confirm a pivot high or low (default 10) Pivot Lookback Right — bars to the right required to confirm a pivot high or low (default 5) Max Pivots Stored — maximum number of pivot highs and lows held in memory (default 30) Quality Check Length — number of recent bars used to score channel containment (default 20) Breakout ATR Mult — ATR multiplier threshold for breakout label generation (default 1.5) Show Pivot Channel — toggle the pivot channel lines on/off Regression Channel Regression Length — bars used in the least-squares fit (default 50) Deviation Mult — standard deviation multiplier for band width (default 2.0) Show Regression Channel — toggle the regression channel lines and fill on/off ATR Settings ATR Length — lookback for ATR calculation used in breakout detection and containment tolerance (default 14) Visuals Bull Color — color for uptrending channels and bullish labels Bear Color — color for downtrending channels and bearish labels Neutral Color — color for channel midlines and neutral dashboard text Show Dashboard — compact structural summary panel Show Watermark Show Breakout Labels — toggle ▲ BRK / ▼ BRK label markers Alerts Webhook JSON Format — switches alert messages to JSON format for automation pipelines How to Use Add PCA to your chart as a main-pane overlay indicator. Let the chart load enough history so both channels initialize. A warmup period of at least 60 bars is enforced before channels begin drawing. Use the Regression Channel to assess macro trend direction. If the midline slope is rising and price is above it, the macro environment is bullish. Use the Pivot Channel to identify the structural support and resistance boundaries formed by actual price pivots. The upper pivot line is the tightest valid resistance. The lower pivot line is the strongest structural support. Watch for Bull Alignment (cyan background) when both systems agree price is in a bullish structural position. This is the highest-conviction environment for long setups. Watch for Bear Alignment (red background) for bearish structural setups. Treat Breakout labels as momentum confirmation signals — they only fire when an ATR-significant price move occurs in the direction of the regression slope. Check the Pivot Quality score in the dashboard. A quality above 65% means the channels are actively containing price well. Below 40% means the channel fit is loose and breakouts are less reliable. Indicator Limitations Pivot channel fitting evaluates only the 8 most recent pivot highs and the 8 most recent pivot lows when searching for the best pair. In very choppy markets with many closely-spaced pivots, the fitted channel may appear narrow or erratic. The regression channel is recalculated on every bar over a fixed lookback window. It will repaint the past visually as new bars are added — the channel reflects the lookback window ending at the current bar, not a fixed historical period. Channel quality scores can be artificially high in low-volatility trending conditions where price barely touches the edges of the channel. Breakout signals require both an ATR threshold move AND a confirming regression slope. In sideways markets the slope condition filters out most breakout candidates, which may lead to missed signals on genuine horizontal range breaks. Originality Statement Prism Channel Architecture is an original Pine Script v6 publication. The dual-engine architecture combining a quality-scored best-fit pivot channel with an independently computed least-squares regression channel, and the definition of alignment confluence as agreement between those two distinct structural systems, is an original design. The pivot quality scoring methodology — measuring the containment ratio of recent bars within the candidate channel bounds with ATR tolerance — is an original technique not derived from any existing published indicator. Disclaimer This indicator is for educational and informational purposes only. Channels, alignment states, and breakout labels are analytical tools and do not constitute financial advice. Channel boundaries can and will be violated without warning. Always apply proper risk management and never trade solely based on indicator signals. -Made with passion by jackofalltrades
Penunjuk Pine Script®
oleh officialjackofalltrades
Quant Edge Ribbon PRO🟦 Quant Edge Ribbon PRO is a multi-kernel divergence ribbon indicator built on the KernelLens Nadaraya–Watson regression library (a_jabbaroff/KernelLens/1). A primary kernel and eleven longer-bandwidth kernels form a dual-ribbon visualization driven by kernel regression mathematics, an integer trend score in , a theme-aware rendering pipeline, four user-configurable reference levels, and a PRO dashboard. All twelve kernels route through the unified library dispatcher, so the user may select any of the eight kernel families and any of the three filter modes from a single configuration panel. 🟦 HOW IT WORKS Quant Edge Ribbon PRO calls the KernelLens library's unified dispatcher (`kl.estimate`) twelve times per bar — once for the primary kernel and once for each of the eleven outer kernels: ``` primary = kl.estimate(type, src, ℓ, α, period, phase, filter) long00 = kl.estimate(type, src, ℓ + 1·s, α, period, phase, filter) long01 = kl.estimate(type, src, ℓ + 2·s, α, period, phase, filter) ... long10 = kl.estimate(type, src, ℓ + 11·s, α, period, phase, filter) ``` where ℓ is the Primary Bandwidth and s is the Bandwidth Step. All twelve kernels share the same kernel type, filter, shape α, period, and phase — only the bandwidth differs. This guarantees the ribbon behaves as a coherent spectrum of kernel scales rather than a mixture of unrelated signals. The library handles all weighted-sum computation, loop-depth selection, NA-safe iteration, division-by-zero guards, and input validation internally. Quant Edge Ribbon PRO does not reimplement any kernel math — every bug fix or optimization in the library automatically propagates to this indicator. 🟦 KERNEL LIBRARY INTEGRATION Quant Edge Ribbon PRO imports the published KernelLens library and uses the following export: | Library Export | Used For | |---|---| | `kl.estimate()` | Unified dispatcher — routes to the correct kernel based on the user's Kernel Type dropdown. Called twelve times per bar, once for the primary kernel and once for each of the eleven outer kernels. | Every regression computation — kernel weight evaluation, NA-safe summation, bandwidth-aware loop termination — is delegated to the library. The indicator itself contains zero kernel math; it only orchestrates twelve library calls and aggregates their outputs into the trend score. 🟦 THE TWELVE-KERNEL RIBBON ARCHITECTURE **Primary kernel (the shortest, the anchor)** — A single kernel at bandwidth ℓ that serves two roles: (1) it is the reference baseline for the trend score calculation, and (2) it is plotted as a dedicated highlighted anchor line when the "Highlight Primary Kernel" toggle is ON. **Eleven outer kernels (progressively wider)** — Kernels at bandwidths ℓ+1·s, ℓ+2·s, …, ℓ+11·s, where s is the Bandwidth Step (default: 1). Each outer kernel is plotted as a line on the main chart, all eleven sharing a single score-driven gradient color — so the entire outer ribbon shifts between the theme's bull and bear hues as the trend score moves between −11 and +11. **Eleven inner ribbon plots (lagged primary snapshots)** — The primary kernel plotted at eleven time-lag offsets (0, 1, 2, …, 10 bars). The resulting visual is a gently flowing shadow that makes expansion and contraction of the outer ribbon easier to perceive. Opacity is user-controlled (default: 40 %). 🟦 INTEGER TREND SCORE The trend score is a signed integer in , computed on every bar by eleven pairwise comparisons between the primary kernel (at progressive lag offsets) and the eleven outer kernels: ``` score = 0 for i in 0..10: if primary < long_i: score += 1 else: score -= 1 ``` **Semantic interpretation** — Each comparison pairs an older snapshot of the shortest kernel against a current snapshot of a progressively wider kernel. In an uptrend, past primary values are lower while current wider-kernel values have caught up above them — the inequality resolves positive on most pairs and the score climbs toward +11. The symmetric argument drives the score toward −11 in a downtrend. **Score parity** — Because the score is a sum of eleven ±1 terms (score = 2k − 11, k ∈ ), it is always odd. Reachable values: { −11, −9, −7, −5, −3, −1, 1, 3, 5, 7, 9, 11 }. The score is never exactly zero. 🟦 STRENGTH CATEGORIZATION The absolute score is bucketed into four bands, each with a matched label glyph used throughout the dashboard and the last-bar signal label: | Score Range | Strength | Label | |---|---|---| | \|score\| = 1 | NEUTRAL | ▰▱▱▱ NEUTRAL | | \|score\| ∈ {3, 5} | WEAK | ▰▰▱▱ WEAK BULL / WEAK BEAR | | \|score\| = 7 | STRONG | ▰▰▰▱ STRONG BULL / STRONG BEAR | | \|score\| ∈ {9, 11} | TRIPLE | ▰▰▰▰ TRIPLE BULL / TRIPLE BEAR | The bull / bear suffix is driven by the sign of the score. The progress-bar glyphs (▰▱) give an instant at-a-glance read of confluence intensity without needing to parse the numeric value. 🟦 NON-REPAINTING BEHAVIOR Quant Edge Ribbon PRO inherits non-repainting behavior directly from the KernelLens library's `_phase` parameter. A single Phase input (default: 2) shifts every one of the twelve kernel centers into the past by that many bars. - Phase = 0 — live estimate, flickers on the current bar (real-time only; history is immutable) - Phase = 1 — 1-bar lag, non-repainting once the bar is confirmed - Phase = 2 — recommended balance between freshness and stability (default) - Phase = 3+ — extra margin against erratic ticks, higher lag Historical repainting never occurs at any phase value. The library contains no `request.security` calls, no lookahead, and no array rotation that could leak future data. Every historical bar's plotted value is final once confirmed. 🟦 VISUAL PIPELINE **Outer Ribbon Gradient** — All eleven outer kernels are plotted with a single shared color driven by the trend score via `color.from_gradient(score, -11, 11, thBear, thBull)`. As the score walks across its range, the entire ribbon shifts continuously between the active theme's bearish and bullish hues — producing a smooth visual feedback loop between the math and the palette. **Inner Ribbon Shadow Trail** — Eleven lag-shifted primary snapshots (primary through primary ) drawn in the theme's accent hue with user-controlled opacity. On a trending chart the trail visually expands; on a reversing chart it contracts. Adjust opacity from 0 (invisible) to 100 (fully opaque) — default 40 balances presence and subtlety. **Primary Kernel Anchor Line** — The primary kernel plotted as a dedicated bold line in the theme's accent color at 80 % opacity, distinct from the shadow trail. Provides a clear centerline amid the ribbon flow. Toggleable. **Oscillator Subplot** — The smoothed trend score plotted in a dedicated subplot with a score-gradient vertical fill between the score line and the zero line. Opacity is user-controlled. An optional bold score line (up to 4 px wide) overlays the fill for sharp numeric reading. **Last-Bar Trend Label** — A right-anchored label at the current bar in the oscillator pane. Format: `▲ TRIPLE BULL 11 / 11` (bull) or `▼ WEAK BEAR −3 / 11` (bear). The label is deleted and redrawn on every bar, so only one instance is ever present on the chart. 🟦 OSCILLATOR STYLES The oscillator subplot ships with two visual presets, selectable from the Oscillator Style dropdown: | Style | Plot Style | Fill | Best For | |---|---|---|---| | Classic Gradient | `plot.style_line` (smooth curve) | Continuous vertical gradient from score to zero | Trend flow, slope momentum | | Stepline | `plot.style_stepline` (staircase) | Stepped gradient mirroring the discrete score plateaus | Signal / threshold trading, discrete level crossings | **Classic Gradient** produces a smooth curve traced through the smoothed score values, with the gradient fill flowing continuously between the score line and the zero line. This is the default and suits traders who read trend direction through slope and curvature. **Stepline** renders each bar as a horizontal plateau joined to the next bar by a vertical edge. Because the raw score is always an odd integer in { −11, −9, …, 9, 11 }, the staircase visualization honors the score's true discrete nature — making it easier to identify exact threshold crossings (e.g. the moment the score enters the ±9 extreme zone). The fill inherits the same stepline style, so the entire oscillator pane stays geometrically consistent. Both styles share the same opacity controls, score line toggle, and line width setting — only the geometry of the score line and its fill changes between them. 🟦 THEME SYSTEM Ten cohesive color palettes tuned to the Quant Edge Ribbon PRO optical brand. One selection drives every visual component — outer ribbon gradient, inner ribbon accent, oscillator fill, reference lines, signal label, dashboard accents — all sharing the same bull / bear / accent color axes: | Theme | Bull | Bear | |---|---|---| | Prism | Forest green | Crimson red | | Focus | Cyan steel | Deep orange | | Solar | Warm amber | Indigo red | | Frost | Sky blue | Soft lavender | | Laser | Neon lime | Hot crimson | | Aurora | Bright gold | Scarlet | | Plasma | Electric aqua | Magenta | | Bloom | Mint green | Hot pink | | Eclipse | Deep navy | Dark crimson | | Carbon | Near-black | Silver grey | The oscillator's zero line uses Pine's `chart.fg_color` so it auto-adapts to the actual chart background (white on dark charts, black on light charts) — independent of the Dashboard's Display Mode setting. 🟦 REFERENCE LEVELS Four user-configurable horizontal reference lines mark the score's structural thresholds inside the oscillator subplot: | Level | Style | Meaning | |---|---|---| | +11 / −11 | Dotted | Ceiling / floor — the mathematical maximum (every comparison aligned) | | +9 / −9 | Dashed | Extreme zone — nine or more of the eleven comparisons agree on direction | Each pair (±11 and ±9) has an independent opacity input (0–100 %). A master toggle (Show Reference Levels) collapses all four lines to fully transparent in a single branch — useful for minimalist layouts. An additional `showOsc` gate hides them automatically when the oscillator itself is disabled. 🟦 PRO DASHBOARD A 2-column, 12-row theme-aware status panel that updates only on the last bar (zero historical overhead). Supports Dark and Light display modes, six docking positions, and four text sizes. Renders via `force_overlay = true` on the main price chart. | Row | Label | Content | |---|---|---| | Header | Q-EDGE PRO | DARK / LIGHT | | Theme | Theme | Active palette name | | Kernel | Kernel | Selected kernel type | | Divider | RIBBON | — | | Primary ℓ | Primary ℓ | Primary bandwidth value | | Span | Span | ℓ → ℓ + 11·s | | Filter | Filter | No Filter / Smooth / Zero Lag | | Divider | SCORE | — | | Score | Score | ▲/▼ + integer score + " / 11" (bull/bear colored) | | Bull / Bear | Bull / Bear | Bull comparison count / bear comparison count | | Strength | Strength | ▰-bar + NEUTRAL / WEAK / STRONG / TRIPLE label | | Primary | Primary | Primary kernel value in chart mintick format | **Bull / Bear breakdown** — The eleven pairwise comparisons split into bulls (resolved +1) and bears (resolved −1). Always sums to 11, so this row gives a direct visual of how many kernel scales agree with the net direction. 🟦 ALERT CONDITIONS Six opt-in alert conditions, each gated by its own toggle: | Alert | Fires When | |---|---| | Bullish Flip | Score crosses from ≤ 0 into positive territory | | Bearish Flip | Score crosses from ≥ 0 into negative territory | | Extreme Bullish | Score reaches +9 or higher (first entry into the zone) | | Extreme Bearish | Score reaches −9 or lower (first entry into the zone) | | Full Confluence Up | Score hits +11 — every outer kernel aligned bullishly | | Full Confluence Down | Score hits −11 — every outer kernel aligned bearishly | All alerts use `alertcondition()` for maximum compatibility with TradingView's alert system including webhooks. Messages are structured as `"Quant Edge Ribbon PRO: "` for easy parsing in downstream automation. 🟦 RECOMMENDED PRESETS | Style | Primary ℓ | Bandwidth Step s | Phase | Filter | Chart | |---|---|---|---|---|---| | Scalper | 8–16 | 1 | 1 | No Filter | 1m–5m | | Day Trader | 16–32 | 1–2 | 2 | Smooth | 15m–1h | | Swing | 25–50 | 1–2 | 2 | Smooth | 4h–1D | | Position | 50–120 | 2–3 | 3 | Smooth | 1D–1W | **Bandwidth Step tuning** — Step = 1 produces a tight ribbon where adjacent outer kernels sit visually close together. Steps of 2–4 spread the eleven outer kernels across a broader spectrum of scales, making expansion / contraction easier to read at a glance. Step 5–6 is reserved for very wide ribbons where each line represents a distinctly different time scale. 🟦 COMPATIBILITY - Pine Script v6 - All exchanges, all asset classes (crypto, forex, equities, commodities, indices) - All timeframes (1 minute through Monthly) - Both Dark and Light chart themes — visual elements auto-adapt via `chart.fg_color` - No exchange-specific logic — fully deterministic 🟦 TECHNICAL NOTES - **Library dependency** — `import a_jabbaroff/KernelLens/1` — all kernel regression math is delegated to the published library - **Plot budget** — 11 outer + 11 inner + 1 primary + 2 oscillator plots + 1 fill + 4 hlines = well under Pine's 64-plot limit - **Table** — Single `var table` created once on `barstate.islast` with `force_overlay = true`; dashboard renders on the main chart pane, zero historical overhead - **No persistent drawing objects** — no `box.new`, `line.new`, no `array.new`; the single trend label is deleted and recreated every bar so only one instance is ever present - **Opacity convention** — every user-facing opacity input follows `0 = invisible, 100 = fully opaque`; conversion to Pine's native transparency is centralized in a single helper function (`f_opac`) - **Non-repainting** — inherits from the library's `_phase` parameter; no `request.security`, no lookahead, no future-bar leakage at any phase value - **Chart background adaptive** — the oscillator's zero line uses `chart.fg_color`, so it always renders with high contrast regardless of the user's chart color scheme 🟦 DISCLAIMER Quant Edge Ribbon PRO is a technical analysis indicator built on the KernelLens Nadaraya–Watson regression library. It is provided solely for educational and research purposes and does not constitute financial, investment, or trading advice. Kernel regression is a local smoothing technique. It estimates the mean of a source series in the neighborhood of the current bar based on historical data, but it does not predict future prices, does not generate trading signals on its own, and does not guarantee the profitability of any strategy built on top of its output. The integer trend score is a geometric summary of kernel alignments — not a forecast — and should always be combined with broader context: higher-timeframe structure, volatility regime, liquidity, news, and risk management. Past performance of any model does not guarantee future results. Markets contain systemic risks that cannot be eliminated by any amount of mathematical rigor. Responsibility for any trading decisions rests entirely with the user. Always apply sound capital management, conduct your own independent analysis, and never risk capital you are not prepared to lose. The author assumes no liability for direct or indirect losses incurred through the use of Quant Edge Ribbon PRO or the underlying KernelLens library.
Penunjuk Pine Script®
oleh a_jabbaroff
Auric Regime Classifier [JOAT]Auric Regime Classifier Introduction Auric Regime Classifier (ARC) is an open-source, multi-factor market regime detection engine that classifies every confirmed bar into one of five distinct market states: Strong Bull, Weak Bull, Ranging, Weak Bear, or Strong Bear — with a special Compression override that fires when volatility is contracting. The engine uses five independent data sources — adaptive ATR volatility ratio, Bollinger Band squeeze detection, SMEMA trend slope, ADX directional index, and RSI momentum bias — fused through a weighted scoring system into a single net score that drives the regime classification. The problem ARC solves is that most traders apply a fixed strategy regardless of whether the market is trending strongly, drifting weakly, compressing before a breakout, or chopping without direction. Each of those conditions demands a completely different approach. Applying a trend-following system in a ranging market produces losses. Trading with tight stops in a compression phase produces whipsaws. ARC gives you a clear, real-time label for the current market phase so you can match your approach to the conditions rather than fighting them. Core Concepts 1. SMEMA Adaptive Baseline The indicator uses SMEMA (Simple Moving Average of Exponential Moving Average) as its core trend baseline — a proprietary double-smoothing construct used throughout the JackOfAllTrades indicator suite. SMEMA applies a standard EMA first to capture responsiveness, then a SMA over the same period to suppress noise. The result is a baseline that reacts faster than a raw SMA but is smoother than a raw EMA: smema(float src, int len) => ta.sma(ta.ema(src, len), len) Two SMEMA lines run in parallel: a slow line over the full period (default 20) and a fast line at half the period. Their slope comparison over three bars determines the trend direction score. When the slow SMEMA slopes upward for three consecutive bars, two bull points are awarded. When it slopes downward, two bear points are awarded. 2. Adaptive ATR Volatility Ratio ATR over the input period (default 14) is compared against a long-run SMA of ATR (default 50 bars) to produce a volatility expansion/contraction ratio: float volRatio = safeDiv(atrRaw, atrBase, 1.0) A ratio above 1.1 in the direction of the existing trend awards a bonus point — recognizing that trend moves are more reliable when accompanied by above-average volatility. This prevents the engine from scoring weak, low-volume drifts as strongly as genuine impulsive moves. 3. Bollinger Band Squeeze Detection The indicator measures Bollinger Band bandwidth (upper minus lower) and compares it against its own 100-bar SMA. When bandwidth falls below the configurable threshold fraction (default 0.75) of its smoothed average, the market is classified as Compressing. Compression overrides all other regime classifications — a compressing market has no valid directional edge regardless of what the other signals say: bool isSqz = bbBW < bbBWAvg * sqzPct A Squeeze Release signal fires when compression ends (isSqz transitions from true to false), marking the potential start of an expansion move. 4. ADX Directional Index ADX (Average Directional Index) and the +DI/-DI directional lines are calculated using Pine Script v6's built-in ta.dmi() function. ADX above the threshold (default 25) confirms that the market is in a genuine trending regime rather than a sideways range. The directional bias of +DI vs -DI adds two bull or bear points to the regime score: = ta.dmi(adxLen, adxLen) bool isBullDir = diPlus > diMinus bool isBearDir = diMinus > diPlus 5. Regime Scoring Engine All five components feed a dual-sided scoring system. Bull and bear points are accumulated independently, and the net score (bull minus bear, range -6 to +6) determines the regime code: int bullPts = (trendUp ? 2 : 0) + (isBullDir ? 2 : 0) + (rsiBull ? 1 : 0) + ((volRatio > 1.1 and trendUp) ? 1 : 0) int netScore = bullPts - bearPts int regCode = isSqz ? 0 : netScore >= 4 ? 2 : netScore >= 1 ? 1 : netScore <= -4 ? -2 : netScore <= -1 ? -1 : 0 Strong Bull requires a net score of +4 or higher (all four components aligned). Weak Bull requires +1 to +3. Ranging sits at 0. The mirror applies for bearish regimes. 6. Trend Strength Score (0-100) Beyond the categorical regime label, ARC produces a continuous trend strength score that measures conviction within the current regime. It combines a distance score (how far price is from the SMEMA baseline in ATR units, capped at 50 points) with a momentum score (RSI deviation from 50 in the trend direction, capped at 50 points). A score of 70+ indicates a strong, high-conviction regime. Below 40 indicates a weak or transitional state. Features Five-State Regime Classification: Every bar labeled Strong Bull, Weak Bull, Ranging, Weak Bear, or Strong Bear with a Compression override — no ambiguity SMEMA Ribbon: Fast and slow SMEMA lines with a gradient fill between them, colored by the current regime state for instant visual context Regime Background Tint: Subtle, semi-transparent background coloring that shifts with the regime — green family for bull states, red family for bear, yellow for compression Candle Coloring: Bar colors inherit the regime color at reduced opacity, giving every candle immediate regime context without obscuring price action Squeeze Markers: Circle markers on the SMEMA baseline during compression, with a diamond signal at the moment of squeeze release Trend Strength Score: A 0-100 numeric score with a Strong/Moderate/Weak label updated each bar, shown in the dashboard Regime Change Alerts: Alert fires on every confirmed regime state transition with either plain text or structured JSON for webhook delivery 12-Row Dashboard (Top Right): Displays current regime, trend strength score, ADX value and trending/ranging status, +DI/-DI directional reading, volatility ratio, Bollinger Band state, RSI, timeframe, and version Watermark: JackOfAllTrades signature rendered at chart center-bottom Input Parameters Core Engine: ATR Length: Period for raw ATR calculation (default: 14) ATR Smoothing Period: Baseline ATR lookback for volatility ratio (default: 50) Directional Index: ADX / DI Length: Period for +DI, -DI, and ADX (default: 14) Trend Threshold: ADX level above which the market is considered trending (default: 25) Volatility Band: BB Length: Bollinger Band period (default: 20) BB Multiplier: Standard deviation multiplier (default: 2.0) Squeeze Threshold: Bandwidth fraction of its 100-bar SMA below which compression is declared (default: 0.75) Trend Engine: SMEMA Length: Period for the double-smoothed baseline (default: 20) RSI Length: Momentum confirmation period (default: 14) Visuals / Dashboard / Alerts: Theme: Auto, Dark, or Light — auto-detects chart background Regime Background Tint: Toggle the subtle background color Show SMEMA Baseline: Toggle the ribbon plots Show Squeeze Markers: Toggle the circle and diamond markers Show Dashboard: Toggle the 12-row information panel Show Watermark: Toggle the JackOfAllTrades signature Webhook JSON Format: Switch alert messages between plain text and JSON Color Palette: All six regime colors are individually customizable How to Use This Indicator Step 1: Read the Regime The dashboard regime field and the background tint tell you exactly where the market stands. This single label is the most actionable piece of information — it drives which strategy is appropriate. Step 2: Match Your Approach to the Regime Strong Bull / Strong Bear: All four scoring components are aligned. High-conviction directional trades, trend-following entries on pullbacks to the SMEMA ribbon Weak Bull / Weak Bear: Only one or two components agree. Lighter position sizing, wider stops, prepare for a possible regime shift Ranging: Net score near zero — avoid directional trades, consider mean-reversion or wait for breakout Compression: All directional analysis is suspended. Reduce exposure, prepare for a breakout in either direction, and watch the squeeze release signal for timing Step 3: Use Trend Strength for Conviction Within any directional regime, the strength score tells you how far into that regime the market has moved. A Strong Bull reading with a strength score of 85 is a very different trade environment from one with a strength score of 42. Use the score to scale position size or filter lower-conviction entries. Step 4: Set Alerts on Regime Transitions The regime-change alert fires the moment a new regime is confirmed on bar close. Enable the Strong Bull and Strong Bear alertconditions specifically to catch the high-conviction regime entrances. Indicator Limitations All five components are backward-looking. The regime label describes what has happened over the lookback windows — not what will happen. A Strong Bull classification can reverse on the very next bar The warmup period (equal to the longest lookback, at least 50 bars) means the indicator produces no signals on the first several bars of any chart, including after switching timeframes Compression detection uses a 100-bar SMA of bandwidth, which is a long-run reference. On very short or illiquid charts with few bars, the bandwidth average may not be reliable The five-state classification uses fixed score thresholds (+4 for Strong, +1 for Weak). These thresholds are not auto-calibrated to the instrument. In range-bound markets where ADX rarely exceeds 20, the Strong Bull/Bear states may rarely appear RSI and ADX both work with default periods. No single set of periods is optimal across all assets and timeframes. Users may need to adjust periods when applying to highly volatile assets or longer timeframes Originality Statement ARC is original in its synthesis approach and the use of SMEMA as the core trend baseline. This indicator is published because: The SMEMA construct (SMA of EMA) is a proprietary double-smoothing formula used consistently across the JackOfAllTrades suite — it provides a smoother baseline than raw EMA while retaining more responsiveness than raw SMA, and it is not a standard available in typical indicator libraries The dual-sided bull/bear point system scores each directional component independently before computing a net score. This is distinct from composite oscillators that blend components into a single signed value — the dual-side approach preserves information about how many bear components are active even when the net score is positive The Compression override takes precedence over all directional scores, explicitly suspending regime analysis during volatility contractions. Most regime indicators simply produce lower directional readings in compression without explicitly declaring the compression state The trend strength score combines an ATR-normalized price distance with an RSI momentum deviation to produce a conviction metric that is distinct from the categorical regime label Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss. Market regime classifications are based entirely on historical data and past behavior. A market classified as Strong Bull can and will reverse at any time. The Compression state does not guarantee a subsequent breakout, and the direction of any eventual breakout cannot be predicted from compression alone. Always apply proper risk management. The author is not responsible for any trading losses resulting from the use of this indicator. -Made with passion by jackofalltrades
Penunjuk Pine Script®
oleh officialjackofalltrades
Trendline Confluence Map [AGPro Series]Trendline Confluence Map Overview Trendline Confluence Map automatically draws ascending and descending trendlines from major and minor swing pivots, then maps the zones where two or more of these trendlines intersect with each other or with key horizontal support and resistance levels. Every confluence zone is scored from 0 to 100 based on touch count, line age, trendline angle and volume, so traders can instantly see which reaction levels are backed by the most structural weight. Drawn as long rectangular zones in the classic S/R style, the map stays anchored to current price and hides old, irrelevant levels automatically. Unique Edge Most trendline tools either draw a single line or flood the chart with dozens of low-quality lines that never interact with each other. This indicator is built around the opposite idea: confluence is the signal, single lines are just noise. Its three design choices make the difference. First, the script maintains a disciplined pool of only the trendlines whose current projection is on-chart, so a zero-visibility clutter problem cannot happen — the panel count always equals what you actually see. Second, horizontal S/R levels participate as first-class confluence sources alongside trendlines, with a guard requiring at least one real trendline per zone so the tool remains true to its name. Third, every zone label shows a source breakdown such as "2 TL" or "1 TL + 1 SR", giving you immediate insight into why a zone was considered high-probability. Methodology Swing pivots are detected at two depths (major and minor) using pivot-high and pivot-low structures, with configurable lookback lengths. Each valid consecutive pivot pair spawns a trendline, and every subsequent bar that wicks into the projected line within an ATR-based tolerance increments its touch count, raising its structural validity. Horizontal S/R levels are extracted from recent major pivots and merged when they fall within 0.4 ATR of each other. On each confirmed bar, the detector builds a combined pool of all on-chart trendline projections plus horizontal levels, then runs a greedy clustering pass using a zone-width-sized tolerance window. Candidate clusters that contain at least one trendline and meet the minimum source count are scored by combining touch strength, line age, slope quality, a rolling volume component and a horizontal S/R bonus. The highest-scoring cluster becomes a confluence zone, drawn as a rectangle whose right edge extends dynamically to the current bar so every active zone stays visually anchored to price. Zones are deduplicated by price and direction, and expire automatically after a configurable age. Signals and Alerts Every new confluence zone is announced with a descriptive alert that carries its mid-price, score and source breakdown. A second alert fires when price first crosses into an active zone from outside, helping traders react to the moment a level is tested. Zone colors encode directional bias: green zones indicate support-favored confluence while pink zones indicate resistance-favored confluence, with the bias derived from the dominant trendline direction in the cluster. Labels on the right edge of each zone show the current score, the source breakdown and the bias tag, so at a glance the trader knows which level is strongest and why. Key Inputs Major Pivot Length and Minor Pivot Length define the swing-detection depth of the two pivot streams. Max Active Trendlines caps the internal pool, while Min Touches to Validate filters out unconfirmed trendlines from the confluence pass. Touch Tolerance in ATR units controls how generously the script counts interactions. Min Sources for Zone sets the confluence requirement, Zone Width in ATR units determines rectangle thickness, and Zone Forward Extension controls how far each zone reaches to the right. Max Active Zones and Max Zone Age in bars keep the map focused on currently relevant structure. Panel Theme, Panel Location and Font Size cover the full visual customization layer. How to Use Start on the timeframe you trade and leave the defaults — they are tuned for 4H crypto and major FX on equivalent timeframes. Scan for zones with a score above 60 in a direction aligned with the higher-timeframe bias. Use the green support-biased zones as pullback entry areas in uptrends and the pink resistance-biased zones as fade or short entry areas in downtrends. Break-and-retest traders can wait for price to pierce a high-score zone and return to its edge; mean-reversion traders can watch for rejection at zones where the source breakdown includes both a trendline and a horizontal level, as those tend to hold more strongly. The panel at the top shows a quick summary of the current map state and the distance to the nearest zone in ATR units, which is useful for position sizing and stop placement. Limitations and Transparency This is not a strategy and not a complete trading system. It does not predict price and does not generate buy or sell instructions. Confluence zones are structural context, not entries, and their score is an internal quality estimate derived from visible price action, not a probability of success. The script needs a reasonable amount of historical bars to build a stable trendline pool. Very low timeframes and very illiquid symbols can produce erratic pivot structures and are not the intended use case. Like all pivot-based tools, the indicator reflects past structure, and levels can weaken or become stale as market regimes change. Zones older than the configured age are removed automatically to reduce this risk. Risk Disclosure Trading involves substantial risk of loss and is not suitable for every investor. Past performance is not indicative of future results. This indicator is provided for educational and analytical purposes only and should not be interpreted as financial advice, an investment recommendation or a solicitation to trade. Always combine multiple forms of analysis, manage position size responsibly, and never risk capital you cannot afford to lose.
Penunjuk Pine Script®
oleh AGProLabs
Smart Trader, Episode 06, Isotropic Trend Lines🔷 WHAT IS ST-EP06 — ISOTROPIC TREND LINES? ST-EP06 is a multi-scale structural trend channel indicator built on a σ-normalized coordinate system. It is designed to solve one of the oldest unaddressed problems in technical analysis: trend angles that cannot be compared across instruments, timeframes, or volatility regimes. A trend line drawn on a chart appears to carry a measurable angle — yet that angle is an artifact of the display window, not a property of the market. Resize the chart horizontally and the slope flattens; compress it and the slope steepens. A given price movement on Gold daily and Bitcoin 1-hour may produce visually identical slopes on screen while reflecting entirely different structural conditions. This happens because traditional charts use a coordinate space where the vertical axis (price) and the horizontal axis (time) share no fixed dimensional relationship. The consequence is not merely cosmetic. A trader cannot meaningfully compare the steepness of a trend on one instrument with another — or even across timeframes on the same instrument — because the weight of "one unit of price per bar" varies with the instrument's current volatility. As the author of this indicator, I sought a coordinate system where trend angles would be an intrinsic structural property of the market, independent of charting software or display settings. The goal: a space where a 30° uptrend on EUR/USD weekly carries the same structural meaning as a 30° uptrend on NASDAQ 5-minute — indicating that each market is moving at the same rate relative to its own realized volatility. The solution draws on the principle of dimensional analysis, well established in physics and engineering. Just as the Reynolds number normalizes fluid flow to make behavior comparable across different pipe sizes and fluid viscosities, this indicator normalizes price movement by realized volatility, producing a dimensionless space we call the Isotropic Coordinate System (ICS). In ICS, price is expressed in natural logarithmic form and scaled by a volatility estimate (σ) derived from the Yang-Zhang (2000) method — a drift-invariant estimator that incorporates Open, High, Low, and Close data. The resulting vertical axis is dimensionless: one unit equals one standard deviation of recent realized price behavior. When trend angles are measured in this space, 45° indicates approximately one σ of movement per bar — whether the chart shows a penny stock, a major currency pair, or a commodity index. Traditional chart coordinates assign no fixed relationship between the price axis and the time axis. Resizing the chart window changes the visual slope of the same price movement — a compressed view may show 52° while a stretched view of the same data shows 25°. The angle is a display artifact, not a market property. The Isotropic Coordinate System (ICS) addresses this by normalizing log-price by realized volatility (σ). In this space, the trend angle is designed to remain constant regardless of how the chart is displayed — because it measures price displacement in units of σ per bar, not in pixels per pixel. 🔷 HOW THE MODULES WORK TOGETHER ST-EP06 operates as a deterministic pipeline where each stage consumes the output of the one before it: Realized volatility estimation (σ) → Structural block construction → Monotonic direction detection → ICS angle measurement → Channel boundary fitting → Six-scale parallel analysis → Consensus aggregation → Breakout and retest state tracking → Dashboard narrative generation The Yang-Zhang σ provides the normalization constant for every downstream computation. Price history is then partitioned into structural blocks, each distilled to a single central tendency that resists close-price bias. Consecutive block centers are compared to identify the longest uninterrupted directional segment. The slope of that segment, measured in σ-normalized space, yields the ICS angle. Four price extremes located within the segment define two log-linear channel boundaries. This complete pipeline runs independently at six temporal scales, and their independent outputs are aggregated into a structural consensus. A finite-state machine then tracks the evolving relationship between price and the primary channel — breakout, retest, confirmation, or failure — and translates it into a single-line human-readable narrative. ST-EP06 operates as a deterministic sequential pipeline. Yang-Zhang volatility (σ) provides the normalization constant that flows into every downstream stage. Price history is partitioned into structural blocks, each reduced to a geometric mean. The longest monotonic segment determines direction, and its slope in σ-normalized space yields the ICS angle. Four price extremes define the channel boundaries. This complete pipeline runs independently at six scales — 3, 7, 13, 19, 29, and 47 bars per block — all prime numbers, chosen to minimize harmonic overlap so that multiple scales are unlikely to lock onto the same cyclical artifact. Scale 19 (highlighted) serves as the primary engine: it is the only scale that maps to the user's Trend Block Period input, and the only scale whose output drives the chart-overlay channel lines, the projection, the diamond markers, and the breakout/retest state machine. The other five scales operate at fixed periods and contribute exclusively to the cross-scale consensus count — providing structural context that a single scale cannot offer alone. When 5 or 6 of the 6 scales agree on direction, it suggests a structural trend visible across a broad range of temporal resolutions. 🔷 DATA ANCHORING Every structural computation in ST-EP06 — volatility, block means, direction, channel coordinates, state machine transitions, and dashboard narrative — is governed by a single anchoring reference, selected through the Calculation Bar input. Live Bar mode (default): the anchor is the current forming bar. Values update with each incoming tick. This is standard TradingView behavior and means the indicator may exhibit intra-bar repaint — the live bar's data enters all computations as it evolves. Close Bar mode: the anchor shifts to the last fully confirmed (closed) bar. The forming bar is excluded from every computation. Values lock once a bar closes and do not change retroactively. This mode is intended for structural analysis, back-testing, and any workflow where historical consistency is a priority. One deliberate exception is maintained in both modes: the dashboard header always displays the current live closing price (Live Exception protocol), preserving real-time price awareness regardless of how the indicator's structural engine is anchored. Two modes, same chart moment. In Live Bar the anchor sits on the forming bar, so every value updates tick-by-tick and may repaint within the bar. In Close Bar the anchor shifts to the last closed bar, locking all structural values once the bar closes. The only exception is the dashboard header row, which always displays the live closing price in both modes, so real-time price awareness is never lost. 🔷 YANG-ZHANG VOLATILITY (σ) The foundation of the ICS is a robust volatility estimate. ST-EP06 uses the Yang-Zhang (2000) realized volatility estimator, an academically established method that combines three variance components: Overnight variance — capturing the gap between consecutive sessions, measured from the prior close to the current open. Intraday variance — capturing the movement from open to close within each session. Range-based variance — using the Rogers-Satchell (1991) estimator, which extracts additional information from the high and low prices without assuming zero drift. These three components are blended using an optimal weight that is designed to minimize estimation error. The resulting σ updates every bar, adapts to changing market conditions, and — crucially — is drift-invariant: it is intended to remain unbiased whether the market is trending strongly or mean-reverting. 🔷 BLOCK CONSTRUCTION Rather than analyzing individual bars, ST-EP06 partitions recent price history into consecutive non-overlapping blocks. Each block spans a user-defined number of bars (the Trend Block Period input) and is reduced to a single representative value: the geometric mean of the block's highest high and lowest low, computed in logarithmic space. This log-midpoint serves as the block's central tendency. Unlike a simple average of closing prices, it captures the structural center of the entire price range within the block, avoiding bias toward any single price point. The number of consecutive blocks compared is controlled by the Trend Block Groups input — more groups means deeper lookback and the ability to detect longer structural trends. Price history is partitioned into consecutive non-overlapping blocks. Each block reduces to a single log-midpoint — the geometric mean of its highest high and lowest low. Connecting the midpoints forms the representative chain used for trend detection. 🔷 DIRECTION DETECTION + ICS ANGLE Once blocks are constructed, the engine compares their geometric means in sequence, starting from the most recent. It identifies the longest consecutive segment where each block's central tendency moves in the same direction — either consistently rising or consistently falling. A single reversal terminates the segment. The slope of this segment is then measured in ICS space: the logarithmic price difference between the oldest and newest blocks in the segment, divided by σ, divided by the number of bars between them. The arctangent of this normalized slope produces the ICS angle in degrees. If the absolute angle falls within the Range Threshold (a user-configurable dead zone in degrees), the direction is classified as ranging rather than trending. This threshold acts as a sensitivity filter — wider values require steeper moves before declaring a trend, narrower values respond to subtler directional shifts. An ICS angle of 45° indicates approximately one σ of price movement per bar. An angle near 0° suggests the market may be structurally flat. Because σ adjusts for volatility and the logarithm adjusts for price level, these angles are intended to be directly comparable across any instrument and any timeframe. 🔷 CHANNEL FITTING Within the identified trending segment, the engine locates four price extremes: the highest high, the lowest high, the highest low, and the lowest low — each paired with its bar position. These four points define two linear boundaries in ICS space. During an uptrend, the upper boundary is fitted through the lowest high and highest high (capturing the rising ceiling), while the lower boundary is fitted through the lowest low and highest low (capturing the rising floor). During a downtrend, the fitting order reverses to capture descending structure. During a ranging market, the channel uses horizontal boundaries at the segment's absolute high and low. All boundary computations occur in the σ-normalized logarithmic coordinate system, meaning the channel lines represent geometric (log-linear) paths in price space — curves that naturally follow multiplicative price behavior rather than additive assumptions. Within the trending segment, four extremes — HH, LH, HL, LL — define two log-linear boundaries. In an uptrend, the upper line fits through LH and HH, the lower through LL and HL. The direction reverses the fitting order for downtrends, and a ranging market uses horizontal boundaries. 🔷 6-SCALE PARALLEL ANALYSIS A single temporal scale may capture the trend at one resolution but miss structure at others. ST-EP06 runs the complete pipeline — volatility normalization, block construction, direction detection, ICS angle, and channel fitting — independently at six different scales: 3, 7, 13, 19, 29, and 47 bars per block. These values were chosen as prime numbers to minimize harmonic overlap between scales. Scale 19 serves as the primary engine and maps to the user's Trend Block Period input. The other five scales use fixed periods, providing a structural context that the primary engine alone cannot offer. The dashboard displays each scale's independent trend direction. A consensus count shows how many of the six scales agree: 5/6 or 6/6 agreement suggests a structural trend that is visible across multiple temporal resolutions, while low agreement may indicate transitional or conflicting structure. 🔷 BREAKOUT / RETEST STATE MACHINE ST-EP06 includes a 5-state finite automaton that tracks price's structural relationship to the primary channel boundaries: Inside — price is observed between the channel floor and ceiling. The dashboard shows the position as a percentage: distance from floor and distance to ceiling (summing to 100%). Breakout Up / Breakout Down — price has exited above the ceiling or below the floor. The dashboard shows the breakout price and the percentage of channel width that price has moved beyond the boundary. Retest Up / Retest Down — after a breakout, price has moved at least one σ away from the boundary (establishing distance), then returned to test it. The dashboard shows both the original breakout price and the current retest level. Transitions between states use dynamic σ-based thresholds rather than fixed percentages, meaning the sensitivity automatically adjusts with market volatility. Additional flags track: ✓ Confirmed — a breakout that has been retested and bounced at least one σ away from the boundary. (gap) — price crossed the entire channel width in a single transition. Failed breakout — price re-entered the channel after initially breaking out. Direction reset — the primary trend direction changed, wiping all breakout state. 🔷 VISUAL TOOLS All chart-overlay elements are drawn from the primary engine (scale 19): Channel lines — solid upper and lower boundaries from the segment start to the anchor bar, colored by trend direction (configurable up/down/range colors, width, and line style). Projection lines — dotted forward extension of the channel slopes beyond the anchor bar, providing a visual reference for potential future support and resistance. The projection offset, width, and style are independently configurable. Channel fill — semi-transparent shading between channel boundaries, with independent color selection and adjustable transparency. Applies to both the solid channel and projection segments. Diamond markers (◆) — placed at the channel endpoints on the anchor bar. Hovering reveals a tooltip with the anchored close price, ceiling level, floor level, and the price's position as a percentage of channel width. Direction label — positioned at the midpoint between segment start and projection end. Displays the trend arrow, direction text, and ICS angle (e.g., "▲ UP +7.3°"). Tooltip includes block count. 🔷 DASHBOARD A compact information table appears at the top-right corner of the chart, organized in 5 rows: Header — indicator name, ticker symbol, timeframe, and live price (always live under the Live Exception protocol, even in Close Bar mode). Period — the six scale values (3, 7, 13, user's period, 29, 47) displayed across columns. The primary engine column is highlighted. Trend — per-scale trend direction with directional arrows (▲ UP, ▼ DN, ◈ RNG) and color coding. Agreement — consensus count (e.g., "5/6 UP") with the primary channel ceiling (▲) and floor (▼) price levels. Narrative — a single merged row presenting the breakout/retest state machine output as a human-readable sentence with distance measurements. This row updates dynamically as price interacts with the channel. All dashboard text, tooltips, and narrative phrases are fully localized. 🔷 ALERT CONDITIONS ST-EP06 provides 19 alert conditions organized in 5 categories, all gated by a master Enable Alerts toggle: D · Direction (3 alerts) — fires when the primary engine trend changes to uptrend, downtrend, or range. B · Breakout (4 alerts) — fires on initial breakout above ceiling or below floor, and separately on confirmed breakout (retested and bounced). R · Retest (2 alerts) — fires when price returns to test the boundary after establishing distance. S · Structural (5 alerts) — fires on gap-through events (price crosses entire channel), failed breakouts (price re-enters channel), and direction resets (trend change wipes state). A · Agreement (5 alerts) — fires when cross-scale consensus reaches significant thresholds: full bullish (6/6), strong bullish (5/6), full bearish (6/6), strong bearish (5/6), or range consensus (≥4/6). Important: alerts require Calculation Bar = Live Bar. In Close Bar mode, all alert conditions are automatically suppressed and a visual warning is displayed on the chart — because Close Bar mode intentionally lags by one bar, which is semantically incompatible with live alert delivery. 🔷 LANGUAGE SUPPORT The dashboard, all tooltips, the breakout/retest narrative, and the alert warning label are available in 7 languages: English · Türkçe · العربية · Русский · Italiano · Português (BR) · 中文 Select the preferred language from the Language dropdown in the Display settings group. All structural and numerical outputs remain unchanged — only the display language of text elements is affected. 🔷 HOW TO USE Apply ST-EP06 to any chart — the indicator is designed to work across instruments (equities, forex, crypto, commodities, indices) and timeframes without parameter re-optimization, because the ICS framework normalizes for volatility and price level automatically. Start with the default settings (Period 26, Groups 5, Sigma Length 20) and observe how the channel captures the dominant structural trend. The 6-scale consensus in the dashboard may help assess whether the observed trend is isolated to one temporal resolution or confirmed across multiple scales. The Calculation Bar setting is a structural decision: use Live Bar for real-time monitoring and alert-driven workflows; use Close Bar for analysis and back-testing where historical stability is prioritized. The ICS angle on the direction label provides a quantitative measure of trend intensity. Comparing angles across different instruments or timeframes is one of the intended use cases of the ICS framework — a 15° angle on one chart and a 15° angle on another may suggest similar structural momentum relative to each market's own volatility. The breakout/retest narrative in the dashboard bottom row is designed to provide context-rich status updates without requiring manual chart reading. The σ-based thresholds ensure that breakout sensitivity adapts to current market conditions rather than relying on fixed values. 🔷 SETTINGS Calculation — Calculation Bar (Live/Close Bar anchoring), Trend Block Period (bars per block), Trend Block Groups (consecutive blocks compared), Range Threshold (ICS dead zone in degrees), Yang-Zhang Sigma Length (volatility lookback). Channel Lines — Up Color, Down Color, Range Color, Line Width, Line Style. Projection Lines — Projection Offset (forward bars), Projection Width, Projection Style. Display — Language (7 options), Show Channel (toggle overlay), Show Fill (toggle shading), Show Dashboard (toggle table), Dashboard Font Size. Channel Fill — Fill Up Color, Fill Down Color, Fill Range Color, Fill Transparency. Alerts — Enable Alerts (master toggle, requires Live Bar mode). 🔷 DISCLAIMER ST-EP06 is an educational and analytical tool. It is designed to provide structural context through σ-normalized trend channels and multi-scale analysis. It does not generate buy or sell signals, does not predict future price movement, and is not intended as financial advice. Historical patterns observed through this indicator do not guarantee future outcomes. All trading decisions remain the sole responsibility of the trader.
Penunjuk Pine Script®
oleh ata_sabanci
8
KernelLens🟦 KernelLens is a professional kernel regression library for Pine Script v6, providing eight mathematically rigorous Nadaraya–Watson estimators, a three-mode filter layer, a unified string dispatcher, and a suite of trading utilities — all built from the ground up on correct non-parametric statistics. Unlike existing Pine smoothing libraries — which inherit a decade-old loop-bound bug that silently reduces every kernel window to a handful of bars, regardless of the bandwidth parameter — KernelLens is built with auditable math, NA-safe iteration, input validation at every entry point, and academic references cited inline next to the formulas they describe. The library integrates eight independent kernel families — Rational Quadratic, Gaussian, Periodic, Locally Periodic, Epanechnikov, Tricube, Triangular, and Cosine — behind a consistent API, with every raw estimator wrapped in a filter layer (None / Smooth / Zero Lag), a unified dispatcher for dropdown-driven kernel selection, and five utility exports covering slope detection, trend state, crossover signaling, residual confidence bands, and Silverman's rule-of-thumb bandwidth recommendation. Every public function validates its inputs, raises descriptive runtime errors on misuse, and returns `na` only when there is genuinely no data — never as a silent fallback. 🟦 MATHEMATICAL FOUNDATION **The Nadaraya–Watson Estimator** Given a source series `y_t` and a symmetric kernel `K` with scale parameter `ℓ` (the "bandwidth"), the Nadaraya–Watson estimator of the regression function `m(x) = E ` evaluated at the current bar is: ``` Σᵢ K(dᵢ / ℓ) · y_{t−i} ŷ(t) = ─────────────────────── Σᵢ K(dᵢ / ℓ) ``` where `dᵢ` is the bar-distance from the kernel center and the sum runs over a finite window determined by the effective support of `K`. The estimator is a locally weighted average: bars close to the kernel center contribute heavily, distant bars contribute proportionally less, and bars outside the support contribute nothing. It is asymptotically unbiased up to `O(ℓ²)` for twice-differentiable `m`, with variance of order `(n·ℓ)⁻¹` — the classical bias–variance trade-off that defines all non-parametric smoothers. **Why Kernel Regression Beats Rolling Means** A simple moving average gives every bar in the window the same weight. Kernel regression gives each bar a weight that decays smoothly with distance, producing: - **Smoother output** — no step artifacts when bars enter / leave the window - **Better bias control** — the peak of the kernel sits exactly on the point being estimated - **Kernel-specific behavior** — compact-support kernels eliminate tail contamination entirely; Rational Quadratic's `α` parameter exposes multi-scale mixing; Periodic kernels resonate with known cycle lengths The math has been the academic standard for non-parametric regression since Nadaraya (1964) and Watson (1964). KernelLens brings it to Pine Script v6 in its correct, bug-free form. 🟦 THE EIGHT KERNELS All eight kernels implement the Nadaraya–Watson weighting scheme. They differ in support (compact versus infinite), smoothness (how many times differentiable), and how weight decays with distance. | # | Kernel | Formula | Support | Smoothness | Character | |---|---|---|---|---|---| | 1 | **Rational Quadratic** | `(1 + d² / (2·α·ℓ²))^(−α)` | ℝ | C∞ | Multi-scale mixer — `α` controls stretch versus wiggle | | 2 | **Gaussian (RBF)** | `exp(−d² / (2·ℓ²))` | ℝ | C∞ | The canonical smoother — smoothest possible with L² optimality | | 3 | **Periodic** | `exp(−2·sin²(π·d/p) / ℓ²)` | ℝ | C∞ | Resonates with repetition distance `p` — ideal for cycles | | 4 | **Locally Periodic** | Periodic · Gaussian | ℝ | C∞ | Seasonal patterns that slowly drift with trend | | 5 | **Epanechnikov** | `(3/4)(1 − u²) · 𝟙{|u|≤1}` | | C⁰ | Asymptotically MSE-optimal (Watson 1964) — no tail contamination | | 6 | **Tricube** | `(70/81)(1 − \|u\|³)³ · 𝟙{|u|≤1}` | | C² | The LOWESS standard — near-Gaussian with compact support | | 7 | **Triangular** | `(1 − \|u\|) · 𝟙{|u|≤1}` | | C⁰ | Simplest non-uniform kernel — fastest to compute | | 8 | **Cosine** | `(π/4)·cos(π·u/2) · 𝟙{|u|≤1}` | | C¹ | Raised-cosine taper — smoother boundary than Epanechnikov | where `u = d/ℓ` and `𝟙` is the indicator function. **Infinite-Support vs Compact-Support — Why Both Matter** | | Infinite Support (RQ, Gauss, Periodic, LocPeriodic) | Compact Support (Epa, Tricube, Triangular, Cosine) | |---|---|---| | **Tail weight** | Never exactly zero | Exactly zero beyond ±ℓ | | **Loop depth** | `3·ℓ` (3-σ cutoff, ≈99.7% mass) | Exactly `ℓ` | | **Bar contamination** | Distant bars still pull the estimate a tiny amount | Distant bars cannot affect the estimate at all | | **Best for** | Smooth trends, Gaussian-process intuition | Robust regression, outlier resistance | KernelLens picks the correct loop depth automatically based on kernel family: `_depthInfinite` for Gaussian-family kernels, `_depthCompact` for bounded kernels, `_depthPeriodic` for Periodic (which must span enough cycles to reach stable weights). **Why Eight, Not Four** Most Pine kernel libraries ship only the four kernels from MacKay's Gaussian process tutorial. KernelLens adds the four compact-support classical kernels because: - **Epanechnikov** minimises asymptotic mean squared error among all non-negative kernels of bounded support (Watson 1964) — it is the MSE-optimal baseline against which all other kernels are measured - **Tricube** is the kernel used by LOWESS (Cleveland 1979), the de-facto standard for robust locally weighted scatterplot smoothing - **Triangular** is the cheapest non-uniform compact kernel — useful when loop-budget matters on intraday charts with huge dataset size - **Cosine** is C¹-continuous at the support boundary, unlike Epanechnikov's C⁰ discontinuity, producing visibly smoother transitions at kernel edges Adding them makes the library an academically complete toolkit, not just a Pine port of one tutorial. 🟦 FILTER LAYER — NONE / SMOOTH / ZERO LAG Every kernel export accepts a `_filter` parameter with three valid values. The filter layer is implemented identically across all eight kernels, so switching kernel families does not change filter behavior. **"No Filter" — Single-Pass Raw Estimate** ``` ŷ = K(y) ``` One Nadaraya–Watson pass over the source. Cheapest mode, most reactive, fully represents the underlying kernel. Use this when you want the kernel's raw behavior with no additional smoothing or lag correction. **"Smooth" — Double-Pass Estimate** ``` ŷ = K(K(y)) ``` The kernel is applied once to the source, then applied again to its own output using the same bandwidth and the same parameters. The result is a more strongly smoothed curve at the cost of one extra loop pass per bar. This is mathematically equivalent to convolving the kernel with itself — the effective kernel is wider and flatter, pulling longer-range context into each estimate without requiring the user to double the bandwidth. **"Zero Lag" — Ehlers De-Lagged Estimate** ``` ŷ = 2·K(y) − K(K(y)) ``` The ZLEMA identity from Ehlers (*Rocket Science for Traders*, 2000): subtract the smoothing lag from the raw estimate, effectively shifting the output back in time to match the source more closely. The intuition: `K(y)` lags `y` by some amount; `K(K(y))` lags `K(y)` by the same amount; so `K(y) − K(K(y))` is an estimate of the lag itself, and adding it back to `K(y)` cancels out. The result tracks the source more tightly than either pass alone, at the cost of slightly noisier turning points. **Lazy Evaluation — No Wasted Cycles** In `"No Filter"` mode, the second pass is skipped entirely — it never runs. The filter branch uses an `if` block (not a ternary), so Pine's short-circuit semantics prevent the unused computation. A single kernel call costs one pass; `"Smooth"` or `"Zero Lag"` costs two. You only pay for what you use. 🟦 KERNEL CENTER OFFSET — THE `_phase` PARAMETER Every KernelLens kernel takes a `_phase` parameter that shifts the kernel center into the past by `_phase` bars. It is the library's non-repainting knob. **_phase = 0 — Live Estimate** The kernel is centered on the current bar. The most recent price has maximum weight, and the estimate is as fresh as possible. Suitable for live signal generation, but the most recent bar can re-evaluate as it develops within its interval — standard Pine real-time behavior. **_phase > 0 — Non-Repainting Historical Estimate** The kernel center is moved `_phase` bars into the past. The estimate becomes the smoothed value *at that historical bar*, not the current bar. Once the bar at `bar_index − _phase` is fully confirmed (`barstate.isconfirmed`), its estimate cannot change again. This is the standard trick for publishing kernel indicators that do not repaint: you get a stable, historically accurate curve at the cost of shifting the entire output `_phase` bars to the right on the chart. A `_phase = 25` call gives a curve that lags live price by 25 bars but is guaranteed stable for every past bar. **Why It Belongs in the Library, Not the Caller** Pushing `_phase` into the kernel's own loop is not the same as evaluating the kernel at a shifted source (`K(src )`). Shifting the source just uses a stale input with a current-bar-centered kernel, which still produces a fresh estimate of a stale series. KernelLens's `_phase` genuinely moves the kernel center, producing a historical-bar estimate that computes over the correct surrounding window. 🟦 NON-REPAINTING BEHAVIOR Repainting is the single most-asked question about any Pine indicator, and the single most common source of silent failure when a retail trader moves from backtest to live. A strategy that looks flawless on historical bars and then bleeds money the moment it is deployed is almost always suffering from some form of repainting. KernelLens is engineered from first principles to eliminate every class of repainting by construction — not by patching symptoms, but by removing the dependencies that cause repainting in the first place. **The Two Forms of Repainting** | Form | Symptom | Typical Cause | |---|---|---| | **Historical repainting** | A bar that was closed days or weeks ago silently changes its plotted value when the chart is refreshed or scrolled | `request.security()` with `lookahead = barmerge.lookahead_on`, un-gated higher-timeframe data, or incorrect array rotation that reads into future bars | | **Real-time repainting** | The plotted value on the live (current developing) bar flickers tick-by-tick as new price ticks arrive, then freezes at a final value when the bar closes | The indicator reads `close ` (or any current-bar value) inside a weighted sum — the current-bar weight changes every tick | KernelLens avoids the first kind **entirely and unconditionally**: the library contains no `request.security` calls, no higher-timeframe lookups, no `lookahead_on` usage, and no array rotation that could leak future bars into the window. Every historical bar plotted by any KernelLens kernel is computed exclusively from bars that existed at the time that bar was closed. The plotted history is immutable. Real-time repainting is controlled explicitly by the `_phase` parameter — it is the user's choice whether to accept tick-by-tick flicker on the live bar in exchange for zero lag (`_phase = 0`) or to eliminate the flicker entirely at the cost of a small fixed lag (`_phase ≥ 1`). **Why Kernel Regression Normally Repaints (And How KernelLens Stops It)** A traditional Nadaraya–Watson call centered on the current bar evaluates: ``` ŷ(t) = Σᵢ K(dᵢ/ℓ) · y_{t−i} for i = 0 … depth ``` On the live bar, the term `y_{t−0} = close ` is the current real-time price — which changes on every tick. Every tick moves the weighted sum, every tick moves the estimate, and the trader watching the chart sees the kernel plot flicker as the bar develops. The historical bars (where `close ` for that past bar is now fixed) are stable, but the live plot is unstable. KernelLens's `_phase` parameter shifts the loop so the kernel runs over `i = _phase … _phase + depth`. With `_phase = 2`: ``` ŷ(t) = Σᵢ K((i−2)/ℓ) · y_{t−i} for i = 2 … 2 + depth ``` The sum no longer touches `close ` or `close ` — every bar it reads is already confirmed and cannot change. The live-bar kernel output is therefore identical from the first tick of the bar to the last tick of the bar, and identical again when the bar finally closes. There is no flicker and nothing to repaint. **The Lag / Stability Trade-Off** | `_phase` | Lag on Live Bar | Live-Bar Flicker | Historical Repainting | Best For | |---|---|---|---|---| | **0** | 0 bars | Yes (real-time only; history is stable) | None | Scalping, academic research, calibration | | **1** | 1 bar | None | None | Fast day-trading; minimum acceptable lag for a live trading desk | | **2** | 2 bars | None | None | Default for most users — the sweet spot between freshness and stability | | **3** | 3 bars | None | None | Swing trading — extra margin against false flickers from erratic ticks | | **5+** | 5+ bars | None | None | Position trading, long-term chart analysis, published signal marks | Even at `_phase = 0`, **historical repainting never occurs** — only the live bar flickers during its own development. Once a bar closes, its plotted value is final; scrolling away and back, refreshing the chart, or re-opening TradingView will never change that historical plot. The flicker is exclusively a live-bar tick-by-tick phenomenon. **KernelLens as a Non-Repainting Primitive** KernelLens exposes real-time flicker as an explicit, user-controlled trade-off rather than a hidden behavior. The caller picks any point on the spectrum from "fully live" (`_phase = 0`, maximum reactivity with tick-by-tick flicker) to "fully confirmed" (`_phase ≥ 1`, one or more bars of lag in exchange for a curve that never redraws) with a single integer parameter. Historical repainting — the dangerous form that silently rewrites past plots — is eliminated unconditionally regardless of `_phase`. **How to Verify Non-Repainting Yourself** Do not trust the word "non-repainting" from any library — always verify. KernelLens can be verified in about thirty seconds: 1. Load a chart with KernelLens on it using `_phase = 2` (or any value > 0). 2. Take a screenshot at any specific historical bar. 3. Scroll far to the left, refresh the chart, or reload the indicator. 4. Return to the same bar. The plotted value at that bar must be pixel-identical to the screenshot — because the computation on that bar used only the bars before it, which have not changed. 5. Repeat with `_phase = 0`. The historical bars must still be pixel-identical — only the live bar's plot can differ between observations, and only because the live bar's `close` is now a different number than it was when you took the screenshot. For a stricter test, use TradingView's **Bar Replay** mode. Enable Bar Replay, step forward one bar at a time, and watch the kernel plot on each newly-closed bar. With `_phase ≥ 1`, the value plotted on each newly-closed bar will exactly match what the indicator shows after you exit replay mode and view the same bar normally. This is the gold-standard test — Bar Replay reproduces live-bar tick arrival in a controlled way. **Common Misconceptions** > *"Any Pine indicator that uses `close` repaints."* False. Using `close` on a confirmed bar does not repaint — the confirmed bar's close is locked. What can repaint is using `close` on the live bar, and only within that live bar's interval. KernelLens with `_phase > 0` never reads the live-bar close at all. > *"`lookahead = barmerge.lookahead_on` is always wrong."* Context-dependent. `lookahead_on` is used correctly in some multi-timeframe indicators to request a higher-TF value that is already settled on the lower TF. KernelLens does not use `request.security` at all, so this question does not apply — but for libraries that do, `lookahead_on` is only problematic when it leaks values from bars that were not yet closed at the lower-TF time of evaluation. > *"Non-repainting means zero lag."* False. Zero lag and non-repainting are orthogonal properties. KernelLens `_phase = 0` is zero lag with real-time flicker; `_phase = 2` is two-bar lag with no flicker. You can have any combination of the two, and the right choice depends on the trading style. > *"The `FILTER_ZEROLAG` mode makes the indicator non-repainting."* False. `FILTER_ZEROLAG` is an Ehlers-style de-lagging filter applied to the kernel output; it reduces the perceived lag of the estimate, but it does not affect whether the live bar flickers. Non-repainting is controlled exclusively by `_phase`. Choose `_phase` for repainting behavior, and `_filter` for smoothness / lag shape — they are independent knobs. **When to Accept Real-Time Flicker (`_phase = 0`)** Despite everything above, there are legitimate reasons to deliberately use `_phase = 0`: - **Academic research and backtesting** — you want the kernel mathematics in its classical form, centered on the point being estimated, with no phase adjustment - **Scalping on very short timeframes** — a 2-bar lag on a 1-minute chart is a 2-minute delay, which can matter when you are exiting within a 4-minute window - **Visual calibration** — when you are choosing a bandwidth by eye, the live-bar flicker actually helps: you see how sensitive the curve is to each incoming tick, which is diagnostic information - **Indicators that read the kernel output only on `barstate.isconfirmed`** — if your signal logic is gated by `if barstate.isconfirmed`, then live-bar flicker is invisible to your signal (it sees only the frozen close-of-bar value), and you can safely use `_phase = 0` with no practical consequence For every other case — and especially for any live alert or automated trading system — use `_phase ≥ 1`. Two bars of lag on a clean, stable curve is almost always worth more than zero lag on a curve that redraws itself several times per bar. 🟦 UNIFIED DISPATCHER — `estimate()` For indicators where the user picks a kernel from a dropdown, writing eight separate ternary branches is tedious and error-prone. KernelLens ships with a unified dispatcher that routes to the correct kernel based on a string argument: ```pine import a_jabbaroff/KernelLens/1 as kl line = kl.estimate( kernelType = kl.KERNEL_GAUSS, src = close, bandwidth = 32, shapeAlpha = 1.0, period = 1, phase = 2, filter = kl.FILTER_SMOOTH) ``` The dispatcher forwards to the matching typed export, so there is no performance penalty versus calling the kernel directly — it is a compile-time routing pass. Unknown kernel names raise a descriptive `runtime.error` naming every valid alternative, so typos fail loudly instead of silently returning `na`. **Public Constants** KernelLens exposes its string constants so callers never type the magic values by hand: | Constant | Value | |---|---| | `FILTER_NONE` | `"No Filter"` | | `FILTER_SMOOTH` | `"Smooth"` | | `FILTER_ZEROLAG` | `"Zero Lag"` | | `KERNEL_RQ` | `"Rational Quadratic"` | | `KERNEL_GAUSS` | `"Gaussian"` | | `KERNEL_PERIODIC` | `"Periodic"` | | `KERNEL_LOCPER` | `"Locally Periodic"` | | `KERNEL_EPA` | `"Epanechnikov"` | | `KERNEL_TRICUBE` | `"Tricube"` | | `KERNEL_TRIANG` | `"Triangular"` | | `KERNEL_COSINE` | `"Cosine"` | Using the constants in your caller code means the Pine compiler — not a runtime string compare — catches typos at edit time. 🟦 UTILITY LAYER — FIVE PROFESSIONAL HELPERS KernelLens ships with five utility exports that complement the core estimators. They are the functions you almost always write immediately after getting a smoothed line, factored out so you don't rewrite them in every indicator. **`slope(estimate, step)` — Discrete First Derivative** Returns `(y_t − y_{t−step}) / step`, the normalized rate of change over `step` bars. Use it to detect whether a kernel output is trending up, flat, or down — the foundation for any trend-following signal built on top of KernelLens. ```pine rising = kl.slope(line, 3) > 0.0 ``` **`trendState(estimate, step)` — Ternary Trend Indicator** Returns `+1` if the estimate is rising, `−1` if falling, `0` if exactly flat over the window. A single-call replacement for hand-rolled `line > line ? 1 : line < line ? -1 : 0` ladders. **`crossSignal(fast, slow)` — Bi-directional Crossover** Returns `+1` on the bar where `fast` crosses above `slow` (bullish), `−1` on a bearish cross, and `0` otherwise. Built on `ta.crossover` / `ta.crossunder`, so the signal is non-repainting once the bar is confirmed. **`confidenceBand(src, estimate, window)` — Residual Standard Deviation** Computes the rolling standard deviation of `(src − estimate)` over a user-defined window. Use the return value as the half-width of a confidence band around the estimate: ```pine est = kl.gaussian(close, 32, 2, kl.FILTER_SMOOTH) sigma = kl.confidenceBand(close, est, 50) upper = est + 1.96 * sigma lower = est - 1.96 * sigma ``` This is a computationally cheap proxy for the full kernel-weighted local variance — ideal when you need visual bands without paying for a second weighted pass. **`silvermanBandwidth(src, window)` — Optimal ℓ Suggestion** Returns the Silverman rule-of-thumb bandwidth: ``` h ≈ 1.06 · σ · n^(−1/5) ``` where `σ` is the rolling standard deviation of the source and `n` is the window size. This is the classical starting point for Gaussian-family bandwidths in academic texts (Silverman 1986). Because Pine requires `simple int` for kernel bandwidth, the returned value is intended for diagnostic display — plot it, read it off the chart, then hard-code the rounded integer into the kernel call. 🟦 INPUT VALIDATION — FAIL LOUDLY, FAIL EARLY Every public function in KernelLens validates its inputs through a set of internal `_assert*` helpers. Invalid arguments never produce silent `na` fallbacks or buried zero-divisions — they raise `runtime.error` with a descriptive message identifying the function, the parameter, and the expected range. | Helper | Checks | Raises On | |---|---|---| | `_assertFilter` | Filter string is `FILTER_NONE`, `FILTER_SMOOTH`, or `FILTER_ZEROLAG` | Typos like `"No FIlter"` (capital I) — a bug that exists in at least one published kernel indicator | | `_assertBandwidth` | Bandwidth is a strictly positive integer | Negative or zero bandwidth, which would cause division by zero or infinite loops | | `_assertPeriod` | Period is a strictly positive integer | Zero period, which would cause `sin(π·d/0)` in Periodic kernels | | `_assertAlpha` | Rational Quadratic shape parameter is strictly positive | Zero or negative `α`, which would invert the RQ formula | Error messages are prefixed `KernelLens:` (or `KernelLens.:`) so they are easy to spot in the TradingView runtime log. Every message names the parameter that failed, the value that was passed, and the set of valid alternatives — so a misconfigured chart tells you exactly what to fix. 🟦 LOOP DEPTH — THE BUG FIX THAT MOTIVATED KERNELLENS The two most popular Pine kernel libraries on TradingView share the same fatal bug: both compute their loop depth as ```pine _size = array.size(array.from(_src)) ``` where `array.from(_src)` creates a **one-element array containing the current value of `_src`**, so `_size` is always `1`. The loop then runs `for i = 0 to 1 + startAtBar`, effectively using only `startAtBar + 2` bars — completely ignoring the user's bandwidth. Every published kernel indicator built on those libraries inherits this silent miscalculation. KernelLens replaces the broken helper with three explicit depth selectors: | Helper | Depth | Used By | |---|---|---| | `_depthInfinite(bw)` | `max(bw · 3, 4)` | Gaussian, Rational Quadratic, Locally Periodic | | `_depthCompact(bw)` | `max(bw, 4)` | Epanechnikov, Tricube, Triangular, Cosine | | `_depthPeriodic(bw, p)` | `max(bw · 3, p · 10, 4)` | Periodic | For Gaussian-family kernels, the `3·ℓ` cutoff captures approximately 99.7% of the kernel mass (the three-sigma rule). For compact-support kernels, the depth equals the bandwidth exactly — the loop terminates at the kernel's natural zero point. For Periodic kernels, the depth is the larger of the scale-based and cycle-based minima, so the loop always spans enough periods to produce a stable weighted average. The loop counter `i` runs over bar offsets starting at `_phase`, every bar lookup is NA-checked before being incorporated into the sum, and the final `num / den` division is guarded against zero denominators. On a fresh chart, the kernel gracefully returns `na` for bars where the window extends past available history, rather than producing poisoned sums from implicit NA arithmetic. 🟦 API REFERENCE **Core Kernel Estimators — Eight Exports** | Export | Signature | |---|---| | `rationalQuadratic` | `(src, bandwidth, shapeAlpha, phase, filter) → float` | | `gaussian` | `(src, bandwidth, phase, filter) → float` | | `periodic` | `(src, bandwidth, period, phase, filter) → float` | | `locallyPeriodic` | `(src, bandwidth, period, phase, filter) → float` | | `epanechnikov` | `(src, bandwidth, phase, filter) → float` | | `tricube` | `(src, bandwidth, phase, filter) → float` | | `triangular` | `(src, bandwidth, phase, filter) → float` | | `cosineKernel` | `(src, bandwidth, phase, filter) → float` | **Unified Dispatcher** | Export | Signature | |---|---| | `estimate` | `(kernelType, src, bandwidth, shapeAlpha, period, phase, filter) → float` | **Utility Layer — Five Exports** | Export | Signature | |---|---| | `slope` | `(estimate, step) → float` | | `trendState` | `(estimate, step) → int` | | `crossSignal` | `(fast, slow) → int` | | `confidenceBand` | `(src, estimate, window) → float` | | `silvermanBandwidth` | `(src, window) → float` | **Parameter Types** | Name | Pine Type | Description | |---|---|---| | `src` | `series float` | Source series (close, hl2, ohlc4, or any other price-derived series) | | `bandwidth` | `simple int` | Kernel scale `ℓ`, must be `> 0` | | `shapeAlpha` | `simple float` | Rational Quadratic shape parameter, must be `> 0` | | `period` | `simple int` | Periodic repetition distance, must be `> 0` | | `phase` | `simple int` | Kernel center offset in bars, must be `≥ 0` | | `filter` | `simple string` | One of `FILTER_NONE`, `FILTER_SMOOTH`, `FILTER_ZEROLAG` | | `kernelType` | `simple string` | One of the eight `KERNEL_*` constants | | `step` | `simple int` | Finite-difference step for `slope` / `trendState`, must be `≥ 1` | | `window` | `simple int` | Rolling window for `confidenceBand` / `silvermanBandwidth`, must be `≥ 2` | 🟦 USAGE EXAMPLES **Minimal — One Gaussian Curve** ```pine //@version=6 indicator("KernelLens — Gaussian Demo", overlay = true) import a_jabbaroff/KernelLens/1 as kl line = kl.gaussian(close, 32, 2, kl.FILTER_SMOOTH) plot(line, "Gaussian", color = color.orange, linewidth = 2) ``` **Fast / Slow Crossover System** ```pine //@version=6 indicator("KernelLens — RQ Crossover", overlay = true) import a_jabbaroff/KernelLens/1 as kl fast = kl.rationalQuadratic(close, 8, 1.0, 2, kl.FILTER_NONE) slow = kl.rationalQuadratic(close, 32, 1.0, 2, kl.FILTER_SMOOTH) cross = kl.crossSignal(fast, slow) plot(fast, "Fast", color = color.aqua, linewidth = 2) plot(slow, "Slow", color = color.orange, linewidth = 2) plotshape(cross == 1, "Bull", location = location.belowbar, color = color.lime, style = shape.triangleup, size = size.tiny) plotshape(cross == -1, "Bear", location = location.abovebar, color = color.red, style = shape.triangledown, size = size.tiny) ``` **Confidence Band Envelope** ```pine //@version=6 indicator("KernelLens — Confidence Band", overlay = true) import a_jabbaroff/KernelLens/1 as kl est = kl.tricube(close, 48, 2, kl.FILTER_SMOOTH) sigma = kl.confidenceBand(close, est, 50) k = 1.96 upper = est + k * sigma lower = est - k * sigma plot(est, "Estimate", color = color.orange, linewidth = 2) p1 = plot(upper, "+1.96σ", color = color.new(color.aqua, 70)) p2 = plot(lower, "−1.96σ", color = color.new(color.aqua, 70)) fill(p1, p2, color = color.new(color.aqua, 92)) ``` **Dropdown-Driven Kernel Selection** ```pine //@version=6 indicator("KernelLens — Dropdown", overlay = true) import a_jabbaroff/KernelLens/1 as kl kernelType = input.string(kl.KERNEL_GAUSS, "Kernel", options = ) bandwidth = input.int(32, "Bandwidth", minval = 2) alphaRQ = input.float(1.0,"RQ Alpha", minval = 0.01, step = 0.25) period = input.int(20, "Period", minval = 1) phase = input.int(2, "Phase", minval = 0) filter = input.string(kl.FILTER_SMOOTH, "Filter", options = ) line = kl.estimate(kernelType, close, bandwidth, alphaRQ, period, phase, filter) plot(line, "KernelLens", color = color.orange, linewidth = 2) ``` 🟦 TIMEFRAME PRESETS — BANDWIDTH BY STYLE Kernel bandwidth is the single most important parameter. It controls the trade-off between reactivity (small `ℓ`, tight fit, noisier) and stability (large `ℓ`, smooth curve, slower to react). The presets below are tested starting points — adjust by ±25 % to taste. --- **SCALPER — 1m / 3m / 5m** | Parameter | Value | |---|---| | Bandwidth (ℓ) | 8 | | Phase | 1 | | Filter | `FILTER_NONE` | | Best Kernel | Rational Quadratic or Gaussian | | RQ shapeAlpha | 1.0 | **Why:** Short bandwidth means the kernel reacts within a handful of bars. `FILTER_NONE` removes the double-pass lag, so the estimate tracks price as tightly as possible. Phase 1 keeps the estimate nearly live while still avoiding the current-bar tick noise. --- **DAY TRADER — 15m / 30m / 1H** | Parameter | Value | |---|---| | Bandwidth (ℓ) | 16 | | Phase | 2 | | Filter | `FILTER_SMOOTH` | | Best Kernel | Gaussian or Tricube | | RQ shapeAlpha | 1.0 | **Why:** Balanced reactivity — the 16-bar Gaussian is the default Silverman range for intraday price data, and `FILTER_SMOOTH` removes most of the bar-to-bar chop without significantly increasing lag. Tricube provides near-identical behaviour with strict compact support and is preferred on noisy assets where outlier bars should not influence the curve. --- **SWING TRADER — 4H / 1D** | Parameter | Value | |---|---| | Bandwidth (ℓ) | 32 | | Phase | 3 | | Filter | `FILTER_SMOOTH` | | Best Kernel | Rational Quadratic | | RQ shapeAlpha | 2.0 | **Why:** Swing trades need structural signals, not intraday noise. Rational Quadratic with `α = 2.0` mixes medium and long length scales, producing a curve that ignores transient spikes but catches genuine regime shifts. Phase 3 shifts the estimate three bars back so each swing decision is made against a fully confirmed kernel output. --- **POSITION / LONG-TERM — 1D / 1W / 1M** | Parameter | Value | |---|---| | Bandwidth (ℓ) | 64 | | Phase | 5 | | Filter | `FILTER_SMOOTH` or `FILTER_ZEROLAG` | | Best Kernel | Gaussian or Locally Periodic | | Period (if LP) | 52 (weekly cycle) | **Why:** Position traders care about the macro trajectory. A Gaussian with ℓ = 64 produces a curve that only turns on genuine multi-month inflections. Locally Periodic with `period = 52` is the ideal choice when a clear seasonal cycle is present — it uses both the long-range Gaussian envelope and the 52-bar periodicity to highlight cycle turns that align with trend. --- **RESEARCH — Academic / Backtest** | Parameter | Value | |---|---| | Bandwidth (ℓ) | Compute via `silvermanBandwidth(src, 200)` | | Phase | 0 | | Filter | `FILTER_NONE` | | Best Kernel | Epanechnikov | **Why:** Epanechnikov is the MSE-optimal kernel; `FILTER_NONE` keeps the estimator in its classical single-pass form; `phase = 0` centers the kernel on the bar being evaluated. This is the configuration that matches the statistical literature exactly — use it when publishing research, running Monte-Carlo studies, or calibrating against reference implementations. 🟦 BANDWIDTH SELECTION Bandwidth `ℓ` is the single most consequential choice in kernel regression. Too small and the estimate overfits local noise; too large and it flattens real structure. KernelLens exposes two helpers to support both manual and semi-automated bandwidth selection. **Manual — Start with ℓ ≈ √n** A practical starting point for financial time series: set `ℓ ≈ √window_of_interest`. If you care about 100-bar structure, try `ℓ = 10`. If you care about 400-bar structure, try `ℓ = 20`. Adjust by ±25 % based on how noisy the result looks. **Silverman's Rule of Thumb** The closed-form optimal bandwidth for Gaussian-family kernels under Gaussian source assumptions: ``` h ≈ 1.06 · σ · n^(−1/5) ``` Call `silvermanBandwidth(src, window)` to compute this value live. Because Pine requires `simple int` bandwidth at compile time, the returned value is for diagnostic use — plot it, read the stable value off the chart, then hard-code the rounded integer into your kernel calls. **Leave-One-Out Cross-Validation (Manual)** For academic rigor, compute the leave-one-out mean squared error for a range of bandwidths and pick the minimum. KernelLens does not automate this (it would require `series int` bandwidth, which Pine does not support inside kernel loops), but the formula is straightforward: ``` LOOCV(ℓ) = (1/n) · Σᵢ (yᵢ − ŷᵢ⁻ⁱ(ℓ))² ``` where `ŷᵢ⁻ⁱ` is the kernel estimate at bar `i` computed without including bar `i` in the sum. Evaluate offline, pick the minimum, hard-code the result. 🟦 FILTER SELECTION — WHEN TO USE EACH | Filter | Best For | Avoid When | |---|---|---| | `FILTER_NONE` | Live signal generation, research / calibration, compact-support kernels on noisy data | Choppy markets where you need extra smoothing | | `FILTER_SMOOTH` | Swing and position trades, confidence band midlines, most day-trading setups | Scalping — the double pass adds measurable lag | | `FILTER_ZEROLAG` | Regime detection, crossover systems that need the curve to track price tightly | Low-volume assets — Zero Lag amplifies high-frequency noise | The three filters use the same underlying kernel with the same bandwidth, so switching between them does not require re-tuning. Default to `FILTER_SMOOTH` when in doubt — it is the best-behaved option across the widest range of assets and timeframes. 🟦 COMPATIBILITY KernelLens targets Pine Script v6 and runs on every TradingView chart — no exchange, asset class, or timeframe restriction. - **Crypto** — Spot, futures, perpetual contracts - **Forex** — All majors, minors, and exotics - **Equities** — Stocks, ETFs, indices - **Commodities** — Metals, energy, agriculture - **Timeframes** — 1 minute through Monthly The library is deterministic — given the same source and parameters, every bar of every symbol produces the same estimate. No calibration is needed across assets; the bandwidth parameter alone controls smoothness, and the kernel formulas are scale-free in the source dimension. Silverman's bandwidth helper automatically adapts to each asset's volatility. 🟦 TECHNICAL NOTES - **Pine Script v6** — uses the modern type system, strict type checking, and the `switch` expression in the unified dispatcher - **Non-repainting** — kernel outputs for any confirmed bar depend only on that bar's history; there is no look-ahead, no `request.security` with lookahead, and no dependency on the unconfirmed current bar unless `_phase = 0` is deliberately chosen - **NA-safe iteration** — every bar lookup inside a kernel loop is guarded by `if not na(y)`, so chart history gaps and warm-up bars cannot poison the weighted sum - **Division-by-zero protection** — every kernel's final division checks `den > 0.0` and returns `na` if the denominator collapses (which can only happen on truly empty windows) - **Input validation** — every public function asserts its preconditions up front via `_assertFilter`, `_assertBandwidth`, `_assertPeriod`, `_assertAlpha`, and raises `runtime.error` with a descriptive message on misuse — no silent `na` fallbacks - **Lazy filter evaluation** — the `"No Filter"` path never executes the second kernel pass; the `if`-branch check short-circuits, so single-pass mode is as cheap as a raw kernel call - **Correct loop bounds** — `_depthInfinite`, `_depthCompact`, and `_depthPeriodic` compute the correct window size per kernel family, fixing the silent `_size = 1` bug that plagues every other published Pine kernel library - **No persistent state** — the library is purely functional: no `var`, no arrays, no history buffers that grow over time; every export is a pure expression of `(inputs) → output`, so Pine's `max_*_count` limits cannot be exceeded and the library cannot leak memory - **O(bandwidth) per bar per kernel call** — the loop depth is bounded by the constants in Section 0; there is no hidden quadratic behavior and the cost scales linearly with the user-chosen bandwidth - **Unicode-safe comments** — the source uses academic notation (`σ`, `ℓ`, `α`, `ŷ`, `ℝ`) where it improves readability; all strings are plain ASCII for runtime compatibility 🟦 ACADEMIC REFERENCES Every kernel and every formula in KernelLens is cited inline in the source. The combined bibliography: - **Nadaraya, E. A. (1964).** On estimating regression. *Theory of Probability & Its Applications*, 9(1), 141–142. - **Watson, G. S. (1964).** Smooth regression analysis. *Sankhyā: The Indian Journal of Statistics, Series A*, 26(4), 359–372. - **Cleveland, W. S. (1979).** Robust locally weighted regression and smoothing scatterplots. *Journal of the American Statistical Association*, 74(368), 829–836. *(Tricube kernel, LOWESS.)* - **Silverman, B. W. (1986).** *Density Estimation for Statistics and Data Analysis*. Chapman & Hall, London. *(Bandwidth rule of thumb.)* - **Wand, M. P. & Jones, M. C. (1995).** *Kernel Smoothing*. Chapman & Hall. *(Unified treatment of all eight kernels.)* - **MacKay, D. J. C. (1998).** Introduction to Gaussian Processes. *NIPS Tutorial*. *(Periodic and Rational Quadratic kernels.)* - **Ehlers, J. F. (2000).** *Rocket Science for Traders*. John Wiley & Sons. *(Zero-lag smoothing trick.)* - **Rasmussen, C. E. & Williams, C. K. I. (2006).** *Gaussian Processes for Machine Learning*. MIT Press. *(Locally Periodic and Rational Quadratic kernels.)* 🟦 VERSIONING & LICENSE - **Version** — 1.0.0 - **Pine Script** — v6 - **License** — Mozilla Public License 2.0 - **Status** — Production-ready KernelLens follows semantic versioning. Minor versions add new exports without breaking existing ones; patch versions fix bugs; major versions may change function signatures and will be announced in the changelog. 🟦 DISCLAIMER KernelLens is a mathematical library for non-parametric regression on financial time series using the Nadaraya–Watson method. The library is provided solely for educational and research purposes and does not constitute financial, investment, or trading advice. Kernel regression is a local smoothing technique. It estimates the mean of a source series in the neighborhood of the current bar based on historical data, but it does not predict future prices, does not generate trading signals on its own, and does not guarantee the profitability of any strategy built on top of its output. Past performance of any model does not guarantee future results. Markets contain systemic risks that cannot be eliminated by any amount of mathematical rigor in the kernel itself. Responsibility for any trading decisions made using this library rests entirely with the user. Always apply sound capital management, conduct your own independent analysis, and never risk capital you are not prepared to lose. The author assumes no liability for direct or indirect losses incurred through the use of KernelLens or any indicator built on top of it.
Perpustakaan Pine Script®
oleh a_jabbaroff
GTS5.alvin - BOS/ChoCH 💥 Smart Market Structure • Break of Structure • Change of Character • Swing Intelligence 🚀 What This Indicator Does The GTS5.alvin - BOS/ChoCH is a precision market structure tracker that automatically identifies: 📈 Higher Highs (HH) & Higher Lows (HL) 📉 Lower Highs (LH) & Lower Lows (LL) ⚡ Breaks of Structure (BOS) 🔄 Change of Character (ChoCH) 🧭 Real-time market bias shifts 📊 Key swing levels with automatic breakout detection This is not a basic structure tool. This is a clean, execution-grade market intelligence system designed to show you what price is actually doing — not what you think it’s doing. 🧬 Why This Indicator Hits Different Most traders lose because they are: Trading random highs/lows Guessing trend direction Entering before structure confirms Reacting emotionally instead of structurally This tool fixes that. It builds a clean narrative of the market: “Where is structure forming?” “Where did it break?” “Was that continuation… or reversal?” Now you always know. ⚔️ Core Features 🧱 True Market Structure Detection MUST use a 11-candle swing model (5 left / pivot / 5 right) to accurately define: Swing highs Swing lows Market structure shifts No noisy micro-swings. Only meaningful structure. 🔥 BOS & ChoCH Engine Automatically distinguishes: 💥 BOS (Break of Structure) → continuation trend behavior 🔁 ChoCH (Change of Character) → potential reversal zones Color-coded and labeled directly on chart for instant clarity. 🧭 Bias Tracking (Hidden Edge) The script continuously tracks directional bias: Bullish (1) Bearish (-1) Neutral (0) This allows perfect classification of: Trend continuation vs reversal breaks LH/HL vs HH/LL behavior shifts 📊 Smart Swing Labels Auto-labels: HH / HL → bullish structure LH / LL → bearish structure Minimal chart clutter. Maximum meaning. 📍 Break Level Mapping Every structural break includes: Horizontal level projection Mid-level BOS/ChoCH label Break candle marker tag You don’t just see the break — you see exactly where it came from. ⚡ Wick or Close Break Logic Choose how structure is validated: Close-based confirmation (cleaner, stricter) Wick-based detection (faster, aggressive) Perfect for scalpers or swing traders. 🔔 Advanced Alert System Built-in alerts for: Bullish Breaks Bearish Breaks HH → BOS continuation events LL → BOS continuation events New Higher Highs New Lower Lows This turns your chart into a semi-automated signal radar. 🧠 How Traders Use It 📈 Trend Following Wait for: BOS in direction of bias Then ride continuation after HH/HL confirmation 🔄 Reversal Detection Look for: ChoCH after strong HH/HL or LL/LH sequence Bias flip + structure break alignment ⚡ Smart Entries Combine: Swing structure Break levels ChoCH confirmation To enter with institutional-style timing instead of retail guessing 🧩 What Makes It Different From Other Structure Tools Most indicators: ❌ Show messy swings ❌ Don’t distinguish BOS vs ChoCH properly ❌ Don’t track bias ❌ Overload charts with noise This one: ✔ Clean structure logic ✔ Context-aware labeling ✔ Bias-driven classification ✔ Minimal but powerful visuals ✔ Designed for real execution decisions 🎯 Ideal For Smart Money Concept traders (SMC) Price action traders ICT-style traders Scalpers & intraday traders Swing traders needing structure confirmation ⚡ Final Thought If you don’t know where structure broke… You’re not trading. You’re guessing. This indicator turns chaos into a readable market story: Structure → Break → Confirmation → Bias → Opportunity No noise. No confusion. Just clarity. ======================= NOTE FROM THE CREATOR: ======================= Combine this indicator with the GTS5.alvin - HMA Tracker 2.0 then use the following strategy to help better your trading edge: Wait for a BOS of a HH/LL Then immediately check to see if the expansion/strength meter measures 50%+ If so, consider entering the trade with the momentum. (If the meter measures less than 50% then consider waiting for another setup.) Take profit: Once expansion/strength meter falls back to 50% then consider exiting trade. Stop Loss: Consider placing behind HH/LL market structure swing. --------------------------------- Disclaimer: This is ONLY for educational purposes only! This indicator will NOT print you money. My success does NOT guarantee your success. I have EXTENSIVE EXPERTISE IN MARKET MANIPULATIONS! Use at your own discretion.
Penunjuk Pine Script®
oleh alvinandfriends
4
Quantitative Linear Regression Channel [Dual Fill + MTF]Abstract / Description: This is a significantly upgraded v6 version of the classic Linear Regression Channel originally created by LonesomeTheBlue. Key Improvements over the original: Updated to Pine Script v6 with modern syntax and better performance. Persistent object pool for lines and labels – eliminates object leakage and improves rendering speed. Optional dual-zone fill (Upper and Lower) with fully customizable colors for clearer visual separation. Broken channel visualization that freezes the previous channel in blue dotted style instead of deleting it immediately. Dynamic MTF Trend Table (5m to 1D) showing slope direction with arrows and color coding. Table position is user-configurable (top_left, top_right, etc.). Support for calculating the channel on a higher timeframe (MTF mode) while displaying it cleanly on the current chart. Cleaner code structure, grouped inputs, improved alerts, and overall more stable behavior across all timeframes. The core regression calculation and broken channel logic remain faithful to the original concept, while adding modern institutional-grade features for better usability and performance. Credits: Base concept and original code by © LonesomeTheBlue (2020). v6 architecture, persistent pool, dual fill, MTF table and optimizations by datapro (2026). Recommended use: Works on any timeframe. Best results when used with the MTF table for confluence.
Penunjuk Pine Script®
oleh datapro
1
QuantEdge Momentum ML [PRO]🟦 QuantEdge Momentum ML PRO is a k-Nearest Neighbors driven momentum oscillator built on an adaptive machine-learning core. Unlike RSI, Stochastic, or MACD — which apply the same static formula to every asset — QE-ML PRO learns the dual-horizon RSI fingerprints that have historically led to bullish versus bearish outcomes on the exact instrument being traded, then scores the current bar against the N closest historical matches. The result is a non-parametric, self-calibrating oscillator whose decision boundary is shaped by the asset's own behaviour rather than a hard-coded curve. The indicator integrates nine independent layers — feature engine, training sampler, k-NN predictor, WMA signal line, stdev-adjusted OB/OS bands, filtered signal dots, gradient channel, theme-adaptive dashboard, and a nine-theme palette — all rendered on a single, clean oscillator panel. 🟦 HOW THE CORE ENGINE WORKS **Dual-Horizon RSI Feature Vector** Each bar, the Feature Engine computes two RSI values at different lookback windows and smooths both through a shared trend-length WMA: - `rsiFast = WMA(RSI(close, FastPeriod), TrendLength)` — reactive short-term momentum - `rsiSlow = WMA(RSI(close, SlowPeriod), TrendLength)` — structural mid-term momentum The pair `(rsiSlow, rsiFast)` is a 2-dimensional point in RSI feature space. Every training sample stores one such point along with a ±1 label that records whether price rose or fell since the previous sample. Over time the dataset accumulates a cloud of labelled points that maps which RSI states historically preceded up-moves versus down-moves on this exact asset. **Training Sampler — Multi-Trigger Collector** Three collection modes decide when to append a new labelled sample: | Mode | Trigger | Use Case | |---|---|---| | **MA Crossover** | Fast WMA crosses Slow WMA | Clean, sparse samples — classic single-trigger behaviour | | **Periodic** | Every N bars (user-set) | Fills dataset fast on new / low-history charts | | **Hybrid** | MA crossover **OR** every N bars | Richest training set — recommended for fresh assets | Sampling is gated by `barstate.isconfirmed` so the dataset never absorbs unconfirmed values from a flickering live bar. **k-NN Predictor with Adaptive k** On every bar, the predictor computes Euclidean distance in the 2D RSI feature space between the live `(rsiSlow, rsiFast)` point and every historical sample: ``` d = sqrt((rsiSlow_now - rsiSlow_hist)² + (rsiFast_now - rsiFast_hist)²) ``` The K closest historical points vote by summing their ±1 labels. The effective K is resolved adaptively using the classical statistical heuristic: ``` kEff = max(3, min(kMax, floor(sqrt(N)))) ``` This means early bars — when only a handful of samples exist — use a small K, and the value stabilises as the dataset fills. On a fresh chart you never get a noisy prediction from an undersized neighborhood, and on a mature dataset K automatically scales up for smoother output. **Bias Correction — Label-Mean Recentering** Raw k-NN output is biased whenever the label distribution is skewed. On a trending asset, Periodic sampling fills the dataset with mostly +1 (or mostly −1) labels, pushing every prediction off zero. QE-ML PRO subtracts the expected value from the raw sum: ``` prediction = neighborLabelSum − (kEff × meanLabelAcrossDataset) ``` This keeps the mid-level visually centred at zero regardless of how trending the underlying asset has been. The correction is applied on every bar and is what makes the oscillator read cleanly on both sideways and strongly trending markets. **Minimum Sample Gate** Until the dataset has reached the user-defined Minimum Training Samples threshold, the predictor outputs exactly zero. This prevents unreliable readings during the warm-up phase on fresh charts. **FIFO Rotation** The dataset is hard-capped at Max Dataset Size. Once the cap is reached, the oldest sample is discarded on every new insertion — classical rolling window memory that keeps the k-NN scan bounded and the indicator fast on long histories. 🟦 PREDICTION LINE — FIVE VISUAL STYLES All five styles are line-based. Only the visual effect differs — the underlying k-NN math is identical across styles. | Style | Character | |---|---| | **Stratum** | Thick adaptive line with zone-based opacity: solid in extreme zones, semi-transparent in the mid zone. Layered intensity aesthetic — default | | **Neon** | Bright core line with an outer glow halo. Cyberpunk luminous effect, best on dark backgrounds | | **Resonance** | LRI-style gradient line that fades near the midline and brightens toward the rolling extremes | | **Pulse** | Adaptive bull/bear color (above midline = bull, below = bear) plus the WMA signal line. The QE-ML PRO classic look | | **Mono** | Single flat theme-bull line, no gradient, no adaptive coloring. Minimalist single-color silhouette | 🟦 SIGNAL LINE A WMA of the raw prediction output, used as a crossover trigger line in the MACD convention. Crossovers between the prediction and signal line mark momentum regime changes. **Two Visual Styles** | Style | Rendering | |---|---| | **Neon** | Bright core line wrapped in a wider semi-transparent glow halo — cyberpunk aesthetic | | **Flat** | Plain single-color line, no halo, no gradient — minimalist clean look | 🟦 SIGNAL DOTS — FILTERED CROSSOVER MARKERS A two-layer neon cross-dot renderer fires on every Prediction × Signal crossover that survives the active filter mode. Four progressive filters decide which raw crosses reach the chart: | Filter Mode | Behaviour | Signal Count | |---|---|---| | **All Crosses** | Every cross becomes a dot | Highest — noisy on choppy assets | | **Zone Only** | Only crosses inside an OB or OS strip | Mean-reversion triggers — strongest reversal setups | | **Mid Aligned** | Bull dots only above mid, bear dots only below | Trend-following — keeps you on regime side | | **Strict** | Zone Only + Mid Aligned + extra strength multiplier on mid-zone crosses | Fewest signals, highest conviction — default | Two additional gates filter out whipsaws: - **Cooldown (bars)** — minimum spacing between consecutive dots, prevents cluster spam in ranges - **Min Strength** — minimum `|prediction − signal|` separation at the moment of the cross, drops razor-thin crossovers that close back on themselves Each dot is a two-layer plot: an outer glow halo with user-adjustable size and opacity, and a bright solid core on top — independently sized and opacity-controlled so users can dial in the exact visual weight they want. The dot is placed at the actual cross point: bull dots at `min(prediction, signalLine)`, bear dots at `max(prediction, signalLine)`. 🟦 DYNAMIC BANDS — STDEV-ADJUSTED OB / OS ZONES QE-ML PRO does not use fixed 80 / 20 overbought / oversold levels. Instead, the bands adapt to the actual historical range of the prediction output: - **Channel Extremes** — rolling highest / lowest of the prediction over a user-configurable lookback - **Stdev Band** — EMA of rolling standard deviation of the prediction, multiplied by the user's stdev length - **OB Level** = `rangeHi − stdevBand` (inner boundary of the overbought strip) - **OS Level** = `rangeLo + stdevBand` (inner boundary of the oversold strip) The result is a pair of mean-reversion zones that tighten during quiet markets and widen during volatile ones — no manual recalibration needed across assets. The strips are rendered as gradient fills anchored on the live prediction plot, so they only appear visually while the prediction is actually inside the zone. 🟦 CHANNEL GRADIENT Two symmetric gradient fills bracket the mid line. The upper fill stretches from `midValue` to `rangeHi`, the lower fill from `midValue` to `rangeLo`. Opacity fades from full intensity at the extremes to fully transparent at the midline — a visual range meter showing how close the prediction is sitting to its historical boundaries. Colors are pulled from the active Theme. A single opacity slider controls the gradient intensity. 🟦 DASHBOARD — LIVE DATA PANEL A compact 2-column × 7-row monospace panel drawn on the last bar only (zero historical overhead). Every field updates in real time on the live bar. | Row | Left | Right | |---|---|---| | Header | QE-ML PRO | Regime (▲ BULL / ▼ BEAR / ■ NEUTRAL) | | Row 1 | Prediction | Raw value + trend arrow vs previous bar | | Row 2 | Signal | WMA trigger line value | | Row 3 | Strength | 10-block gauge of `|prediction − signal|` normalised against rolling channel | | Row 4 | Zone | OB / MID / OS tag | | Row 5 | Dataset | Sample count / effective k | | Row 6 | Mode | Active Learning Mode (MA Cross / Periodic / Hybrid) | **Theme-Aware Auto-Invert** The panel background scaffolds auto-switch: - **Tropic / Amber / Pastel / Cyber / Gold / Electric / Candy** → dark panel with bright theme accent text - **Midnight / Graphite** → light panel with dark theme accent text This guarantees legibility on every theme without breaking the theme's color identity — because Midnight and Graphite use deep dark bull tones that would drown against a black panel. **Direction via Glyphs, Not Color** Both columns share the same full-strength theme tone. Regime direction is conveyed by `▲ ▼ ■` glyphs rather than color shifts, which keeps the panel reading cleanly even on the most minimal themes. 🟦 NINE COLOR THEMES One theme selector drives every colored component — Prediction line, Signal line, Channel fill, OB / OS strips, Mid-level line, Signal Dots, and Dashboard panel. No per-color manual inputs. | Theme | Character | Bull | Bear | |---|---|---|---| | **Tropic** | Cyan steel + deep orange — electric contrast (default) | Cyan | Deep Orange | | **Amber** | Warm amber + indigo blue — fire tones | Amber | Red | | **Pastel** | Sky blue + soft lavender — cool arctic glow | Sky Blue | Lavender | | **Cyber** | Neon lime + hot crimson — cyber terminal | Neon Green | Crimson | | **Gold** | Bright gold + scarlet — solar warmth | Yellow Gold | Red | | **Electric** | Electric aqua + magenta — high-voltage neon | Aqua | Magenta | | **Candy** | Neon green + hot pink — dark energy pop | Mint Green | Hot Pink | | **Midnight** | Deep navy + dark crimson — dark depth (auto light dashboard) | Navy Blue | Dark Red | | **Graphite** | Near-black + silver grey — monochrome minimal (auto light dashboard) | Near Black | Grey | 🟦 ALERT SYSTEM — TEN CONDITIONS Every alert is gated by its matching "Show X" visibility toggle — if a component is hidden from the chart, its alerts are automatically suppressed. This eliminates the mismatch between visual signals and alert signals that plagues many indicators. | Alert | Condition | Gated By | |---|---|---| | Crossover OB | Prediction crosses above the overbought boundary | Show OB/OS Fill | | Crossunder OB | Prediction crosses back down through OB | Show OB/OS Fill | | Crossover OS | Prediction crosses up through oversold boundary | Show OB/OS Fill | | Crossunder OS | Prediction crosses below the oversold boundary | Show OB/OS Fill | | Crossover Mid | Prediction crosses above the mid line — bullish regime flip | Show Mid Level | | Crossunder Mid | Prediction crosses below the mid line — bearish regime flip | Show Mid Level | | Crossover Signal | Prediction crosses above its WMA signal line (MACD bullish) | Show Signal Line | | Crossunder Signal | Prediction crosses below its WMA signal line (MACD bearish) | Show Signal Line | | Bull Signal Dot | A filtered Bull Signal Dot is plotted (uses Filter Mode + Cooldown + Min Strength) | Show Signal Dots | | Bear Signal Dot | A filtered Bear Signal Dot is plotted (uses Filter Mode + Cooldown + Min Strength) | Show Signal Dots | 🟦 SETTINGS REFERENCE **Visual** - Theme — nine cohesive palettes. Default: Tropic **Machine Learning** - Neighbors (k) — upper bound on neighbors used by the predictor. Default: 100 - Adaptive k — scales k with dataset size using the `floor(sqrt(N))` heuristic. Default: ON - Learning Mode — MA Crossover / Periodic / Hybrid. Default: MA Crossover - Sample Every (bars) — bar interval for the Periodic / Hybrid trigger. Default: 5 - Minimum Training Samples — warm-up gate, predictor outputs zero until reached. Default: 30 - Max Dataset Size — hard FIFO cap. Default: 500 (safe on all timeframes) **Feature Engine** - Trend Length — WMA smoothing applied to both RSI features. Default: 20 - RSI Fast Period — first feature dimension. Default: 5 - RSI Slow Period — second feature dimension. Default: 20 - MA Fast Period — fast WMA for the crossover training trigger. Default: 5 - MA Slow Period — slow WMA for the crossover training trigger. Default: 20 **Prediction Line** - Show Prediction Line — master toggle. Default: ON - Prediction Style — Stratum / Neon / Resonance / Pulse / Mono. Default: Stratum - Prediction Width — 1 to 5. Default: 2 **Signal Line** - Show Signal Line — toggle. Default: ON - Signal Style — Neon / Flat. Default: Neon - Signal Period — WMA length of the signal line. Default: 20 - Signal Width — 1 to 5. Default: 1 **Signal Dots** - Show Signal Dots — toggle. Default: ON - Filter Mode — All Crosses / Zone Only / Mid Aligned / Strict. Default: Strict - Cooldown (bars) — minimum spacing between dots. Default: 5 - Min Strength — minimum `|prediction − signal|` at the cross. Default: 0.5 - Core Dot Size — 1 to 8. Default: 3 - Core Dot Opacity — 0 to 100. Default: 100 - Glow Dot Size — 1 to 12. Default: 8 - Glow Dot Opacity — 0 to 100. Default: 30 **Channel Fill** - Show Channel Fill — toggle. Default: ON - Channel Opacity — 0 to 100. Default: 25 - Channel Lookback — rolling highest / lowest window. Default: 500 **OB / OS Fill** - Show OB/OS Fill — toggle. Default: ON - Zone Stdev Length — stdev window that offsets the OB / OS boundaries inward. Default: 20 **Mid Level** - Show Mid Level — toggle. Default: ON - Mid Level Value — Y-value of the reference line. Default: 0 - Mid Level Style — Solid / Dashed / Dotted. Default: Dashed **Dashboard** - Show Dashboard — toggle. Default: ON - Panel Position — six slots (Top/Middle/Bottom × Right/Left). Default: Middle Right - Panel Text Size — Tiny / Small / Normal / Large. Default: Small **Alerts** - Ten opt-in toggles, one per alert condition. All default: ON 🟦 TRADER PRESETS — SETTINGS BY STYLE QE-ML PRO is volatility-agnostic thanks to the adaptive bands and bias correction, but the reactivity of the predictor scales directly with the feature and sampler parameters. The four presets below are tested starting points you can drop straight into the settings panel — adjust by ±20% to taste. --- ** SCALPER — 1m / 3m / 5m** High-frequency entries, tight stops, many signals per session. Priority is reaction speed — you want the predictor to flip states within a handful of bars of an actual move. | Setting | Value | |---|---| | Trend Length | 10 | | RSI Fast Period | 3 | | RSI Slow Period | 14 | | MA Fast Period | 3 | | MA Slow Period | 10 | | Signal Period | 8 | | Neighbors (k) | 40 | | Adaptive k | ON | | Learning Mode | **Hybrid** | | Sample Every | 2 | | Minimum Training Samples | 20 | | Max Dataset Size | **300** (keeps 1m charts fast) | | Filter Mode | **All Crosses** or Zone Only | | Cooldown | 2 | | Min Strength | 0.3 | | Channel Lookback | 200 | | Zone Stdev Length | 10 | | Prediction Style | Neon or Stratum | **Why:** Low smoothing (Trend=10) + short RSI pair (3/14) keeps the features razor-sharp. Hybrid learning means you never wait for an MA crossover during quiet 1m sessions. Max Dataset capped at 300 protects you from the TradingView per-bar calculation limit on long 1m histories. --- ** DAY TRADER — 15m / 30m / 1H** Balanced reactivity and conviction — the default profile. You want clean crosses without noise spam, and signals that survive the open / close volatility spikes. | Setting | Value | |---|---| | Trend Length | 20 (default) | | RSI Fast Period | 5 (default) | | RSI Slow Period | 20 (default) | | MA Fast Period | 5 (default) | | MA Slow Period | 20 (default) | | Signal Period | 20 (default) | | Neighbors (k) | 100 (default) | | Adaptive k | ON | | Learning Mode | **MA Crossover** (default) | | Minimum Training Samples | 30 (default) | | Max Dataset Size | 500 (default) | | Filter Mode | **Strict** (default) | | Cooldown | 5 (default) | | Min Strength | 0.5 (default) | | Channel Lookback | 500 (default) | | Zone Stdev Length | 20 (default) | | Prediction Style | Stratum (default) | **Why:** Every default value was tuned for this range. Strict filter + 5-bar cooldown keeps the dot count honest on a 30m chart. MA Crossover sampling gives you clean sparse data since 15m+ charts already have enough crossover events. --- ** SWING TRADER — 4H / 1D** Lower signal frequency, higher conviction per signal. You're holding for days or weeks — every dot needs to mean something. | Setting | Value | |---|---| | Trend Length | 30 | | RSI Fast Period | 7 | | RSI Slow Period | 30 | | MA Fast Period | 7 | | MA Slow Period | 30 | | Signal Period | 30 | | Neighbors (k) | 150 | | Adaptive k | ON | | Learning Mode | MA Crossover | | Minimum Training Samples | 50 | | Max Dataset Size | 800 | | Filter Mode | **Strict** | | Cooldown | 10 | | Min Strength | 0.8 | | Channel Lookback | 800 | | Zone Stdev Length | 30 | | Prediction Style | Stratum or Mono | **Why:** Longer feature periods mean the predictor only moves on genuine structural shifts. Larger k (150) + bigger dataset (800) gives the k-NN vote a wider base so outliers don't flip the sign. Cooldown of 10 bars on a 4H chart = 40 hours minimum between dots — exactly what a swing trader wants. --- ** POSITION / LONG-TERM — 1D / 1W / 1M** Macro regime detection. You're looking for the handful of generational setups per year — noise is the enemy. | Setting | Value | |---|---| | Trend Length | 50 | | RSI Fast Period | 10 | | RSI Slow Period | 40 | | MA Fast Period | 10 | | MA Slow Period | 40 | | Signal Period | 40 | | Neighbors (k) | 200 | | Adaptive k | ON | | Learning Mode | **Hybrid** | | Sample Every | 3 | | Minimum Training Samples | 40 | | Max Dataset Size | 1000 | | Filter Mode | **Strict** | | Cooldown | 15 | | Min Strength | 1.0 | | Channel Lookback | 1000 | | Zone Stdev Length | 40 | | Prediction Style | Mono or Pulse | **Why:** Weekly and monthly charts have few crossover events per year — without Hybrid mode the dataset starves. Sample Every = 3 on a weekly chart means one sample every 3 weeks, which is plenty of structural density. Min Strength 1.0 filters out every shallow cross — you only see dots on generational momentum inflections. --- **Tuning Tip** If the predictor feels **too reactive** → increase Trend Length and Signal Period by 25%, raise Cooldown. If the predictor feels **too sluggish** → switch Learning Mode to Hybrid, decrease Min Samples, lower Trend Length. If the dashboard shows **Dataset N is stuck low** → switch Learning Mode from MA Crossover to Hybrid — crossover events are too rare on your current settings. If you see **runtime / timeout errors** on long histories → drop Max Dataset Size to 300 and Channel Lookback to 300. 🟦 COMPATIBILITY Works on all asset classes and all timeframes in TradingView Pine Script v6. - **Crypto** — Spot, futures, perpetual contracts - **Forex** — All pairs - **Equities** — Stocks, ETFs, indices - **Commodities** — Metals, energy, agriculture - **Timeframes** — 1m through Monthly The k-NN engine learns each asset's own RSI fingerprint distribution, and the stdev-adjusted bands auto-scale to the volatility of that distribution, so the indicator is truly self-calibrating across assets and timeframes — no manual recalibration required. 🟦 TECHNICAL NOTES - Pine Script v6 - No repainting — training samples are gated by `barstate.isconfirmed` so the dataset never absorbs unconfirmed live-bar values - Dataset is hard-capped via FIFO rotation; no unbounded memory growth - Dashboard renders only on `barstate.islast` — zero historical overhead - All drawing objects are stateless plots (no label / box / line object pools), so `max_*_count` limits cannot be exceeded - k-NN distance pass is O(N), sort is O(N log N), both bounded by Max Dataset Size - Default Max Dataset Size of 500 is tuned to stay within TradingView's per-bar calculation budget on histories up to ~50,000 bars - Bias correction uses a single extra accumulator pass during the distance sweep — no performance penalty 🟦 DISCLAIMER This indicator is provided for educational and informational purposes only. It does not constitute financial advice. Past performance does not guarantee future results. The k-NN engine learns from historical patterns, but markets do not guarantee that historical patterns will repeat. Always conduct your own analysis and apply proper risk management.
Penunjuk Pine Script®
oleh a_jabbaroff
24
Liquidity Zone Harvester [JOAT]Liquidity Zone Harvester Introduction Institutional order flow leaves footprints in market structure. When a large buyer or seller places a significant order, the execution of that order creates an imbalance between supply and demand at a specific price level — and markets frequently return to these levels to test whether the original interest remains. These price areas are commonly referred to as order blocks or liquidity zones, and they form one of the core concepts in institutional and Smart Money trading methodology. The Liquidity Zone Harvester is an automated order block detection and management system that identifies these zones using statistically validated momentum signals rather than arbitrary manual placement. Instead of drawing boxes wherever a trader's eye thinks supply or demand may exist, this indicator uses Z-score cumulative impulse detection to identify when directional momentum has reached statistically significant levels — and only then marks the most recent opposing-close candle as the source order block. Volume quality gates ensure that only high-participation impulses create zones, filtering out low-conviction moves that are less likely to represent genuine institutional activity. What sets this indicator apart from standard order block tools is what happens after zone creation. Every active zone is tracked through a dual-mechanism aging system. The Bayesian exponential decay model progressively reduces zone visual intensity over time with a configurable half-life, providing a continuous probability signal about zone freshness. Simultaneously, a Kaplan-Meier survival analysis engine — borrowed from medical statistics — estimates the probability that a given zone will survive future price tests, based on the historical survival rates of all previously observed zones in the training window. Each zone displays both its current age and its estimated survival probability directly on the chart, turning static boxes into dynamically updated probability estimates. Core Concepts 1. Z-Score Cumulative Impulse Detection Zone creation is triggered only when directional momentum reaches a statistically defined threshold. The system accumulates a running streak of directional closes — when consecutive bars close higher than their open, the bull accumulator grows; when consecutive bars close lower, the bear accumulator grows. The streak resets when direction reverses. This cumulative streak is then normalized against its own rolling mean and standard deviation, producing a Z-score that measures how unusual the current momentum streak is relative to recent history. cumBull := close > open ? nz(cumBull ) + (close - open) : 0 cumBear := close < open ? nz(cumBear ) + (open - close) : 0 zBull = (cumBull - ta.sma(cumBull, zLen)) / ta.stdev(cumBull, zLen) zBear = (cumBear - ta.sma(cumBear, zLen)) / ta.stdev(cumBear, zLen) bullEvent = ta.crossover(zBull, zThresh) and barstate.isconfirmed and volOK bearEvent = ta.crossover(zBear, zThresh) and barstate.isconfirmed and volOK When a bullEvent fires (bull Z-score crosses the threshold with volume confirmation), the system looks backward to find the most recent down-close candle — the last bar where sellers were dominant before the impulse began. This becomes the demand zone. Similarly, a bearEvent marks the most recent up-close candle as the supply zone. 2. Volume Quality Gate Not all Z-score impulses are created equal. An impulse that occurs on abnormally low volume represents weak conviction — possibly a thin-market price drift rather than genuine institutional momentum. The volume gate applies RSI to the volume series to normalize it against its own history. Only when volume RSI exceeds the configurable threshold is the volOK condition true, enabling zone creation. volRsi = ta.rsi(volume, 14) volOK = volRsi > volThresh This filter meaningfully reduces the number of zones created during low-participation conditions such as pre-market sessions, lunch hours, or holiday-period trading — precisely the times when order block levels are least likely to represent significant institutional interest. 3. Order Block Zone Construction When a signal event is confirmed, the most recent opposing candle is identified using ta.valuewhen(). For a bullEvent, the system finds the most recent bar where close was less than open (a down candle) — its high and low define the demand zone boundaries. For a bearEvent, it finds the most recent up candle — its high and low define the supply zone boundaries. A box object is created spanning from that historical bar to the current bar, with height defined by the candle's actual high-low range. lastDnHigh = ta.valuewhen(close < open, high, 0) lastDnLow = ta.valuewhen(close < open, low, 0) lastDnBar = ta.valuewhen(close < open, bar_index, 0) if bullEvent newBox = box.new(lastDnBar, lastDnHigh, bar_index, lastDnLow, ...) bullBoxes.push(newBox) 4. Overlap Prevention (f_no_overlap) To avoid cluttering the chart with redundant zones that occupy the same price territory, an overlap check function evaluates whether a proposed new zone overlaps with any existing zone of the same type. The function iterates over all existing bull or bear boxes and compares the new zone's top and bottom against each existing box's top and bottom. A guard condition (nBull > 0) prevents the iteration from running on an empty array, which would cause an index -1 crash. f_no_overlap(newTop, newBot, boxes) => noOverlap = true if boxes.size() > 0 for i = 0 to boxes.size() - 1 b = boxes.get(i) if newTop >= box.get_bottom(b) and newBot <= box.get_top(b) noOverlap := false noOverlap 5. Bayesian Exponential Decay Each zone's visual transparency is driven by an exponential decay function that represents the diminishing probability of zone relevance over time. The half-life parameter (default: 75 bars) defines how quickly a zone fades. At age 0, the zone is fully opaque. At age 75 bars, the zone is at 50% opacity. At age 150 bars, 25% opacity. This continuous decay — rather than a binary active/expired switch — provides an analog probability signal directly encoded in the zone's visual intensity. decayFactor = math.exp(-0.693 * age / halfLife) zoneAlpha = math.round(decayFactor * 200) box.set_bgcolor(b, color.new(zoneColor, 255 - zoneAlpha)) 6. Kaplan-Meier Survival Analysis The Kaplan-Meier estimator is a nonparametric statistical method originally developed to measure survival probabilities in clinical trial data. In this indicator, "survival" is defined as a liquidity zone remaining unmitigated (not breached by a closing price on two separate occasions). Each time a zone is mitigated, it is recorded as a "death event" at its current age. Zones that expire by age limit without mitigation are recorded as "censored events" — incomplete observations. The KM formula multiplies survival probabilities across all observed events up to a given age. // For each completed event (death at age t_i with n_i at-risk zones): S_t := S_t * (1.0 - d_i / n_i) // Product over all event times <= query age For each active zone, the indicator queries the KM estimate at the zone's current age and displays the result as a percentage label. A zone at age 40 showing "Age 40 | 72%" means that historically, 72% of zones survived to at least 40 bars without being mitigated — giving traders a quantitative assessment of how likely the zone is to hold on the next test. Features Z-Score Cumulative Impulse: Statistical momentum threshold using normalized cumulative directional streaks to gate zone creation. Volume Quality Gate: Volume RSI filter ensures only high-participation impulses create zones. Precise Order Block Identification: Most recent opposing candle (last down-close for bull event, last up-close for bear event) defines zone boundaries. Overlap Prevention: f_no_overlap function checks all existing zones before creating a new one, preventing chart clutter from redundant levels. Bayesian Exponential Decay: Zone opacity decays over time with configurable half-life, encoding freshness as a visual probability signal. Kaplan-Meier Survival Analysis: Medical-statistics survival estimator applied to zone longevity, displayed as a percentage probability label on each active zone. Dynamic Zone Extension: Box right edge extends to the current bar on every update, keeping zones visually connected to the present. Mitigation Tracking: Zones that are closed through twice are flagged as mitigated and removed, with the event recorded for KM analysis. Seven-Row Dashboard: Active demand count, active supply count, bull Z, bear Z, volume RSI, KM training size, and signal status. Two Alert Conditions: Zone created alert and zone rejection (price tests and bounces back) alert. Input Parameters Z-Score Settings: Z Lookback: Rolling window for Z-score normalization (default: 50) Z Threshold: Sigma level required to trigger an impulse event (default: 2.0) Volume Gate Settings: Volume RSI Period: RSI lookback for volume normalization (default: 14) Volume RSI Threshold: Minimum volume RSI for zone creation eligibility (default: 55) Zone Management Settings: Max Zone Age: Maximum bars a zone remains active before forced removal (default: 300) Mitigation Count: Number of closes through a zone required for mitigation (default: 2) Max Active Zones Per Side: Maximum simultaneous demand or supply zones displayed (default: 5) Decay Settings: Decay Half-Life: Number of bars at which zone opacity reaches 50% of initial value (default: 75) KM Settings: KM Training Window: Bar lookback for Kaplan-Meier training data collection (default: 500) Show Survival Labels: Toggle KM probability labels on active zones (default: true) Display Settings: Show Demand Zones: Toggle demand (bull) zone boxes (default: true) Show Supply Zones: Toggle supply (bear) zone boxes (default: true) Show Dashboard: Toggle the seven-row information table (default: true) How to Use This Indicator Step 1: Understand Zone Creation Conditions Zones are not created on every bar — they are created only when a statistically significant directional impulse (Z-score above threshold) occurs on above-average volume. This selectivity is intentional. In any given trading session, you will likely see only a few zone creation events, each backed by a genuine momentum surge that suggests institutional participation. When you see a new zone appear, note the Z-score values in the dashboard and the volume RSI reading — higher values on both indicate a stronger impulse and more confident zone placement. Step 2: Prioritize Fresh, High-Survival Zones Not all zones on the chart are equally relevant. A fresh zone (low age, full opacity) at a KM survival rate of 80% is a far stronger candidate for price reaction than an old zone (high age, near-transparent) at 30% survival probability. Use both the visual opacity and the KM label together: as a zone ages and fades, reduce your expectation that it will provide meaningful support or resistance. When price approaches a zone that is both visually fresh and shows high KM survival probability, the statistical expectation of reaction is at its highest. Step 3: Watch for Zone Rejection Alerts The zone rejection alert fires when price tests a zone (enters the box boundary) and then closes back away from it without mitigating it. This is the core trade setup: price returning to the institutional order block level, briefly penetrating it, and then reversing. The rejection alert provides a timely notification for potential entries in the direction of the original impulse that created the zone, with the zone's near boundary serving as the natural stop-loss reference. Step 4: Monitor KM Training Size for Statistical Validity The dashboard displays the KM training sample size — the number of completed zone events (both mitigated and aged-out) available for the survival analysis. With fewer than 10 training events, the KM estimate has high variance and should be treated as rough guidance. With 30 or more training events, the estimate becomes statistically stable. On instruments or timeframes where the indicator has run for extended periods, the KM estimates become increasingly reliable as the training dataset grows. Indicator Limitations The Z-score cumulative impulse and volume gate require sufficient chart history for the rolling normalization periods to be seeded. In the first Z-lookback bars of a new chart, zone creation signals may be less reliable as the mean and standard deviation are not yet fully established. Kaplan-Meier survival estimates are only as reliable as the training dataset. On instruments or timeframes that have not accumulated many completed zone events, the survival probabilities should be treated as rough estimates rather than statistically precise values. The mitigation definition (two closes through the zone) is a configurable approximation. In real order block theory, mitigation can be defined in several ways; this indicator's specific definition may not match every trader's conceptual framework. Zones are based on the most recent opposing candle at the time of the impulse event. In fast markets where multiple large candles cluster closely together, the marked candle may not represent the most significant institutional order location. This indicator requires volume data. On instruments where volume is unavailable or unreliable (some synthetic indices, certain forex pairs), the volume gate will not function as intended and should be disabled or its threshold lowered significantly. The exponential decay model assumes a constant half-life across all market conditions. In reality, zone relevance can be regime-dependent — a zone formed during a trending market may remain relevant longer than one formed during a range, or vice versa. Maximum active zones per side is a hard limit. If the limit is reached, new valid zone creation events will be rejected until an existing zone is mitigated or aged out. Originality Statement The Liquidity Zone Harvester is a genuinely original indicator that applies statistical and mathematical frameworks from outside the trading domain to a problem common in technical analysis. The Z-score cumulative impulse detection — using consecutive close-open accumulation normalized against rolling sma/stdev — as the primary trigger for order block marking is an original signal architecture. Most order block indicators use visual pattern matching (e.g., a large candle followed by a gap) rather than statistical significance thresholds. Applying the Kaplan-Meier survival estimator — a nonparametric method from biostatistics — to estimate the probability that a liquidity zone will survive future price tests is a novel application of medical statistics to market analysis. This provides a mathematically grounded probability estimate that no standard order block indicator offers. The Bayesian exponential decay applied to zone visual transparency — using a configurable half-life to continuously encode zone freshness as opacity — is an original visual design that treats zone relevance as a continuously diminishing probability rather than a binary active/inactive state. The overlap prevention function that iterates over all existing zone arrays before creating a new zone — with the index-crash guard for empty arrays — is a specific engineering solution to a concrete problem in box-based indicator design. The volume RSI quality gate, applied specifically to filter Z-score impulse events rather than as a standalone signal, is an original confluence filter design that specifically addresses the problem of thin-market false signals in order block detection. Disclaimer The Liquidity Zone Harvester is provided for educational and informational purposes only. It is a technical analysis tool and does not constitute financial advice. Liquidity zones and order blocks are analytical constructs; they do not guarantee price reactions. Past zone behavior as encoded in Kaplan-Meier estimates does not predict future zone performance. All trading involves risk of loss. Users are solely responsible for their own trading decisions. Please consider your individual risk tolerance and consult a licensed financial professional before engaging in any trading activity. -Made with passion by officialjackofalltrades
Penunjuk Pine Script®
oleh officialjackofalltrades
Self-Aware Trend System [WillyAlgoTrader]🧠 Self-Aware Trend System (SATS) is an adaptive SuperTrend-based trend-following system that continuously measures its own operating environment through a 4-factor Trend Quality Index (TQI) and modulates band width, asymmetry, and flip logic in real time. Unlike a fixed SuperTrend — which uses the same ATR multiplier forever — SATS knows when the market is trending vs. chopping, compresses bands in clean trends to lock profit tighter, widens them in noisy conditions to avoid whipsaws, and can detect regime collapse through a "character-flip" even when price hasn't broken the band yet. Each confirmed signal comes with a full trade plan (Entry, SL, TP1/TP2/TP3 at user-defined R multiples), and the system tracks its own realized R, win rate, drawdown, and per-regime edge — building an honest, instrument-specific performance log directly on your chart. The name "Self-Aware" refers to one specific property: the indicator measures the quality of its own environment every bar and feeds that measurement back into its band width and flip conditions. It doesn't predict the future — it reacts to present conditions with mathematically defined adaptation rules. 🧩 WHY THESE COMPONENTS WORK TOGETHER A classic SuperTrend has one problem: its ATR multiplier is fixed. In a clean trending market the bands are too wide, giving back profit on every pullback. In a choppy market the bands are too tight, generating whipsaw after whipsaw. Traders try to fix this by manually switching multipliers per timeframe or per instrument — but that's guesswork. SATS chains a different approach: Market state measurement (TQI) → Non-linear band modulation → Asymmetric band widths → Character-flip detection → R-multiple trade plan → Outcome tracking → Regime-aware statistics The TQI engine measures market quality from four independent angles each bar (efficiency, volatility regime, structure, momentum persistence). The non-linear modulation translates that quality into band width — high quality compresses bands, low quality expands them, using a power curve that avoids both over-reacting to mild fluctuations and under-reacting to severe regime changes. Asymmetric bands tighten the active side (in the direction of the trend) while loosening the passive side — creating a "ratchet with leverage" that locks in profit faster than it invalidates the trend. Character-flip detection catches regime collapses (high quality → low quality) even when price hasn't broken the band — critical for exiting stale trends before they fully reverse. And performance tracking records every signal's realized R, building a real statistical picture of how the system performs on your specific instrument and timeframe. Without TQI, the bands are blind. Without asymmetry, profit-taking lags. Without character-flip, exits happen too late. Without performance tracking, you have no idea if the system has a real edge on your instrument. All four work together — each layer addresses a specific weakness of classic SuperTrend. 🔍 WHAT MAKES IT ORIGINAL 1️⃣ Trend Quality Index (TQI) — 4-factor continuous quality measurement. TQI is computed every bar as a weighted combination of four independent 0..1 factors: — 🧭 Efficiency (default weight 0.35) : Kaufman Efficiency Ratio = |close − close | / sum(|close − close |). Measures directional movement vs. total path. 1.0 = perfect straight line, 0.0 = pure noise. Default window 20 bars. — 📊 Volatility Regime (weight 0.20) : uses Volume Z-score when volume data is available (z = (volume − sma) / stdev, mapped from to ), or falls back to ATR ratio (current ATR vs. long-baseline ATR) on volume-less instruments. — 🏗️ Structure (weight 0.25) : price position within its recent range. pricePos = (close − lowest) / (highest − lowest). Then tqiStruct = |pricePos − 0.5| × 2. Trends pin price to one edge (1.0), chop oscillates around the midpoint (0.0). No ATR dependency. — ⏩ Momentum Persistence (weight 0.20) : of the last N bars, what fraction moved in the same direction as the overall window change? alignedBars / N. Default 10 bars. Final TQI = (factor1 × w1 + factor2 × w2 + factor3 × w3 + factor4 × w4) / sum(weights), clamped to 0..1. Each weight is user-configurable. 2️⃣ Non-linear band modulation with power curve. Instead of a linear "multiplier × (1 − tqi)", SATS uses a power curve: qualityDeviation = (1 − tqi)^curvePower tqiMult = 1 − qStrength + qStrength × (0.6 + 0.8 × qualityDeviation) With curvePower = 1.5 (default), mild quality drops (from 0.9 to 0.7) cause small band expansion, but severe drops (0.5 to 0.2) cause rapid expansion. This matches how traders actually think: ignore small wobbles, react strongly to clear regime changes. 3️⃣ Asymmetric band widths — ratchet with leverage. In a strong uptrend, the lower band (active, trailing price up) tightens while the upper band (passive, not used as stop) widens: activeMult = symMult × (1 − asymStrength × tqi × 0.3) passiveMult = symMult × (1 + asymStrength × tqi × 0.4) Effect: as trend quality rises, the trailing stop moves closer to price (locking profit faster) while the opposite band moves away (so an accidental pullback doesn't trigger a flip). This is the "leverage" — asymmetric response to confirmed trend strength. 4️⃣ EMA-smoothed multipliers before ratchet application. Raw TQI can spike bar-to-bar. If those spikes fed directly into the SuperTrend ratchet logic, bands would compress at a high-TQI bar and stay stuck there (SuperTrend math never loosens active bands against the trend). SATS EMA-smooths the multipliers (alpha 0.15) before ratchet application — preventing stickiness. This is the critical fix that makes adaptive SuperTrend actually work in practice. 5️⃣ Efficiency-weighted ATR. Used for band construction and SL/TP sizing (not for TQI itself, to avoid circular feedback): effATR = rawATR × (0.5 + 0.5 × ER) Clean trending volatility counts full (ER = 1.0 → effATR = rawATR). Noisy chop volatility is halved (ER = 0.0 → effATR = 0.5 × rawATR). This makes SL/TP distances proportional to "useful" volatility, not total volatility. 6️⃣ Character-flip detection with age guard. Classic SuperTrend only flips on price breaks. But a trend can die internally — quality collapses, momentum fades — before price actually breaches the band. Character-flip catches this: charFlipDown = prevTQI > 0.55 (high) AND currentTQI < 0.25 (low) AND trendAge ≥ minAge AND close < source The age guard (default 5 bars) prevents whipsaw on fresh trends — a newborn trend hasn't had time to establish quality, so early TQI noise can't kill it. After the age threshold, a quality collapse triggers an immediate flip even without price break. 7️⃣ Auto-fixed TP order. If a user accidentally sets TP1 > TP2 (or TP3 < TP2), the indicator automatically sorts them. Math: fixedMin = min(all), fixedMax = max(all), middle = sum − min − max. The three TP lines always end up in correct order on the chart regardless of user input order. 8️⃣ R-multiple trade planning with pivot-anchored SL. On each signal: — Entry = close at bar of confirmed flip — SL = min(pivot − slMult×ATR, entry − slMult×ATR) for longs (mirror for shorts) — TP1/2/3 = entry ± risk × R-multiple The SL uses whichever is further from entry — the recent pivot (if available) or a pure ATR distance. This ensures the stop always has a minimum ATR buffer regardless of how close the nearest pivot is. 9️⃣ Performance tracking with realized R accounting. Every signal is tracked bar-by-bar for TP hits, SL hits, and timeout (default 100 bars). On close-out, realized R is calculated assuming 1/3 position per TP: — TP3 hit: realized = (tp1R + tp2R + tp3R) / 3 (all three filled) — SL hit after TP1: realized = (1/3) × tp1R + (2/3) × (−1R) — SL hit after TP1+TP2: realized = (1/3) × tp1R + (1/3) × tp2R + (1/3) × (−1R) — Pure SL: realized = −1R — Timeout: realized = sum of already-hit TP portions (no penalty) Results feed a rolling buffer (up to 100 signals), which drives: — Rolling Win Rate — Rolling Avg R — Rolling drawdown (window DD) — All-time drawdown — Current and max win/loss streaks 🔟 9-cell regime edge tracking. Every completed signal is bucketed by the market regime at entry time: Efficiency bin (low/mid/high) × Volatility bin (low/normal/high) = 3×3 = 9 cells. Each cell accumulates its own EWMA of realized R. The dashboard shows the current regime's historical edge — e.g., "Trending + High Vol: +0.85R (23 trades)". This lets you see which market conditions the system actually profits in. 1️⃣1️⃣ Experimental self-calibration (off by default). When enabled, the system monitors its rolling avg R and drifts the Quality Influence parameter toward the user default if recent edge is poor (below threshold). This is explicitly marked experimental — no claim of improved results — and recommended off until validated on your instrument. ⚙️ HOW IT WORKS — CALCULATION FLOW Step 1 — TQI computation : Compute four factors (Efficiency, Volatility Regime, Structure, Momentum Persistence). Weight and combine into a single 0..1 value. Step 2 — ATR and effective ATR : rawATR = ta.atr(len). effATR = rawATR × (0.5 + 0.5 × ER). Step 3 — Adaptive multiplier : Apply legacy ER adaptation (optional) and non-linear TQI curve. If asymmetric bands enabled, split into active/passive multipliers. Step 4 — EMA smoothing : Smooth both multipliers with alpha 0.15 to prevent ratchet stickiness. Step 5 — SuperTrend bands : upperBand = source + upperMult × effATR. lowerBand = source − lowerMult × effATR. Ratchet logic: lower only rises, upper only falls, until a flip. Step 6 — Flip detection : Price flip (close crosses opposite band) OR character-flip (TQI collapse + age guard). On flip: reset trend age, start new segment. Step 7 — Trade plan : On confirmed flip, compute Entry/SL/TP1/TP2/TP3. Draw lines and labels. Cache the market regime (ER bin × Vol bin) for later edge attribution. Step 8 — Outcome tracking : Each bar, check active trade for TP1/TP2/TP3/SL hits and timeout. On close-out, calculate realized R, push to history buffer, update rolling stats, drawdown, streaks, and regime cell. Step 9 — Dashboard render : On last bar, render live state (Trend, TQI, regime, performance stats, TQI breakdown, regime edge). 📖 HOW TO USE 🎯 Quick start: 1. Add indicator — preset is "Auto" (adapts to your current timeframe) 2. Green line = bullish trend, red = bearish trend 3. Line transparency reflects TQI: bright = high quality, faded = low quality 4. ▲ BUY / ▼ SELL labels appear on confirmed flips 5. Entry, SL, TP1, TP2, TP3 lines drawn automatically at the signal 6. Copy levels to your exchange, let the dashboard track outcomes 👁️ Reading the chart: — 🟢 Bright green line = bullish trend with high TQI — aggressive participation — 🟢 Faded green line = bullish trend with low TQI — cautious, possible regime shift — 🔴 Bright red line = bearish trend with high TQI — 🔴 Faded red line = bearish trend with low TQI — Line flip + label = new trade signal — Dashed TP lines turning solid + "✓" = TP was hit — Score on label (e.g., "85/102") = multi-factor confluence strength 📊 Dashboard fields: — Preset: Auto-resolved (Scalping / Default / Swing / Crypto) — Trend: Bullish ▲ / Bearish ▼ — TQI: current quality index (0..1) — Q.Strength: effective Quality Influence (may drift if auto-calibration enabled) — Signal: current bar signal (BUY / SELL / —) — Regime: Trending / Mixed / Choppy + Low/Norm/High Vol — ER / RSI / Vol Z: raw filter values — TQI Components breakdown: Efficiency / Volatility / Structure / Momentum (each 0..1) — Performance section: Win Rate, Avg R, Window DD, All-Time DD, Streak W/L, Regime Edge 🔧 Tuning guide: — Too many whipsaws : increase Quality Influence (0.5–0.7), increase Structure weight, increase Base Band Width — Missing moves / signals too late : decrease Quality Influence (0.2–0.3), decrease Base Band Width, increase asymmetry — Choppy instrument : use Swing preset, enable Character-Flip, raise minAge to 10+ — Strong trending instrument : use Scalping preset, enable Asymmetric Bands with strength 0.6+ — No volume data : automatically falls back to ATR ratio for volatility regime — no action needed ⚙️ KEY SETTINGS REFERENCE ⚙️ Main: — Preset : Auto / Custom / Scalping / Default / Swing / Crypto 24/7 (auto-adapts ATR, band width, ER window, RSI, SL multiplier) — ATR Length (13), Base Band Width (2.0 × ATR) 📐 Trend Quality Engine: — Enable TQI (default On) — Quality Influence (0.4): how strongly TQI compresses/expands bands — Quality Curve Power (1.5): non-linearity — Smooth Adaptive Multipliers (On): critical fix for ratchet stickiness — Asymmetric Bands (On) + Asymmetry Strength (0.5) — Efficiency-Weighted ATR (On) — Character-Flip (On) + Min Age (5) + High/Low TQI thresholds (0.55 / 0.25) — TQI factor weights : ER 0.35, Volatility 0.20, Structure 0.25, Momentum 0.20 🎯 Risk: — SL Buffer (1.5 × ATR), TP1/2/3 R-multiples (1.0 / 2.0 / 3.0), Trade Timeout (100 bars) 🤖 Self-Learning (experimental): — Auto-calibration (default Off), calibration window, bad/good R thresholds, quality step, cooldown, floor/ceiling — Reset Learning Memory button 📊 Dashboard: position, TQI breakdown toggle, performance stats toggle, score breakdown toggle 🔔 Alerts — 🟢 BUY — ticker, TF, price, TQI, score, SL, TP1, TP2, TP3 — 🔴 SELL — same payload Plain text and JSON webhook formats supported. Bar-close confirmed. ⚠️ IMPORTANT NOTES — 🚫 No repainting. All signals require barstate.isconfirmed. SuperTrend ratchet logic is monotonic — once the trailing band moves, it cannot move back against the trend until a flip. Character-flip uses only previous-bar TQI and current-bar close, both available at bar close. — 📊 TQI is descriptive, not predictive. It measures current market quality from 4 factors — it does not forecast future price. A high TQI reading means "the market is currently behaving like a trend" — it can still fail on the next bar. — 📏 Performance stats are walk-forward, not backtested. The rolling buffer records signals as they happen, bar by bar. Drawdown, win rate, and regime edge are honest forward-looking statistics on your specific instrument and timeframe — not curve-fitted optimization results. — ⚖️ The realized R accounting assumes 1/3 position per TP . This mirrors a standard "scale out at each target" approach. Traders who hold full position to a single target should interpret the R values accordingly. — 🔄 Auto-calibration is experimental. It's off by default and should stay off until you've validated it on your specific instrument. The drift is mean-reverting (toward your user default), not profit-maximizing — no claim of improvement is made. — 🔒 The Reset Learning Memory button clears the rolling buffer, regime cells, drawdown, and streak stats. Use when changing instruments or after significant market regime shifts. — 🛠️ SATS is a decision-support and trade-planning tool , not an automated bot. It identifies trend conditions, measures environmental quality, provides structured trade plans with R-based targets, and tracks outcomes — trade decisions and execution remain yours. — 🌐 Works on all markets and timeframes. Volume-dependent features (Volume Z in TQI) auto-fall-back to ATR-based measurement when volume data is unavailable.
Penunjuk Pine Script®
oleh WillyAlgoTrader
76
Golden Pocket Syndicate Mini (GPSM)This indicator is an overlay toolkit that combines multi-timeframe Golden Pocket-style zones (Fibonacci-derived ranges between user-defined high/low ratios), optional GP-anchored VWAPs that reset when price interacts with the matching zone, and a confluence framework with optional visuals (signals, divergences, order-block-style markers, sweeps, trails). It is intended to help traders see where higher-timeframe ranges and optional filters overlap on the chart—not to automate trading or promise outcomes. What it does Pulls prior completed higher-timeframe highs/lows via request.security() and derives upper/lower pocket levels from your fib inputs. Plots pocket bands (and fills where used) for the timeframes you enable. Optionally plots volume-weighted averages anchored to touches of the corresponding pocket. Combines user-toggled filters into a confluence score and optional bull/bear markers; all signal logic can be turned off in settings. How to use Open settings, enable only the pocket timeframes and visuals you need. Adjust fib inputs, touch tolerance, and filter groups to match your process. If you use alerts, treat them as notifications only—confirm every trade in your own plan. Important limitations This is not financial, investment, or tax advice. Markets involve risk; past or hypothetical chart behavior does not guarantee future results. Higher-timeframe data and request.security() behavior depend on symbol, session, and chart timeframe. Validate outputs on your instruments before relying on them. Scripts cannot execute orders; you are responsible for compliance, sizing, and risk. Companion For separate 1H / 4H / 8H pocket bands (to reduce plot limits when combined with heavy scripts), use the author’s “Golden Pocket Syndicate mini” (GPSM) publication if offered. Golden Pocket Syndicate mini (GPSM) — public description Use this in the publication description field (English first). GPSM is a lightweight companion overlay focused on 1-hour, 4-hour, and 8-hour Golden Pocket-style zones: two fib ratios applied to the prior completed bar’s range on each timeframe, with optional filled bands and optional GP-anchored VWAPs (off by default) that reset when price touches the matching pocket. The 1-hour band can optionally switch color using a simple prior closed 1H close vs EMA rule so you can see a regime-style split at a glance. What it does Uses request.security() on "60", "240", and "480" minute timeframes with the same prior-bar anchoring idea as the author’s main GPS Pro script. Keeps the script small so it can run alongside heavier indicators without hitting Pine’s plot limits as quickly. How to use Add it to your chart, toggle 1H/4H/8H zones and fills, then optionally enable individual VWAPs. Match fib settings to your main workflow if you use GPS Pro on the same chart. Important limitations Not financial advice. No performance or profitability claims. Past chart behavior does not predict future prices. HTF behavior varies by symbol and session (especially 8H). Confirm levels on your market. You are solely responsible for trading decisions and risk. Relationship to GPS Pro GPSM does not duplicate the full confluence, SMC filters, or alerts stack from GPS Pro; it is meant as a focused HTF pocket + optional VWAP add-on.
Penunjuk Pine Script®
oleh RickyTSpanish
Pulse Trend Radar [WillyAlgoTrader]⦿ Pulse Trend Radar is an overlay indicator built on a Kaufman Adaptive Moving Average (KAMA) core with median-ATR volatility bands — producing an adaptive trend system that speeds up in trending markets and slows down in noise. Every trend flip generates a signal scored by a 4-factor quality engine (0–100) with letter grades (A+ through C). The indicator also detects and visualizes liquidity zones from pivot highs/lows, marks order blocks from the last opposite candle before each trend flip, tracks real-time P&L with a live trade tracker, and monitors win/loss outcomes — creating a complete trend-following framework with Smart Money context. 🧩 WHY THESE COMPONENTS WORK TOGETHER A trend indicator alone tells you direction — but not whether the entry is near a liquidity pool (where stops cluster), not whether there's institutional supply/demand nearby (order blocks), not how strong the signal is (all flips treated equally), and not how the system performs over time (no feedback). This indicator layers four analysis dimensions onto the adaptive trend core: KAMA adaptive trend + median ATR bands → Trend direction and flip detection Liquidity zones from pivots → Where stop-hunts and liquidity grabs are likely Order blocks from pre-flip candles → Where institutional supply/demand was established 4-factor signal scoring → Quality filtering — not all flips are equal Win/loss tracker → Performance feedback on this instrument and timeframe The KAMA core adapts its speed via the Efficiency Ratio — in a strong trend, the MA tracks price closely and the bands tighten, producing early signals. In choppy conditions, the MA barely moves and the bands widen, filtering out noise. The liquidity zones show where clusters of stops sit (above pivot highs, below pivot lows) — entries near these zones have higher follow-through because the liquidity grab fuels the move. The order blocks mark the institutional footprint before each trend change — these zones often act as support/resistance on retests. And the signal score combines trend strength, volume delta, efficiency acceleration, and liquidity proximity into a single quality metric — letting you prioritize A+ setups over C-grade ones. 🔍 WHAT MAKES IT ORIGINAL 1️⃣ Kaufman Adaptive Moving Average (KAMA) trend core. The KAMA computes a smoothing constant from the Efficiency Ratio: ER = |price − price | / sum(|price − price |, N) fastSc = 2 / (fastLen + 1), slowSc = 2 / (slowLen + 1) sc = (ER × (fastSc − slowSc) + slowSc)² KAMA = KAMA + sc × (price − KAMA ) When ER → 1 (pure trend): sc approaches fastSc² → KAMA tracks price tightly. When ER → 0 (pure noise): sc approaches slowSc² → KAMA barely moves. This produces a line that accelerates into trends and goes flat in chop — without any manual period switching. 2️⃣ Median ATR volatility bands. Instead of standard ATR (arithmetic mean of true ranges), the indicator uses a median of recent true ranges computed via a ring buffer over the volatility lookback (default 50 bars). The median is more robust to outlier spikes (gap bars, flash wicks) than the mean — producing smoother, more stable band widths. Bands: upper = KAMA + medianATR × multiplier, lower = KAMA − medianATR × multiplier. Trend flips when the previous bar's source price crosses beyond a band: source > upper → bullish, source < lower → bearish. The active band (lower in uptrend, upper in downtrend) is plotted as the trend line. 3️⃣ Displacement-based gradient fill. The fill between the trend line and price is not a fixed transparency — it scales with displacement: displacement = |price − KAMA| / (medianATR × multiplier). The further price stretches from KAMA, the more intense the fill becomes (transparency decreases from 95 to 60). This creates a visual "heat map" effect: faint fill near KAMA (low extension), bright fill far from KAMA (overbought/oversold). This gives immediate visual feedback on how extended the current move is without needing a separate oscillator. 4️⃣ Liquidity zone detection and sweep tracking. Pivot highs and lows (configurable lookback, default 4 bars) are marked as liquidity zones: — Above pivot highs → bearish liquidity (buy stops cluster above swing highs — potential sell-side liquidity) — Below pivot lows → bullish liquidity (sell stops cluster below swing lows — potential buy-side liquidity) Each zone extends rightward as a thin box (height = 0.15× medianATR). Zones are automatically removed when price sweeps through them (high crosses above bearish zone top, or low crosses below bullish zone bottom) — representing the liquidity grab event. Up to 15 zones per side (configurable). The signal scoring engine measures the nearest liquidity zone distance on each trend flip — entries closer to a liquidity pool receive a higher quality score because the stop-hunt provides fuel for the ensuing move. 5️⃣ Order block detection on trend flips. When the trend flips, the previous bar is marked as an order block: — Bullish flip → demand order block (the last bearish candle before the reversal — where institutional buying absorbed selling pressure) — Bearish flip → supply order block (the last bullish candle before the drop — where institutions distributed) Each OB is drawn as a box from the previous candle's high to low, extending rightward. OBs are automatically invalidated (deleted) when price closes beyond the opposite edge after 3+ bars — indicating the zone has been broken. Up to 10 OBs per side (configurable). 6️⃣ 4-factor signal quality scoring (0–100). Each trend flip is scored on four factors: — 📐 Trend strength (25 pts) : combined from ER (directional efficiency) and displacement from KAMA — measures how strong the trend is at the moment of the flip — 📊 Volume delta alignment (25 pts) : buy volume vs sell volume accumulated during the previous trend leg — bullish flip with positive volume delta scores higher (smart money was accumulating) — ⚡ Efficiency acceleration (25 pts) : current ER minus previous ER — positive acceleration means the trend is gaining momentum, not losing it — 💧 Liquidity proximity (25 pts) : distance to the nearest liquidity zone — closer = higher score (the flip is near a liquidity grab point) Grades: A+ (≥ 80), A (≥ 60), B (≥ 40), C (< 40). Signal labels display "Long A+" / "Short B" etc. 7️⃣ OBV-based volume regime detection. On Balance Volume (OBV) delta = OBV − SMA(OBV, 20). Classified as: — Accumulation : OBV delta > 0 — more volume on up-moves than down-moves (institutional buying) — Distribution : OBV delta < 0 — more volume on down-moves (institutional selling) Displayed in the dashboard with directional coloring. Auto-displays "N/A" on instruments without volume data. 8️⃣ Live trade tracker with P&L. On each signal: a dashed entry line extends horizontally, a vertical connector line tracks from entry to current price, and a P&L label updates in real-time showing percentage gain/loss. Green = profit, red = loss. Replaced on each new signal. 9️⃣ Win/loss markers + win rate tracking. Each signal is tracked as a mini-trade: entry at signal close, SL at entry ± medianATR × SL multiplier, TP1 at entry ± risk × TP1 multiplier. If TP1 is reached before SL → green ● marker at the signal bar (win). If SL is reached first → red ● marker (loss). Running win rate displayed in the dashboard as "67% (4W/2L)". 🔟 ATR-based TP/SL with hit tracking. Three take-profit levels as risk multiples (default 1.0/2.0/3.0 × risk) plus SL (default 3× medianATR from entry). Lines extend rightward with labels showing price + percentage. Labels update with ✓ on hit (green) or ✗ on SL hit (red). Active until the next signal replaces them. ⚙️ HOW IT WORKS — CALCULATION FLOW Step 1 — KAMA: Efficiency Ratio from configurable lookback → adaptive smoothing constant → KAMA line that accelerates in trends, goes flat in chop. Step 2 — Median ATR bands: True ranges stored in ring buffer → median computed → upper/lower bands = KAMA ± median × multiplier. Step 3 — Trend detection: Previous bar's source > upper band → bullish flip. Source < lower band → bearish flip. Active band plotted as trend line. Gradient fill scales with displacement. Step 4 — Liquidity zones: Pivot highs/lows → boxes above/below. Swept zones auto-deleted. Step 5 — Order blocks: On flip → previous candle becomes OB. Invalidated when price closes beyond opposite edge. Step 6 — Signal scoring: 4 factors (trend strength, volume delta, ER acceleration, liquidity proximity) → 0–100 → A+/A/B/C grade. Step 7 — Trade tracking: SL/TP placed, lines extend, win/loss evaluated per trade. 📖 HOW TO USE 🎯 Quick start: 1. Add the indicator — adaptive trend line, liquidity zones, and order blocks appear 2. "Long A+" / "Short B" labels = trend flip signals with quality grade 3. Green/red liquidity zone boxes = where stops cluster (potential sweep targets) 4. Green/red order blocks = institutional supply/demand zones 5. SL/TP lines auto-appear with P&L tracker 👁️ Reading the chart: — 🟢 Green trend line = bullish (lower band active) — 🔴 Red trend line = bearish (upper band active) — 🟢/🔴 Gradient fill = displacement from KAMA (brighter = more extended) — 🟢 Small boxes below price = bullish liquidity zones (buy-side stops) — 🔴 Small boxes above price = bearish liquidity zones (sell-side stops) — 🟢 Larger boxes = demand order blocks (institutional buying zone) — 🔴 Larger boxes = supply order blocks (institutional selling zone) — 🟢 ● = win (TP1 reached), 🔴 ● = loss (SL hit) — Dashed line + PnL label = live trade tracker 📊 Dashboard fields: — Trend: ▲ Bullish / ▼ Bearish — Last Signal: BUY/SELL with grade — Score: 0–100 quality rating — Strength: trend strength percentage — P&L: current trade percentage — Win Rate: wins/losses with percentages — SL / TP1: current trade levels with ✓/✗ status — Vol Regime: Accumulation / Distribution — Vol Delta: buy vs sell volume percentage — Efficiency: current ER percentage 🔧 Tuning guide: — Too many signals: increase Band Multiplier (2.0–2.5) or ER Length (15–20) — Too few signals: decrease Band Multiplier (1.2–1.5) or ER Length (8–10) — Signals too late: decrease Slow Smoothing (15–20), decrease Volatility Length (20–30) — Stops too tight: increase SL ATR Multiplier (2.5–4.0) — Want only A+/A signals: monitor grades in dashboard, skip B/C entries ⚙️ KEY SETTINGS REFERENCE ⚙️ Main: — Efficiency Ratio Length (default 13): KAMA lookback — higher = smoother — Fast/Slow Smoothing (default 2/30): KAMA acceleration/deceleration — Band Multiplier (default 1.8): band width in median ATR — Volatility Length (default 50): median ATR ring buffer size 🎯 SL/TP: — SL (× ATR) (default 3): stop distance in median ATR — TP1/TP2/TP3 (× risk) (default 1.0/2.0/3.0): R:R multiples 💧 Liquidity: — Pivot Lookback (default 4) / Max Zones (default 15) 🟧 Order Blocks: — Max Order Blocks (default 10) 🎨 Visual: — Gradient fill, trade tracker, win/loss markers (all toggleable) — Configurable signal label size (Tiny–Large) — Configurable dashboard font size (Tiny–Normal) — Auto / Dark / Light theme 🔔 Alerts — 🟢 BUY / 🔴 SELL — ticker, price, TF, SL, TP1, TP3 All support plain text and JSON webhook format. Bar-close confirmed. ⚠️ IMPORTANT NOTES — 🚫 No repainting. All signals require barstate.isconfirmed. Trend flips use the previous bar's source vs the previous bar's band value — the signal fires on the bar after the crossing bar closes. KAMA and band values are deterministic once a bar is confirmed. — 📐 The median ATR is more robust than standard ATR . A single flash wick or gap bar shifts the mean (standard ATR) significantly but barely affects the median. This produces more stable band widths and fewer false flips during anomalous bars. — 📊 Volume delta is accumulated within each trend leg and resets on every trend flip. It represents the buy/sell balance during the specific move — not the overall volume profile. The pre-reset delta value is used for the signal score (capturing the exiting leg's character). — 💧 Liquidity zones are automatically swept and removed when price touches them. This prevents stale zones from cluttering the chart. If a zone disappears, it means price swept through it — the liquidity has been taken. — 🟧 Order blocks are invalidated after 3+ bars if price closes beyond the opposite edge. This prevents old OBs that have clearly failed from persisting. — ⚖️ The 4-factor score uses the volume delta from before the trend reset (preResetVolDelta) — not the current leg's delta, which would be zero at the moment of the flip. This correctly captures whether the previous leg had accumulation or distribution behind it. — 📏 Win/loss tracking evaluates TP1 vs SL only — if TP1 is reached before SL, it's a win. The trade closes on the first event and is not re-evaluated. — 🛠️ This is a trend-following signal and analysis tool , not an automated trading bot. It provides adaptive trend detection, liquidity context, order block zones, and signal quality grading — trade decisions remain yours. — 🌐 Works on all markets and timeframes. Volume features auto-adapt to instruments without volume data (OBV and volume delta show "N/A").
Penunjuk Pine Script®
oleh WillyAlgoTrader
9
Dynamic Support & Resistance V3Dynamic SRT V3 by Anonycryptous inspired by Ilja V. Compared to the previous version, a completely new and accelerated concept. Dynamic SRT V3 is a professional structural mapping suite that identifies high-density liquidity zones through a dual-engine calculation process. By merging a 6-Tiered Pivot Architecture with a Validated Diagonal Scoring Engine, it provides a surgical view of market boundaries, allowing traders to distinguish between minor price fluctuations and major institutional walls. *How the Engine Operates This indicator functions as a mathematical filter for price action, operating on two distinct layers: -1. Tiered Institutional Anchors (Horizontal) Instead of looking at a single fractal period, V3 tracks six different "memory depths" simultaneously (ranging from 5 to 200 bars). The Concept: Markets move in cycles. Small cycles (Pivot 1-2) represent retail positioning, while large cycles (Pivot 5-6) represent institutional buy/sell walls. State-Aware Logic: Each level uses an ATR-Volatility Buffer to determine its current state. If price is above the level, it acts as Support (Green); if below, it is Resistance (Red). If price is currently slicing through it, the level turns Grey (Neutral), signaling a "No-Trade Zone" or a consolidation phase. -2. Slope-Intercept Validation Engine (Diagonal) The dynamic trendlines are not just simple "peak-to-peak" connectors. They are calculated using a Linear Regression Scoring System. *The Concept: A trendline's strength is defined by its "cleanliness." -The Filter: Unlike standard tools, V3 uses a Price-Action Scan. It calculates the path of a potential trendline and automatically discards it if it cuts through the bodies of intermediate candles. This ensures that the wedges and channels you see are statistically valid structural boundaries. -Strategic Application: LTF vs. HTF *Performance & User Manual -Optimized Execution: V3 utilizes Last-Bar Offloading. It scans 1000+ bars of history in milliseconds by executing the heavy diagonal math only on the most recent candle, ensuring zero chart lag. -Price Tags: Dynamic labels are pinned professionally above the levels. Use these as your Take-Profit (TP) or Stop-Loss (SL) targets. -Customization: Adjust the Touch Tolerance in the settings to make the trendlines more or less strict depending on the asset's "wickiness" (e.g., higher for BTC, lower for Forex). *Lower Timeframes (1m – 15m): Scalping & Intraday -LTF Focus: Prioritize Pivot Levels 1, 2, and 3. These are highly reactive and will map the micro-pullbacks of the current session. -Early Signal: Look for price to reject a Dynamic Trendline while a micro-pivot (Level 1) is acting as support. This provides an aggressive "Early Entry" with a very tight risk-to-reward ratio. -The Trap: Avoid trading when the LTF candles are consistently Grey, as this indicates the market is trapped inside a static pivot zone. -Higher Timeframes (1H – Daily): Swing & Position Trading *HTF Focus: Prioritize Pivot Levels 5 and 6. These represent the "Major Floors and Ceilings" of the weekly or monthly trend. -The Macro Wall: If price hits a Level 6 Pivot (200-bar lookback), expect a significant reaction. Institutional orders are often clustered at these depths. -Structural Confluence: The most powerful HTF setup is "The Confluence Cross." This occurs when a diagonal Resistance Trendline and a horizontal Level 5/6 Resistance meet at the same price point. This is the mathematical "End of Trend" zone where heavy reversals typically begin. ! Notice: This tool is for institutional-grade structural mapping and educational purposes only. It is not financial advice. Structural levels are areas of high probability, not guaranteed reversal points. Always trade with a stop-loss.
Penunjuk Pine Script®
oleh Anonycryptous
Prismatic Trend Matrix [JOAT]Prismatic Trend Matrix Introduction The Prismatic Trend Matrix is an advanced open-source multi-dimensional trend analysis system that combines Hull Moving Average, SuperTrend, ADX strength filtering, and moving average confluence into a unified trend detection engine. This indicator analyzes trend across multiple dimensions simultaneously, creating a prismatic view of market direction with gradient visualization that reveals trend strength and conviction. Unlike single-indicator trend systems, the Prismatic Trend Matrix provides multi-layered trend intelligence through Hull MA smoothing, SuperTrend band analysis, ADX strength measurement, and EMA/SMA alignment detection. The indicator is designed for traders who understand that strong trends require confirmation across multiple analytical dimensions. Why This Indicator Exists This indicator addresses the need for comprehensive trend analysis that goes beyond simple moving averages. By combining four distinct trend methodologies with gradient visualization, it reveals: Hull Moving Average: Weighted moving average with reduced lag for responsive trend detection SuperTrend Component: ATR-based bands that identify trend direction and support/resistance ADX Strength Filter: Measures trend strength to separate strong trends from weak/choppy conditions Moving Average Matrix: Three EMAs and two SMAs create alignment-based trend confirmation Trend Classification: Five-state system (Strong Bull, Weak Bull, Sideways, Weak Bear, Strong Bear) Counter-Trend Detection: Identifies potential reversals when price moves against established trend Prismatic Gradient: Visual fill between Hull MA and SuperTrend shows trend intensity Core Components Explained 1. Hull Moving Average (HMA) The Hull MA uses weighted moving averages to create a smooth trend line with minimal lag: The calculation involves three steps: Step 1: Calculate WMA of half-length period Step 2: Calculate WMA of full-length period Step 3: Calculate WMA of the difference using square root of length The result is a moving average that responds quickly to price changes while maintaining smoothness. The indicator applies additional EMA smoothing (default 3 periods) to reduce noise. 2. SuperTrend Calculation SuperTrend uses ATR-based bands to identify trend direction: Source: Average of high and low (HL2) Upper Band: Source + (ATR × Factor) Lower Band: Source - (ATR × Factor) Direction: Bullish when close > upper band, bearish when close < lower band Three modes control band adjustment: Strict Mode: Bands adjust only when price crosses or previous band is breached Quick Mode: Bands adjust when price crosses previous band Quicker Mode: Bands adjust immediately with price SuperTrend provides dynamic support/resistance levels that adapt to volatility. 3. ADX Strength Measurement The Average Directional Index measures trend strength: DI+ (Directional Indicator Plus): Measures upward directional movement DI- (Directional Indicator Minus): Measures downward directional movement ADX: Smoothed average of the difference between DI+ and DI-, normalized Threshold: ADX above threshold (default 25) indicates strong trend Rising ADX: Indicates strengthening trend momentum ADX filters out weak trends and choppy conditions, ensuring signals occur only during strong directional movement. 4. Moving Average Matrix Five moving averages create a trend alignment system: Fast EMA (9): Short-term trend direction Medium EMA (21): Intermediate trend direction Slow EMA (50): Primary trend direction Fast SMA (50): Smoothed primary trend Slow SMA (200): Long-term institutional trend Alignment is measured by comparing the order of these averages: Bullish Alignment: EMA9 > EMA21 > EMA50 > SMA50 (all in ascending order) Bearish Alignment: EMA9 < EMA21 < EMA50 < SMA50 (all in descending order) Mixed Alignment: Averages not in order (choppy or transitional conditions) Perfect alignment indicates strong institutional conviction in the trend direction. 5. Composite Trend Classification [/b> The indicator combines all components into a five-state trend classification: Strong Bull (State 2): Hull rising + SuperTrend bullish + MA alignment bullish + ADX strong Weak Bull (State 1): Hull rising + (SuperTrend bullish OR MA alignment bullish) Sideways (State 0): Mixed signals or weak trend conditions Weak Bear (State -1): Hull falling + (SuperTrend bearish OR MA alignment bearish) Strong Bear (State -2): Hull falling + SuperTrend bearish + MA alignment bearish + ADX strong This classification provides clear trend assessment at a glance. 6. Counter-Trend Detection [/b> The indicator identifies potential reversals when price moves against established trend: Counter-Trend Bull: Trend state neutral/bearish + close > open + price rising + DI+ > DI- + close > Hull + ADX > 20 Counter-Trend Bear: Trend state neutral/bullish + close < open + price falling + DI- > DI+ + close < Hull + ADX > 20 Counter-trend signals include anti-overlap logic to prevent signal clustering and ensure clean placement. 7. Prismatic Gradient Visualization The indicator creates a gradient fill between Hull MA and SuperTrend: Gradient Layers: Multiple intermediate values calculated between Hull and SuperTrend (default 15 layers) Color Intensity: Transparency increases from Hull (solid) to SuperTrend (transparent) Dynamic Coloring: Gradient color matches trend state (green = bullish, red = bearish, cyan = sideways) Visual Effect: Creates a glowing prismatic effect that emphasizes trend strength The gradient provides intuitive visual feedback on trend intensity and direction. 8. Platform Levels Platform levels are horizontal lines at the current Hull MA value: Extension: Lines extend forward and backward from current bar (default 7 bars each direction) Color Coding: Platform color matches current trend state Purpose: Provides visual reference for potential support/resistance at Hull MA level Platforms help identify key levels where price may find support or resistance. Visual Elements Hull Trend Line: Thick line (3px) with regime-based coloring showing primary trend SuperTrend Line: Medium line (2px) with step-line style showing dynamic support/resistance EMA Matrix: Three thin lines showing fast, medium, and slow EMAs with transparency Prismatic Gradient: Multi-layer fill between Hull and SuperTrend creating glow effect Platform Levels: Horizontal lines at Hull MA value extending forward/backward Counter-Trend Signals: Triangles marking potential reversal points Background Coloring: Subtle background tint for strong bull/bear states Information Dashboard: Displays trend state, Hull direction, ADX strength, momentum, alignment, SuperTrend, DI balance, price vs Hull, gradient zone, counter-trend status, and signal How to Use This Indicator Step 1: Check Trend State Monitor the dashboard for current trend state (Strong Bull, Weak Bull, Sideways, Weak Bear, Strong Bear). Trade in the direction of strong states. Step 2: Verify ADX Strength Ensure ADX is above threshold (default 25) for strong trends. Low ADX indicates choppy conditions - avoid trend-following strategies. Step 3: Confirm MA Alignment Check if moving averages are aligned (Bullish/Bearish/Mixed). Perfect alignment confirms institutional conviction. Step 4: Monitor Hull Direction Hull rising = bullish bias, Hull falling = bearish bias. Hull provides the primary trend direction signal. Step 5: Use SuperTrend for Support/Resistance SuperTrend line acts as dynamic support in uptrends and resistance in downtrends. Breaks of SuperTrend warn of trend changes. Step 6: Watch for Counter-Trend Signals Counter-trend signals at extreme levels may indicate reversals. Use these cautiously and confirm with other factors. Step 7: Assess Gradient Zone Price in upper gradient zone (near Hull) = strong trend, price in lower zone (near SuperTrend) = weak trend or potential reversal. Best Practices Trade only in Strong Bull or Strong Bear states for highest probability Avoid trading in Sideways state - wait for clear trend establishment Use ADX as a filter - only trade when ADX > 25 for strong trends Confirm trend with MA alignment before entering positions Use SuperTrend as trailing stop level in trending markets Counter-trend signals work best at extreme levels with divergence Monitor gradient zone - price near SuperTrend may indicate trend exhaustion Combine with higher timeframe trend for additional confirmation Input Parameters Hull Trend Engine: Hull Length: Period for Hull MA calculation (default: 20) Hull Smoothing: Additional EMA smoothing (default: 3) SuperTrend Layer: ATR Period: Period for ATR calculation (default: 10) ATR Factor: Multiplier for band width (default: 3.0) Mode: Strict, Quick, or Quicker (default: Quick) Trend Strength: ADX Length: Period for ADX calculation (default: 14) ADX Smoothing: Smoothing period for ADX (default: 14) Strength Threshold: Minimum ADX for strong trend (default: 25) MA Matrix: Fast EMA: Short-term EMA (default: 9) Medium EMA: Intermediate EMA (default: 21) Slow EMA: Primary EMA (default: 50) Fast SMA: Smoothed primary (default: 50) Slow SMA: Long-term institutional (default: 200) Visual Configuration: Bullish/Bearish Trend Colors: Customizable colors for trend states Sideways/Weak Trend Colors: Colors for neutral and weak states Gradient Layers: Number of gradient fills (default: 15) Show Platforms: Toggle platform level display (default: enabled) Platform Extension: Bars to extend platforms (default: 7) Originality Statement This indicator is original in its multi-dimensional trend approach. While individual components (Hull MA, SuperTrend, ADX, EMAs) are established concepts, this indicator is justified because: It combines four distinct trend methodologies into a unified classification system The five-state trend classification provides clear trend assessment Prismatic gradient visualization creates intuitive trend intensity display Counter-trend detection with anti-overlap logic identifies potential reversals MA alignment analysis measures institutional conviction Integration of Hull MA smoothness with SuperTrend adaptability creates balanced trend detection The comprehensive dashboard presents all trend dimensions simultaneously Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Trend analysis does not guarantee profitable trades. Past trends do not guarantee future trends. Always use proper risk management and never risk more than you can afford to lose. -Made with passion by officialjackofalltrades
Penunjuk Pine Script®
oleh officialjackofalltrades
Smart Reversal EntrySmart Reversal Entry Smart Reversal Entry is an open-source reversal-entry indicator built around one specific analytical idea: after a short, directional three-candle expansion move, the first confirmed candle closing back in the opposite direction can create a structured reversal-entry opportunity when it appears in the correct EMA context. This script is not designed to mark every bullish or bearish candle, and it is not intended to behave like a generic trend-following overlay, a standard candlestick-pattern indicator, or a broad “signal generator” that reacts to every small reversal. Its purpose is to measure short-term directional exhaustion in a standardized way, filter that move through an EMA context, require close-confirmed reversal behavior, and then project a fixed-risk trade structure directly on the chart for analysis and review. The script also includes an internal background optimizer and review tables so users can compare how the same reversal framework behaves under different parameter combinations. These review tools are included to support study and comparison, not to imply future performance. OPEN-SOURCE NOTE This script is published open-source so users can inspect the logic directly, verify what the script is doing, and adapt parts of the workflow for their own research if they wish. Even though the code is open, this description is intentionally detailed because many TradingView users do not read Pine Script. The goal is for a user to understand what the script does, how it works, why its parts belong together, and how it may be used in practice without having to study the code line by line. OVERVIEW At a high level, the script does six things: 1. It measures whether the last three candles produced a directional move large enough to matter in pip terms. 2. It checks whether price is positioned on the correct side of a selected EMA filter. 3. It requires the current candle to close in the opposite direction as confirmation of a possible reversal. 4. It maps a fixed stop-loss and a selectable take-profit multiple directly onto the chart. 5. It tracks projected trade outcomes and summarizes them in a review table and a daily PnL table. 6. It runs a hidden background optimizer over multiple EMA and move-threshold combinations so the user can compare the current settings to an internal parameter sweep. The script is therefore meant to function as a complete reversal-entry and review framework rather than as a single-purpose candle-pattern marker. CORE IDEA Many reversal-style tools identify isolated candles or basic candlestick formations, but they do not standardize the market context around them. This script is built around the idea that a reversal signal becomes more meaningful when three specific things happen together: 1. price has already made a clear short-term directional move, 2. that move is large enough to matter relative to the chosen pip structure, 3. and the next confirmed candle closes back in the opposite direction while price remains on the correct side of an EMA filter. The model is intentionally narrow. It does not try to identify every turning point in the market. It does not try to classify broad market structure. It does not use discretionary support and resistance interpretation. It does not rely on vague candle descriptions such as “looks weak” or “looks exhausted”. Instead, it defines reversal-entry conditions using a fixed sequence: first measure a three-candle directional push, then filter it using EMA context, then require an opposite close-confirmed candle, then project a standardized risk framework, then review the resulting projected outcomes over time. That narrower focus is the main reason this script exists in its current form. WHY THIS SCRIPT IS NOT A SIMPLE MASHUP This script combines multiple components, but they are not included simply to place more features into one publication. Each component has a specific function inside the same analytical workflow: - The EMA filter defines directional context. - The three-candle move measurement defines whether a short-term push is large enough to qualify. - The reversal candle confirmation defines the actual entry trigger. - The pip-based stop-loss and RR framework standardize trade projection. - The summary and daily review tables organize projected outcomes into a readable review structure. - The internal optimizer compares the same reversal logic across multiple hidden EMA and move-threshold combinations. These layers are interdependent. Without the three-candle move measurement, the script would react to many small candles that do not represent meaningful short-term expansion. Without the EMA filter, the script would lose its directional context and become a more generic reversal marker. Without the close-confirmed reversal candle, the script would identify momentum but not the actual reversal-entry moment. Without the risk projection layer, the user would still need to manually draw the entry, stop, and target after every signal. Without the review tables, the user would have less organized feedback when reviewing results under the selected settings. Without the internal optimizer, the user would see only the current configuration and not how the same logic behaves across a broader parameter range. For that reason, the script is intended as a single reversal-entry framework, not as a random collection of unrelated features. WHAT THE SCRIPT DOES The script identifies reversal-entry setups using a strict, rule-based structure. Long setup requirements: - price must be above the selected EMA, - the prior three candles must all be bearish, - the combined bearish move across that sequence must reach the minimum pip threshold, - the current candle must close bullish. Short setup requirements: - price must be below the selected EMA, - the prior three candles must all be bullish, - the combined bullish move across that sequence must reach the minimum pip threshold, - the current candle must close bearish. When a valid signal appears, the script can: - place a BUY or SELL label, - project a fixed stop loss in pips, - project a take-profit level using the selected RR multiple, - draw TP/SL boxes, - draw an entry line, - keep historical projected trades visible for later review, - summarize projected outcomes in a summary table, - summarize recent daily projected behavior in a daily PnL table. The script also evaluates an internal optimizer in the background. That optimizer tests multiple EMA lengths and minimum-move combinations using the same reversal logic and displays the best-performing parameter combination inside the summary table over the shared analysis window. HOW THE SCRIPT WORKS 1) EMA CONTEXT FILTER The script uses a single EMA as a directional filter. For long setups: price must close above the selected EMA. For short setups: price must close below the selected EMA. This does not turn the script into a pure trend-following system. Instead, it acts as a directional context filter so that reversal entries are only considered when price is positioned on the chosen side of the EMA. In practical terms, the EMA filter is used to reduce context-free reversal signals. A bullish candle appearing after a bearish push is not enough by itself. The script still wants price to be trading above the selected EMA for longs, and below it for shorts. 2) THREE-CANDLE DIRECTIONAL MOVE MEASUREMENT The script looks at the three candles immediately before the signal candle. For a long setup: those three candles must all be bearish. For a short setup: those three candles must all be bullish. The script then measures the total directional move across that sequence in pip terms. For long setups, it calculates the bearish move from the open of the first candle in the sequence to the close of the third bearish candle. For short setups, it calculates the bullish move from the open of the first candle in the sequence to the close of the third bullish candle. That move must be at least as large as the user-defined “Minimum 3-Candle Move (Pips)” setting. This is one of the key parts of the script’s logic. It ensures that the setup is not based on three arbitrary candles, but on a directional push that is large enough to meet the minimum threshold selected by the user. 3) REVERSAL CANDLE CONFIRMATION After the three-candle directional push is identified, the current candle must close in the opposite direction. For long setups: the current candle must close bullish. For short setups: the current candle must close bearish. This requirement is intentionally strict. The script does not treat intrabar movement or unfinished candles as a valid signal. Signals are confirmed only when the bar closes. This matters because a reversal that looks valid intrabar can disappear by the close. By waiting for close confirmation, the script reduces premature signal marking. 4) COOLDOWN FILTER The script includes a cooldown period between signals. Once a signal has fired, a new signal is not allowed until a defined number of bars has passed. In the current implementation, that cooldown is handled internally. The purpose of this filter is to reduce signal clustering and prevent the chart from producing multiple nearby entries from the same short-term market behavior. 5) PIP-BASED RISK PROJECTION When a valid signal appears, the script creates a projected trade framework using: - entry at the signal close, - a fixed stop-loss distance in pips, - a take-profit level based on the selected risk/reward multiple. This makes the projection logic standardized across signals. For long setups: - stop loss is placed below entry, - take profit is placed above entry. For short setups: - stop loss is placed above entry, - take profit is placed below entry. The script can draw: - entry line, - TP box, - SL box, - BUY / SELL label, - TP / SL hit labels. This projection layer is not meant to claim that a setup will succeed. Its purpose is to reduce manual chart annotation and make the behavior of the signal model easier to inspect after the fact. 6) SAME-BAR TP/SL PRIORITY RULE The script uses a strict and conservative rule when both target and stop would appear to be touched on the same bar after entry: if TP and SL are both reached on the same bar, SL takes priority. This is an important implementation detail because it directly affects projected statistics. It makes the review logic more conservative and avoids optimistic ambiguity when bar data alone cannot determine exact intrabar order. 7) SHARED ANALYSIS WINDOW The script uses a shared analysis window internally. Projected results and optimizer comparisons are evaluated over a rolling historical range rather than over the full unlimited chart history. This keeps the internal review process more controlled and makes the optimizer comparison consistent inside the same defined lookback window. 8) INTERNAL OPTIMIZER One of the script’s more advanced components is the internal optimizer. The optimizer runs in the background and is intentionally not exposed as a user-facing optimization panel. Instead of asking the user to manually test every variation, the script internally evaluates combinations of: - 10 EMA values, - 10 minimum-move thresholds. That produces 100 total internal combinations. Each combination uses the same reversal logic: - EMA context, - three-candle directional sequence, - minimum move threshold, - opposite close-confirmed candle, - same stop-loss and RR structure. The optimizer then tracks projected wins, losses, net R, gross profit, and gross loss for each combination, and the summary table displays the current best combination based on the script’s internal comparison rules. This optimizer is not intended to present a “perfect setting”. It is a comparative review aid that helps the user understand how the same reversal framework behaves across multiple hidden parameter combinations. WHAT MAKES THIS SCRIPT ORIGINAL This script uses familiar technical-analysis building blocks such as: - EMA filtering, - candle-sequence logic, - pip-based move measurement, - fixed stop-loss projection, - risk/reward mapping, - performance review tables. Those building blocks are not original by themselves. The originality of this script is not in inventing a completely new primitive indicator. The originality lies in how these familiar elements are arranged into one tightly defined reversal-entry workflow: EMA context → three-candle directional expansion → minimum pip-threshold validation → opposite candle close confirmation → fixed-risk trade projection → on-chart review → internal background parameter comparison That full sequence is the main reason this script exists as its own publication. It is not intended to be simply another EMA filter, another candlestick marker, another TP/SL visualizer, or another optimizer dashboard. It is specifically a short-term reversal-entry framework that combines directional context, expansion measurement, confirmation logic, risk mapping, and review in one workflow. WHAT APPEARS ON THE CHART Depending on settings, the chart may display: - EMA line, - BUY labels, - SELL labels, - signal-bar background highlights, - entry line, - TP box, - SL box, - TP hit labels, - SL hit labels, - summary table, - daily PnL table. Users who want a cleaner chart can disable some visual layers and keep only the ones most relevant to their workflow. HOW TO USE THE SCRIPT A practical workflow is: 1. Add the script to a standard candlestick chart. 2. Select the EMA length you want to use as directional context. 3. Set the minimum three-candle move threshold in pips. 4. Set the pip preset correctly for the instrument, or use manual pip size if needed. 5. Choose the stop-loss distance in pips. 6. Select the RR mode used for take-profit projection. 7. Wait for a valid long or short setup to appear. 8. Use the projected entry, stop, and target structure as a chart-analysis framework rather than as a blind instruction. 9. Review projected trade behavior in the summary table and daily table. 10. Compare your selected settings with the optimizer’s best internal combination, but do not treat the optimizer output as a guaranteed best future configuration. This script is best understood as a structured decision-support and reversal-review tool, not as a self-sufficient trading system. SETTINGS REFERENCE Signal Settings - EMA Length: sets the EMA used as the directional filter. - Minimum 3-Candle Move (Pips): defines how large the directional three-candle move must be before a reversal candle can qualify. Pip Settings - Pip Preset: selects a predefined pip-size interpretation for common instrument types. - Manual Pip Size: allows direct control when the selected symbol needs a custom pip conversion. Risk Management - Stop Loss (Pips): sets the fixed stop-loss distance in pip units. - Take Profit RR: sets the projected target multiple relative to the stop-loss distance. Visual Settings - Show Buy/Sell Labels: shows or hides the signal labels. - Highlight Signal Bars: adds background color to signal bars. - Show Entry Line: shows or hides the projected entry line. - Show TP/SL Hit Labels: controls whether projected outcomes are labeled. - Show TP Hit Labels: controls TP hit labels specifically. - Show SL Hit Labels: controls SL hit labels specifically. Summary Table - Show Summary Table: enables or disables the main review table. - Table Position: sets the table location. - Table Text Size: controls summary-table text size. Daily PnL Table - Show Daily PnL Table: enables or disables the daily review table. - Daily Table Position: sets the daily table location. - Daily Table Text Size: controls daily-table text size. INTERNAL LOGIC NOTES The current code also includes internal settings that are not exposed as user-facing optimization controls. These include: - signal cooldown, - shared analysis window, - maximum stored closed-trade visuals, - hidden optimizer activation, - internal optimizer parameter combinations. These internal elements exist to keep the public interface simpler while still allowing the script to maintain consistent review behavior in the background. IMPORTANT PRACTICAL NOTE ON PIP SIZE The script uses pip-based calculations for: - the minimum three-candle move, - stop-loss distance, - take-profit distance, - optimizer comparison logic. Because of that, correct pip interpretation is extremely important. If signals appear too frequent, too rare, too compressed, or visually inconsistent for the instrument being analyzed, the first setting to verify is Pip Preset or Manual Pip Size. This matters especially for: - gold symbols, - 5-digit forex symbols, - JPY forex pairs, - indices and CFD-style instruments, - custom broker symbols with unusual decimal formatting. LIMITATIONS AND SHORTCOMINGS This script has important limitations: - It is a short-term reversal model, not a full market-structure engine. - It only evaluates one specific reversal pattern based on a three-candle directional push and an opposite close-confirmed candle. - It does not use support/resistance structure, volume profile, or discretionary context. - It relies on pip conversion, so poor pip settings can distort signal behavior. - The internal optimizer compares parameter combinations only inside the defined shared analysis window. - The optimizer output is a comparative review tool, not a guarantee that the best historical combination will remain best in future market conditions. - Projected results depend on the script’s own simplified outcome logic. - If TP and SL are both touched on the same bar, SL is prioritized by design, which makes the logic more conservative but also affects outcome statistics. - Historical projected trades and review metrics are chart-based review aids, not proof of tradable real-world execution. - No reversal-entry model can remove all false signals or all regime-dependent behavior. For those reasons, the script should be used as a structured analysis and review framework, not as a promise of future profitability. WHO THIS SCRIPT MAY BE USEFUL FOR This script may be useful for traders who: - want a rules-based short-term reversal-entry model, - want EMA-based directional context, - want a minimum expansion threshold before a reversal is allowed, - want fixed-risk trade projection on the chart, - want review tables for projected outcomes, - want background comparison of multiple EMA and move-threshold combinations. It may be less suitable for traders who: - want a broad trend-following system, - want a discretionary support/resistance engine, - want a multi-pattern candlestick library, - want a fully automated strategy with no outside confirmation, - want outcome metrics interpreted as live performance promises. DISCLAIMER This script is provided for educational and informational purposes only. It does not constitute financial, investment, or trading advice. Market conditions change, historical behavior does not guarantee future results, and users should perform their own analysis, validation, and risk management before using the script in live decision-making.
Penunjuk Pine Script®
oleh trade_w_samet
2
Alpha Signal Engine [MarkitTick]💡 The Alpha Signal Engine is an advanced, multi-dimensional trend-following system designed to provide traders with highly filtered, high-probability market signals. At its core, it dynamically calculates a volatility-adjusted trailing band to determine the primary market direction. However, unlike traditional trend indicators that rely on a single data point, this engine passes every potential trend reversal through a rigorous, six-layer filtering mechanism. By requiring confluence across higher timeframe trends, momentum, volume, volatility regimes, and price action strength, it drastically reduces the noise and false signals inherent in choppy markets. It also features a built-in heads-up dashboard and fully formatted JSON webhook capabilities for automated trading integration. ✨ Originality and Utility ● A Dynamic, Adaptive Baseline Standard trailing stop or trend indicators, such as the classic Supertrend, typically use a static multiplier against the Average True Range (ATR). The Alpha Signal Engine innovates by introducing a "Dynamic Factor." This factor continuously adapts the band's distance from price by factoring in the current baseline multiplier, the relative volatility (ATR normalized by price), and the immediate price change momentum. This allows the bands to tighten during periods of strong, directional momentum and widen during erratic volatility, providing a more responsive and intelligent trailing mechanism. ● The Six-Pillar Filtering Gateway The true utility of this indicator lies in its modular filtering engine. Traders often have to clutter their charts with half a dozen indicators to confirm a setup. This script centralizes that logic. Users can selectively enable or disable filters based on their specific asset and trading style, turning the indicator into a customizable algorithmic engine. Whether you need volume confirmation, ADX trend strength, or simple RSI momentum, the script handles the complex boolean logic internally and only outputs a signal when your precise market conditions are met. 🔬 Methodology and Concepts ● Dynamic Factor Calculation The indicator establishes its baseline trend using an upper and lower band. The distance of these bands from the median price is dictated by a dynamically calculated factor. This factor is the sum of a base value, a volatility component (ATR divided by Close, scaled by a user weight), and a price movement component (percentage change of the close, scaled by a user weight). This raw factor is then smoothed using a Simple Moving Average (SMA) to prevent erratic band shifts. ● Trend Determination The trend direction flips when the closing price crosses the active dynamic band. If the price closes above the upper band, the trend shifts bullish, and the lower band becomes the active support. Conversely, closing below the lower band shifts the trend bearish, making the upper band the active resistance. ● The Filter Matrix A signal is only generated when a trend flip aligns with all activated filters: HTF Alignment: Uses the request context to pull the trend direction from a higher timeframe, ensuring you are not trading against the macro trend. ADX Trending: Measures the Average Directional Index to ensure the market is in an active trending phase (above a defined threshold) rather than a sideways chop. Volume Surge: Compares current volume against a Volume SMA. The current bar must exhibit a volume spike greater than the defined multiplier to confirm institutional participation. RSI Momentum: A simple but effective gatekeeper requiring the Relative Strength Index to be above 50 for longs and below 50 for shorts. ATR Volatility Regime: Compares the current ATR against a 50-period SMA of the ATR. It ensures the market is operating within a "normal" volatility ratio, preventing entries during extreme, unpredictable volatility spikes or dead, illiquid periods. Candle Body Strength: Calculates the absolute size of the candle body (Open to Close) and mandates it must be larger than a specific fraction of the ATR, ensuring the signal candle has true directional conviction. 🎨 Visual Guide ● Chart Elements Up Trend Line: Displayed as a solid, teal-colored line trailing below the price action during a bullish phase. It acts as dynamic support. Down Trend Line: Displayed as a solid, bright pink/red line trailing above the price action during a bearish phase. It acts as dynamic resistance. Trend Cloud (Fill): A colored gradient fill exists between the median price and the active trend line. A teal cloud visually represents bullish dominance, while a pink/red cloud represents bearish dominance. Buy Signals: Indicated by small, teal "B" labels positioned below the signal candle. Sell Signals: Indicated by small, pink/red "S" labels positioned above the signal candle. ● Filter Dashboard Located in the top right corner of the chart, this HUD (Heads-Up Display) provides a real-time status check of your system. The left column lists the available filters (HTF Align, ADX Trend, Vol Surge, RSI Gate, ATR Regime, Body Str). The right column displays the current status of each filter. A gray "OFF" indicator means the user has disabled the filter in the settings. A green "ON" or "Aligned" text indicates the condition is currently met. A red "Opposed" or unlit indicator means the condition is active but currently failing to meet the required criteria. The bottom rows clearly state the current overarching trend direction and whether a signal is pending or waiting. 📖 How to Use • Interpreting the System To effectively use the Alpha Signal Engine, begin by observing the main trend lines and the color of the cloud. This provides your baseline bias. Do not take trades purely on the band flipping. Instead, rely on the explicit "B" and "S" labels. • Signal Execution When a "B" (Buy) or "S" (Sell) label appears, it means the price has successfully flipped the trend AND all user-activated filters in the dashboard are glowing green. This is your entry trigger. The active trend line (the teal line for longs, the pink line for shorts) serves as an ideal, dynamic stop-loss placement. • Customizing the Engine The system is designed to be tuned. If you are trading a highly liquid asset like major forex pairs, you may want to enable the ADX and HTF filters to catch long, sustained moves. If you are trading volatile crypto assets, enabling the Volume Surge and Candle Body filters can help you avoid fake-outs and trap wicks. Monitor the on-chart dashboard to see which filters are keeping you out of bad trades and adjust your settings accordingly. ⚙️ Inputs and Settings • Supertrend Settings ATR Length: The lookback period for calculating the Average True Range. Base Factor: The starting multiplier for the dynamic bands. Volatility & Price Change Weights: Determines how aggressively the bands react to sudden spikes in relative volatility and price momentum. Factor Smoothing: Applies an SMA to the final dynamic multiplier to keep the bands stable. • Filter Settings Enable HTF Alignment: Toggle and define the higher timeframe (e.g., Daily) to align with. ADX Settings: Toggle the filter, define the lookback length, and set the minimum trend strength threshold (default is 20). Volume Settings: Toggle the filter, define the Volume MA length, and set the multiplier required to classify as a "surge." RSI Settings: Toggle the filter and set the RSI lookback length. ATR Regime Settings: Define the minimum and maximum acceptable ratios of current ATR versus historical ATR. Candle Body Settings: Define the minimum required size of the candle body as a fraction of the current ATR. • Webhook Action Names These text inputs allow you to define specific payload strings (e.g., "long", "closeshort") that the indicator will output via JSON alerts, perfectly formatting the data for third-party automation services like 3Commas or PineConnector. 🔍 Deconstruction of the Underlying Scientific and Academic Framework The Alpha Signal Engine is grounded in several well-documented tenets of quantitative financial analysis and statistical market theory. ● Volatility-Adjusted Trailing Stops The foundation of the indicator relies on the Average True Range (ATR), introduced by J. Welles Wilder Jr. The ATR is a measure of the degree of price volatility. By tying the trailing stop (the dynamic band) to the ATR, the system acknowledges the statistical reality of market variance. The innovation here is the dynamic multiplier. By adjusting the distance based on the normalized rate of change (momentum), the script attempts to solve the lagging nature of fixed-multiplier trailing stops, utilizing principles found in adaptive moving averages (like Kaufman's AMA), where sensitivity increases alongside directional conviction. ● Multi-Dimensional Confluence Theory The filtering engine operates on the academic principle of conditional probability and confluence. In market microstructure, no single indicator holds a permanent statistical edge. The HTF filter is rooted in Dow Theory, prioritizing the primary trend over secondary reactions. The ADX filter utilizes Wilder's Directional Movement Index to mathematically separate trending environments from mean-reverting environments, applying a statistical threshold to directional strength. The Volume Surge filter relies on the Volume Price Trend concepts, positing that significant price movements must be sponsored by outsized volume to validate institutional participation and avoid anomalous low-liquidity spikes. The ATR Regime filter applies mean-reverting principles to volatility itself (volatility clustering), ensuring that entries are only taken when the variance of the asset is within historically "normal" parameters, avoiding the fat tails of extreme market shocks. By chaining these disparate mathematical models (trend, momentum, volume, volatility) via Boolean logic, the system mathematically reduces the frequency of trades while theoretically increasing the probability of the remaining sample size. ⚠️ Disclaimer All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
Penunjuk Pine Script®
oleh MarkitTick
4
ORB Pro Suite v6ORB Pro Suite v6 — Multi-Session + HTF ORB Build ORB Pro Suite v6 is an advanced Opening Range Breakout (ORB) tool designed for traders who want clarity, structure, and adaptability across NY, London, and Asia sessions — without changing the core ORB logic that works. This update expands the original ORB Pro Suite to support overnight markets and multi-timeframe workflows, while keeping the strategy behavior consistent and familiar. ✅ Multi-Session Presets Choose from built-in session presets: NY AM (RTH) — original behavior (unchanged) London Asia Custom Each preset aligns the ORB window with the selected session and pairs seamlessly with session-appropriate filters. ✅ ORB Build Mode You now have two ways to build your ORB: 1️⃣ Time Window (Classic ORB) Uses session start/end times Identical to previous versions 2️⃣ HTF Candle Count (Advanced) Build the ORB from 5m / 15m / 30m / 60m candles Works on any chart timeframe Ideal for traders who want ORB consistency across TFs Example: Build a 15-minute ORB from 1× 15m candle, even while trading on a 5m chart. ✅ Session Profile Defaults ORB Pro Suite introduces Session Profiles that automatically tune filters for different market conditions — without changing the strategy logic. Profiles include: NY (Default) London (Breakout) Asia (Slow Session) Custom You can toggle Profile Defaults ON or OFF at any time. 🧠 Core ORB Logic (Unchanged) Original ORB framework: Opening range high/low Breakout confirmation Optional retest logic Golden Pocket (0.5–0.618) validation Local + higher-timeframe trend filters Cooldown protection Visual risk/reward mapping If you traded NY with earlier versions, nothing has changed. ⚙️ Recommended Starting Settings For most users: ORB Build Mode: Time Window Session Profile: Auto Strictness: Balanced Advanced users: Enable HTF Candle Count Select desired ORB TF (5m–60m) Adjust candle count to match your style All inputs remain fully customizable. 📊 Designed For Futures (ES, NQ, YM, RTY) Forex pairs Gold & major indices Intraday price-action traders Session-based trading workflows ⚠️ Disclaimer This indicator is for educational and informational purposes only. It does not constitute financial advice or trade recommendations. Trading involves risk. Always manage risk appropriately and trade responsibly.
Penunjuk Pine Script®
oleh TradeswithB
Sessions + Prev + PDH/PDL + Killzones SuiteDescription This indicator is designed to provide time-based and price-based market context by combining session ranges with commonly referenced prior levels into a single, unified framework. The purpose of the script is contextual analysis, not signal generation. What the script does The script tracks and plots the following elements directly on the price chart: • High and Low ranges for multiple trading sessions (Asia, London, New York morning, and New York afternoon) • High and Low levels from the previous occurrence of each session • Prior Day High (PDH) and Prior Day Low (PDL) • Optional session “killzone” boxes that visually mark active session time windows All calculations are performed using time-based session boundaries and price extrema (high/low) within those windows. Why these components are combined Sessions, previous session levels, and prior day levels are frequently analyzed together by discretionary traders because they represent: • Where liquidity formed earlier in the day or previous day • Where price previously paused, expanded, or reversed • Natural reference points for intraday structure and range analysis Instead of plotting these elements using multiple separate scripts, this indicator integrates them into one consistent framework so that all levels are calculated using the same timezone, session logic, and display rules. This avoids mismatched session times, duplicate levels, or conflicting calculations that can occur when multiple scripts are used simultaneously. How the script works (high-level) • Each session is defined using user-selectable session times and timezone • During a session, the script tracks the highest and lowest traded price • When a session ends, its final high and low are stored as the “previous session” levels • PDH and PDL are calculated using the completed trading day • Lines and labels are anchored to the bars where levels are formed, rather than extending indefinitely • Optional display filters allow users to show only the current trading day to reduce chart clutter No forward-looking logic, prediction, alerts, or trade execution logic is included. How to use it This script is intended to be used as a visual reference tool to help traders: • Identify session boundaries and intraday ranges • Observe how price reacts near prior session highs and lows • Assess where price is trading relative to PDH and PDL • Maintain consistent session timing across different timezones The script does not provide trade entries, exits, alerts, or performance claims. Important notes • This indicator does not generate buy or sell signals • It does not predict future price movement • It is not a trading strategy • All decisions remain the responsibility of the user Disclaimer This script is provided for educational and informational purposes only. It does not constitute financial advice. Trading involves risk, and users should apply appropriate risk management and personal judgment when using any technical tool.
Penunjuk Pine Script®
oleh TradeswithB
Kalman Volume Trend [BigBeluga]🔵 OVERVIEW Kalman Volume Trend is an advanced trend-following system that combines the predictive power of a Kalman Filter with real-time volume delta analysis. Unlike standard moving averages that suffer from significant lag, the Kalman Filter uses a recursive mathematical algorithm to estimate the "true" trend by filtering out market noise. The indicator not only identifies directional regimes but also visualizes the intensity of buying and selling pressure directly on the trend line, providing a multi-dimensional view of market conviction. 🔵 CONCEPT Kalman Filter Logic — A state-space model that predicts price movement and then corrects itself based on new data, resulting in a smoother yet more responsive trend line than traditional EMAs. Adaptive ATR Bands — The trend direction is determined by price breaking through volatility-adjusted bands, reducing whipsaws in sideways markets. Volume-Weighted Trend Lines — The indicator plots "Volume Bars" extending from the trend line, where the length and color represent the relative strength of the volume delta. Cumulative Trend Statistics — It tracks the total buy volume, sell volume, and net delta from the exact moment a new trend begins. 🔵 HOW IT WORKS (IN-DEPTH) 1️⃣ The Kalman Filtering Process The script utilizes two primary parameters: Process Noise (Q) and Measurement Noise (R) . It calculates a "State Estimate" (the trend) by balancing its previous prediction against the current price. If the price is "jittery" (high R), the filter smooths the line; if the trend is moving decisively (low Q), it tracks the price more aggressively. 2️⃣ Trend Direction & Volatility Bands Two bands are projected around the Kalman line based on a multiplier of the Average True Range (ATR) . A Bullish trend is triggered when price closes above the upper band. A Bearish trend is triggered when price closes below the lower band. Once a trend is established, the opposite band acts as the trailing "Trend Line" to provide a clear buffer for price fluctuations. 3️⃣ Volume Delta Visualization Small vertical candles ("Volume Bars") are plotted along the trend line. These bars represent the Normalized Volume Delta (Close vs. Open and Volume intensity). Large bars indicate high-conviction participation, while small bars suggest waning interest or consolidation. 4️⃣ Extreme Volume & Cumulative Dashboard When volume exceeds 1.5x its recent average, an "X" label appears on the chart to mark an Exhaustion or Ignition point. A bottom-right dashboard displays a vertical histogram showing the balance of power (BUY vs. SELL vs. DELTA) for the current trend only . 🔵 KEY FEATURES Recursive Kalman Algorithm: High-accuracy trend tracking with minimal lag. Integrated Volume Profiling: See volume delta without needing a separate sub-window. Dynamic Trend Dashboard: Automatically resets at every trend flip to show fresh volume stats. Volatility-Aware: Uses 200-period ATR to ensure bands adapt to changing market conditions. Volume Extreme Alerts: Identifies high-volume spikes that often precede trend reversals. 🔵 DASHBOARD METRICS BUY — Total volume accumulated on bullish candles since the trend started. SELL — Total volume accumulated on bearish candles since the trend started. DELTA — The net difference between buying and selling pressure. TOTAL VOLUME — The total "fuel" spent during the current directional regime. 🔵 HOW TO USE Riding the Trend: Stay in the trade as long as the Kalman line color remains consistent. Spotting Weakness: If the Kalman line is Bullish (Blue) but the Volume Bars are consistently negative or shrinking, the trend may be losing steam. High-Volume Breakouts: Look for the "X" labels at the start of a trend shift; this confirms institutional participation in the new direction. Dashboard Confirmation: Use the vertical histogram to confirm if the buyers or sellers are truly in control during a pullback to the trend line. 🔵 CONCLUSION Kalman Volume Trend offers a sophisticated approach to trend analysis by merging high-level signal processing with raw volume data. By focusing on "clean" price data and weighting it with volume delta, it helps traders filter out market noise and focus on high-conviction movements.
Penunjuk Pine Script®
oleh BigBeluga
7
OHLC Lookback Price Analyzer# 📊 OHLC Lookback Price Analyzer **Stop scrolling back through your chart to find yesterday's close. Everything you need is right there, in one clean dashboard.** --- ## What it does **OHLC Lookback Price Analyzer** is a multi-timeframe dashboard that displays Open, High, Low, Close, percentage change and direction for three timeframes — Daily, Weekly and Monthly — directly overlaid on your chart. Built on **H1 candles** for maximum precision, it detects day, week and month boundaries natively in UTC, avoiding any timezone-related misalignment. No guesswork, no offsets — just clean, accurate data. --- ## Features - **Daily** — shows the last N sessions (default 10, up to 50), each with full OHLC, % variation and bullish/bearish direction - **Weekly** — last 4 weeks, with period shown as `DD/MM > DD/MM` (Monday to Sunday) - **Monthly** — last 3 months, with period shown as `DD/MM > DD/MM` (first to last trading day of the month) - **Color-coded rows** — green for bullish candles, red for bearish, at a glance - **Fully customizable** — colors, text size and table position are all adjustable from the settings panel - **Overlay** — sits directly on your main chart, no extra panel needed - **Works on any instrument** — forex, indices, crypto, commodities, stocks --- ## How to use 1. Add the indicator to any chart running on **H1 or higher timeframe** 2. Set your chart timezone to **UTC** for best accuracy 3. Open the settings panel to enable/disable timeframes, adjust the number of daily bars shown, and customize colors and position 4. Use the dashboard as a quick reference for recent price action — ideal for weekly reviews, journaling and multi-timeframe analysis --- ## Settings | Setting | Description | |---|---| | Daily — Active | Show/hide the daily section | | Daily — Giorni | Number of daily bars to display (1–50, default 10) | | Weekly — Active | Show/hide the weekly section (always shows last 4 weeks) | | Monthly — Active | Show/hide the monthly section (always shows last 3 months) | | Header / TF colors | Background colors for each section | | Rialzo / Ribasso | Bullish and bearish candle colors | | Text size | tiny / small / normal / large / huge | | Position | 9 positions available (corners, edges, center) | --- ## Notes - For best results, use the indicator on a **1H chart set to UTC** - The weekly period runs **Monday to Sunday** - The monthly period runs from the **first to the last trading day** of each month - Data is sourced from H1 bars — the indicator requires sufficient historical H1 data to populate all rows --- ## Disclaimer *This indicator is provided for informational and educational purposes only. It does not constitute financial advice, investment recommendations or trading signals of any kind. Past price data displayed by this tool does not guarantee future results. Always do your own research and consult a qualified financial advisor before making any trading or investment decisions. The author assumes no responsibility for any losses incurred from the use of this indicator.* --- *If you find this tool useful, please leave a comment or a like — it helps a lot and motivates further development. Feel free to share your feedback or suggestions!* 🙏
Penunjuk Pine Script®
oleh Burdiga84
StealthTrail SuperTrend ML Pro [WillyAlgoTrader]🤖 StealthTrail SuperTrend ML Pro is an overlay indicator that builds on the adaptive SuperTrend core from StealthTrail and adds three layers of intelligence: an instrument profiling engine that classifies the market into Trending, Ranging, or Volatile regimes and auto-tunes all SuperTrend parameters accordingly; a 13-feature machine learning scoring system that evaluates every candidate signal on momentum, trend, volatility, structure, volume, HTF alignment, divergence, session quality, and regime context — producing a 0–100 confidence score; and a self-learning mechanism that tracks signal outcomes over time and dynamically adjusts the confidence gate to optimize signal quality. The result is a SuperTrend that configures itself, scores its own signals, and learns from its results. 🧩 WHY THESE COMPONENTS WORK TOGETHER A standard SuperTrend has fixed parameters that work well in one market regime and fail in another. Manually tuning ATR length, multiplier, and filters for each instrument and timeframe is time-consuming and becomes outdated when conditions change. This indicator solves the problem through a three-layer intelligence stack: Layer 1 — Regime classification → Auto-tuning: The instrument profiler continuously measures efficiency ratio (trend strength), autocorrelation (serial dependence), volatility clustering, and normalized volatility — classifying the market into TRENDING, RANGING, or VOLATILE. Each regime produces different optimal SuperTrend parameters. The auto-tuner interpolates ATR length, multiplier, cushion, cooldown, and RSI threshold using regime-weighted blending — so the SuperTrend self-configures for current conditions. Layer 2 — ML signal scoring: Even with auto-tuned parameters, not every SuperTrend flip is a good trade. The 13-feature ML engine evaluates each flip against momentum, volume, trend efficiency, volatility shock, band distance, MACD, price structure, regime confidence, MTF alignment, ADX strength, RSI divergence, volume profile zone, and session quality. Each feature is normalized to 0–100, weighted, passed through a sigmoid function, and combined into a single confidence score. Signals below the confidence gate are rejected — they pass the classic SuperTrend logic but fail the multi-dimensional quality check. Layer 3 — Self-learning gate: The ML confidence gate itself adapts over time. The system tracks each signal's outcome (win/loss evaluated after N bars using the ATR at entry for fair comparison). When the win rate exceeds 70%, the gate lowers (allowing more signals). When it drops below 50%, the gate raises (becoming stricter). A decay factor prevents old signals from dominating. This creates a feedback loop: the indicator learns which confidence level produces profitable signals on this specific instrument and timeframe. Without auto-tuning, the SuperTrend uses static parameters. Without ML scoring, good and bad flips are treated equally. Without self-learning, the confidence gate is a fixed guess. Each layer eliminates a specific category of bad signals that the previous layer can't catch. 🔍 WHAT MAKES IT ORIGINAL 1️⃣ Instrument profiling and regime classification. The profiler computes four metrics over a configurable lookback (default 100 bars): — 📐 Efficiency Ratio : ER = |close − close | / sum(|close − close |, N). Measures net directional movement vs total path. ER → 1.0 = pure trend, ER → 0.0 = pure chop. — 🔄 Autocorrelation : correlation between return and return over a sliding window. High positive autocorrelation = trending persistence. Near-zero = random. Negative = mean-reverting. — 📏 Volatility Clustering : ratio of short-term ATR (20 bars) to long-term ATR (lookback). Values > 1.0 indicate a volatility spike (breakout or crash). Values < 1.0 indicate compression. — 📈 Normalized Volatility : ATR / close × 100. Measures absolute volatility as percentage of price — allows cross-instrument comparison. These are smoothed with EMA and combined into three regime scores: — trendScore = ER × regimeSensitivity — rangeScore = (1 − ER) × (1 − |autocorrelation|) × regimeSensitivity — volatScore = clamp(volClustering − 1, 0, 2) × regimeSensitivity The highest score determines the regime: TRENDING, RANGING, or VOLATILE. Regime confidence = highest_score / total_scores × 100%. 2️⃣ Regime-weighted auto-tuning (6 parameters). Each SuperTrend parameter is computed as a weighted blend of regime-optimal values: effectiveParam = wT × trendOptimal + wR × rangeOptimal + wV × volatileOptimal Where wT, wR, wV are the normalized regime weights. The parameters and their regime-optimal ranges: — ATR Length: Trending=10, Ranging=16, Volatile=21 (further scaled by normalized volatility) — Base Multiplier: Trending=2.0, Ranging=3.2, Volatile=3.8 (scaled by normalized vol) — Flip Cushion: Trending=0.05, Ranging=0.25, Volatile=0.15 — Signal Cooldown: Trending=2, Ranging=5, Volatile=3 — RSI Threshold: Trending=40, Ranging=52, Volatile=45 — RSI Length: derived as ATR Length × 0.9 — Adaptive Smoothing: ATR Length × 4.0 This means in a trending regime, the SuperTrend uses shorter ATR, lower multiplier (tighter bands), minimal cushion, and permissive RSI — catching trends early. In a ranging regime: longer ATR, wider multiplier, large cushion, strict RSI — avoiding chop. In volatile conditions: intermediate settings with wider bands to accommodate spikes. 3️⃣ 13-feature ML scoring engine. Each feature is normalized to 0–100, multiplied by its weight, summed, normalized by total weight, and passed through a sigmoid function to produce the final 0–100 confidence score. Features and their weights: — 💪 F1: Momentum (RSI alignment with trend) — w=0.15 — 📈 F2: Volume Surge (volume / SMA ratio) — w=0.08 — 📐 F3: Trend Efficiency (ER × 100) — w=0.15 — ⚡ F4: Volatility Shock (inverted vol clustering) — w=−0.08 (negative = penalizes vol spikes) — 📏 F5: Band Distance (sigmoid of close-to-band ATR distance) — w=0.10 — 📊 F6: MACD (normalized histogram vs ATR) — w=0.08 — 🏗️ F7: Price Structure (HH/HL for bull, LL/LH for bear over 10 bars) — w=0.08 — 🧠 F8: Regime Confidence (% confidence in current regime) — w=0.04 — 🌐 F9: MTF Confluence (aligned with HTF = 100, not = 0) — w=0.12 — 💪 F10: ADX Strength (ADX × 2.5, capped at 100) — w=0.10 — 🔀 F11: RSI Divergence (aligned div = 100, counter div = 10) — w=0.08 — 📊 F12: Volume Profile Zone (proximity to highest-volume bar) — w=0.06 — 🕐 F13: Session Quality (hour-based scoring for optimal trading sessions) — w=0.04 The raw weighted sum is normalized, then passed through: mlScore = 100 / (1 + exp(−0.08 × (normalizedScore − 50))). This sigmoid compresses extreme values and centers the output around 50. All 13 weights plus the bias term are user-configurable — you can adjust how much each factor contributes. Negative weights penalize a factor (e.g., W4 = −0.08 means high volatility shock reduces the score). 4️⃣ Self-learning confidence gate. The system maintains 5 pending signal slots. Each stores the entry price, direction, bar index, and ATR at entry. After the evaluation horizon (default 15 bars), the outcome is assessed: win = (close − entryPrice) × direction > 0.5 × entryATR (profitable by at least half an ATR, using the ATR at entry time — not current ATR — for fair comparison) A decay factor (0.98) is applied to historical totals before adding new results, preventing stale data from dominating. When total tracked signals ≥ 8: — Win rate > 70% → gate decreases by 1.5 (more permissive) — Win rate < 50% → gate increases by 1.5 (more restrictive) — Gate is clamped to The dashboard shows the current gate value, whether it's auto-adjusted, and the tracked win rate with sample size. 5️⃣ Multi-timeframe confluence with auto-HTF selection. The indicator automatically selects the appropriate higher timeframe based on your current TF: 1M→15M, 5M→30M, 15M→1H, 1H→4H, 4H→D, D→W. Or you can manually specify the HTF. Three strictness levels: — Loose: MTF misalignment adds negative ML score but doesn't block — Moderate: MTF misalignment reduces signal quality — Strict: MTF misalignment hard-blocks the signal entirely HTF trend is determined by EMA(20) vs EMA(50) on the higher timeframe. 6️⃣ Adaptive SuperTrend core (from StealthTrail). The band calculation uses pre-computed interpolated ATR values (ATR bank at 9 fixed periods with lerp interpolation) for smooth adaptation to any auto-tuned ATR length. Same mechanics as StealthTrail: adaptive multiplier (ATR / SMA ratio), band ratcheting, flip cushion, and cooldown — but now all parameters are regime-driven. 7️⃣ Three-mode trailing TP/SL system. Three SL modes: ATR (entry ± mult × ATR), Band (SuperTrend band as SL), Fixed % (percentage from entry). Three trailing modes: — ATR : trail at fixed ATR distance from price — simple, consistent — Band : use the SuperTrend band itself as trailing stop — structurally anchored — ATR → Band (hybrid) : start with tight ATR trail, switch to band when it catches up — best of both The trail only ratchets in the profit direction (never moves away from price). TP1/TP2/TP3 hit markers (✓) appear on the chart. On TP3 or SL hit, the trade closes and lines are removed. 8️⃣ RSI divergence detection. Scans for bullish divergence (price lower low, RSI higher low, RSI < 40) and bearish divergence (price higher high, RSI lower high, RSI > 60). Divergences aligned with the signal direction boost the ML score (F11 = 100). Counter-divergences penalize it (F11 = 10). Optional visualization as dots on the chart. 9️⃣ Volume profile zone proximity. Tracks the highest-volume bar in the last 50 bars. Proximity to this bar's price level is scored: within 1.5× ATR = 100 (near institutional activity), further = proportionally lower. Signals near high-volume zones tend to have more follow-through. 🔟 Session quality scoring. For intraday timeframes, each hour receives a quality score based on typical institutional activity: London/NY overlap (13–17 UTC) = 100, European session (8–12) = 80, US afternoon (18–20) = 70, Asian session (0–7) = 30. An optional kill zone filter suppresses all signals during specified hours. ⚙️ HOW IT WORKS — CALCULATION FLOW Step 1 — Instrument profiling: ER, autocorrelation, vol clustering, normalized vol → EMA-smoothed → regime classification (TRENDING / RANGING / VOLATILE) with confidence %. Step 2 — Auto-tuning: Regime weights blend optimal parameters for ATR length, multiplier, cushion, cooldown, RSI threshold/length. Step 3 — SuperTrend calculation: Interpolated ATR from pre-computed bank → adaptive multiplier (ATR/SMA ratio) → upper/lower bands → ratcheting → flip detection with cushion + cooldown. Step 4 — Classic filters: Momentum (RSI), volume, session, MTF hard-block (if strict). Step 5 — ML feature extraction: 13 features normalized to 0–100 from current market state. Step 6 — ML scoring: Weighted sum → weight normalization → sigmoid → 0–100 confidence score. Step 7 — Signal decision: Classic filters pass AND (ML disabled OR mlScore ≥ gate) → confirmed signal. Step 8 — Self-learning: Signal stored in pending slot → evaluated after horizon bars → win rate updated → gate adjusted. Step 9 — TP/SL placement: SL from band/ATR/fixed% → TPs as ATR multiples → trailing stop ratchets per mode. 📖 HOW TO USE 🎯 Quick start: 1. Add the indicator — Auto-Tune is ON by default, parameters self-configure 2. The SuperTrend band and Long/Short labels appear 3. Dashboard shows: trend, strength, regime, ML score, gate, win rate, TP/SL status 4. Enable ML Filter for quality scoring (off by default — enable after reviewing signal quality) 5. Self-Learning auto-adjusts the gate over time 👁️ Reading the chart: — 🟢 Green band + "Long" label = confirmed bullish signal — 🔴 Red band + "Short" label = confirmed bearish signal — ⚫ Gray dot = classic filter blocked the flip — 🟡 Yellow triangle = ML rejected the signal (score below gate) — 🟢 Green dot below bar = bullish RSI divergence — 🔴 Red dot above bar = bearish RSI divergence — 📈 Regime badge = current market classification (📈 TRENDING / 📊 RANGING / ⚡ VOLATILE) — 🟢 Green dashed = TP1/TP2/TP3, 🔴 Red dashed = SL, 🔵 Blue dotted = entry — "TP1 ✓" / "SL ✗" labels = outcome markers 📊 Dashboard sections: — Main: trend, signal, strength, ADX, HTF alignment — 🤖 ML Engine: ML score, confidence gate (fixed or auto), win rate with sample size — 🎯 Position: status (LONG/SHORT/FLAT), entry, SL (with trail mode icon), TP levels (✓ for hit), R:R ratio — 🧠 Regime: classification + confidence % 🔧 Tuning guide: — Start simple: Auto-Tune ON, ML OFF — let the regime engine handle parameters — Add ML: Enable ML Filter after 50+ signals to see which score level produces winners — Enable Self-Learning: after 100+ bars of ML being active — let the gate auto-calibrate — Adjust weights: if you know your market (e.g., volume is unreliable on forex → set W2 to 0) — Strict MTF: for higher-timeframe alignment — reduces signals, increases quality — Trail Mode: Band for trend-following, ATR for scalping, ATR→Band for hybrid ⚙️ KEY SETTINGS REFERENCE ⚙️ Main: — Auto-Tune (default On): regime-driven parameter self-configuration — ATR Length / Base Multiplier : manual overrides when auto-tune is off 🧠 Adaptive Engine: — Profiling Lookback (default 100): bars for regime classification — Regime Sensitivity (default 1.0): scaling factor for regime scores 📐 Multi-Timeframe: — MTF Confluence (default On): Auto/Manual HTF selection — Strictness (default Moderate): Loose / Moderate / Strict 🤖 ML Signal Filter: — Enable ML (default Off): activate 13-feature scoring — Confidence Gate (default 21): minimum ML score to pass — Self-Learning (default On): auto-adjust gate from tracked outcomes — Evaluation Horizon (default 15 bars): outcome assessment window — 13 individual weights + bias : fully configurable feature importance 🔍 Filters: — Flip Cushion, Cooldown, RSI Momentum, Volume, Session Kill Zone 🎯 TP/SL: — SL Mode (default Band): ATR / Band / Fixed % — Trail Mode (default Band): ATR / Band / ATR → Band — TP Levels (1–3): ATR multipliers (default 1.5 / 2.5 / 4.0) 🔔 Alerts — 🟢 LONG / 🔴 SHORT — ticker, price, TF, band, ML score, ADX, HTF, SL, TP1, regime — 🎯 TP1 / TP2 / TP3 HIT — trade progress — 🛑 SL HIT — stopped out All support plain text and JSON webhook format. Bar-close confirmed. ⚠️ IMPORTANT NOTES — 🚫 No repainting. All signals require barstate.isconfirmed. The confirmed trend direction is stored separately from the real-time calculation — signals only fire on closed bars. HTF data uses lookahead_off. A warmup period (max of profiling lookback and 55 bars) prevents signals during insufficient data. — 🤖 The ML scoring is not machine learning in the neural network sense . It's a weighted linear model with sigmoid activation — a logistic regression analog. The 13 features are hand-crafted from market microstructure, and the weights are configurable by the user. There is no gradient descent, backpropagation, or training phase. The "self-learning" adjusts only the confidence gate threshold, not the feature weights. — 📐 Auto-tuning produces different parameters on every bar as the regime shifts. The ATR length and multiplier change continuously — this is by design. If you prefer fixed parameters, disable Auto-Tune. — ⚖️ The self-learning gate requires at least 8 tracked signals before it begins adjusting. With the decay factor (0.98), the effective sample is weighted toward recent signals. The gate moves slowly (±1.5 per adjustment) and is clamped to . — 📊 Win rate evaluation uses ATR at entry time , not current ATR. A signal is a "win" if price moves > 0.5× entry ATR in the signal direction within the evaluation horizon. This prevents volatile periods from inflating win counts. — 🔄 The pre-computed ATR bank (9 fixed periods with lerp interpolation) is a performance optimization that allows smooth ATR adaptation to any auto-tuned length without calling ta.atr() dynamically — which Pine Script doesn't support with variable-length arguments. — 📏 Volume Profile Zone tracks the single highest-volume bar in the last 50 bars, not a full volume profile. It decays after 50 bars with no new volume peak. — 🕐 Session scoring uses UTC-based hours. On daily or higher timeframes, session quality defaults to 50 (neutral) as intraday session distinctions don't apply. — 🛠️ This is a signal scoring and analysis tool , not an automated trading bot. It classifies regimes, scores signals, tracks outcomes, and manages TP/SL — trade decisions remain yours. — 🌐 Works on all markets and timeframes. Volume features auto-adapt to instruments without volume data.
Penunjuk Pine Script®
oleh WillyAlgoTrader
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