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Torsion Range [JOAT]Torsion Range Introduction Torsion Range is an open-source compression, expansion, and wave-energy panel designed to track whether the market is coiling, releasing, trending, or exhausting. The script combines wave direction, weighted energy accumulation, compression scoring, and exhaustion logic into one regime-aware panel. The problem Torsion Range solves is fragmentation between wave tools and volatility tools. Traditional wave studies often ignore whether the market is compressing or expanding, while compression indicators often ignore directional wave state. Torsion Range merges both worlds so the user can monitor directional energy and structural volatility state together. Core Concepts 1. Wave Direction Engine The script supports multiple methods for determining directional wave state, including impulse, pressure, and hybrid behavior. This allows the model to adapt to different styles of price movement. 2. Weighted Energy Accumulation Wave state is not just directional. It is weighted by body, range, ATR context, and optional relative volume to produce a more informative torsion core. 3. Compression Index A 0-100 compression framework is built from fast and slow comparisons of range, body, and energy behavior. This allows the indicator to identify tight conditions before release. 4. Release and Exhaustion Logic Confirmed-bar release events occur when compression gives way to directional expansion. Exhaustion logic looks for overstretched waves with weakening internal behavior. 5. Dashboard and Regime Visualization The panel includes adaptive colors, background overlays, event flashes, and a top-right dashboard that summarizes wave state, bias, compression, and most recent event. Features Multi-method wave direction: Impulse, pressure, or hybrid state engine Weighted energy accumulation: Uses price and optional relative volume Compression scoring: Tracks coiling conditions on a normalized scale Confirmed release logic: Detects transition from compression to directional expansion Exhaustion detection: Flags overextended wave conditions Adaptive gradient styling: Institutional panel presentation for dark charts Regime overlays and event flashes: Highlights important state transitions cleanly Top-right dashboard: Summarizes live wave and compression state Confirmed event mode: Optional bar-close only event gating Alertconditions: Wave flips, release events, and exhaustion states How to Use This Indicator Step 1: Read the Wave State Determine whether directional energy currently favors bullish, bearish, or neutral behavior. Step 2: Check Compression High compression means stored energy but not yet confirmed release. Low compression with directional energy implies active movement rather than coiling. Step 3: Watch Release Events Release events matter most when they occur after genuine compression and align with the active wave bias. Step 4: Respect Exhaustion Exhaustion conditions can warn that a strong wave is becoming less efficient, even if trend has not yet fully reversed. Indicator Limitations Compression and exhaustion are relative, not absolute, conditions Wave direction depends on the selected method and will respond differently across market types This panel does not identify exact reversal bars and should not be treated as one Release events are strongest when combined with separate structural context Originality Statement Torsion Range is original in its fusion of wave-state persistence, weighted energy accumulation, compression analysis, and exhaustion logic within one panel. It is not a basic Weis-style clone and not a plain volatility gauge. Its value comes from combining directional energy and volatility state into a single workflow. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Compression, release, and exhaustion states are analytical conditions derived from historical chart behavior and do not guarantee future outcomes. - Made with passion by jackofalltrades
Pine Script® 인디케이터
officialjackofalltrades의
Quotient Divergence Ledger [JOAT]Quotient Divergence Ledger Introduction Quotient Divergence Ledger is an open-source divergence engine built on a signed participation wave. It tracks regular and hidden divergences, wave energy, directional streaks, and the quality of each divergence event so the trader can judge whether momentum disagreement is minor or meaningful. Core Concepts 1. Signed participation wave The oscillator blends directional volume participation, body pressure, and smoothed wave behavior into one normalized structure. 2. Confirmed pivot-based divergence logic Bullish and bearish divergences are only evaluated from confirmed oscillator pivots, reducing repaint behavior and maintaining clean historical structure. 3. Divergence quality scoring Each event is scored using oscillator displacement, price displacement, and wave energy. This helps separate weak visual divergence from stronger disagreement. Features Regular and hidden divergence modes Wave energy and stretch diagnostics Directional streak tracking Bottom ribbon and institutional dashboard Confirmed-bar divergence and regime alerts Disclaimer This indicator is provided for educational purposes only. Divergence can persist or fail entirely in strong trend conditions.
Pine Script® 인디케이터
officialjackofalltrades의
Adaptive Wave Pressure Index [JOAT]Adaptive Wave Pressure Index Introduction Adaptive Wave Pressure Index is a normalized slope oscillator built to measure directional pressure through the relationship between regression slope and volatility. By scaling a manually calculated OLS slope with ATR, the script produces a dimensionless momentum reading that can be compared across instruments and timeframes much more cleanly than raw slope alone. This indicator is designed for traders who want wave pressure, not just speed. It tracks directional force, smooths that force into fast and slow lines, colors the histogram using structural swing context, and adds divergence detection for potential exhaustion. Why This Indicator Exists Volatility-Normalized Momentum: Regression slope is scaled by ATR to improve comparability Fast / Slow Pressure Read: Reveals acceleration and deceleration of directional force Structure Overlay: Swing-sequence counts add context to histogram strength Zone Framework: Overbought and oversold thresholds define pressure extremes Divergence Layer: Flags when price reaches new extremes without matching pressure Core Components Explained 1. Manual OLS Slope rawSlope = f_olsSlope(regLength) The script calculates slope directly from the last N closes rather than relying on a built-in regression shortcut. This provides more control over normalization and display logic. 2. ATR Normalization normSlope = rawSlope / ta.atr(atrNormPeriod) Dividing slope by ATR transforms it into a volatility-aware measure of pressure. A positive slope on a low-volatility asset and a positive slope on a high-volatility asset become more comparable after normalization. 3. Fast / Slow Pressure System Two EMAs are applied to the normalized slope: Fast Line: More responsive pressure state Slow Line: More stable reference Histogram: Spread between fast and slow, showing acceleration or fade 4. Structural Sequence Layer The indicator also counts consecutive higher lows and lower highs in price. When structure strongly supports the current pressure direction, histogram colors intensify. This adds a valuable distinction between pressure that is statistically rising and pressure that is also structurally confirmed. 5. Divergence and Zone Logic The script highlights: Fast-line crosses of overbought and oversold thresholds Fast/slow line crosses Bullish and bearish divergences Divergence lines are retained with a fixed cap so the pane stays readable over time. Visual Elements Histogram: Pressure spread with structural-intensity color logic Fast Line: Main directional read Slow Line: Reference pressure line Zero Fill: Directional bias area fill OB/OS Background: Soft zone shading for extreme pressure Markers: Crosses and divergence markers Dashboard: Raw slope, normalized slope, trend, structure sequence, divergence, and active zone Input Parameters Regression Length: Window for OLS slope calculation ATR Norm Period: Volatility baseline used for normalization Fast / Slow EMA: Pressure responsiveness controls OB / OS Levels: Extreme pressure thresholds Pivot Left / Right: Sensitivity for structural and divergence logic How to Use This Indicator Step 1: Read whether fast is above or below slow. Step 2: Check the histogram to see whether pressure is expanding or contracting. Step 3: Use the sequence readout to judge whether price structure agrees with the oscillator. Step 4: Treat divergences as warnings that pressure may be weakening. Step 5: Use OB/OS events to identify stretched pressure, especially after large runs. Best Practices Use on instruments with clean swings and sufficient range Respect signals more when sequence direction agrees with fast/slow direction Use divergence with structure, not by itself Increase regression length for smoother wave pressure Lower lengths react faster but create more noise Indicator Limitations Normalized slope improves comparison but does not eliminate market differences Pressure can stay elevated in strong trends Divergences can persist before price turns Short settings increase false transitions Structure counts are descriptive, not predictive Technical Implementation Built in Pine Script v6 using: Manual OLS slope computation ATR normalization Dual-EMA pressure smoothing Pivot-based structure counting Capped divergence-line management Confirmed-bar signal generation Originality Statement This indicator is original in the way it combines normalized regression slope, structural sequence intensity, and divergence management into a single wave-pressure framework. Its purpose is not just to show direction, but to show how forceful and how structurally supported that direction is. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Momentum and divergence tools can fail, especially during volatile transitions. Always use proper risk management and independent confirmation. -Made with passion by officialjackofalltrades
Pine Script® 인디케이터
officialjackofalltrades의
1
Quantum Momentum Wave [Pineify]Quantum Momentum Wave Quantum Momentum Wave is a signed peak-momentum oscillator paired with a Kaufman-style adaptive filter. Instead of measuring the net change from one point to another, the wave scans the full lookback window and returns the largest absolute price displacement with its original sign. The wave is linearly smoothed and then tracked by an adaptive signal line, so entry markers fire on context-filtered reversals rather than every momentum flip. Key Features Signed peak-momentum engine that isolates the strongest move inside the lookback Linear regression smoothing to tame the step-change behavior of raw peak readings Adaptive signal line that speeds up when the wave is stretched and slows down in noise Zone-filtered BUY and SELL markers that only fire when the wave is past zero in the opposite direction Gradient fill between the wave and zero — bullish above, bearish below Alerts for reversal setups and for zero-line regime shifts How It Works The core function is a QMW scan. On each bar it walks the last Momentum Lookback bars, computes price minus each historical bar, and keeps the comparison that produced the largest absolute change. It returns that value with its original sign. So a -6 reading means the single strongest move inside the window was a 6-unit drop, even if the bar-to-bar change right now is small. Raw peak readings step when a new candle takes over as the dominant move, so the output is passed through linear regression over the Signal Smoothing length. LR fits a straight line through the recent raw values and returns its endpoint, preserving signed direction while cutting short-term jitter. The adaptive signal runs a Kaufman-style update on top of the smoothed wave. Its coefficient is the ratio of the wave's absolute value to the sum of its recent absolute one-bar changes. When momentum is clearly extended and directional, the ratio is high and the signal follows quickly. When the wave oscillates near zero, the ratio collapses and the line nearly freezes. How the Components Work Together Three layers combine into one output. The peak scan answers how hard has price moved inside the window, not just where it ended. Linear regression answers what is the underlying direction of the peak reading after filtering out one-bar swaps between competing peak bars. The adaptive signal answers when has that direction actually shifted by providing a responsive reference line. Entries layer a location filter on top. A crossover only qualifies when the wave is on the opposite side of zero — buys look for the wave turning up while stretched below zero, sells for the wave rolling over while stretched above it. Crosses near the midline are ignored because they tend to be low-conviction flips without prior displacement, so the setup is biased toward reversals from extended states rather than trend-chasing entries. Trading Ideas and Insights BUY triangles below zero can flag exhaustion of a downside push and a possible rotation back toward the mean SELL triangles above zero can flag fading upside strength after an extended rally Zero-line crosses act as broader regime markers; alerts on those can frame intraday bias During strong trends the wave may stay on one side of zero for many bars. Counter-trend BUY or SELL labels in that state often need extra confirmation from price structure or higher-timeframe context Signals are context aids, not standalone trade instructions. Past reactions at these conditions do not guarantee future ones. Unique Aspects The momentum calculation returns the peak signed displacement within the lookback, not a simple close-minus-close value. The reading stays elevated while the dominant move is still inside the window, which tracks the strongest displacement in memory rather than just endpoint delta The adaptive signal uses a non-standard variation of Kaufman's efficiency ratio — absolute wave value divided by total absolute wave travel — tuned for oscillator input rather than raw price Entry markers require both a crossover and a zone match. No label prints for a crossover that happens in the neutral zone around zero, which filters out the weakest setups How to Use Add the indicator to the chart and start with the defaults. The thicker line is the Quantum Wave; the fainter line is the Adaptive Signal Read the wave color and the gradient fill to see which side of zero the wave is on and how stretched it is Watch for BUY triangles when the wave is clearly below zero and turning up through the signal line Watch for SELL triangles when the wave is clearly above zero and turning down through the signal line Configure alerts for buy, sell, or zero-cross conditions if you want notifications outside the chart Customization Source Data (default: close) — Price series used for the peak momentum scan Momentum Lookback (default: 14) — Window size for the peak displacement scan. Higher values capture broader swings; lower values react faster Signal Smoothing (default: 9) — Linear regression length applied to the raw wave. Higher values produce a cleaner line with more lag Bullish Color / Bearish Color — Wave color in positive and negative territory, and the BUY/SELL marker colors Adaptive Signal Color — Color of the adaptive reference line; a subtle contrast against the wave usually reads cleanest Limitations Linear regression smoothing introduces a small amount of lag. On fast reversals the wave can reach the crossover a few bars after price has already moved Because the adaptive ratio uses the wave's own value, very small wave readings near zero make the signal line nearly freeze. This is deliberate but means the reference reacts slowly while momentum is mild Mean-reversion style BUY and SELL labels can occur repeatedly during strong trends. Pair them with price structure or higher-timeframe bias instead of using them in isolation Conclusion Quantum Momentum Wave is for traders who want an oscillator that reflects the strongest recent move rather than the endpoint delta. Its value comes from the combination of peak-momentum measurement, linear regression smoothing, and an adaptive reference line that only produces labels when the wave is both stretched past zero and visibly turning back.
Pine Script® 인디케이터
Pineify의
Artemis Volatility Bands PRO🟦 Artemis Volatility Bands PRO is a price-overlay volatility 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 Basis line. Around it, two outer bands fan outward by a fixed multiple of the residual standard deviation, creating an envelope whose width is model-consistent with the kernel. The interior is washed with a 6-layer neon halo that mirrors the statistical density of price residuals under normality. A signal engine detects basis-breaks with confirming slope direction, guarded by Confirmed (zero-repaint) or Realtime mode. Twelve cohesive color themes, a theme-aware Dark / Light dashboard, and four opt-in alert conditions complete the indicator. 🟦 HOW IT WORKS Artemis Volatility Bands PRO fuses two mathematical operations on every bar — a single kernel regression pass and a residual standard deviation calculation: basis = kl.estimate(type, src, ℓ, α, period, phase, filter) sigma = kl.confidenceBand(src, basis, window) upper = basis + k · σ lower = basis − k · σ where ℓ is the Primary Bandwidth, k is the Band Multiplier, and window is the Residual σ Window. The kernel regression produces the Basis line, and the residual standard deviation (σ of price − basis) produces the per-σ band half-width. This architecture collapses classical Bollinger band duality: in Bollinger bands, the moving average and volatility estimate live in different statistical universes. In Artemis, both emerge from a single kernel pass, so the envelope is internally consistent by construction. The Basis is always non-parametric, and the band width always measures deviation relative to the Basis, never to a disconnected moving average. The library handles all weighted-sum computation, kernel weight evaluation, NA-safe iteration, division-by-zero guards, and input validation internally. Artemis Volatility Bands PRO does not reimplement any kernel math — every bug fix or optimization in the library automatically propagates to this indicator. 🟦 KERNEL LIBRARY INTEGRATION Artemis Volatility Bands PRO 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 Basis line. | | `kl.confidenceBand()` | Rolling residual standard deviation — computes σ of (source − basis) over the specified window. | | `kl.trendState()` | Ternary trend detector — returns +1 (rising), 0 (flat), or −1 (falling) based on a 1-bar finite difference of the Basis. | 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 three library calls and aggregates their outputs into the band envelope and signal logic. 🟦 KERNEL REGRESSION & RESIDUAL VOLATILITY **The Basis line** — A non-parametric kernel regression anchored to the user's choice of price source (default: close; also supports hl2, ohlc4, custom). Eight kernel families available: | Kernel | Behavior | Best For | |---|---|---| | Rational Quadratic | Multi-scale mixer; α controls stretch | Default, balanced responsiveness | | Gaussian / RBF | Canonical smoother, infinitely differentiable | Smooth trend, minimize noise | | Periodic | Resonates with a known repetition distance p | Cyclic markets, seasonal patterns | | Locally Periodic | Periodic × gaussian blend | Seasonal + trend drift | | Epanechnikov | MSE-optimal, compact support | Minimal tail contamination | | Tricube | LOWESS standard, near-Gaussian profile | Fast computation, robust | | Triangular | Simplest compact kernel | Lightweight, real-time responsiveness | | Cosine | Raised-cosine, smooth boundary transition | Smooth rolloff, professional appearance | Three filter modes applied on top of the raw kernel estimate: | Filter | Description | Use Case | |---|---|---| | No Filter | Single-pass Nadaraya–Watson | Rawest output, maximum responsiveness | | Smooth | Double-pass: kernel applied to its own output | Cleaner, slightly more lag | | Zero Lag | Ehlers de-lagging: 2·raw − smooth | Sharpens edges without increasing lag | **Residual volatility** — Once the Basis is computed, the per-bar residual is (source − basis). The residual standard deviation is the rolling σ of this residual over a configurable window (default: 14 bars). This is the model-consistent volatility estimate — price deviations are measured relative to the kernel Basis, guaranteeing alignment between trend and volatility. **Band levels** — upper = basis + k · σ and lower = basis − k · σ, where k is the Band Multiplier (default: 2.0, Bollinger-style). The multiplier is user-adjustable from 0.5 (tight) to 5.0 (wide). 🟦 SIGNAL ENGINE Artemis fires Long / Short signals when price breaks the Basis with a confirming slope direction: Long → close > basis AND basis rising Short → close < basis AND basis falling The signal state is persistent — once a direction flips, it remains latched until the opposite condition fires. This state machine (vii ∈ {−1, 0, +1}) ensures the Basis hue stays coherent across bars even when the raw trigger is a single-bar event. Signal markers fire ONLY on the bar the state flips, not on every bar that satisfies the raw condition. This keeps the chart uncluttered and mirrors how discretionary traders consume trend-flip information. **Signal Mode** — Two gating options: | Mode | Behavior | Repaint | |---|---|---| | Confirmed | Signals fire ONLY after the bar closes via `barstate.isconfirmed` | Zero repaint, fully reliable for live trading | | Realtime | Signals fire on the current (open) bar as soon as the condition is met | Fastest reaction; signal may vanish if price reverses before bar closes | Historical repainting never occurs at any Signal Mode value. The library's `_phase` parameter shifts every kernel center into the past by that many bars, so historical bars' plotted values are final once confirmed. 🟦 6-LAYER NEON HALO VISUALIZATION The interior between the Basis and each outer band is filled with a 6-layer gradient: 5 interior step plots plus the outer band, creating a stepped transparency schedule that mirrors the statistical density of price residuals under normality. **Transparency schedule:** | Layer | Transparency | Meaning | |---|---|---| | Outer band ↔ 1st interior | 70 % | Densest layer | | 1st ↔ 2nd | 78 % | | | 2nd ↔ 3rd | 85 % | | | 3rd ↔ 4th | 90 % | | | 4th ↔ 5th | 95 % | | | 5th ↔ Basis | 98 % | Nearly invisible fade to centerline | Every transparency value is scaled by the Gradient Intensity input (0–100 %), so the user can dial the visual density from invisible (0) to heavy fills (100). **Color assignment:** - Upper band + gradient: Bearish theme hue (short signal color) - Lower band + gradient: Bullish theme hue (long signal color) - Basis line: Slope-adaptive color (thBull when rising, thBear when falling, previous color on flat bars) **Signal markers** — Two-layer neon glow plotshapes: - Halo: size.small, 40 % opaque theme hue (glow layer) - Core: size.tiny, 100 % opaque theme hue (bright center) 🟦 THEME SYSTEM Twelve cohesive color palettes tuned to every trading aesthetic. One selection drives every visual component — Basis line, outer bands, gradient halos, long / short signal markers, dashboard accents — all sharing the same bull / bear / neutral color axes: | Theme | Bull | Bear | Usage | |---|---|---|---| | Tropic | Cyan steel | Deep orange | Default, electric contrast | | Amber | Warm amber | Indigo blue | Fire tones | | Pastel | Sky blue | Soft lavender | Cool arctic glow | | Cyber | Neon lime | Hot crimson | Cyber terminal aesthetic | | Helios | Bright gold | Scarlet | Solar warmth | | Electric | Electric aqua | Magenta | High-voltage neon | | Candy | Neon green | Hot pink | Dark energy pop | | Bloomberg | Terminal orange | Cyan | Wall Street finance heritage (PRO) | | Solar | Solarized olive | Crimson | Developer palette, easy on eyes (PRO) | | Royal | Imperial gold | Deep purple | Luxury signature (PRO) | | Midnight | Deep navy | Dark crimson | Dark depth | | Graphite | Near-black | Silver grey | Monochrome minimal | **Dashboard display modes** — Dark (black background, bright accents) or Light (white background, darker accents), auto-adapting visual contrast regardless of chart background. 🟦 DASHBOARD A 2-column, 9-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 PRO | DARK / LIGHT | | Theme | Theme | Active palette name | | Divider | KERNEL | — | | Type | Type | Selected kernel type | | Bandwidth ℓ | Bandwidth ℓ | Primary bandwidth + Phase φ | | Basis | Basis | Current Basis value in chart mintick format | | Divider | VOLATILITY | — | | Residual σ | Residual σ | Current residual standard deviation | | Signal | Signal | ▲ LONG / ▼ SHORT / ━ FLAT (direction-colored) | 🟦 ALERT CONDITIONS Four opt-in alert conditions, each gated by its own toggle: | Alert | Fires When | |---|---| | Long Signal | Price breaks above the Basis with a rising slope (confirmed state flip) | | Short Signal | Price breaks below the Basis with a falling slope (confirmed state flip) | | Upper Band Touch | Price touches or exceeds the upper outer band (raw crossover) | | Lower Band Touch | Price touches or falls below the lower outer band (raw crossunder) | Band touch alerts are useful as pre-signal early warnings in trending markets. Signal alerts are gated by the Signal Mode setting, so Confirmed mode ensures zero-repaint alerts suitable for live trading. All alerts use `alertcondition()` for maximum compatibility with TradingView's alert system including webhooks. Messages are structured as `"Artemis Volatility Bands: "` for easy parsing in downstream automation. 🟦 RECOMMENDED PRESETS | Trading Style | Bandwidth ℓ | Filter | Residual σ Window | Phase | |---|---|---|---|---| | Scalper | 10–20 | No Filter | 8–10 | 1 | | Day Trader | 20–40 | Smooth | 14 | 2 | | Swing | 30–60 | Smooth | 20–40 | 2 | | Position | 60–120 | Smooth | 40–60 | 3 | **Bandwidth tuning** — Smaller ℓ produces a tighter fit to price and faster reaction; larger ℓ produces smoother curves and more stability. Experiment with ℓ in your preferred style's range, then adjust the Residual σ Window and Filter mode for visual smoothness. **Phase tuning** — Phase = 0 is live (flickers on the current bar); Phase = 2 is the recommended balance; Phase = 3+ adds margin against noise at the cost of lag. Historical charts are immutable at any phase value. 🟦 KERNEL-ONLY DESIGN PHILOSOPHY Artemis Volatility Bands PRO contains zero classical technical analysis bolt-ons. No Bollinger Bands, no Keltner Channels, no linear regression, no ATR, no moving averages — only kernel regression and residual volatility. This kernel-only architecture guarantees that: 1. **Internal consistency** — The Basis and band width emerge from a single statistical model, not from mixing independent techniques. 2. **Unified parameterization** — All visual outputs (Basis, bands, gradient) are controlled by a single set of kernel-theoretic parameters. 3. **No analytical compromise** — Every choice in the indicator is mathematically motivated; no ad-hoc decorations. The philosophy is defensive: traders who layer Artemis on top of their own edge strategies get a pure kernel-regression envelope that will not conflict with classical-TA signals already in use. Traders seeking a standalone kernel-based indicator get a complete, coherent system. 🟦 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 to chart background - No exchange-specific logic — fully deterministic Indicator renders on the main overlay chart with `force_overlay = true`. No secondary panes, no subplot logic. 🟦 TECHNICAL NOTES - **Library dependency** — `import a_jabbaroff/KernelLens/1` — all kernel regression math is delegated to the published library - **Plot budget** — 2 outer bands + 10 gradient interior plots + 1 Basis + 1 transparent anchor + 4 signal shapes + 12 fills + 4 alertconditions = 34 outputs, well under Pine's 64-output hard limit (50 % margin) - **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`; all visuals are plots and fills - **Opacity convention** — every user-facing opacity / transparency 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 - **Residual consistency** — The residual σ is computed as `ta.stdev(source − basis, window)`, ensuring the band width always measures deviation relative to the kernel Basis 🟦 DISCLAIMER Artemis Volatility Bands 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 signal engine is a mechanical detector of basis breaks and slope direction — 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 Artemis Volatility Bands PRO or the underlying KernelLens library.
Pine Script® 인디케이터
a_jabbaroff의
Velocity Spectrum Analyzer [JOAT]Velocity Spectrum Analyzer Introduction The Velocity Spectrum Analyzer is an advanced open-source momentum wave system that combines Munich Wave methodology with ALMA enhancement and multi-basis momentum tracking. This indicator analyzes momentum across five distinct velocity layers, creating a spectrum of momentum waves that reveal trend strength, regime shifts, and momentum alignment across multiple timeframes. Unlike single-line momentum indicators, the Velocity Spectrum Analyzer provides multi-dimensional momentum analysis through layered EMA calculations, ALMA enhancement, regime classification, and spread analysis. The indicator is designed for traders who understand that momentum flows in waves and that multi-layer alignment signals institutional conviction. Why This Indicator Exists This indicator addresses the need for multi-dimensional momentum analysis. By combining five momentum layers with ALMA enhancement and regime detection, it reveals: Five Velocity Layers: Fast (9), Medium (21), Slow (55), Very Slow (100), and Ultra Slow (200) EMAs create a momentum spectrum ALMA Enhancement: Arnaud Legoux Moving Average provides adaptive smoothing with reduced lag Basis Calculations: Averages between EMA layers create intermediate momentum levels Regime Classification: Extreme Bull/Bear detection using Bollinger-style bands Spread Analysis: Distance between fast and slow layers measures momentum strength Wave State Detection: All layers bullish or bearish signals strong directional momentum Background Coloring: Visual regime indication shows extreme conditions Core Components Explained 1. Core Momentum Calculation The indicator starts with basic momentum (current close minus close N bars ago), then applies ALMA for adaptive smoothing: The ALMA offset (default 0.85) and sigma (default 6) parameters control the balance between responsiveness and smoothness. Higher offset values shift the average toward recent prices, while higher sigma values increase smoothness. 2. Five EMA Layers Five EMAs are calculated on the momentum values: Fast EMA (9): Captures short-term momentum shifts Medium EMA (21): Tracks intermediate momentum trends Slow EMA (55): Identifies primary momentum direction Very Slow EMA (100): Reveals long-term momentum bias Ultra Slow EMA (200): Shows institutional momentum positioning Each layer responds at different speeds, creating a spectrum of momentum perspectives. 3. Basis Calculations Five basis levels are calculated as averages between EMA layers: Basis 1: Average of Fast and Medium EMAs Basis 2: Average of Medium and Slow EMAs Basis 3: Average of Slow and Very Slow EMAs Basis 4: Average of Very Slow and Ultra Slow EMAs Basis 5: Average of Ultra Slow and Fast EMAs (wraps around) These basis levels create intermediate momentum zones that smooth transitions between layers. 4. Trend Classification Functions Two functions classify momentum direction: Growing: Momentum > basis (bullish momentum) Falling: Momentum <= basis AND momentum <= ALMA (bearish momentum) Each basis is classified independently, creating five separate momentum assessments. 5. Regime Detection with Bollinger-Style Bands The indicator calculates bands around the average of all five basis levels: Origin: SMA of basis average (default 25 periods) Deviation: Standard deviation multiplied by factor (default 6.0) Top Band: Origin + deviation (extreme bullish threshold) Bottom Band: Origin - deviation (extreme bearish threshold) When basis 1 and ALMA both exceed the top band with rising momentum, the indicator signals extreme bullish conditions. When both fall below the bottom band with falling momentum, it signals extreme bearish conditions. 6. Mean Range Calculation A long-term mean range (default 415 bars) tracks the highest and lowest basis average values. The center of this range serves as a reference point for ALMA positioning. When ALMA is above the center mean with all layers bullish, strong upward momentum is confirmed. 7. Wave State Analysis The indicator tracks when all five basis levels are simultaneously bullish or bearish: All Bullish: All five basis levels show growing momentum - strong uptrend All Bearish: All five basis levels show falling momentum - strong downtrend Mixed: Some layers bullish, some bearish - transitional or choppy conditions Wave state alignment indicates institutional conviction across all momentum timeframes. 8. Spread Calculation The spread between Basis 1 (fastest) and Basis 5 (slowest) measures momentum divergence: Positive Spread (> 10): Fast momentum exceeds slow momentum - bullish acceleration Negative Spread (< -10): Fast momentum below slow momentum - bearish acceleration Extreme Spread (> 20 or < -20): Very strong momentum divergence - potential exhaustion Large spreads indicate strong directional momentum, while narrowing spreads warn of momentum loss. Visual Elements Five Velocity Layer Lines: Thick colored lines showing each basis level with dynamic coloring (cyan = bullish, yellow = bearish, white = neutral) ALMA Enhanced Line: Separate line showing ALMA-adjusted momentum with tri-color scheme Wave State Line: Zero line colored based on overall wave state Background Regime: Red background for extreme bull, green background for extreme bear Information Dashboard: Displays wave state, regime, spread, ALMA position, momentum value, layer alignment, and signal status Signal Generation The indicator generates four types of signals: Lean Short: Bearish crossover with falling Basis 1 and 2, spread <= -10 Maybe Buy: Bearish crossover with falling Basis 1 and 2, extreme bear regime, spread <= -20 (oversold) Lean Long: Bullish crossover with growing Basis 1 and 2, spread >= 10 Maybe Sell: Bullish crossover with growing Basis 1 and 2, extreme bull regime, spread >= 20 (overbought) Additional signals: All Aqua: All layers bullish for 4+ consecutive bars - strong uptrend confirmation All Yellow: All layers bearish for 4+ consecutive bars - strong downtrend confirmation How to Use This Indicator Step 1: Check Wave State Monitor the dashboard for wave state (All Bullish, All Bearish, or Mixed). Trade in the direction of wave state alignment. Step 2: Analyze Regime Watch for extreme bull/bear regimes (red/green backgrounds). These often precede reversals or strong continuation moves. Step 3: Monitor Spread Large spreads (> 20 or < -20) indicate strong momentum but potential exhaustion. Narrowing spreads warn of momentum loss. Step 4: Check ALMA Position ALMA above center mean with bullish layers confirms uptrend. ALMA below center mean with bearish layers confirms downtrend. Step 5: Count Layer Alignment The dashboard shows how many layers are bullish (X/5). 5/5 bullish = strongest uptrend, 0/5 bullish = strongest downtrend. Step 6: Wait for Signal Confirmation Lean Long/Short signals work best when wave state aligns. Maybe Buy/Sell signals at extremes offer reversal opportunities. Best Practices Trade with wave state alignment, not against it Use extreme regimes as reversal warnings, not continuation signals Monitor spread for momentum strength - large spreads indicate strong trends Wait for all layers to align (5/5) before taking aggressive positions Use Maybe Buy/Sell signals only at extreme regimes with high spread Combine with price action - momentum shows intent, price shows result Be cautious when layers are mixed (2/5 or 3/5) - indicates choppy conditions Watch for spread narrowing as early warning of trend exhaustion Input Parameters Momentum Engine: Source: Price input (default: close) Momentum Length: Period for momentum calculation (default: 21) ALMA Offset: Offset parameter for ALMA (default: 0.85) ALMA Sigma: Sigma parameter for ALMA (default: 6) Momentum Layers: Fast EMA: Short-term momentum (default: 9) Medium EMA: Intermediate momentum (default: 21) Slow EMA: Primary momentum (default: 55) Very Slow EMA: Long-term momentum (default: 100) Ultra Slow EMA: Institutional momentum (default: 200) Regime Classification: Mean Lookback: Period for mean range (default: 415) StdDev Length: Period for standard deviation (default: 25) StdDev Multiplier: Band width multiplier (default: 6.0) Background Offset: Shift background display (default: 0) Visual Configuration: Bullish Color: Color for bullish momentum (default: cyan) Bearish Color: Color for bearish momentum (default: yellow) Neutral Color: Color for neutral momentum (default: white) Enable Alerts: Toggle alert conditions (default: enabled) Originality Statement This indicator is original in its multi-layer momentum approach. While individual components (EMAs, ALMA, momentum) are established concepts, this indicator is justified because: It combines five distinct momentum layers into a unified spectrum analysis The basis calculation system creates intermediate momentum zones between layers ALMA enhancement provides adaptive smoothing with reduced lag Regime detection using Bollinger-style bands on basis average identifies extremes Wave state analysis tracks alignment across all five layers simultaneously Spread calculation measures momentum divergence between fast and slow layers The comprehensive dashboard presents all momentum dimensions simultaneously Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Momentum analysis does not guarantee profitable trades. Past momentum patterns do not guarantee future results. Always use proper risk management and never risk more than you can afford to lose. -Made with passion by officialjackofalltrades
Pine Script® 인디케이터
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Quantum Flux Oscillator [JOAT]Quantum Flux Oscillator Introduction The Quantum Flux Oscillator is an advanced open-source momentum detection system that synthesizes six distinct analytical methodologies into a unified institutional-grade oscillator. This indicator combines Volume Flux Indicator (VFI), Laguerre RSI, Fisher Transform, True Strength Index (TSI), Money Flow Index (MFI), and On-Balance Volume (OBV) with Accumulation/Distribution analysis to create a comprehensive momentum engine that reveals institutional positioning and market regime shifts. Unlike traditional single-dimension oscillators, the Quantum Flux Oscillator provides multi-layered momentum intelligence through weighted composite calculations, regime classification, velocity tracking, and divergence detection. The indicator is designed for traders who understand that momentum precedes price and that institutional footprints can be detected through systematic multi-indicator confluence. Why This Indicator Exists This indicator addresses a critical gap in momentum analysis: the ability to detect institutional momentum shifts before they become obvious to retail traders. By combining multiple momentum methodologies with volume-weighted analysis, this indicator reveals: Volume Flux Intelligence: Detects unusual volume-price relationships that signal institutional activity Laguerre RSI: Zero-centered adaptive RSI that responds faster to price changes while filtering noise Fisher Transform: Converts momentum into a Gaussian normal distribution for clearer extreme identification True Strength Index: Double-smoothed momentum that separates genuine trends from noise Money Flow Analysis: Tracks buying and selling pressure through volume-weighted price movements Volume Confirmation: Integrates OBV and A/D Line to confirm momentum with volume flow Regime Classification: Categorizes market conditions as Extreme Bull, Bullish, Neutral, Bearish, or Extreme Bear Multi-Timeframe Alignment: Confirms momentum across higher timeframes for conviction measurement Each component provides a different perspective on momentum. VFI shows volume-driven momentum, Laguerre RSI shows adaptive momentum, Fisher Transform shows statistical extremes, TSI shows smoothed directional momentum, MFI shows money flow momentum, and OBV/A/D show cumulative volume momentum. Together, they create a comprehensive view of institutional momentum positioning. Core Components Explained 1. Volume Flux Indicator (VFI) VFI measures the relationship between price movement and volume to identify institutional accumulation or distribution. The calculation uses logarithmic price changes and volume cutoffs to filter significant moves: The indicator classifies volume-price relationships by comparing actual volume against average volume with a cutoff threshold. When price moves significantly with volume above the cutoff, it signals institutional participation. VFI is scaled and smoothed to create a momentum baseline that responds to volume-confirmed price movements. 2. Laguerre RSI (Zero-Centered) Laguerre RSI applies a four-stage Laguerre filter to price data, creating an adaptive RSI that responds faster to recent price changes while maintaining smoothness. The zero-centered output ranges from -50 to +50, making it easier to identify bullish and bearish momentum: The Laguerre filter uses a gamma parameter (default 0.4) to control responsiveness. Lower gamma values create faster response, while higher values create smoother output. The zero-centered format allows direct comparison with other momentum components. 3. Fisher Transform The Fisher Transform converts the composite momentum into a Gaussian normal distribution, making extreme values more identifiable. This transformation compresses the middle range and expands the tails, creating clearer overbought and oversold signals: The Fisher Transform output oscillates around zero with extreme values typically beyond +2 and -2. These extremes often precede reversals as momentum reaches unsustainable levels. 4. True Strength Index (TSI) TSI applies double exponential smoothing to price momentum, creating a smooth oscillator that filters out short-term noise while preserving trend direction. The calculation uses two EMA periods (default 25 and 13) to separate signal from noise: TSI values above zero indicate bullish momentum, while values below zero indicate bearish momentum. The double smoothing reduces whipsaws while maintaining responsiveness to genuine momentum shifts. 5. Money Flow Index (MFI) MFI is a volume-weighted RSI that measures buying and selling pressure. It calculates the ratio of positive money flow (volume on up days) to negative money flow (volume on down days): MFI values above 80 indicate overbought conditions with high volume, while values below 20 indicate oversold conditions with high volume. The indicator normalizes MFI to a zero-centered scale for integration with other components. 6. On-Balance Volume (OBV) and Accumulation/Distribution (A/D) OBV and A/D track cumulative volume flow to confirm momentum direction. OBV adds volume on up days and subtracts on down days, while A/D weights volume by the close's position within the day's range: Both indicators are normalized to a 0-100 scale and then zero-centered for composite integration. Rising OBV/A/D with rising momentum confirms institutional accumulation, while falling OBV/A/D with rising price warns of distribution. Quantum Flux Core Calculation The Quantum Flux Core combines all components using weighted averaging: Quantum Flux = (VFI × 0.20) + (Laguerre RSI × 0.20) + (Fisher × 0.15) + (TSI × 0.15) + (MFI × 0.10) + (OBV × 0.10) + (A/D × 0.05) + (CMF × 0.05) This weighted approach emphasizes volume-driven components (VFI, Laguerre) while incorporating smoothed momentum (Fisher, TSI) and volume confirmation (MFI, OBV, A/D, CMF). The result is smoothed with an EMA to create the final Quantum Flux line. Regime Classification System The indicator classifies market conditions into five regimes based on Quantum Flux levels: Extreme Bull (QF > 35): Institutional buying pressure at extreme levels, potential exhaustion Bullish (QF > 25): Strong bullish momentum with institutional participation Neutral (-25 < QF < 25): Balanced conditions, no clear institutional bias Bearish (QF < -25): Strong bearish momentum with institutional selling Extreme Bear (QF < -35): Institutional selling pressure at extreme levels, potential capitulation Regime shifts often precede significant price moves as institutional positioning changes. The indicator tracks regime changes and generates signals when momentum confirms directional bias. Multi-Timeframe Alignment The indicator requests Quantum Flux data from three customizable higher timeframes (default: 5m, 15m, 60m) and calculates alignment: Strong Aligned (3/3): All timeframes show bullish/bearish momentum - high conviction Aligned (2/3): Majority timeframes confirm - moderate conviction Weak (1/3): Only one timeframe confirms - low conviction No Alignment (0/3): No timeframe confirmation - conflicting signals Strong alignment across multiple timeframes indicates institutional participation at scale, as large orders are often split across timeframes to minimize market impact. Velocity and Acceleration Tracking The indicator calculates momentum velocity (rate of change) and acceleration (change in velocity): Velocity: Current Quantum Flux minus previous bar's value Acceleration: Current velocity minus previous velocity (second derivative) Accelerating momentum often precedes breakouts as institutional orders hit the market. Decelerating momentum warns of potential reversals or consolidation. Visual Elements Quantum Flux Line: Main oscillator with regime-based color coding (cyan = extreme bull, aqua = bullish, yellow = neutral, red = bearish, magenta = extreme bear) Threshold Lines: Horizontal lines at +35 (extreme overbought), +25 (overbought), 0 (zero line), -25 (oversold), -35 (extreme oversold) Velocity Histogram: Shows momentum velocity with color-coded bars (green = rising, red = falling) Acceleration Columns: Displays momentum acceleration to identify momentum shifts early Regime Strength Bars: Visual regime indicator showing current market condition strength Gradient Glow Effect: Multiple layered fills create a glowing effect that emphasizes momentum intensity Information Dashboard: Comprehensive table displaying all metrics in real-time with color-coded cells The dashboard displays 10 key metrics: Regime, Flux Value, HTF Confirmation, MFI, CMF, Velocity, Divergence, Volume, and Signal status. Signal Generation The indicator generates two types of signals: Primary Reversal Signals: Bullish Reversal: Quantum Flux in extreme oversold (< -35), rising momentum, positive velocity acceleration, and HTF confirmation Bearish Reversal: Quantum Flux in extreme overbought (> 35), falling momentum, negative velocity acceleration, and HTF confirmation Momentum Crossover Signals: Bullish Momentum: Quantum Flux crosses above -25 (oversold threshold) with positive velocity and volume confirmation Bearish Momentum: Quantum Flux crosses below +25 (overbought threshold) with negative velocity and volume confirmation Signals include anti-overlap logic to prevent signal clustering and ensure clean chart presentation. Divergence Detection The indicator detects both regular and hidden divergences between price and Quantum Flux: Regular Bullish Divergence: Price makes lower low, Quantum Flux makes higher low (potential reversal up) Regular Bearish Divergence: Price makes higher high, Quantum Flux makes lower high (potential reversal down) Hidden Bullish Divergence: Price makes higher low, Quantum Flux makes lower low (trend continuation up) Hidden Bearish Divergence: Price makes lower high, Quantum Flux makes higher high (trend continuation down) Divergences are drawn with clean lines (solid for regular, dashed for hidden) without text clutter. How to Use This Indicator Step 1: Monitor Regime Classification Watch for regime shifts between Extreme Bear, Bearish, Neutral, Bullish, and Extreme Bull. Regime changes often precede significant price moves. Step 2: Check Multi-Timeframe Alignment Strong alignment (3/3) across timeframes confirms institutional conviction. Weak or no alignment suggests retail-driven moves that may lack follow-through. Step 3: Analyze Velocity and Acceleration Accelerating momentum (positive acceleration) often precedes breakouts. Decelerating momentum (negative acceleration) warns of potential reversals. Step 4: Look for Divergences Regular divergences at extreme levels (QF > 35 or < -35) often signal reversals. Hidden divergences confirm trend continuation. Step 5: Confirm with Volume Metrics Check MFI, CMF, OBV, and A/D for confirmation. Rising volume metrics with rising Quantum Flux confirms institutional accumulation. Step 6: Wait for Signal Confirmation Primary reversal signals at extreme levels with HTF confirmation provide highest probability setups. Momentum crossover signals work best in trending markets. Best Practices Use on liquid instruments (major forex pairs, large-cap stocks, major crypto) for most reliable signals Combine with price action analysis - momentum shows intent, price shows result Pay attention to extreme levels (QF > 35 or < -35) as these often precede reversals MTF alignment is most reliable in trending markets, less reliable in choppy conditions Extreme momentum can persist longer than expected during strong trends - use stops Look for momentum divergences at key support/resistance levels for highest probability setups Monitor velocity and acceleration for early warning signs of momentum shifts Use the dashboard to quickly assess overall market condition and signal status Indicator Limitations Momentum analysis works best on liquid instruments with consistent volume patterns Low-volume instruments or off-market hours can produce unreliable readings MTF alignment requires sufficient data on all timeframes - may not work on newly listed instruments Momentum precedes price but doesn't guarantee direction - high momentum can occur on both breakouts and fakeouts Extreme momentum levels can persist longer than expected during major news events or market dislocations The indicator shows what is happening, not why - fundamental catalysts can override technical momentum patterns Divergences are more reliable at extreme levels than in neutral zones Multiple components mean the indicator can be slower to respond than single-component oscillators Input Parameters Core Engine: Primary Length: Period for momentum calculations (default: 14) Smoothing Period: EMA smoothing for final output (default: 7) Sensitivity Factor: Multiplier for Fisher Transform input (default: 1.5) Volume Flux Engine: VFI Coefficient: Cutoff multiplier for significant moves (default: 0.2) Volume Cutoff: Maximum volume multiplier (default: 2.5) Scale Multiplier: VFI output scaling (default: 4.0) Laguerre Transform: Gamma: Responsiveness parameter (default: 0.4, lower = faster) Threshold Zones: Extreme Overbought: Upper extreme threshold (default: 35) Overbought: Upper threshold (default: 25) Oversold: Lower threshold (default: -25) Extreme Oversold: Lower extreme threshold (default: -35) Money Flow & Volume: MFI Length: Period for Money Flow Index (default: 14) OBV Smoothing: Smoothing period for OBV (default: 14) A/D Smoothing: Smoothing period for A/D Line (default: 14) Multi-Timeframe Analysis: Enable Higher Timeframe: Toggle MTF calculations (default: enabled) HTF Timeframe 1/2/3: Customizable timeframes (default: 5m, 15m, 60m) Visual Configuration: Color Theme: Choose from Gradient Glow, Professional Dark, Neon Spectrum, or Institutional Grey Bullish/Bearish Spectrum: Customizable colors for momentum direction Glow Layers: Number of gradient layers for glow effect (default: 20) Show Divergence: Toggle divergence detection (default: enabled) Show Volume Profile: Toggle volume profile histogram (default: enabled) Technical Implementation Built with Pine Script v6 using: Custom VFI calculations with logarithmic price changes and volume cutoffs Four-stage Laguerre filter for adaptive RSI Fisher Transform for Gaussian distribution conversion Double-smoothed TSI for noise filtering Volume-weighted MFI calculations Normalized OBV and A/D Line integration Multi-timeframe security requests with proper lookahead settings Velocity and acceleration calculations for momentum derivatives Real-time regime classification system Dynamic dashboard with 10 metrics and color-coded cells Gradient glow effect with multiple layered fills Divergence detection with pivot analysis The code is fully open-source and can be modified to suit individual trading styles and preferences. Originality Statement This indicator is original in its comprehensive integration approach. While individual components (VFI, Laguerre RSI, Fisher Transform, TSI, MFI, OBV, A/D) are established concepts, this indicator is justified because: It synthesizes six distinct momentum methodologies into a unified weighted composite system The regime classification provides institutional momentum measurement not available in standard oscillators Multi-timeframe alignment detection measures institutional conviction across timeframes Velocity and acceleration calculations provide early warning of momentum shifts The gradient glow visualization creates intuitive momentum intensity display Integration of volume-weighted components (VFI, MFI) with smoothed momentum (Fisher, TSI) and cumulative volume (OBV, A/D) creates layered confirmation The comprehensive dashboard presents 10 metrics simultaneously for holistic momentum analysis Each component contributes unique information: VFI shows volume-driven momentum, Laguerre RSI shows adaptive momentum, Fisher Transform shows statistical extremes, TSI shows smoothed momentum, MFI shows money flow, OBV shows cumulative volume, and A/D shows distribution. The indicator's value lies in presenting these complementary perspectives simultaneously with a unified regime classification system. 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 and is not suitable for all investors. Momentum analysis is a tool for understanding market dynamics, not a crystal ball for predicting future price movement. High momentum does not guarantee profitable trades. Past momentum patterns do not guarantee future momentum patterns. Market conditions change, and strategies that worked historically may not work in the future. The metrics displayed are mathematical calculations based on current market data, not predictions of future price movement. Extreme momentum levels, regime classifications, and signal generation do not guarantee profitable trades. Users must conduct their own analysis and risk assessment before making trading decisions. Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions. The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool. -Made with passion by officialjackofalltrades
Pine Script® 인디케이터
officialjackofalltrades의
Lattice Trend Helix [JOAT]Lattice Trend Helix Introduction The Lattice Trend Helix is an open-source trend analysis indicator built in Pine Script v6. It combines a GMMA-inspired multi-EMA fan system (19 exponential moving averages across fast and slow groups) with a pivot-center SuperTrend, RSI momentum confirmation, and a comprehensive trend strength scoring system. The indicator detects EMA fan alignment, measures trend strength on a 0-100 scale, identifies fan expansion/contraction dynamics, and generates priority-ranked signals including full confluence locks, fan crosses, SuperTrend flips, EMA 200 reclaims, fan burst breakouts, SuperTrend bounces, and displacement impulses. The Guppy Multiple Moving Average (GMMA) concept, originally developed by Daryl Guppy, uses two groups of EMAs to visualize the behavior of short-term traders (fast group) and long-term investors (slow group). When both groups are aligned and separated, a strong trend is in place. When they converge and cross, a trend change is developing. This indicator extends the GMMA concept by adding a pivot-based SuperTrend for dynamic support/resistance, RSI filtering for momentum confirmation, and a quantified scoring system that turns visual alignment into a measurable number. Why This Indicator Exists Single moving average crossover systems are prone to whipsaws. Even dual-MA systems produce frequent false signals in choppy markets. The GMMA approach solves this by requiring alignment across many EMAs simultaneously — a much higher bar than a simple crossover. This indicator takes that concept further: 19-EMA Fan System: 11 fast EMAs (periods 3 through 23) capture short-term trader sentiment. 8 slow EMAs (periods 25 through 60) capture longer-term investor positioning. Full alignment of all 11 fast EMAs in order is a strong signal that short-term traders agree on direction. Full alignment of all 8 slow EMAs confirms institutional agreement. Pivot-Center SuperTrend: Unlike standard SuperTrend which uses HL2 as the center, this implementation uses a weighted average of detected pivot points. Each new pivot high or low updates the center using the formula: center = (center * 2 + pivot) / 3. This creates a more responsive center line that adapts to actual market structure rather than simple bar midpoints. ATR-based bands around this center define the trend direction. Trend Strength Score (0-100): Quantifies trend strength from three components — fast EMA alignment (50 points), slow EMA alignment (30 points), and price position relative to EMA 200 (20 points). A score of 100 means all 19 EMAs are perfectly aligned and price is on the correct side of the 200 EMA. Fan Spread Dynamics: The distance between the fastest EMA (3) and slowest fast EMA (23), normalized by ATR, measures how "open" the fan is. An expanding fan indicates strengthening trend momentum. A contracting fan warns of potential trend exhaustion or reversal. RSI Momentum Filter: RSI must agree with the fan direction for the highest-confidence signals. This prevents false confluence signals during momentum divergences. EMA 200 Macro Filter: Price must be above the 200 EMA for confirmed bullish signals and below for confirmed bearish signals, ensuring alignment with the macro trend. How the EMA Fan Alignment Works The fast fan consists of 11 EMAs at periods 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, and 23. For bullish alignment, every EMA must be above the next longer one: // Full fast fan bull alignment requires ALL 10 pairs in order bool fastBull = ef3 > ef5 and ef5 > ef7 and ef7 > ef9 and ef9 > ef11 and ef11 > ef13 and ef13 > ef15 and ef15 > ef17 and ef17 > ef19 and ef19 > ef21 and ef21 > ef23 This is an extremely high bar. In choppy markets, the fast EMAs will be tangled and neither fastBull nor fastBear will be true. Only in genuine trending conditions do all 11 EMAs sort into perfect order. The same logic applies to the 8 slow EMAs. The indicator counts how many adjacent pairs are aligned (0-10 for fast, 0-7 for slow) to produce a granular alignment score even when full alignment is not achieved. This allows the trend strength score to reflect partial alignment — a market with 8/10 fast pairs aligned is stronger than one with 4/10, even though neither achieves full alignment. Pivot-Center SuperTrend The SuperTrend component uses a unique center calculation based on detected pivot points: Pivot highs and lows are detected using ta.pivothigh() and ta.pivotlow() with a configurable period Each new pivot updates the center line using an exponentially weighted formula that gives 2/3 weight to the existing center and 1/3 to the new pivot Upper and lower bands are calculated as center +/- (ATR Factor * ATR) Trend direction flips when price crosses the opposite band The trailing stop ratchets in the trend direction — it can only move favorably, never against the trend This pivot-based center produces a SuperTrend that is more responsive to actual market structure than the standard HL2-based version. It adapts to the rhythm of the market's swing points rather than just the midpoint of each bar. Signal Priority System The indicator generates 8 types of signals, ranked by priority with cooldown-based anti-overlap: P1 — HELIX LOCK (highest): Full fan alignment (fast + slow) + RSI confirmation + price above/below EMA 200. This is the maximum confluence signal — every factor agrees. A highlight box is drawn around the signal candle. P2 — LATTICE SYNC: Full fan alignment (fast + slow) without RSI/EMA200 confirmation. Strong but not maximum confluence. P3 — TREND FLIP: SuperTrend direction change. The pivot-center SuperTrend has flipped from bearish to bullish or vice versa. P4 — FAN CROSS: The fast fan median (EMA 13) crosses the slow fan median (EMA 40). This is the GMMA equivalent of a moving average crossover, but using the center of each fan group. P5 — MACRO CROSS: Price crosses the EMA 200 — a major structural event that changes the macro trend context. P6 — FAN BURST: The fan spread transitions from contracting to expanding while the trend score is above 50. This indicates a breakout from compression — similar to a Bollinger squeeze release but measured through EMA dynamics. P7 — ST BOUNCE: Price touches the SuperTrend line and bounces in the trend direction. This is a pullback-to-support/resistance signal unique to this indicator. A separate 5-bar cooldown prevents repeated bounce signals during extended touches. P8 — IMPULSE (lowest): Displacement candle detection — large body (>70% of range, >2x average body). These indicate aggressive institutional order flow. Trend Strength Score Breakdown The 0-100 score is computed from three weighted components: Fast EMA Alignment (50 points): The number of aligned adjacent pairs (max 10) divided by 10, multiplied by 50. Full fast alignment = 50 points. Half alignment = 25 points. Slow EMA Alignment (30 points): The number of aligned adjacent pairs (max 7) divided by 7, multiplied by 30. Full slow alignment = 30 points. EMA 200 Filter (20 points): If price is above EMA 200 and the fast fan leans bullish, or below EMA 200 and the fast fan leans bearish, 20 points are added. This rewards macro-aligned trends. The score is displayed in the HUD with both a number and a visual bar (||||......). Scores above 70 indicate strong, tradeable trends. Scores between 40-70 indicate developing or weakening trends. Below 40 indicates choppy or transitional conditions. Visual Design The indicator uses a "Cyberpunk" color theme — electric cyan, hot magenta, neon yellow, deep violet, and chrome accents: Fast EMA Fan: All 11 lines in a single color that adapts to alignment — cyan for bullish, magenta for bearish, steel grey for neutral. Configurable opacity. Slow EMA Fan: All 8 lines in deeper tones — teal for bullish, violet for bearish, steel grey for neutral. EMA 200: Three-layer neon glow effect (outer glow, mid glow, core line) that shifts between cyan (above) and violet (below). Holographic Ribbon: Fill between the fastest (EMA 3) and slowest (EMA 23) fast EMAs, creating a ribbon that expands with trend strength and contracts during consolidation. SuperTrend: Four-layer neon glow step-line (88%, 72%, 50%, 10% transparency) in cyan (bullish) or magenta (bearish). Regime Background: Subtle background tinting for confirmed bull (cyan) or confirmed bear (magenta) conditions. Candle Coloring: Multi-tier coloring based on confirmation level — confirmed bull/bear, strong bull/bear, weak bull/bear, or neutral. HUD Dashboard The HUD displays 14 metrics: Trend direction (Bullish/Bearish/Neutral) Strength score with visual bar (||||......) Fan state (Strong Bull/Bear, Weak Bull/Bear, Converging) SuperTrend direction EMA 200 position (Above/Below) Alignment counts (Fast: X/10, Slow: X/7) Fan Spread value with state (Expanding/Contracting/Stable) RSI value with bull/bear/neutral classification Confluence count (0-5): fast alignment + slow alignment + SuperTrend agreement + RSI agreement + EMA 200 agreement SuperTrend distance from price Volume ratio (current vs 20-bar average) Confirmed signal status (CONFIRMED BULL/BEAR or ---) Input Parameters EMA Fan: Show Fast/Slow EMAs: Toggle each fan group Show EMA 200: Toggle macro filter line Fast/Slow EMA Opacity: Control transparency of each fan group SuperTrend: Show SuperTrend: Toggle the pivot-center SuperTrend Pivot Period: Lookback for pivot detection (default: 3) ATR Factor: Band width multiplier (default: 2.5) ATR Length: Period for ATR calculation (default: 14) Visual: Show Trend Ribbon: Toggle holographic ribbon fill Show Fan Crosses: Toggle fan cross signals Show Regime Background: Toggle background tinting SuperTrend Neon Glow: Toggle 4-layer glow effect Color Candles: Toggle multi-tier candle coloring HUD Panel: Toggle dashboard Momentum Filter: Show RSI Confirmation: Toggle RSI requirement for confirmed signals RSI Length: Period (default: 14) RSI Bull/Bear Threshold: Directional thresholds (default: 55/45) How to Use This Indicator Step 1: Check Fan Alignment Look at the fan state in the HUD. "Strong Bull" or "Strong Bear" means both fast and slow fans are fully aligned — the strongest trend condition. "Weak" means only the fast fan is aligned — a developing or weakening trend. Step 2: Verify with SuperTrend The SuperTrend should agree with the fan direction. Fan bullish + SuperTrend bullish = high conviction. Disagreement suggests a transitional market. Step 3: Check the Strength Score Scores above 70 are strong trends. Use the visual bar for quick assessment. The confluence count (0-5) tells you how many independent factors agree. Step 4: Trade the Signals HELIX LOCK is the highest-conviction entry — all factors agree. LATTICE SYNC and TREND FLIP are strong. FAN CROSS and MACRO CROSS are structural. ST BOUNCE provides pullback entries within established trends. Step 5: Monitor Fan Spread Expanding fan = strengthening trend. Contracting fan = weakening trend or approaching reversal. FAN BURST signals mark the transition from contraction to expansion. Best Practices The 19-EMA fan is most effective on timeframes of 5 minutes and above. Very low timeframes produce too much noise for meaningful alignment. Full fan alignment is rare and powerful. Do not expect it on every trade — it represents the highest-conviction conditions. The SuperTrend bounce signal works best in established trends. In choppy markets, bounces may fail. Fan crosses (fast median vs slow median) are the GMMA equivalent of MA crossovers — they confirm trend changes but lag the actual turn. The EMA 200 filter is a macro-level gate. Ignoring it means trading against the larger trend, which reduces probability. Use the fan spread dynamics to time entries — entering when the fan is expanding gives you momentum. Entering when it is contracting means you are fighting exhaustion. The confluence count (0-5) is a quick decision filter. 4-5 = high conviction. 2-3 = moderate. 0-1 = low conviction. Limitations EMAs are lagging indicators. Full fan alignment is confirmed after the trend has already started, not at the exact turn. The 19-EMA system uses significant computational resources. On very long charts with many bars, loading may be slower. Pivot-center SuperTrend depends on pivot detection, which has an inherent delay equal to the pivot period. Fan alignment can persist in overextended trends. Full alignment does not mean the trend will continue indefinitely. The RSI filter can occasionally prevent valid signals during strong momentum divergences. The indicator is optimized for trending markets. In range-bound conditions, the fan will be tangled and few signals will fire — which is by design. EMA periods are fixed (3-23 fast, 25-60 slow). Different instruments or timeframes might benefit from different period sets, but the GMMA standard periods are well-tested across markets. Technical Implementation Built with Pine Script v6 using: 19 EMA calculations at global scope (11 fast + 8 slow) for Pine v6 compliance Pivot-based SuperTrend center with exponentially weighted pivot averaging Granular alignment counting (0-10 fast, 0-7 slow) for trend strength scoring Fan spread normalization by ATR for cross-instrument comparability 8-tier priority signal system with cooldown-based anti-overlap Separate cooldown tracking for SuperTrend bounce signals 4-layer neon glow rendering for SuperTrend and EMA 200 Holographic ribbon fill between fan extremes Multi-tier candle coloring based on confirmation level barstate.isconfirmed gating on all signal generation 9 alert conditions covering alignment changes, fan crosses, SuperTrend flips, confirmed signals, and fan expansion Originality Statement This indicator is original in its synthesis of the GMMA fan concept with pivot-center SuperTrend and quantified trend scoring. While GMMA and SuperTrend are established concepts, this indicator is justified because: The pivot-center SuperTrend uses a weighted average of actual market pivots rather than simple HL2, creating a more structurally responsive trend line The trend strength score (0-100) quantifies fan alignment into a single actionable metric with three weighted components Fan spread dynamics (expansion/contraction tracking normalized by ATR) provide momentum acceleration/deceleration information not available in standard GMMA implementations The 8-tier priority signal system with separate cooldown tracking for SuperTrend bounces prevents visual clutter while capturing all significant events RSI momentum filtering and EMA 200 macro gating create a multi-layer confirmation framework that reduces false signals The confluence count (0-5) provides an instant assessment of how many independent factors agree The Cyberpunk theme with 4-layer neon glow and holographic ribbon creates a distinctive visual identity where trend strength is immediately apparent from the fan's visual character 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. Moving average systems identify trends after they have started — they do not predict trend changes in advance. Full fan alignment can occur in overextended trends that are about to reverse. SuperTrend bounces can fail. Past alignment patterns do not guarantee future trend behavior. Always use proper risk management and never risk more than you can afford to lose. The author is not responsible for any losses incurred from using this indicator. -Made with passion by officialjackofalltrades
Pine Script® 인디케이터
officialjackofalltrades의
Hosoda Wave Targets [UAlgo]Hosoda Wave Targets is a wave projection tool inspired by the classical Hosoda target framework. The script identifies a three point swing structure using confirmed pivots, classifies that structure as either bullish or bearish, and then projects forward target levels from the completed A B C wave. Instead of drawing generic extension levels, it uses Hosoda style wave arithmetic to produce V, N, and E targets, while also calculating NT internally. The logic begins with swing detection. Once the script has three valid pivots in sequence, it checks whether they form a proper bullish wave or bearish wave. A bullish wave requires a low, then a high, then a higher low. A bearish wave requires a high, then a low, then a lower high. If the structure is valid and does not overlap with the previous completed pattern, the script creates a new wave object and calculates its targets. The visual output is built around the A B C D sequence. A, B, and C label the measured swing points, while D is placed at the projected N target time and price. The script also draws a dotted path for the original A to B and B to C legs, then projects the C to D path toward the N objective. In addition, horizontal V, N, and E target lines are plotted forward so the user can see the three main Hosoda price objectives at a glance. This makes the indicator useful for traders who want a structured wave target model rather than a standard Fibonacci extension tool. Because the wave must pass a specific pivot sequence test before targets are shown, the script stays focused on clean swing structures instead of projecting from every random oscillation in price. In practical use, Hosoda Wave Targets can help with wave mapping, objective placement, and scenario planning after a three point structure is completed. It works especially well as a framework for comparing potential continuation targets once a valid A B C sequence is already in place. 🔹 Features 🔸 Automatic A B C Wave Detection The script continuously tracks confirmed pivot highs and pivot lows, then checks whether the latest three pivots form a valid bullish or bearish Hosoda wave structure. 🔸 Bullish and Bearish Pattern Recognition A bullish structure requires a low, then a high, then a higher low. A bearish structure requires a high, then a low, then a lower high. This keeps the wave logic clear and directional. 🔸 Hosoda Style Price Targets Once a valid wave is found, the script calculates classic Hosoda V, N, and E targets. It also computes the NT target internally as part of the target model. 🔸 Time Projection for D Point The projected D point is placed using the Hosoda N time relationship. This means the script is not only projecting price, but also projecting where that objective sits in bar time. 🔸 Clear Wave Path Visualization The A to B and B to C legs are drawn as dotted structural guides, while the C to D path toward the N target is drawn as the forward projection leg. 🔸 Color Coded Target Lines V, N, and E targets are drawn with separate colors so the user can distinguish between the major Hosoda objectives quickly. 🔸 Last Wave Only Option The script can either show only the most recent valid wave or keep multiple wave drawings on the chart, depending on the selected setting. 🔸 Overlap Protection A new wave is only accepted when its first pivot does not overlap the prior completed pattern. This reduces repeated stacking of wave projections over the same structure. 🔹 Calculations 1) Defining the Core Data Objects type Point int bar_index float price bool isHigh type HosodaTargets float v float n float e float nt float p int time_n type Wave Point a Point b Point c int direction This is the structural foundation of the indicator. A Point stores one pivot with its bar index, price, and whether it is a high or low. A HosodaTargets object stores the projected target values: V, N, E, NT, an unused placeholder P, and the projected time for the N target. A Wave object stores the three actual swing points A, B, and C, along with the direction of the structure. So before any drawing is done, the script already has a clear data model for pivots, waves, and targets. 2) Detecting Pivot Highs and Pivot Lows float ph = ta.pivothigh(high, pivotLength, pivotLength) float pl = ta.pivotlow(low, pivotLength, pivotLength) This is the pivot discovery step. The script uses the selected pivot length on both the left and right side, which means a pivot is only confirmed after enough bars have passed. This avoids premature wave labeling on unconfirmed turning points. When a pivot high is confirmed, it becomes a candidate high point. When a pivot low is confirmed, it becomes a candidate low point. So the whole wave engine is built from confirmed swing structure rather than from temporary price fluctuations. 3) Storing Recent Pivot Points if not na(ph) pivots.push(Point.new(bar_index - pivotLength, ph, true)) if not na(pl) pivots.push(Point.new(bar_index - pivotLength, pl, false)) if pivots.size() > 10 pivots.shift() Once a pivot is confirmed, the script converts it into a Point object and stores it in the pivot array. The true pivot bar is pivotLength bars in the past, so the script uses bar_index - pivotLength as the pivot location. The pivot history is capped at ten points. This keeps the structure engine focused on recent swings and avoids unnecessary buildup of old pivots. 4) Building the Candidate A B C Structure if pivots.size() >= 3 and (not na(ph) or not na(pl)) Point pA = pivots.get(pivots.size() - 3) Point pB = pivots.get(pivots.size() - 2) Point pC = pivots.get(pivots.size() - 1) This block selects the latest three stored pivots and treats them as a potential A B C sequence. The script does not skip around the array looking for arbitrary combinations. It always evaluates the latest three pivots in sequence. That keeps the pattern logic straightforward and tied to the most recent confirmed structure. So every wave begins as a direct test of the newest three pivot points. 5) Bullish Wave Validation bool isValidUptrend = not pA.isHigh and pB.isHigh and not pC.isHigh and pB.price > pA.price and pB.price > pC.price and pC.price > pA.price This line defines the bullish Hosoda wave condition. For a bullish structure: A must be a low, B must be a high, C must be a low, B must sit above both A and C, and C must remain above A. That last condition is important because it ensures the pullback at C is a higher low rather than a full structural failure. So the script only accepts bullish waves that look like an advancing move followed by a constructive retracement. 6) Bearish Wave Validation bool isValidDowntrend = pA.isHigh and not pB.isHigh and pC.isHigh and pB.price < pA.price and pB.price < pC.price and pC.price < pA.price This is the mirror condition for bearish waves. For a bearish structure: A must be a high, B must be a low, C must be a high, B must sit below both A and C, and C must remain below A. This ensures the move represents a decline followed by a lower high retracement, which is the bearish counterpart of the bullish wave structure. So the script only projects bearish targets when the A B C pattern shows proper downside sequence. 7) Preventing Pattern Overlap bool isNotOverlapping = pA.bar_index >= lastPatternEnd This is the overlap protection rule. The script tracks the end of the previous completed pattern and requires the new A point to begin at or after that location. If the new structure starts inside the previous wave’s territory, it is ignored. This reduces repetitive wave printing over the same swing area and keeps the output cleaner. So the chart shows more distinct wave structures instead of heavily overlapping ones. 8) Assigning Wave Direction int dir = isValidUptrend ? 1 : -1 Wave newWave = Wave.new(pA, pB, pC, dir) Once the script confirms that the latest three pivots form a valid structure, it assigns a direction: 1 for bullish, -1 for bearish. That direction then controls how the target formulas are calculated. So all later math depends on whether the wave is an advancing or declining structure. 9) Calculating the Hosoda Time Projection int _time_n = this.c.bar_index + (this.b.bar_index - this.a.bar_index) This is the time projection used for the D point and the N target. The script measures the bar distance from A to B, then adds that same distance forward from C. That projected index becomes time_n . So the wave does not only project price objectives. It also projects when the N objective would align in bar space according to the A to B rhythm. 10) Bullish V, N, E, and NT Target Formulas if this.direction == 1 _v := this.b.price + (this.b.price - this.c.price) _n := this.c.price + (this.b.price - this.a.price) _e := this.b.price + (this.b.price - this.a.price) _nt := this.c.price + (this.c.price - this.a.price) These are the bullish target formulas. For bullish waves: V projects the B to C distance upward from B. N projects the A to B distance upward from C. E projects the A to B distance upward from B. NT projects the A to C distance upward from C. Each target represents a different Hosoda style price relationship. The script calculates all four, although only V, N, and E are drawn visually in the current implementation. So the script provides a full bullish target set, with one of the values kept internally for future expansion or interpretation. 11) Bearish V, N, E, and NT Target Formulas else if this.direction == -1 _v := this.b.price - (this.c.price - this.b.price) _n := this.c.price - (this.a.price - this.b.price) _e := this.b.price - (this.a.price - this.b.price) _nt := this.c.price - (this.a.price - this.c.price) These are the bearish equivalents. For bearish waves: V projects the C to B distance downward from B. N projects the A to B distance downward from C. E projects the A to B distance downward from B. NT projects the A to C distance downward from C. So the same target families are preserved, but the math is reversed to match bearish structure. 12) Returning the Target Object HosodaTargets.new(_v, _n, _e, _nt, 0.0, _time_n) After the formulas are calculated, the script packages them into a HosodaTargets object. This makes it easy to pass all target information into the drawing method in one structured object. Note that the p field is currently filled with 0.0 and is not used elsewhere in the script. So the active practical targets in this version are V, N, E, and the projected time index for N. 13) Drawing the A B C Labels label lA = label.new(this.a.bar_index, this.a.price, "A", color=lblBgColor, textcolor=lblTextColor, style=this.direction == 1 ? label.style_label_up : label.style_label_down, size=size.small) label lB = label.new(this.b.bar_index, this.b.price, "B", color=lblBgColor, textcolor=lblTextColor, style=this.direction == 1 ? label.style_label_down : label.style_label_up, size=size.small) label lC = label.new(this.c.bar_index, this.c.price, "C", color=lblBgColor, textcolor=lblTextColor, style=this.direction == 1 ? label.style_label_up : label.style_label_down, size=size.small) These labels mark the confirmed swing points of the pattern. Their placement style changes according to wave direction so the labels sit in a visually readable position relative to price. So the user can immediately see the measured A B C structure before looking at any target levels. 14) Placing the Projected D Label label lD = label.new(targets.time_n, targets.n, "D", color=lblBgColor, textcolor=lblTextColor, style=this.direction == 1 ? label.style_label_down : label.style_label_up, size=size.small) The D label is placed at the projected N time and N price. This does not mean D is a confirmed pivot. It is the projected completion point of the main wave objective according to the N relationship. So D acts as a forward reference point, not a confirmed market event. 15) Drawing the Structural A to B and B to C Legs line ab = line.new(this.a.bar_index, this.a.price, this.b.bar_index, this.b.price, color=color.gray, width=1, style=line.style_dotted) line bc = line.new(this.b.bar_index, this.b.price, this.c.bar_index, this.c.price, color=color.gray, width=1, style=line.style_dotted) These two dotted lines draw the original measured wave. They are not target lines. They simply show the actual A to B impulse and B to C correction that were used to build the projection. So the user can visually separate measured structure from projected structure. 16) Drawing the C to D Projection Path line cd = line.new(this.c.bar_index, this.c.price, targets.time_n, targets.n, color=cN, width=1) This line connects the confirmed C pivot to the projected D point at the N target. It visually represents the expected continuation path of the wave according to the N target model. So the script does not leave the targets floating independently. It ties the projected move directly back to the completed C point. 17) Drawing the V, N, and E Target Lines line vLine = line.new(this.c.bar_index, targets.v, targets.time_n + 10, targets.v, color=color.new(cV, 0), style=line.style_dashed, width=1) line nLine = line.new(this.c.bar_index, targets.n, targets.time_n + 10, targets.n, color=color.new(cN, 0), style=line.style_dashed, width=1) line eLine = line.new(this.c.bar_index, targets.e, targets.time_n + 10, targets.e, color=color.new(cE, 0), style=line.style_solid, width=1) These horizontal lines display the three visible price objectives. V and N are drawn as dashed lines. E is drawn as a solid line. Each starts from the C area and extends forward beyond the projected D time. This makes it easier to compare the three target levels side by side. It is worth noting that NT is calculated but not drawn in this version of the script. So the visible target framework here focuses on V, N, and E. 18) Drawing the Target Labels label vLbl = label.new(targets.time_n + 10, targets.v, "V " + str.tostring(targets.v, format.mintick), textcolor=cV, style=noBgStyle, size=size.small, textalign=text.align_left) label nLbl = label.new(targets.time_n + 10, targets.n, "N " + str.tostring(targets.n, format.mintick), textcolor=cN, style=noBgStyle, size=size.small, textalign=text.align_left) label eLbl = label.new(targets.time_n + 10, targets.e, "E " + str.tostring(targets.e, format.mintick), textcolor=cE, style=noBgStyle, size=size.small, textalign=text.align_left) Each visible target line gets its own text label showing both the target family and the exact projected price. This is useful because Hosoda targets often overlap or sit close to one another. The labels remove ambiguity and make it easy to read exact values directly from the chart. 19) Show Only Last Wave Mode if showOnlyLast while drawings.size() > 0 WaveDrawing d = drawings.shift() d.clear() This setting controls how much history remains visible. If enabled, every time a new valid wave appears, all prior wave drawings are deleted first. That leaves only the most recent wave and its targets on the chart. If disabled, multiple valid waves can remain visible. So the user can choose between a clean single setup view or a historical wave map. 20) Clearing Old Drawings method clear(WaveDrawing this) => if not na(this) this.lineAB.delete() this.lineBC.delete() this.lineCD.delete() this.lblA.delete() this.lblB.delete() this.lblC.delete() this.lblD.delete() this.lineV.delete() this.lineN.delete() this.lineE.delete() this.lblV.delete() this.lblN.delete() this.lblE.delete() This cleanup method deletes every visual object associated with one stored wave drawing. That includes: the structural lines, the projected line, the point labels, the target lines, and the target labels. So the indicator can safely remove prior patterns without leaving stale objects on the chart.
Pine Script® 인디케이터
UAlgo의
Bollinger Bands Bull/Bear B2Bollinger Bands Bull/Bear by MasterTony Overview & How It Works: This indicator enhances classic Bollinger Bands by the legendary John Bollinger with emphasizing dynamic bull/bear coloring, gradient strength fills, overextension highlights, and an orange squeeze overlay to visualize volatility contraction. Core Calculations: Basis Line: User-selectable moving average (SMA by default) of the source (close by default). Upper / Lower Bands: Basis ± (Multiplier × Standard Deviation over the chosen length). Default multiplier = 2.0. Bull / Bear State Determination: Price position relative to the basis and outer or lower bollinger bands, smoothed with a short EMA. Hysteresis is applied to prevent rapid flipping during consolidation. Result: stable bullish state when price is convincingly above the basis, bearish when below. Proximity & Gradient Strength: Distance from price to each band is measured and normalized against current band width. The closer price is to a band, the stronger the signal and the more opaque the fill becomes. Colored Band Fills: Bullish state → mint-green fills around both bands (brighter/opaquer when price hugs the upper band). Bearish state → red fills around both bands (brighter/opaquer when price hugs the lower band). Special Overextension Fill: When price is very close (>85%) to the "active" band (upper in bull, lower in bear) and the state is confirmed, a Brighter gradient of the band green (bull) or lred (bear). Strong Vs Weak Bollinger Thicker the Bollinger Band stronger the trend, thinner the weaker. Gradient going from brighter to more transparent highlights potential exhaustion of strong Bollinger. Squeeze Detection (Orange Overlay): Bollinger Band Width Percentile (BBWP) is calculated over a user-defined lookback (default 100 bars). Squeeze = BBWP ≤ 25% (bands are historically narrow). Tight squeeze = BBWP < 15% → brighter orange. Orange fill covers the entire area between upper and lower bands during squeeze periods. Toggle available to hide squeeze fills if desired. How to Read & Trade This Indicator Visual Interpretation: Green-dominant chart (mint fills + possible light-green basis-to-band fill) = bullish bias. The brighter and fuller the green, the stronger the momentum (price pushing against or touching the upper band). Red-dominant chart (red fills + possible light-red basis-to-band fill) = bearish bias. Brighter/fuller red = stronger downward pressure. Light special fills (light green or light red from basis to band) = overextension zone. Often seen near trend extremes — watch for continuation (breakout) or reversal (failure to hold the band). Orange overlay = low volatility / squeeze. Two shades: Lighter orange = regular squeeze (potential move brewing). Brighter orange = very tight squeeze (high probability of imminent explosive move). Trading Guidelines: Trend Following (Shows Bull Zone and Bear Zones): In green fills → favor longs or hold existing longs. In red fills → favor shorts or hold existing shorts. Strength increases as fills become more opaque (price near the outer band). Squeeze Breakouts (Highest Probability Setups): Wait for orange squeeze to appear. When price closes outside the bands (breakout), enter in the direction of the break. Bias the trade toward the prevailing color:Breakout upward during green fills = strong long signal. Breakout downward during red fills = strong short signal. If breakout direction opposes the color bias, be cautious (lower probability). Entry/Exits: Follow the Band color, Band color with special fill green or red is strong to determining Bull or Bear states. Green=price up Red= Price down Risk Management: Use the basis line as dynamic support/resistance. Stops can be placed just beyond the opposite band or basis during strong trends. Combine with volume or other confirmation for higher conviction. This visual upgrade makes Bollinger Bands far more intuitive — the chart literally colors itself bullish or bearish while highlighting volatility cycles and overextension zones. Great for trend, breakout, and mean-reversion strategies across any timeframe. Enjoy fellow traders, this is V1 more versions will be updated. Please also boost and comment would love your ideas on advancements to this. Cheers, MasterTony
Pine Script® 인디케이터
MasterTonyTA의
2
Divergence Confirmation System [JOAT]Divergence Confirmation System Introduction The Divergence Confirmation System (DCS) is an advanced open-source multi-oscillator divergence detection indicator that combines RSI, MFI, Stochastic, MACD, CCI, and Stochastic RSI analysis to identify high-probability divergence setups through systematic pivot comparison and multi-oscillator confirmation. This indicator reveals when price action diverges from underlying momentum across six independent oscillators, providing traders with early warning signals of potential trend reversals or continuations through rigorous confirmation requirements. Unlike basic divergence indicators that rely on a single oscillator, DCS employs a sophisticated 6-oscillator confirmation system that detects both regular divergences (trend reversal signals) and hidden divergences (trend continuation signals) across multiple momentum indicators. The indicator requires minimum oscillator confirmation (default 2/6) to filter false signals and provides divergence strength scoring based on oscillator count, volume confirmation, and price momentum. Why This Indicator Exists This indicator addresses the challenge of identifying reliable divergence signals in noisy market conditions. Single-oscillator divergences often produce false signals, but when multiple independent oscillators confirm the same divergence pattern, probability of successful reversal increases significantly. DCS systematically reveals: 6-Oscillator Analysis: RSI, MFI, Stochastic, MACD, CCI, Stochastic RSI for comprehensive momentum assessment Regular Divergence Detection: Price makes new high/low but oscillators don't confirm (reversal signal) Hidden Divergence Detection: Price makes higher low/lower high but oscillators show opposite (continuation signal) Multi-Oscillator Confirmation: Requires 2+ oscillators to agree before generating signal Divergence Strength Scoring: 0-100% score based on oscillator count, volume, and momentum Multi-Timeframe Divergence: Confirms divergences on higher timeframe for added conviction Divergence Clustering: Detects multiple divergences in short period indicating strong reversal potential Each component provides unique intelligence. Multiple oscillators reduce false signals, regular divergences show reversals, hidden divergences show continuations, strength scoring quantifies quality, MTF confirmation adds conviction, and clustering shows intensity. Core Components Explained 1. Multi-Oscillator Divergence Detection System DCS calculates six independent oscillators and detects divergences on each: // RSI float rsi = ta.rsi(close, rsi_period) float rsi_high = ta.pivothigh(rsi, pivot_left, pivot_right) float rsi_low = ta.pivotlow(rsi, pivot_left, pivot_right) // MFI (Money Flow Index - volume-weighted RSI) float mfi = ta.mfi(hlc3, mfi_period) // Stochastic float stoch_k = ta.stoch(close, high, low, stoch_period) // MACD Histogram = ta.macd(close, macd_fast, macd_slow, macd_signal) // CCI (Commodity Channel Index) float cci = ta.cci(close, 20) // Stochastic RSI float rsi_for_stoch = ta.rsi(close, rsi_period) float stoch_rsi_k = ta.stoch(rsi_for_stoch, rsi_for_stoch, rsi_for_stoch, stoch_period) Each oscillator provides independent momentum perspective. RSI shows price momentum, MFI adds volume weighting, Stochastic shows position in range, MACD shows trend momentum, CCI shows deviation from mean, and Stochastic RSI shows RSI momentum. 2. Regular Divergence Detection (Reversal Signals) Regular bullish divergence occurs when price makes lower low but oscillator makes higher low: f_detect_bull_regular_div(float osc_val, float osc_pivot) => bool detected = false if not na(osc_pivot) and not na(price_low) and array.size(price_lows) >= 2 float curr_price = array.get(price_lows, last_idx) float prev_price = array.get(price_lows, prev_idx) // Price makes lower low, oscillator makes higher low if curr_price < prev_price and osc_pivot > osc_pivot if (bar_index - prev_bar) <= max_pivot_distance detected := true detected Regular bearish divergence occurs when price makes higher high but oscillator makes lower high. These signal potential trend reversals. 3. Hidden Divergence Detection (Continuation Signals) Hidden bullish divergence occurs when price makes higher low but oscillator makes lower low: f_detect_bull_hidden_div(float osc_val, float osc_pivot) => bool detected = false if detect_hidden and not na(osc_pivot) and not na(price_low) float curr_price = array.get(price_lows, last_idx) float prev_price = array.get(price_lows, prev_idx) // Price makes higher low, oscillator makes lower low if curr_price > prev_price and osc_pivot < osc_pivot if (bar_index - prev_bar) <= max_pivot_distance detected := true detected Hidden bearish divergence occurs when price makes lower high but oscillator makes higher high. These signal trend continuation after pullback. 4. Multi-Oscillator Confirmation Aggregation DCS counts how many oscillators confirm each divergence type: int bull_reg_count = (rsi_bull_reg ? 1 : 0) + (mfi_bull_reg ? 1 : 0) + (stoch_bull_reg ? 1 : 0) + (macd_bull_reg ? 1 : 0) + (cci_bull_reg ? 1 : 0) + (srsi_bull_reg ? 1 : 0) bool confirmed_bull_regular = bull_reg_count >= min_oscillators // Optional volume confirmation float vol_avg = ta.sma(volume, 20) bool vol_confirm = volume > vol_avg * 1.2 bool final_bull_regular = confirmed_bull_regular and (not require_volume_confirm or vol_confirm) Minimum oscillator requirement (default 2/6) filters false signals. Volume confirmation adds additional filter. 5. Divergence Strength Scoring System Strength score (0-100%) calculated from multiple factors: f_divergence_strength(int osc_count, bool vol_confirm_param, float price_momentum) => float score = 0.0 // Oscillator count (0-50 points) score += osc_count * 8.33 // 6 oscillators max = 50 points // Volume confirmation (0-25 points) score += vol_confirm_param ? 25 : 0 // Price momentum (0-25 points) float momentum_score = math.min(math.abs(price_momentum) * 5, 25) score += momentum_score math.min(score, 100) Strength classification: - 75-100%: Very Strong (highest probability) - 60-74%: Strong (high probability) - 40-59%: Moderate (medium probability) - 0-39%: Weak (low probability) 6. Multi-Timeframe Divergence Confirmation DCS checks for divergences on higher timeframe (default 15m): f_get_htf_divergence(string tf) => = request.security(syminfo.tickerid, tf, ) float htf_rsi_high = ta.pivothigh(htf_rsi, pivot_left, pivot_right) float htf_rsi_low = ta.pivotlow(htf_rsi, pivot_left, pivot_right) bool htf_bull = f_detect_bull_regular_div(htf_rsi, htf_rsi_low) bool htf_bear = f_detect_bear_regular_div(htf_rsi, htf_rsi_high) bool mtf_bull_confirmed = final_bull_regular and htf_bull_div bool mtf_bear_confirmed = final_bear_regular and htf_bear_div MTF confirmation significantly increases signal reliability. 7. Divergence Clustering Detection Clustering identifies multiple divergences in short period: var array div_bars = array.new_int(0) if final_bull_regular or final_bear_regular array.push(div_bars, bar_index) // Count divergences in last 50 bars int recent_div_count = 0 for i = 0 to array.size(div_bars) - 1 int div_bar = array.get(div_bars, i) if bar_index - div_bar <= 50 recent_div_count += 1 bool in_div_cluster = recent_div_count >= 3 string cluster_intensity = recent_div_count >= 5 ? "High" : recent_div_count >= 3 ? "Moderate" : "Low" Clusters indicate strong reversal pressure building. Visual Elements Primary Oscillator Display: User-selectable (RSI/MFI/Stochastic/MACD) with gradient shadow effect Reference Lines: 70 (overbought), 50 (midline), 30 (oversold) Oscillator Histogram: Gradient-colored bars showing oscillator deviation from 50 Background Zones: Cyan for bullish divergence, red for bearish divergence Divergence Labels: "BULL DIV" or "BEAR DIV" with oscillator count (e.g., "4/6") Hidden Divergence Markers: Small "H" circles for hidden divergences Elite Signals: Large labels for 4+ oscillator confirmation with strength >75% MTF Confirmation: Triangle markers when higher timeframe confirms Multi-Oscillator Confirmation: Labels showing oscillator count (e.g., "3/6 CONF") Institutional Flow: "INST BUY/SELL" labels when delta confirms divergence Input Parameters Oscillator Settings: RSI Period: RSI calculation period (default: 14) MFI Period: MFI calculation period (default: 14) Stochastic Period: Stochastic calculation period (default: 14) MACD Fast: MACD fast EMA (default: 12) MACD Slow: MACD slow EMA (default: 26) MACD Signal: MACD signal line (default: 9) Divergence Detection: Pivot Left Bars: Bars to left of pivot (default: 5) Pivot Right Bars: Bars to right of pivot (default: 2) Detect Hidden Divergences: Toggle hidden divergence detection (default: true) Max Pivot Distance: Maximum bars between pivots (default: 60) Confirmation Rules: Minimum Oscillator Confirmation: Required oscillators (default: 2/6) Require Volume Confirmation: Toggle volume filter (default: false) Visualization: Show Divergence Lines: Toggle divergence line drawing (default: true) Show Labels: Toggle divergence labels (default: true) Primary Display: Select oscillator to display (RSI/MFI/Stochastic/MACD) How to Use This Indicator Step 1: Monitor Primary Oscillator Watch selected oscillator (default RSI) for overbought/oversold conditions. Step 2: Wait for Divergence Labels "BULL DIV" or "BEAR DIV" labels appear when 2+ oscillators confirm divergence. Step 3: Check Oscillator Count Higher count = higher probability. 4/6 or better is ideal. Step 4: Assess Divergence Strength Tooltip shows strength percentage. >75% is very strong, >60% is strong. Step 5: Confirm with MTF Triangle markers indicate higher timeframe confirmation - highest probability setups. Step 6: Watch for Elite Signals Large "BULL DIV" or "BEAR DIV" labels with 4+ oscillators and >75% strength are highest conviction. Best Practices Focus on divergences with 3+ oscillator confirmation for best results Regular divergences work best at price extremes (support/resistance) Hidden divergences confirm trend continuation - trade with trend MTF confirmation adds significant edge - wait when possible Divergence clustering indicates strong reversal pressure Volume confirmation reduces false signals but adds lag Elite signals (4+ oscillators, >75% strength) have highest win rate Use cooldown system (15 bars minimum) to avoid overtrading Combine with price action - divergence shows momentum, price shows structure Indicator Limitations Divergence detection requires clear pivot formation - lags by pivot_right bars Multiple oscillators can produce conflicting signals during choppy markets Hidden divergences are less reliable than regular divergences Strength scoring is probabilistic, not deterministic MTF confirmation adds lag but increases reliability Clustering detection has fixed lookback - may miss longer-term patterns Volume confirmation may not work well on illiquid instruments Extreme market conditions can invalidate divergence signals Technical Implementation Built with Pine Script v6 using: 6-oscillator system (RSI, MFI, Stochastic, MACD, CCI, Stochastic RSI) Pivot-based divergence detection with array tracking Regular and hidden divergence algorithms Multi-oscillator confirmation aggregation Divergence strength scoring (oscillator count + volume + momentum) Multi-timeframe security requests for HTF confirmation Divergence clustering detection (50-bar lookback) Signal cooldown system (15 bars minimum) Gradient visualization with dynamic coloring Institutional flow integration (CVD delta analysis) Elite signal filtering (4+ oscillators, >75% strength) The code is fully open-source and can be modified to suit individual trading styles. Originality Statement This indicator is original in its comprehensive multi-oscillator divergence confirmation approach. While individual oscillator divergences are established concepts, this indicator is justified because: It combines 6 independent oscillators (RSI, MFI, Stochastic, MACD, CCI, Stochastic RSI) for robust confirmation The multi-oscillator confirmation system (2-6 required) significantly reduces false signals Divergence strength scoring quantifies setup quality through multi-factor analysis Multi-timeframe divergence confirmation adds conviction layer Divergence clustering detection identifies high-probability reversal zones Integration of institutional flow (CVD delta) with divergence analysis is unique Elite signal filtering (4+ oscillators, >75% strength) isolates highest probability setups Signal cooldown system prevents overtrading while maintaining signal quality Each component contributes unique information: multiple oscillators reduce false signals, regular divergences show reversals, hidden divergences show continuations, strength scoring quantifies quality, MTF confirmation adds conviction, clustering shows intensity, and institutional flow confirms with volume. The indicator's value lies in presenting these complementary perspectives simultaneously with rigorous confirmation requirements. Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Divergence signals do not guarantee reversals. Trading involves substantial risk of loss. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose. -Made with passion by officialjackofalltrades
Pine Script® 인디케이터
officialjackofalltrades의
Apex Momentum Wave [Pineify]Apex Momentum Wave The Apex Momentum Wave (AMW) is a noise-filtered momentum oscillator that combines Hull Moving Average price smoothing with RSI momentum measurement and an EMA-based signal line to produce cleaner, more actionable momentum readings. Instead of applying RSI directly to raw price — which often generates noisy, whipsaw-prone signals — AMW first smooths the close price through an HMA filter to strip out short-term market noise, then measures momentum on this cleaned data. The result is a responsive yet smooth oscillator that highlights genuine momentum shifts while suppressing false signals. Buy and sell markers are further refined by a midline filter that only triggers entries when momentum is turning from a favorable position, making this indicator a practical tool for timing entries across any market and timeframe. Key Features HMA-smoothed RSI oscillator — applies RSI to a Hull Moving Average of price rather than raw close, dramatically reducing noise and false momentum readings. EMA signal line with crossover detection — a trailing signal line provides clear, objective crossover-based entry triggers. Midline-filtered buy/sell signals — BUY signals only fire when momentum crosses up below the 50 midline (turning from weakness), and SELL signals only fire when crossing down above 50 (turning from strength), filtering out low-conviction entries. Multi-layer visual zone system — overbought/oversold zones, dynamic breach fills, and oscillator-signal cloud fills provide instant visual context of market momentum state. Built-in alert conditions — configurable alerts for both BUY and SELL signals for hands-free monitoring. How It Works The indicator follows a three-stage calculation pipeline designed to extract clean momentum information from price: Price smoothing via Hull Moving Average (HMA): The closing price is first passed through an HMA with a configurable period (default: 7). The Hull Moving Average uses a combination of weighted moving averages with a square-root-period final smoothing step, producing a curve that closely tracks price with significantly less lag than traditional SMA or EMA. This step acts as a pre-filter, removing intrabar noise and minor fluctuations before momentum is measured. Momentum measurement via RSI: The Relative Strength Index is then calculated on the HMA-smoothed price over a configurable lookback (default: 14). Because the input price has already been cleaned by the HMA, the resulting RSI oscillator produces smoother momentum readings that more accurately reflect the underlying trend's strength, rather than reacting to every minor price tick. Signal line via EMA: An Exponential Moving Average (default period: 9) is applied to the oscillator output, creating a trailing signal line. Crossovers between the oscillator and this signal line indicate momentum direction changes — the oscillator crossing above the signal suggests strengthening momentum, while crossing below suggests weakening momentum. Trading Ideas and Insights The AMW is designed to be versatile across different trading styles and market conditions. Here are practical approaches: Momentum reversal entries: The primary use case — when a BUY triangle appears (oscillator crosses above signal below the 50 midline), it indicates momentum is shifting from bearish to bullish while still in the lower half of the range, catching the turn early. Enter long and target the midline or overbought zone. The SELL signal is the mirror for short entries. Overbought/oversold confluence: When the oscillator enters the shaded overbought zone (above 80) or oversold zone (below 20), the market is at a momentum extreme. Wait for the oscillator to turn and cross below the signal line (in overbought) or above it (in oversold) for high-probability mean reversion trades. Trend confirmation: Use the cloud fill between the oscillator and signal line as a trend filter. A sustained bullish (green) cloud suggests maintaining long bias; a sustained bearish (red) cloud suggests maintaining short bias. Only take signals aligned with the prevailing cloud color for higher win rates. Divergence analysis: Compare the oscillator's peaks and troughs with price action. If price makes a higher high but the oscillator makes a lower high, bearish divergence suggests weakening momentum — and vice versa for bullish divergence. These divergences often precede significant reversals. How Multiple Indicators Work Together The AMW integrates three distinct technical components into a cohesive system, each serving a specific role: Hull Moving Average (noise reduction): The HMA serves as the foundation layer, transforming noisy raw price data into a clean input signal. Its near-zero-lag property is critical — if a lagging average like SMA were used instead, the subsequent RSI calculation would inherit that lag, making the entire oscillator slow to react. HMA preserves responsiveness while eliminating the noise that causes false RSI signals. Relative Strength Index (momentum quantification): RSI converts the smoothed price movement into a bounded 0–100 oscillator that measures the speed and magnitude of price changes. Applied to the HMA-filtered price, it produces a momentum reading that is both responsive and stable — capturing genuine momentum shifts without the jitter that plagues standard RSI on raw price. Exponential Moving Average signal line (timing mechanism): The EMA of the oscillator creates a reference line that the oscillator oscillates around. Crossovers between the two provide objective, rule-based entry timing. The EMA's inherent smoothing prevents the signal line from reacting to every minor oscillator fluctuation, ensuring crossovers represent meaningful momentum changes rather than noise. The synergy is sequential: HMA cleans the price → RSI measures momentum on clean data → EMA provides a timing reference for the momentum reading. Each layer builds on the previous one, and the midline filter on top adds a final directional bias check, ensuring the complete system produces signals only when multiple conditions align. Unique Aspects Pre-filtered RSI approach: Most oscillator indicators apply RSI (or similar) directly to raw price. AMW's innovation is the HMA pre-smoothing step, which fundamentally changes the quality of the RSI output. This two-stage approach produces a momentum oscillator that behaves more like a "true" momentum reading rather than a noisy derivative of price. Midline directional filter: Unlike simple crossover systems that generate signals anywhere in the oscillator range, AMW restricts BUY signals to the lower half (below 50) and SELL signals to the upper half (above 50). This ensures entries occur when momentum is turning from a relatively extreme position, significantly reducing false signals during choppy, range-bound conditions. Multi-layer visual feedback: The indicator provides four distinct visual layers — oscillator-signal cloud fill, horizontal zone lines, extreme zone background shading, and dynamic breach fills — giving traders an immediate, at-a-glance understanding of the current momentum state without needing to interpret raw numbers. Clean sub-panel design: As a non-overlay oscillator, AMW keeps the price chart uncluttered while providing all momentum information in a dedicated panel, making it easy to combine with overlay-based indicators like moving averages or support/resistance tools. How to Use Add the indicator to your chart. It will appear in a separate panel below the price chart, displaying the oscillator (thick line), signal line (orange), and reference levels. Watch for BUY triangles (green, at the bottom of the panel) — these appear when the oscillator crosses above the signal line while below the 50 midline, indicating a bullish momentum shift from a weak state. Watch for SELL triangles (red, at the top of the panel) — these appear when the oscillator crosses below the signal line while above the 50 midline, indicating a bearish momentum shift from a strong state. Use the cloud fill color between the oscillator and signal line to gauge the prevailing momentum direction — green for bullish, red for bearish. Monitor the overbought (80) and oversold (20) zones. When the oscillator enters these shaded areas and the breach fill activates, the market is at a momentum extreme — be alert for potential reversals. Set up alerts using the built-in "AMW Buy Signal" and "AMW Sell Signal" alert conditions to receive real-time notifications. Customization Momentum Length (default: 14): The RSI lookback period. Lower values (e.g., 8–10) make the oscillator more sensitive and responsive, suitable for scalping or lower timeframes. Higher values (e.g., 21–30) produce smoother, more stable readings for swing trading or higher timeframes. Price Smoothing / HMA (default: 7): Controls the Hull Moving Average period applied to price before RSI calculation. Lower values preserve more price detail but allow more noise through; higher values produce a cleaner oscillator but introduce slight additional lag. Find the balance that suits your timeframe. Signal Line Length (default: 9): The EMA period for the signal line. Shorter periods make the signal line more reactive, generating more frequent crossovers; longer periods produce fewer but potentially more reliable crossover signals. Overbought Level (default: 80): The upper threshold for the extreme zone. Raise to 85 or 90 for fewer but more extreme overbought readings; lower to 70 or 75 for earlier warnings. Oversold Level (default: 20): The lower threshold for the extreme zone. Lower to 10 or 15 for fewer but more extreme oversold readings; raise to 25 or 30 for earlier warnings. All colors — bullish, bearish, and signal line — are fully customizable through the Colors & Aesthetics settings group. Conclusion The Apex Momentum Wave reimagines the classic RSI oscillator by introducing an HMA pre-smoothing stage that fundamentally improves signal quality. Combined with an EMA signal line for objective crossover timing and a midline directional filter that restricts entries to favorable momentum positions, AMW delivers a momentum oscillator that is both cleaner and more actionable than standard RSI implementations. Its multi-layer visual design — featuring cloud fills, zone shading, and dynamic breach highlights — provides traders with immediate, intuitive momentum context. Whether you trade stocks, forex, crypto, or futures, the Apex Momentum Wave adapts to your market and timeframe, offering a refined approach to momentum-based trading decisions.
Pine Script® 인디케이터
Pineify의
Adaptive Spectral Forecast [WillyAlgoTrader]📡 Adaptive Spectral Forecast is an overlay indicator that applies Goertzel spectral analysis to decompose price into its dominant cyclical components, reconstructs them as a harmonic sum, and then extrapolates the resulting waveform forward in time to generate a visual forecast with confidence bands. Signals fire when the forecast direction changes with sufficient signal-to-noise ratio and trend alignment — projecting where price is likely to oscillate next based on the cycles detected in recent history. This is a fundamentally different approach from trend-following or momentum-based indicators. Instead of asking "where is price going based on its direction and speed?", spectral analysis asks "what recurring cycles exist in this price data, and where do they project to next?" The Goertzel algorithm is a targeted frequency detector — it scans a range of cycle periods, measures the power (amplitude²) at each frequency, identifies the dominant peaks, computes their exact phase and amplitude via DFT projection, and recombines them into a multi-harmonic forecast that decays toward the adaptive trend as it extends into the future. 🧩 WHY THESE COMPONENTS WORK TOGETHER Raw price is a mix of trend, cycles, and noise. Attempting to forecast raw price directly fails because trend and cycles require different extrapolation methods: trend continues linearly, cycles repeat sinusoidally, and noise should not be extrapolated at all. This indicator solves the problem through decomposition and reassembly: EMA trend extraction → Hann-windowed detrending → Goertzel spectral scan → SNR peak detection → DFT coefficient extraction → Harmonic recombination with decay → Trend re-addition → Confidence bands Each stage addresses a specific challenge: — Trend extraction separates the slow directional component so it can be extrapolated linearly (via slope), not sinusoidally — Hann windowing reduces spectral leakage — without it, the finite data window creates false frequency peaks that contaminate the analysis — Goertzel scanning efficiently measures power at each candidate frequency without computing a full FFT — enabling targeted, adaptive-resolution frequency detection — SNR filtering ensures only cycles with meaningful signal strength are included — weak noise-level frequencies are discarded — DFT coefficient extraction computes the exact amplitude and phase of each selected cycle on un-windowed data (the Hann window is only for spectral scanning, not for coefficient calculation — this preserves correct amplitudes) — Harmonic decay causes the cyclic component to gradually fade toward the trend as the forecast extends forward — reflecting the reality that detected cycles have limited persistence — Confidence bands widen with √(bars ahead) × ATR, showing that forecast uncertainty grows with distance Removing any component breaks the pipeline: without detrending, the Goertzel scan detects the trend as a low-frequency "cycle". Without Hann windowing, spectral leakage creates phantom peaks. Without SNR filtering, the forecast includes noise-level harmonics that produce random oscillations. Without decay, the harmonic projection repeats forever at full amplitude (unrealistic). The full pipeline is required. 🔍 WHAT MAKES IT ORIGINAL 1️⃣ Goertzel spectral analysis for cycle detection. The Goertzel algorithm is a single-frequency DFT that computes the power at one specific period using a recursive formula: s0 = data + 2×cos(2π/period) × s1 − s2 After iterating through all N data points, the power is: (s1 − s2×cos(ω))² + (s2×sin(ω))², normalized by N² for cross-period comparability. The indicator scans every integer period from Min Cycle Period (default 8) up to N/2, with an adaptive step size: every period for fast cycles (≤30 bars), step of 2 for longer cycles (reducing computation without losing resolution where it matters most). For each period, the Goertzel power is computed, producing a power spectrum — a map of which cycle lengths carry the most energy in the current price data. This is fundamentally more targeted than an FFT. An FFT computes power at all frequencies simultaneously but at fixed resolution (determined by window size). The Goertzel approach allows scanning exactly the frequency range of interest with customizable resolution. 2️⃣ SNR-based peak detection with fallback. From the power spectrum, the indicator identifies local peaks (frequencies where power is higher than both neighbors) and computes the Signal-to-Noise Ratio for each: SNR = peak_power / mean_power_across_all_frequencies. Only peaks with SNR ≥ Min Cycle SNR (default 2.0) are accepted as genuine cycles — the rest are considered noise-level fluctuations. If no peaks pass the SNR filter (possible in highly random or trend-dominated price action), the algorithm falls back to the single strongest frequency. In this case, the dashboard displays "Weak*" strength and buy/sell signals are suppressed — the forecast is shown for visual reference only, but the indicator acknowledges that no reliable cyclical structure was found. The top N peaks (sorted by power, N = Harmonics Count, default 5) are selected as the dominant cycles. 3️⃣ DFT coefficient extraction on un-windowed data. For each selected cycle period, the indicator computes exact sine and cosine coefficients using standard DFT projection: a = (2/N) × Σ data × sin(2π × i / period) b = (2/N) × Σ data × cos(2π × i / period) Critically, this computation uses the raw detrended data (without Hann windowing). The Hann window was only applied for the spectral scan (to identify which frequencies are dominant). Using windowed data for coefficient extraction would distort the amplitude of the harmonics. This two-pass approach — windowed scan for detection, raw data for coefficients — is a key design choice that preserves forecast accuracy. 4️⃣ Harmonic extrapolation with configurable decay. The forecast is constructed by evaluating the harmonic sum at each future bar: forecast = trend_projection + Σ (a_k × sin(ω_k × t) + b_k × cos(ω_k × t)) × decay^(t − t_base) Where trend_projection = trend_last + trend_slope × bars_ahead (linear extrapolation of the EMA trend). The decay factor (default 0.97) causes harmonic amplitude to reduce by 3% per bar, so the cyclic component gradually fades and the forecast converges toward the trend line. At decay = 1.0, harmonics repeat at full amplitude forever (pure cycle projection). At decay = 0.95, they fade rapidly (forecast becomes trend-only within ~20 bars). Default 0.97 provides meaningful oscillation for the first 20–30 bars before fading. The forecast line is colored by segment: green segments where the forecast is rising, red where falling. Reversal dots mark predicted peaks and troughs. 5️⃣ ATR-based confidence bands with √t scaling. Uncertainty in the forecast grows with distance. The confidence band width is calculated as: band_width = ATR(14) × confidence_multiplier × √(bars_ahead) The √t scaling follows the mathematical principle that forecast variance grows linearly with time horizon (standard deviation grows with square root). The ATR provides the natural volatility scale of the instrument. At bar +1, the band is approximately ATR × multiplier. At bar +25, it's 5× wider. This gives a realistic visual envelope of where price might actually be, not just the central harmonic forecast. 6️⃣ 3-bar consensus forecast direction. Instead of using a simple "is the next bar higher or lower?" check (which is noisy), the forecast direction is determined by majority vote over 3 bars: The indicator evaluates the forecast at t+0, t+1, t+2, t+3 and counts upward moves: upVotes = (y1>y0 ? 1:0) + (y2>y1 ? 1:0) + (y3>y2 ? 1:0). If ≥2 of 3 transitions are upward → forecast direction is bullish. If ≤1 → bearish. This consensus approach prevents a single-bar oscillation from flipping the forecast direction. 7️⃣ Trend alignment filter for signal quality. When enabled (default on), buy signals require the EMA trend slope to be positive, and sell signals require negative slope. This prevents the indicator from generating counter-trend signals when a cycle oscillation temporarily points against the broader trend — which is the most common source of false signals in cycle-based systems. If the trend filter blocks a direction change, the forecast visualization still updates (you can see the cycle projection) but no signal label is emitted. Additionally, signals are suppressed when the SNR is below the minimum threshold or when the spectral scan fell back to a single non-significant frequency. 8️⃣ Adaptive trend extraction with linear extrapolation. The trend component is extracted using an EMA with configurable smoothing (default 30 bars), applied forward using the buildTrendArray function. The trend's slope is computed via weighted linear regression over the last 5 points of the trend array — providing a stable slope estimate that isn't dominated by a single bar. The trend is extrapolated linearly into the forecast: trend_forecast = trend_last + slope × i. This linear projection is appropriate for the short-term forecast horizon (15–55 bars) where trend curvature is typically negligible. 9️⃣ Historical fit visualization. The reconstructed harmonic sum + trend is plotted over historical data as a polyline, showing how well the detected cycles explain the actual price movement. This serves as an immediate visual validation: if the fit tracks price well, the detected cycles are meaningful. If the fit diverges significantly, the current market regime may not have strong cyclical structure (reflected in low SNR scores in the dashboard). The fit line is colored by trend direction (green for bullish slope, red for bearish) and decimated for performance (every 1–3 bars depending on lookback length). 🔟 Four presets with coordinated parameter scaling. — Conservative : lookback ≥ 150, harmonics ≤ 3, forecast ≥ 40 bars — stable, long-term cycles, less overfitting risk — Default : user settings unchanged — Aggressive : lookback ≤ 80, harmonics +1, forecast ≤ 25 — faster adaptation, more cycles included — Scalping : lookback ≤ 50, harmonics +2, forecast ≤ 15 — shortest window, most harmonics, very short projection Each preset adjusts lookback (spectral window), harmonics count (complexity), and forecast length (projection horizon) as a coordinated unit. Longer lookback needs fewer harmonics (the cycles are more stable). Shorter lookback needs more harmonics (to capture the faster fluctuations within the compressed window). ⚙️ HOW IT WORKS — CALCULATION FLOW Step 1 — Data collection: The last N bars (Analysis Lookback, default 144) of the selected price source are collected into an array, oldest first. Step 2 — Trend extraction: An EMA with the Trend Smoothing period (default 30) is applied across the array using a forward-pass recursive formula: trend = α × price + (1−α) × trend . This produces a smooth trend array. Step 3 — Detrending + Hann window: Each bar's trend value is subtracted from its price. The residual is multiplied by a Hann window: w = 0.5 × (1 − cos(2π×i/(N−1))). This isolates the cyclical component while minimizing spectral leakage at the data boundaries. Step 4 — Goertzel spectral scan: For each candidate period from minPeriod to N/2 (adaptive step: 1 for periods ≤30, 2 for longer), the Goertzel algorithm computes power. The result is a power spectrum across all scanned frequencies. Step 5 — Peak detection: Local maxima in the power spectrum are identified (power > both neighbors). Each peak's SNR is computed against the mean power. Peaks with SNR ≥ threshold are accepted. If none pass, the strongest single frequency is used as fallback (with "Weak*" marking). Step 6 — Coefficient extraction: For the top N peaks (by power), sine and cosine coefficients are computed via DFT projection on raw (un-windowed) detrended data. This gives exact amplitude and phase for each cycle. Step 7 — Forecast generation: The trend is extrapolated linearly using its 5-bar regression slope. Each harmonic is evaluated at future time steps with decay applied. The sum of trend + decayed harmonics produces the central forecast line. Confidence bands = ATR × multiplier × √(bars_ahead). Step 8 — Direction and signals: 3-bar consensus determines forecast direction. Trend filter and SNR check gate signal emission. Buy/sell labels appear on bar-close confirmation. 📖 HOW TO USE 🎯 Quick start: 1. Add the indicator — the historical fit and forecast line appear on the last bar 2. The colored dotted line extending right is the forecast (green = rising, red = falling) 3. Colored dots (●) on the forecast mark predicted peaks and troughs 4. Dotted lines above and below = confidence bands (forecast uncertainty zone) 5. ▲/▼ labels = buy/sell signals when the forecast direction changes 👁️ Reading the chart: — 🟢 Green solid line on history = harmonic fit (uptrend slope) — 🔴 Red solid line on history = harmonic fit (downtrend slope) — 🟢🔴 Dotted line extending right = forecast (colored by direction: green rising, red falling) — 🔵 Upper/lower dotted lines = confidence bands (uncertainty grows with distance) — 🟢 ● dots = predicted troughs (potential support) — 🔴 ● dots = predicted peaks (potential resistance) — 🟢 ▲ below bar = buy signal (forecast changed to bullish) — 🔴 ▼ above bar = sell signal (forecast changed to bearish) 📊 Dashboard fields: — Trend: current EMA trend direction (Bullish / Bearish / Neutral) — Forecast: predicted direction (▲ Up / ▼ Down / — Flat) — Signal: current state (BUY / SELL / Bullish Bias / Bearish Bias / Wait) — Strength: cycle quality based on average SNR (Strong > 5.0 / Medium > 2.5 / Weak / Weak* = fallback) — Dom. Cycle: dominant cycle period in bars (e.g., "34 bars") — Cycles: how many cycles passed SNR filter vs. requested (e.g., "3 / 5") — Timeframe, preset, version 🔧 Tuning guide: — Forecast too noisy: reduce Harmonics Count (3), increase Min SNR (3.0+), increase Trend Smoothing — Forecast too smooth: increase Harmonics Count (5–7), decrease Min SNR (1.5), decrease Trend Smoothing — Cycles don't match price: increase Analysis Lookback (200+) for more stable cycle detection, or decrease for faster adaptation — Forecast fades too fast: increase Decay Rate toward 0.99–1.0 — Forecast unrealistic long-term: decrease Decay Rate toward 0.95, reduce Forecast Bars — Too many false signals: enable Trend Alignment Filter, increase Min SNR — Scalping 1–5M: use Scalping preset (lookback ≤50, 7 harmonics, 15-bar forecast) — Swing 4H–1D: use Conservative preset (lookback ≥150, 3 harmonics, 40-bar forecast) ⚙️ KEY SETTINGS REFERENCE ⚙️ Main: — Analysis Lookback (default 144): spectral analysis window — the last N bars analyzed — Harmonics Count (default 5): how many dominant cycles to include in the forecast — Min Cycle Period (default 8): shortest cycle to scan for (bars) — Preset (default Default): Conservative / Default / Aggressive / Scalping — Trend Alignment Filter (default On): require trend-forecast agreement for signals 🔮 Forecast: — Forecast Bars (default 55): projection length into the future — Confidence Band Width (default 1.5× ATR): band multiplier — Trend Smoothing (default 30): EMA period for trend component — Harmonic Decay Rate (default 0.97): amplitude reduction per bar (1.0 = no decay) — Min Cycle SNR (default 2.0): signal-to-noise threshold for cycle acceptance 🎨 Visual: — Historical fit, trend line, reversal dots, confidence bands (all toggleable) — Auto / Dark / Light theme 🔔 Alerts — 🟢 BUY — ticker, price, timeframe, time — 🔴 SELL — same fields Both support plain text and JSON webhook format. Signals are bar-close confirmed, direction-locked, trend-filtered, and SNR-gated. ⚠️ IMPORTANT NOTES — 📐 This is spectral analysis, not trend following. The indicator detects and projects recurring cycles. In markets with strong cyclical structure (commodities, forex majors, crypto with regular oscillations), it performs well. In news-driven or momentum-dominated markets with no cyclical structure, the SNR will be low and the forecast unreliable — the dashboard reflects this via the Strength reading. — 🚫 No repainting of signals. The spectral analysis runs on barstate.islast (updating the forecast in real time on the current forming bar). Signals only fire on the next barstate.isconfirmed bar, after the forecast direction has been set. This means the forecast line itself updates in real time (by design — it's a live projection), but buy/sell signals are confirmed and do not change retroactively. — 📊 The forecast is a projection, not a prediction. It shows where price would go if the detected cycles continue with their current amplitude and phase. Real markets introduce new information that disrupts cycles. The confidence bands reflect this growing uncertainty. Treat the forecast as a probabilistic zone, not a target. — 🔄 "Weak*" strength means no cycles passed the SNR filter and the indicator fell back to the single strongest frequency. In this state, signals are suppressed. The forecast is still shown for visual reference but should not be trusted for trading decisions. — ⚖️ The Hann window is applied only for spectral scanning , not for coefficient extraction. This is deliberate: the window prevents spectral leakage during frequency detection, but the un-windowed data preserves correct harmonic amplitudes for the forecast. — 📏 The forecast extends a fixed number of bars into the future. Accuracy degrades with distance — the first 10–15 bars are typically the most reliable. The confidence bands quantify this degradation visually. — 🛠️ This is a spectral analysis and forecasting tool , not an automated trading bot. It detects cycles, projects them forward, and generates directional signals — trade decisions remain yours. — 🌐 Works on all markets and timeframes. Cycle periods adapt automatically to whatever timeframe you apply it on.
Pine Script® 인디케이터
WillyAlgoTrader의
Variable Sine Wave Fit [LuxAlgo]The Variable Sine Wave Fit indicator uses Ordinary Least Squares (OLS) to fit a dynamic, damped, or expanding sine wave with an underlying linear trend to recent price action. This tool aims to identify cyclical patterns and project their potential continuation into the future, providing a mathematical framework for understanding market regimes and turning points. This indicator is subject to repainting and is displayed retrospectively. 🔶 USAGE The indicator fits a complex trigonometric model to the price data within a user-defined window. The resulting fit is displayed as a solid line over historical bars and transitions into a dashed extrapolation for the forecasted period. To use the indicator effectively, traders should observe the relationship between the price and the RMSE bands. If the price remains within these bands, the current cyclical model is considered to be tracking the price action effectively. If the price breaks significantly outside, the cycle may be shifting or breaking down. 🔹 Extrema Markers Small dot markers are placed at the local maxima and minima of the dashed forecast line. These serve as visual guides for the timing of potential future turning points based on the current mathematical fit. 🔹 Market Regime Dashboard The dashboard provides a real-time summary of the fitted model's characteristics: State: Classified based on the amplitude behavior (Damped, Expanding, or Constant) and the linear component (Trending or Ranging). Best Period: The cycle length (in bars) that currently provides the best fit to the data. RMSE: The Root Mean Square Error, representing the average deviation of price from the fit. 🔶 DETAILS The script solves for the best parameters of the following equation: y = e^(λ * t) * (a * sin(ω * t) + b * cos(ω * t)) + m * t + c Where: e^(λ * t): The damping/expansion factor. If λ > 0, the cycle is expanding; if λ < 0, it is damping. a, b: Coefficients determining the phase and initial amplitude of the sine wave. m * t + c: A linear regression component that accounts for the underlying price trend. The "Best Period" is determined through a grid search that minimizes the Sum of Squared Errors (SSE), ensuring the frequency (ω) matches the most dominant local cycle within the search range. 🔶 SETTINGS 🔹 Settings Window Size (N): The number of historical bars used to calculate the fit. Auto Period: When enabled, the script searches for the best period within the specified min/max range. Fixed Period (P): The period used if Auto Period is disabled. Min/Max Search Period: Defines the boundaries for the automatic cycle search. Forecast Length: The number of bars to project the fit into the future. RMSE Band Multiplier: Determines the width of the bands surrounding the fit based on the fit error. 🔹 Visuals Bullish/Bearish Color: Colors used for the fit line and extrema markers based on the final slope. Band Color: The color of the RMSE-based envelope. 🔹 Dashboard Dashboard: Toggles the visibility of the data table. Position: Moves the dashboard to different corners of the chart. Size: Adjusts the text and table scale.
Pine Script® 인디케이터
LuxAlgo의
Harmonic Frequency Visualizer [BackQuant]Harmonic Frequency Visualizer Overview Harmonic Frequency Visualizer is a cycle-analysis and cross-asset resonance tool that uses a simplified Discrete Fourier Transform (DFT) to measure how strongly specific cycle periods are present in price. It is not a “trend indicator” and it is not trying to predict direction by itself. Its job is to quantify rhythm: which repeating periods (in bars) are currently dominant, whether those cycles are expanding or contracting (phase direction), and whether multiple instruments are sharing the same dominant periods at the same time (resonance). This indicator has two main output modes: Spectrum : a frequency “snapshot” showing amplitude at each tested period for up to five instruments. Spectrogram : a history heatmap showing how the spectrum evolves through time (for the chart instrument). Spectrum Spectrogram On top of that, it produces a Dominant Cycle Oscillator derived from the dominant cycle’s phase, which gives a continuous cycle position metric (peak/trough style zones) without repainting. This is designed for traders who want cycle context the same way they want volatility context: not as a magic signal, but as structure. What “frequency” and “cycles” mean in trading terms A cycle period (say 21 bars) means: “a repeating pattern that tends to complete one full oscillation every 21 bars.” If price contains such a pattern, the DFT will detect a strong correlation between price and a 21-bar sine/cosine wave. Markets do not have perfectly stable periodic motion, but they often show: Mean-reverting swings around value. Trend pulses with pullback cadence. Volatility clustering that creates rhythmic expansions and contractions. Cycle tools are trying to measure those repeating components, and DFT is the standard mathematical way to do it. Where DFT comes from (the core idea) The Discrete Fourier Transform comes from Fourier analysis, a foundational signal processing concept: Fourier’s idea : any sufficiently well-behaved signal can be expressed as a sum of sine and cosine waves at different frequencies, each with: An amplitude (how strong that wave is). A phase (where you are within the wave cycle). In continuous math you get the Fourier Transform. In sampled data (like candles) you use the Discrete Fourier Transform. It converts a time series (price over time) into a frequency description (strength of different cycles). In markets: Time domain: candles and price series. Frequency domain: cycle periods and their strengths. Why sine and cosine, not just sine A sine wave alone cannot represent every phase alignment cleanly. DFT uses both cosine and sine components because together they form an orthogonal basis that can represent any phase shift. You can think of it like this: Cosine component captures “in-phase” alignment with the cycle. Sine component captures “quadrature” (90-degree shifted) alignment. Combining them gives full information: amplitude + phase. Mathematically, a single frequency component can be written as: A * cos(ωt + φ) But DFT estimates A and φ by separately accumulating cosine and sine projections. How this script implements the DFT (and what it is actually measuring) This is not a full-spectrum FFT across every frequency. It is a targeted DFT across a fixed set of cycle periods: Tested periods The script tests 8 predefined periods: 5, 8, 13, 21, 34, 55, 89, 120 These are Fibonacci-like cycle candidates commonly used in cycle/market structure work. The point is not that Fibonacci is magic. The point is that these represent a reasonable spread from short to long rhythms without needing hundreds of frequencies (which would be heavy in Pine). Normalization step (important) Before computing the DFT, the script normalizes the series: mn = SMA(src, lookback) sd = stdev(src, lookback) norm = (src - mn) / sd (if sd != 0) Why normalize: DFT amplitude depends on the scale of the input series. If you compare BTC and TLT raw prices, the magnitude is meaningless. Z-score normalization makes amplitude more comparable across instruments and regimes. So the spectrum is measuring “cyclical structure in standardized deviations,” not raw dollars. Projection onto cosine and sine For each tested period P: ω = 2π / P (angular frequency for that period) Compute: - sCos = Σ(norm * cos(ωk)) - sSin = Σ(norm * sin(ωk)) Interpretation: You are correlating the last window of normalized price with a cosine wave of period P. And also correlating it with a sine wave of period P. If the price has a strong P-bar rhythm, these sums grow in magnitude. Window length detail The script uses: window = min(lookback - 1, 99) So even if lookback is 200, the internal DFT accumulation caps at 100 bars for performance stability. This is a deliberate trade: stable computation in Pine, while still letting you define normalization lookback and overall context. Amplitude computation Once sCos and sSin are computed: raw magnitude = sqrt(sCos² + sSin²) This is the length of the vector (sCos, sSin). That vector length is the standard way to combine the orthogonal components into one strength metric. Then it scales it into a 0–100 “display amplitude”: amp = sqrt(sCos² + sSin²) / lookback * 100 * sensitivity amp is capped to 100 So: Higher amplitude means stronger alignment with that cycle period. Sensitivity is a user control to amplify or damp the display scaling. Important: amplitude here is not a probability, and it is not guaranteed “signal quality.” It is a standardized “how much of that cycle exists in the recent window” metric. Phase computation Phase is computed using atan2(sSin, sCos). That matters because: A simple atan(sin/cos) fails in different quadrants. atan2 correctly resolves the angle from -π to +π. Phase tells you where you are within the cycle: Two cycles can have same amplitude but opposite phase. Phase is what lets you infer “approaching peak vs trough” behavior. Dominant cycle selection The script chooses the dominant cycle as the period with the highest amplitude among the tested periods: domIdx = argmax(amp ) domAmp = max amplitude domPhase = phase at domIdx This dominant cycle is used for: Spectrogram history matrix (chart symbol). Dominant cycle oscillator. Data window outputs (dominant period, oscillator value). Spectrum View: what you see and how to read it In Spectrum mode, the indicator draws a frequency snapshot for up to five instruments. Each instrument gets a spectrum line (or bars/area depending on style) plotted across the 8 periods on the x-axis, with amplitude (0–100) on the y-axis. X-axis meaning Each x position corresponds to a period (5 → 120 bars). You are not looking at “frequency in Hz.” You are looking at “period in bars,” which is more intuitive in trading. Y-axis meaning Amplitude is a scaled measure of how strongly that period is present in the recent normalized data. Higher means stronger. Plot styles Waveform: connects amplitude points into a continuous shape, best for seeing spectrum shape. Bars: draws vertical bars per period, best for quick comparison. Area: similar to waveform but filled toward baseline for emphasis. Dominant peaks and phase direction labels The script highlights dominant cycles per symbol (if enabled): If max amplitude > 20, it labels that peak with the symbol name. If Show Phase Direction is enabled, it appends ▲ or ▼. Phase direction logic: rising = sin(phase) < 0 ▲ means cycle is in a “rising” phase segment ▼ means cycle is in a “falling” phase segment This is not “price will rise now.” It is “the dominant cycle’s instantaneous phase suggests you are on the upward vs downward half of that oscillation.” In real markets, you use this as context, not as a standalone trade trigger. It also draws small ▲/▼ markers on secondary peaks (amp > 15) to show phase direction of other meaningful cycles, giving you a richer picture than “one dominant period.” Resonance Zones: cross-asset harmonic alignment Resonance is where this tool becomes more than a single-chart curiosity. What resonance means here A resonance zone is flagged when at least 3 out of 5 instruments have strong amplitude at the same tested period. Mechanically: For each period i: - Count instruments with amp > 30 - If count >= 3, mark resonance at that period When resonance is detected: A vertical highlight box is drawn behind that period. A ⚡ marker is printed at the top. Interpretation: Multiple assets are expressing a similar cycle length at the same time. This can indicate macro rhythm, shared liquidity timing, or cross-market synchronization. This is especially useful when your instrument set includes: Rates proxy (TLT), commodities (oil, gold), and crypto indices. You can visually spot when markets are “vibrating” together at a shared period. Resonance is not automatically bullish or bearish. It is telling you “cycle length agreement,” which can help with timing models and contextual trade planning. Spectrogram View: frequency over time Spectrum mode is a snapshot. Spectrogram mode adds time evolution. What a spectrogram is A spectrogram is a 2D heatmap where: Rows = different periods (frequency bands). Columns = time history (bars ago → now). Color = amplitude strength. This allows you to see: Which cycles are persistent vs fleeting. When dominant cycle shifts occur (energy moves from one period to another). Cycle regime transitions (short cycles dominating in chop vs longer cycles dominating in trend). How the script builds the spectrogram matrix It maintains a matrix with: NUM_PERIODS rows (8 periods) histBars columns (history length) Each bar: Remove the oldest column. Append the newest amplitude array from chartSpec. So the spectrogram is always a rolling history of the chart symbol’s cycle amplitudes. It does not attempt to store five symbols (too heavy), it focuses on the active chart for time evolution. Heat coloring Amplitude values map to a custom gradient: Low = dark blue Mid = blue/cyan to orange High = yellow This makes dominant energy bands visually obvious. A stable bright band means persistent cycle dominance. Dominant Cycle Oscillator: phase mapped to a 0–100 oscillator The oscillator is derived from the dominant cycle phase (chart symbol): oscRaw = cos(domPhase) oscValue = 50 + 50 * oscRaw (maps -1..1 into 0..100) Interpretation: When cos(phase) ≈ +1, oscillator near 100 (cycle peak zone). When cos(phase) ≈ -1, oscillator near 0 (cycle trough zone). Midline 50 corresponds to the quarter-cycle transition points. It also colors the oscillator by phase direction: oscRising = sin(domPhase) < 0 Rising phase = green-ish Falling phase = red-ish This gives you a clean timing reference: The dominant period tells you the cycle length. The oscillator tells you where you are within that cycle. It is not forecasting price. It is telling you the current phase position of the strongest detected cycle component. Alerts and practical timing usage Alerts are based on the oscillator: Cross above 80: dominant cycle entering peak zone. Cross below 20: dominant cycle entering trough zone. Cross 50: midline cross (phase transition). In practice, you use these as “timing context” alerts, for example: If your trend model is bullish and cycle oscillator enters trough zone, it can hint at a favorable pullback timing window. If you are mean-reversion trading and cycle peak zone aligns with resistance, that confluence matters. Again: cycle timing needs structure confirmation. The oscillator alone is not a trade system. Multi-instrument design and non-repaint behavior The indicator requests five external instruments via request.security. It uses: close with lookahead_on This forces the data to be “previous confirmed close” so the spectral calculations do not repaint intra-bar. That matters because cycle measures can change drastically within a bar if you let them use live values. So: Spectra for external symbols are based on confirmed historical closes. Chart symbol spectrogram and oscillator are also stable in the sense they depend on confirmed series values (dominant phase updates bar-to-bar). Key parameters and how they change behavior Analysis Lookback Affects normalization and the DFT window cap: Higher lookback stabilizes mean/stdev normalization and reduces random shifts. Lower lookback makes the tool more reactive but more prone to regime noise. Because the inner DFT accumulation caps at 100 bars, very high lookback mostly affects normalization rather than the raw projection length. Sensitivity Scales displayed amplitude: Higher sensitivity makes peaks stand out more. Lower sensitivity compresses amplitude. It is a display control, not a physics constant. View Mode Spectrum: cross-asset snapshot comparison, resonance detection. Spectrogram: time evolution of cycle energy for chart symbol. Show Phase Direction Adds ▲/▼ markers derived from sin(phase). Useful for quick cycle position intuition, but do not treat ▲ as “buy.” Show Resonance Zones Marks periods where many instruments share strong energy. Useful for macro rhythm alignment. Highlight Dominant Cycles Labels peaks. If you disable it, the chart becomes cleaner but less informative. Spectrogram History Controls how many columns are stored. Higher makes a longer heatmap but costs more drawing. Limitations and what not to assume This tool is honest DSP applied to market data, but market data is not a stationary sine wave generator. Key limitations: Cycles drift. Dominant period can shift as regime changes. The tool only tests 8 candidate periods. If the true dominant period is 30, it will express as energy near 34 or distributed across neighbors. Normalization helps comparability, but does not make amplitude “absolute truth.” DFT assumes a stable frequency over the window. Markets often violate that. Phase-based oscillators are timing aids, not predictors. This is why the indicator is best used as: Context for entries/exits, not a standalone system. A way to see when cycle energy concentrates or disperses. A way to detect when multiple markets share a timing rhythm. How to use it properly (workflows) 1) Cycle regime identification If short periods (5–13) dominate, market is often choppy, reactive, and mean-reverting. If mid periods (21–55) dominate, market often shows swing structure. If long periods (89–120) dominate, market can be in slower macro drift, trend legs, or compressed volatility regimes. 2) Timing layer for an existing strategy Use your trend model to decide direction. Use dominant cycle oscillator to decide timing within that direction. Use spectrogram to avoid trading when dominant period is unstable or flipping rapidly. 3) Cross-asset confirmation If you see resonance at a period, watch whether your main instrument is also showing strength there. Resonance can justify holding a cycle-based timing thesis with more confidence because it is not isolated. 4) Expectation management If the spectrum is flat (no peaks above threshold), that is information: No clean dominant cycle, randomness dominates. Cycle-based timing will be unreliable. Summary Harmonic Frequency Visualizer uses a targeted Discrete Fourier Transform across predefined cycle periods to measure amplitude and phase of cyclical components in price. It supports multi-instrument spectrum comparison, resonance detection when several markets share strong energy at the same periods, and a spectrogram heatmap for the chart instrument showing how cycle dominance evolves over time. A dominant cycle oscillator maps phase into a 0–100 timing readout with alerts for peak/trough/midline transitions. It is a cycle context engine designed to complement trend, structure, and risk models, not replace them.
Pine Script® 인디케이터
BackQuant의
Luminous Trend Wave [Pineify]``` Luminous Trend Wave - Hull MA Based Normalized Momentum Oscillator The Luminous Trend Wave (Pineify) is a momentum oscillator designed to provide clear, responsive trend signals while minimizing the lag commonly associated with traditional momentum indicators. By combining Hull Moving Average (HMA) calculations with ATR-based normalization and hyperbolic tangent transformation, LTW delivers a bounded oscillator that works consistently across different assets and timeframes. Key Features Hull Moving Average foundation for reduced lag trend detection ATR normalization for universal applicability across all markets Bounded output range (-100 to +100) using mathematical tanh transformation Dynamic gradient coloring that reflects momentum intensity Built-in signal line for momentum confirmation Automatic alerts for trend reversals and momentum shifts How It Works The indicator operates through a four-stage calculation process: Trend Basis Calculation: The indicator first calculates a Hull Moving Average (HMA) of the closing price. HMA was chosen specifically because it provides significantly less lag compared to Simple or Exponential Moving Averages while maintaining smoothness. This allows the oscillator to respond quickly to genuine price movements. Distance Measurement: The raw distance between the current close price and the HMA trend line is calculated. This distance represents how far price has deviated from its smoothed trend. ATR Normalization: The distance is then divided by the Average True Range (ATR) over the same lookback period. This normalization step is crucial - it makes the oscillator readings comparable across different assets regardless of their price levels or typical volatility. A stock trading at $500 and one at $5 will produce equivalent readings when their relative movements are similar. Tanh Transformation: Finally, the normalized value is passed through a hyperbolic tangent function scaled by a sensitivity multiplier. The mathematical formula (e^2x - 1) / (e^2x + 1) naturally bounds the output between -100 and +100, preventing extreme spikes while preserving the directional information. Trading Ideas and Insights Zero Line Crossovers: When the oscillator crosses above zero, it indicates a shift from bearish to bullish momentum. Conversely, crossing below zero signals bearish momentum. These crossovers can be used as entry triggers when confirmed by other analysis. Overbought/Oversold Levels: Readings above +80 suggest overbought conditions where price has extended significantly above its trend. Readings below -80 indicate oversold conditions. These extremes often precede mean reversion moves. Signal Line Divergence: When the main oscillator (histogram) is above the signal line, momentum is increasing. When below, momentum is decreasing. This relationship helps identify the strength of the current move. Momentum Fading: The indicator automatically fades the color intensity when the oscillator value is closer to the signal line than to the extremes, visually indicating weakening momentum before potential reversals. How Multiple Indicators Work Together LTW integrates three distinct technical concepts into a cohesive system: Hull MA + ATR Integration: The Hull Moving Average provides the trend direction while ATR provides the volatility context. Together, they answer not just "where is the trend?" but "how significant is the current deviation relative to normal market movement?" Mathematical Bounding + Visual Mapping: The tanh transformation ensures readings stay within predictable bounds, while the gradient coloring maps these bounded values to intuitive visual feedback. Strong bullish readings appear in bright green, strong bearish in bright red, with smooth transitions between. Oscillator + Signal Line System: Similar to MACD's relationship between the MACD line and signal line, LTW uses a WMA-smoothed signal line to filter noise and confirm momentum direction. The interplay between the faster oscillator and slower signal creates actionable crossover signals. Unique Aspects Universal Normalization: Unlike many oscillators that produce different reading ranges on different assets, LTW's ATR normalization ensures consistent interpretation whether trading forex, crypto, stocks, or commodities. Sensitivity Control: The sensitivity parameter allows traders to adjust how aggressively the oscillator responds to price changes. Higher values make it more responsive (useful for scalping), while lower values smooth out noise (better for swing trading). Visual Momentum Feedback: The gradient coloring and transparency adjustments provide immediate visual feedback about trend strength without requiring traders to interpret numerical values. How to Use Add the indicator to your chart - it displays in a separate pane below price. Watch for zero line crossovers as primary trend signals. Bullish when crossing above, bearish when crossing below. Use the ±80 levels as caution zones where reversals become more likely. Monitor the relationship between the histogram and signal line - histogram above signal indicates strengthening momentum. Pay attention to color intensity - faded colors indicate weakening momentum and potential reversal zones. Set alerts for automated notifications on trend changes and momentum shifts. Customization Trend Lookback (default: 21): Controls the HMA period. Lower values increase responsiveness but may generate more false signals. Higher values provide smoother trends but with more lag. Signal Smoothing (default: 5): Adjusts the WMA period for the signal line. Higher values create a slower signal line with fewer crossovers. Sensitivity (default: 1.5): Multiplier for the tanh transformation. Increase for more reactive signals, decrease for smoother readings. Colors: Fully customizable bullish and bearish colors to match your chart theme. Gradients: Toggle gradient coloring on/off based on preference. Conclusion The Luminous Trend Wave indicator offers traders a mathematically sound approach to momentum analysis. By combining the low-lag properties of Hull Moving Average with ATR-based normalization and bounded output transformation, LTW provides consistent, interpretable signals across any market. The visual feedback system makes trend strength immediately apparent, while the signal line crossovers offer clear entry and exit timing. Whether used as a standalone tool or combined with price action analysis, LTW helps traders identify trend direction, momentum strength, and potential reversal zones with clarity. ```
Pine Script® 인디케이터
Pineify의
ADX Volatility Waves [BOSWaves]ADX Volatility Waves - Trend-Weighted Volatility Mapping with State-Based Wave Transitions Overview ADX Volatility Waves is a regime-aware volatility framework designed to map statistically significant price extremes through adaptive wave structures driven by trend strength. Rather than treating volatility as a static dispersion metric, this indicator conditions all volatility expansion, contraction, and zone placement on ADX-derived trend intensity. Price behavior is interpreted through wave-like transitions between balance, expansion, and exhaustion states rather than isolated band interactions. The result is a dynamic, gradient-based wave system that visually encodes volatility cycles and regime shifts in real time, allowing traders to contextualize price movement within trend-weighted volatility waves. Price is evaluated not by static thresholds, but by its position and progression within adaptive volatility waves shaped by directional strength. Conceptual Framework ADX Volatility Waves is built on the premise that volatility unfolds in waves, not straight lines. Traditional volatility tools identify dispersion but fail to account for how volatility behaves differently across trend regimes. By embedding ADX directly into volatility construction, this indicator ensures that volatility waves expand during strong directional phases and compress during weak or transitioning regimes. Three guiding principles define the framework: Volatility must be conditioned on trend strength Extremes occur within zones, not at lines Signals should emerge from completed wave transitions, not instantaneous touches This reframes analysis from reactive mean-reversion toward regime-aware wave interpretation. Theoretical Foundation The indicator fuses directional movement theory with statistical volatility modeling. Bollinger-derived dispersion provides the structural base, while ADX normalization controls the amplitude of volatility waves. As ADX increases, volatility waves widen and deepen; as ADX weakens, waves compress and tighten around equilibrium. From this foundation, extended upper and lower wave zones are constructed and smoothed to represent statistically significant expansion and contraction phases. At its core are three interacting systems: ADX-Controlled Volatility Engine : Standard deviation is dynamically scaled using normalized ADX values, producing trend-weighted volatility waves. Wave Zone Construction : Smoothed volatility boundaries are offset and expanded to form upper and lower wave zones, defining overextension and compression regions. State-Based Wave Transition Logic : Signals occur only after price completes a full wave cycle: expansion into an extreme wave zone followed by a confirmed return to equilibrium. This structure ensures that signals reflect completed volatility waves, not transient noise. How It Works ADX Volatility Waves processes price action through layered wave mechanics: Trend-Weighted Volatility Calculation : Volatility boundaries are dynamically adjusted using ADX influence, allowing wave amplitude to scale with trend strength. Structural Smoothing : Volatility boundaries are smoothed to stabilize wave geometry and reduce short-term distortions. Wave Offset & Expansion : Upper and lower wave zones are positioned beyond equilibrium and expanded proportionally to volatility range, forming clearly defined expansion waves. Gradient Wave Depth Mapping : Each wave zone is subdivided into multiple gradient layers, visually encoding increasing extremity as price moves deeper into a wave. Wave State Tracking & Cooldown Control : The system tracks prior wave occupancy, enforces neutral stabilization periods, and applies cooldowns to prevent overlapping wave signals. Compression Detection : Volatility width monitoring identifies compression phases, highlighting conditions where new volatility waves are likely to form. Together, these processes create a continuous, adaptive wave map of volatility behavior. Interpretation ADX Volatility Waves reframes market reading around volatility cycles: Upper Volatility Waves (Red Gradient) : Represent upside expansion phases. Deeper wave penetration indicates increased overextension relative to trend-adjusted volatility. Lower Volatility Waves (Green Gradient) : Represent downside expansion phases. Sustained presence signals pressure, while exits toward balance suggest wave completion. Equilibrium Zone : The neutral region between volatility waves. Confirmed re-entry into this zone marks the completion of a wave cycle and forms the basis for BUY and SELL signals. Regime Context via ADX : Strong ADX regimes widen waves, reducing premature reversal signals. Weak ADX regimes compress waves, increasing sensitivity to reversion. Wave progression and completion matter more than single-bar interactions. Signal Logic & Visual Cues ADX Volatility Waves produces single-entry BUY and SELL labels as its visual cues, plotted only when price first enters a volatility wave zone after the defined cooldown period. Buy Signal (Bottom Zone Entry) : A BUY label appears when price enters the lower volatility wave (oversold zone). This highlights potential expansion into undervalued extremes, providing visual context for trend assessment rather than a guaranteed execution trigger. Sell Signal (Top Zone Entry) : A SELL label appears when price enters the upper volatility wave (overbought zone). This marks potential overextension into upper volatility extremes, serving as a contextual indicator of trend stress. All labels respect cooldown tracking to prevent clustering. Alerts are tied directly to these zone-entry signals, and a separate alert monitors volatility squeezes for awareness of compression periods. Strategy Integration ADX Volatility Waves integrates cleanly into volatility-aware trading frameworks: Wave Context Mapping : Use wave depth to assess expansion and exhaustion risk rather than forcing immediate entries. Transition-Based Execution : Prioritize BUY and SELL signals formed after confirmed wave completion. Trend-Regime Filtering : In strong ADX regimes, treat waves as continuation pressure. In weak regimes, favor completed wave reversions. Volatility Cycle Awareness : Monitor compression phases to anticipate the emergence of new volatility waves. Multi-Timeframe Alignment : Apply higher-timeframe ADX regimes to contextualize lower-timeframe wave behavior. Technical Implementation Details Core Engine : ADX-normalized volatility expansion Wave System : Smoothed, offset, expanded volatility waves Visualization : Multi-layer gradient wave zones Signal Logic : State-based wave transitions with cooldown enforcement Alerts : Wave entry, wave completion, volatility compression Performance Profile : Lightweight, real-time optimized overlay Optimal Application Parameters Timeframe Guidance: 1 - 5 min : Short-term volatility waves and intraday transitions 15 - 60 min : Structured intraday wave cycles 4H - Daily : Macro volatility regimes and expansion phases Suggested Baseline Configuration: BB Length : 20 BB StdDev : 1.5 ADX Length : 14 ADX Influence : 0.8 Wave Offset : 1.0 Wave Width : 1.0 Neutral Confirmation : 5 bars These suggested parameters should be used as a baseline; their effectiveness depends on the asset volatility, liquidity, and preferred entry frequency, so fine-tuning is expected for optimal performance. Performance Characteristics High Effectiveness: Markets exhibiting rhythmic volatility expansion and contraction Assets with responsive ADX regime behavior Reduced Effectiveness: Erratic, news-driven price action Illiquid markets with distorted volatility metrics Integration Guidelines Confluence : Combine with BOSWaves structure or trend tools Discipline : Respect wave completion and cooldown logic Risk Framing : Interpret wave depth probabilistically, not predictively Regime Awareness : Always contextualize waves within ADX strength Disclaimer ADX Volatility Waves is a professional-grade volatility and regime-mapping tool. It does not predict price and does not guarantee profitability. Performance depends on market conditions, parameter calibration, and disciplined execution. BOSWaves recommends using this indicator as part of a comprehensive analytical framework incorporating trend, volatility, and structural context.
Pine Script® 인디케이터
BOSWaves의
16
Elliott Wave Full Fractal System v2.0Elliott Wave Full Fractal System v2.0 – Q.C. FINAL (Guaranteed R/R) Elliott Wave Full Fractal System is a multi-timeframe wave engine that automatically labels Elliott impulses and ABC corrections, then builds a rule-based, ATR-driven risk/reward framework around the “W3–W4–W5” leg. “Guaranteed R/R” here means every order is placed with a predefined stop-loss and take-profit that respect a minimum Reward:Risk ratio – it does not mean guaranteed profits. Core Idea This strategy turns a full fractal Elliott Wave labelling engine into a systematic trading model. It scans fractal pivots on three wave degrees (Primary, Intermediate, Minor) to detect 5-wave impulses and ABC corrections. A separate “Trading Degree” pivot stream, filtered by a 200-EMA trend filter and ATR-based dynamic pivots, is then used to find W4 pullback entries with a minimum, user-defined Reward:Risk ratio. Default Properties & Risk Assumptions The backtest uses realistic but conservative defaults: // Default properties used for backtesting strategy( "Elliott Wave Full Fractal System - Q.C. FINAL (Guaranteed R/R)", overlay = true, initial_capital = 10000, // realistic account size default_qty_type = strategy.percent_of_equity, default_qty_value = 1, // 1% risk per trade commission_type = strategy.commission.cash_per_contract, commission_value = 0.005, // example stock commission slippage = 0 // see notes below ) Account size: 10,000 (can be changed to match your own account). Position sizing: 1% of equity per trade to keep risk per idea sustainable and aligned with TradingView’s recommendations. Commission: 0.005 cash per contract/share as a realistic example for stock trading. Slippage: set to 0 in code for clarity of “pure logic” backtesting. Real-life trading will experience slippage, so users should adjust this according to their market and broker. Always re-run the backtest after changing any of these values, and avoid using high risk fractions (5–10%+) as that is rarely sustainable. 1. Full Fractal Wave Engine The script builds and maintains four pivot streams using ATR-adaptive fractals: Primary Degree (Macro Trend): Captures the large swings that define the major trend. Labels ①–⑤ and ⒶⒷⒸ using blue “Circle” labels and thicker lines. Intermediate Degree (Trading Degree): Captures the medium swings (swing-trading horizon). Uses teal labels ( (1)…(5), (A)(B)(C) ). Minor Degree (Micro Structure): Tracks short-term swings inside the larger waves. Uses red roman numerals (i…v, a b c). ABC Corrections (Optional): When enabled, the engine tries to detect standard A–B–C corrective structures that follow a completed 5-wave impulse and plots them with dashed lines. Each degree uses a dynamic pivot lookback that expands when ATR is above its EMA, so the system naturally requires “stronger” pivots in volatile environments and reacts faster in quiet conditions. 2. Theory Rules & Strict Mode Normal Mode: More permissive detection. Designed to show more wave structures for educational / exploratory use. Strict Mode: Enforces key Elliott constraints: Wave 3 not shorter than waves 1 and 5. No invalid W4 overlap with W1 (for standard impulses). ABC Logic: After a confirmed bullish impulse, the script expects a down-up-down corrective pattern (A,B,C). After a bearish impulse, it looks for up-down-up. 3. Trend Filter & Pivots EMA Trend Filter: A configurable EMA (default 200) is used as a non-wave trend filter. Price above EMA → Only long setups are considered. Price below EMA → Only short setups are considered. ATR-Adaptive Pivots: The pivot engine scales its left/right bars based on current ATR vs ATR EMA, making waves and trading pivots more robust in volatile regimes. 4. Dynamic Risk Management (Guaranteed R/R Engine) The trading engine is designed around risk, not just pattern recognition: ATR-Based Stop: Stop-loss is placed at: Entry ± ATR × Multiplier (user-configurable, default 2.0). This anchors risk to current volatility. Minimum Reward:Risk Ratio: For each setup, the script: Computes the distance from entry to stop (risk). Projects a take-profit target at risk × min_rr_ratio away from entry. Only accepts the setup if risk is positive and the required R:R ratio is achievable. Result: Every order is created with both TP and SL at a predefined distance, so each trade starts with a known, minimum Reward:Risk profile by design. “Guaranteed R/R” refers exclusively to this order placement logic (TP/SL geometry), not to win-rate or profitability. 5. Trading Logic – W3–W4–W5 Pattern The Trading pivot stream (separate from visual wave degrees) looks for a simple but powerful pattern: Bullish structure: Sequence of pivots forms a higher-high / higher-low pattern. Price is above the EMA trend filter. A strong “W3” leg is confirmed with structure rules (optionally stricter in Strict mode). Entry (Long – W4 Pullback): The “height” of W3 is measured. Entry is placed at a configurable Fibonacci pullback (default 50%) inside that leg. ATR-based stop is placed below entry. Take-profit is projected to satisfy min Reward:Risk. Bearish structure: Mirrored logic (lower highs/lows, price below EMA, W3 down, W4 retrace up, W5 continuation down). Once a valid setup is found, the script draws a colored box around the entry zone and a label describing the type of signal (“LONG SETUP” or “SHORT SETUP”) with the suggested limit price. 6. Orders & Execution Entry Orders: The strategy uses limit orders at the computed W4 level (“Sniper Long” or “Sniper Short”). Exits: A single strategy.exit() is attached to each entry with: Take-profit at the projected minimum R:R target. Stop-loss at ATR-based level. One Trade at a Time: New setups are only used when there is no open position (strategy.opentrades == 0) to keep the logic clear and risk contained. 7. Visual Guide on the Chart Wave Labels: Primary: ①,②,③,④,⑤, ⒶⒷⒸ Intermediate: (1)…(5), (A)(B)(C) Minor: i…v, a b c Trend EMA: Single blue EMA showing the dominant trend. Setup Boxes: Green transparent box → long entry zone. Red transparent box → short entry zone. Labels: “LONG SETUP / SHORT SETUP” labels mark the proposed limit entry with price. 8. How to Use This Strategy Attach the strategy to your chart Choose your market (stocks, indices, FX, crypto, futures, etc.) and timeframe (for example 1h, 4h, or Daily). Then add the strategy to the chart from your Scripts list. Start with the default settings Leave all inputs on their defaults first. This lets you see the “intended” behaviour and the exact properties used for the published backtest (account size, 1% risk, commission, etc.). Study the wave map Zoom in and out and look at the three wave degrees: Blue circles → Primary degree (big picture trend). Teal (1)…(5) → Intermediate degree (swing structure). Red i…v → Minor degree (micro waves). Use this to understand how the engine is interpreting the Elliott structure on your symbol. Watch for valid setups Look for the coloured boxes and labels: Green box + “LONG SETUP” label → potential W4 pullback long in an uptrend. Red box + “SHORT SETUP” label → potential W4 pullback short in a downtrend. Only trades in the direction of the EMA trend filter are allowed by the strategy. Check the Reward:Risk of each idea For each setup, inspect: Limit entry price. ATR-based stop level. Projected take-profit level. Make sure the minimum Reward:Risk ratio matches your own rules before you consider trading it. Backtest and evaluate Open the Strategy Tester: Verify you have a decent sample size (ideally 100+ trades). Check drawdowns, average trade, win-rate and R:R distribution. Change markets and timeframes to see where the logic behaves best. Adapt to your own risk profile If you plan to use it live: Set Initial Capital to your real account size. Adjust default_qty_value to a risk level you are comfortable with (often 0.5–2% per trade). Set commission and slippage to realistic broker values. Re-run the backtest after every major change. Use as a framework, not a signal machine Treat this as a structured Elliott/R:R framework: Filter signals by higher-timeframe trend, major S/R, volume, or fundamentals. Optionally hide some wave degrees or ABC labels if you want a cleaner chart. Combine the system’s structure with your own trade management and discretion. Best Practices & Limitations This is an approximate Elliott Wave engine based on fractal pivots. It does not replace a full discretionary Elliott analysis. All wave counts are algorithmic and can differ from a manual analyst’s interpretation. Like any backtest, results depend heavily on: Symbol and timeframe. Sample size (more trades are better). Realistic commission/slippage settings. The 0-slippage default is chosen only to show the “raw logic”. In real markets, slippage can significantly impact performance. No strategy wins all the time. Losing streaks and drawdowns will still occur even with a strict R:R framework. Disclaimer This script is for educational and research purposes only and does not constitute financial advice or a recommendation to buy or sell any security. Past performance, whether real or simulated, is not indicative of future results. Always test on multiple symbols/timeframes, use conservative risk, and consult your financial advisor before trading live capital.
Pine Script™ 전략
mbedaiwi2의
Ichimoku PourSamadi Signal [TradingFinder] KijunSen Magic Number🔵 Introduction The Ichimoku Kinko Hyo system is one of the most comprehensive market analysis tools ever created. Developed by Goichi Hosoda, a Japanese journalist in the 1930s, its purpose was to allow traders to recognize the balance between price, time, and momentum at a single glance. (In Japanese, Ichimoku literally means “one look.”) At the core of the system lie five key components: Tenkan-sen (Conversion Line), Kijun-sen (Baseline), Chikou Span (Lagging Line), and the two leading spans, Senkou Span A and Senkou Span B, which together form the well-known Kumo or cloud representing both temporal structure and equilibrium zones in the market. Although Ichimoku is commonly used to identify trends and support/resistance levels, a deeper layer of time philosophy exists within it. Ichimoku was not designed solely for price analysis but equally for time analysis. In the classical model, the numerical cycles 9, 26, 52 reflect the natural rhythm of the market originally based on the Tokyo Stock Exchange’s trading schedule in the 1930s. These values repeat across the system’s calculations, forming the foundation of Ichimoku’s time symmetry where price and time ultimately seek equilibrium. In recent years, modern analysts have explored new approaches to extract time-based turning points from Ichimoku’s structure. One such approach is the analysis of flat segments on the Kijun-sen and Senkou B lines. Whenever one of these lines remains flat for a period, it signals temporary balance between buyers and sellers; when the flat breaks, the market exits equilibrium and a new cycle begins. This indicator is built precisely upon that philosophy. Following the timing methodology introduced by M.A. Poursamadi, the focus shifts away from price signals and line crossovers toward identifying flat periods on Kijun-sen (period 52) as time anchors. From the first candle that changes the line’s slope, the tool begins a temporal count using a fixed sequence of key numbers: 5, 9, 13, 17, 26, 35, 43, 52, 63, 72, 81, 90. Derived from both classical Ichimoku cycles and empirical testing, these numbers mark potential timing nodes where a market wave may end, a correction may begin, or a new leg may form. Thus, this method serves not merely as another Ichimoku tool but as a temporal metronome for market structure a way to visualize moments when the market is ready to change rhythm, often before candles reveal it. 🔵 How to Use The Kijun Timing BoX is built entirely on Ichimoku’s concept of time analysis. Its core idea is that within every flat segment of the Kijun-sen, the market enters a temporary balance between opposing forces. When that flat breaks, a new time cycle begins. From that first breakout candle, the indicator starts counting forward through the predefined time sequence(5, 9, 13, 17, 26, 35, 43, 52, 63, 72, 81, 90). This counting framework creates a temporal map of market behavior, where each number represents an area where meaningful price fluctuations often occur. A “meaningful fluctuation” does not necessarily imply reversal or continuation; rather, it marks a moment when the market’s internal energy balance shifts, typically visible as noticeable reactions on lower timeframes. 🟣 Identifying the Anchor Point The first step is recognizing a valid flat zone on the Kijun-sen. When this line remains flat for several candles and then changes slope, the indicator marks that bar as the Anchor, initiating the time count. From that point onward, vertical gray lines appear at each interval in the key-number sequence, visualizing the time nodes ahead. 🟣 Reading the Timing Lines Each numbered line represents a timing node a temporal point where a change in price rhythm is statistically more likely to occur. At these nodes, the market may : Enter a consolidation or minor correction phase. Develop range-bound movement. Or simply alter the speed and intensity of its move. These behaviors do not imply a specific direction; they only highlight zones where time-based activity tends to cluster, giving traders a clearer view of cyclical rhythm. 🟣 Applying Time Analysis The indicator’s primary use is to observe temporal order, not to predict price direction. By tracking the distance between Anchors and the reactions that appear near major timing lines, traders can empirically identify each market’s characteristic rhythm—its own time DNA. For example, one asset may consistently show significant fluctuations around the 13- and 26-bar marks,while another might react closer to 9 or 52. Recognizing such patterns helps traders understand how long typical cycles last before new phases of volatility emerge. 🟣 Combining with Other Tools The indicator does not generate buy/sell signals on its own. Its best use is in combination with price- or structure-based methods, to see whether meaningful price reactions occur around the same timing nodes. In practice, it helps distinguish structured time-based fluctuations from random, noise-driven moves an insight often overlooked in conventional market analysis. 🔵 Settings 🟣 Logical Settings KijunSen Period : Defines the baseline period used for timing analysis. Default = 52. It is the main line for detecting flats and generating time anchors. Flat Event Filter : Controls how flat segments are validated before triggering a new timing event. All : Every flat triggers a new Timing Box. Automatic : Only flats longer than the historical average are used (recommended). Custom : User manually defines the minimum flat length via Custom Count. Update Timing Analysis BoX Per Event : If enabled, a new Timing Box is drawn each time a new flat event occurs. If disabled, the box completes its 90-bar window before refreshing. 🟣 Ichimoku Settings TenkanSen Period : Defines the period for the Conversion Line (Tenkan-sen). Default = 9. KijunSen Period : Sets the standard Ichimoku baseline (not the timing line). Default = 26. Span B Period : Defines the period for Senkou Span B, the slower cloud boundary. Default = 52. Shift Lines : Offsets cloud projection into the future. Default = 26. 🟣 Display Settings Users can show or hide all Ichimoku lines Tenkan-sen, Kijun-sen, Chikou Span, Span A, and Span B as well as the Ichimoku Cloud. They can also customize the color of each element to match personal chart preferences and improve visibility. 🔵 Conclusion This analytical approach transforms Ichimoku’s time philosophy into a visual and measurable framework. A flat Kijun-sen represents a moment of market equilibrium; when its slope shifts, a new temporal cycle begins. The purpose is not to forecast price direction but to highlight periods when meaningful fluctuations are more likely to develop. Through this perspective, traders can observe the hidden rhythm of market time and expand their analysis beyond price into a broader time-cycle dimension. Ultimately, the method revives Ichimoku’s original principle: the market can only be truly understood through the simultaneous harmony of price, time, and balance.
Pine Script® 인디케이터
TFlab의
5
Guppy MMA [Alpha Extract]A sophisticated trend-following and momentum assessment system that constructs dynamic trader and investor sentiment channels using multiple moving average groups with advanced scoring mechanisms and smoothed CCI-style visualizations for optimal market trend analysis. Utilizing enhanced dual-group methodology with threshold-based trend detection, this indicator delivers institutional-grade GMMA analysis that adapts to varying market conditions while providing high-probability entry and exit signals through crossover and extreme value detection with comprehensive visual mapping and alert integration. 🔶 Advanced Channel Construction Implements dual-group architecture using short-term and long-term moving averages as foundation points, applying customizable MA types to reduce noise and score-based averaging for sentiment-responsive trend channels. The system creates trader channels from shorter periods and investor channels from longer periods with configurable periods for optimal market reaction zones. // Core Channel Calculation Framework maType = input.string("EMA", title="Moving Average Type", options= ) // Short-Term Group Construction stMA1 = ma(close, st1, maType) stMA2 = ma(close, st2, maType) // Long-Term Group Construction ltMA1 = ma(close, lt1, maType) ltMA2 = ma(close, lt2, maType) // Smoothing Application smoothedavg = ma(overallAvg, 10, maType) 🔶 Volatility-Adaptive Zone Framework Features dynamic score-based averaging that expands sentiment signals during strong trend periods and contracts during consolidation phases, preventing false signals while maintaining sensitivity to genuine momentum shifts. The dual-group averaging system optimizes zone boundaries for realistic market behavior patterns. // Dynamic Sentiment Adjustment shortTermAvg = (stScore1 + stScore2 + ... + stScore11) / 11 longTermAvg = (ltScore1 + ltScore2 + ... + ltScore11) / 11 // Dual-Group Zone Optimization overallAvg = (shortTermAvg + longTermAvg) / 2 allMAAvg = (shortTermAvg * 11 + longTermAvg * 11) / 22 🔶 Step-Like Boundary Evolution Creates threshold-based trend boundaries that update on smoothed average changes, providing visual history of evolving bullish and bearish levels with performance-optimized threshold management limited to key zones for clean chart presentation and efficient processing. 🔶 Comprehensive Signal Detection Generates buy and sell signals through sophisticated crossover analysis, monitoring smoothed average interaction with zero-line and thresholds for high-probability entry and exit identification. The system distinguishes between trend continuation and reversal patterns with precision timing. 🔶 Enhanced Visual Architecture Provides translucent zone fills with gradient intensity scaling, threshold-based historical boundaries, and dynamic background highlighting that activates upon trend changes. The visual system uses institutional color coding with green bullish zones and red bearish zones for intuitive market structure interpretation. 🔶 Intelligent Zone Management Implements automatic trend relevance filtering, displaying signals only when smoothed average proximity warrants analysis attention. The system maintains optimal performance through smart averaging management and historical level tracking with configurable MA periods for various market conditions. 🔶 Multi-Dimensional Analysis Framework Combines trend continuation analysis through threshold crossovers with momentum detection via extreme markers, providing comprehensive market structure assessment suitable for both trending and ranging market conditions with score-normalized accuracy. 🔶 Advanced Alert Integration Features comprehensive notification system covering buy signals, sell signals, strong bull conditions, and strong bear conditions with customizable alert conditions. The system enables precise position management through real-time notifications of critical sentiment interaction events and zone boundary violations. 🔶 Performance Optimization Utilizes efficient MA smoothing algorithms with configurable types for noise reduction while maintaining responsiveness to genuine market structure changes. The system includes automatic visual level cleanup and performance-optimized visual rendering for smooth operation across all timeframes. This indicator delivers sophisticated GMMA-based market analysis through score-adaptive averaging calculations and intelligent group construction methodology. By combining dynamic trader and investor sentiment detection with advanced signal generation and comprehensive visual mapping, it provides institutional-grade trend analysis suitable for cryptocurrency, forex, and equity markets. The system's ability to adapt to varying market conditions while maintaining signal accuracy makes it essential for traders seeking systematic approaches to trend trading, momentum reversals, and sentiment continuation analysis with clearly defined risk parameters and comprehensive alert integration.
Pine Script® 인디케이터
AlphaExtract의
2
Fibonacci Cycle Finder🟩 Fibonacci Cycle Finder is an indicator designed to explore Fibonacci-based waves and cycles through visualization and experimentation, introducing a trigonometric approach to market structure analysis. Unlike traditional Fibonacci tools that rely on static horizontal levels, this indicator incorporates the dynamic nature of market cycles, using adjustable wavelength, phase, and amplitude settings to visualize the rhythm of price movements. By applying a sine function, it provides a structured way to examine Fibonacci relationships in a non-linear context. Fibonacci Cycle Finder unifies Fibonacci principles with a wave-based method by employing adjustable parameters to align each wave with real-time price action. By default, the wave begins with minimal curvature, preserving the structural familiarity of horizontal Fibonacci retracements. By adjusting the input parameters, the wave can subtly transition from a horizontal line to a more pronounced cycle,visualizing cyclical structures within price movement. This projective structure extends potential cyclical outlines on the chart, opening deeper exploration of how Fibonacci relationships may emerge over time. Fibonacci Cycle Finder further underscores a non-linear representation of price by illustrating how wave-based logic can uncover shifts that are missed by static retracement tools. Rather than imposing immediate oscillatory behavior, the indicator encourages a progressive approach, where the parameters may be incrementally modified to align wave structures with observed price action. This refinement process deepens the exploration of Fibonacci relationships, offering a systematic way to experiment with non-linear price dynamics. In doing so, it revisits fundamental Fibonacci concepts, demonstrating their broader adaptability beyond fixed horizontal retracements. 🌀 THEORY & CONCEPT 🌀 What if Fibonacci relationships could be visualized as dynamic waves rather than confined to fixed horizontal levels? Fibonacci Cycle Finder introduces a trigonometric approach to market structure analysis, offering a different perspective on Fibonacci-based cycles. This tool provides a way to visualize market fluctuations through cyclical wave motion, opening the door to further exploration of Fibonacci’s role in non-linear price behavior. Traditional Fibonacci tools, such as retracements and extensions, have long been used to identify potential support and resistance levels. While valuable for analyzing price trends, these tools assume linear price movement and rely on static horizontal levels. However, market fluctuations often exhibit cyclical tendencies , where price follows natural wave-like structures rather than strictly adhering to fixed retracement points. Although Fibonacci-based tools such as arcs, fans, and time zones attempt to address these patterns, they primarily apply geometric projections. The Fibonacci Cycle Finder takes a different approach by mapping Fibonacci ratios along structured wave cycles, aligning these relationships with the natural curvature of market movement rather than forcing them onto rigid price levels. Rather than replacing traditional Fibonacci methods, the Fibonacci Cycle Finder supplements existing Fibonacci theory by introducing an exploratory approach to price structure analysis. It encourages traders to experiment with how Fibonacci ratios interact with cyclical price structures, offering an additional layer of insight beyond static retracements and extensions. This approach allows Fibonacci levels to be examined beyond their traditional static form, providing deeper insights into market fluctuations. 📊 FIBONACCI WAVE IMPLEMENTATION 📊 The Fibonacci Cycle Finder uses two user-defined swing points, A and B, as the foundation for projecting these Fibonacci waves. It first establishes standard horizontal levels that correspond to traditional Fibonacci retracements, ensuring a baseline reference before wave adjustments are applied. By default, the wave is intentionally subtle— Wavelength is set to 1 , Amplitude is set to 1 , and Phase is set to 0 . In other words, the wave starts as “stretched out.” This allows a slow, measured start, encouraging users to refine parameters incrementally rather than producing abrupt oscillations. As these parameters are increased, the wave takes on more distinct sine and cosine characteristics, offering a flexible approach to exploring Fibonacci-based cyclicity within price action. Three parameters control the shape of the Fibonacci wave: 1️⃣ Wavelength Controls the horizontal spacing of the wave along the time axis, determining the length of one full cycle from peak to peak (or trough to trough). In this indicator, Wavelength acts as a scaling input that adjusts how far the wave extends across time, rather than a strict mathematical “wavelength.” Lower values further stretch the wave, increasing the spacing between oscillations, while higher values compress it into a more frequent cycle. Each full cycle is divided into four quarter-cycle segments, a deliberate design choice to minimize curvature by default. This allows for subtle oscillations and smoother transitions, preventing excessive distortion while maintaining flexibility in wave projections. The wavelength is calculated relative to the A-B swing, ensuring that its scale adapts dynamically to the selected price range. 2️⃣ Amplitude Defines the vertical displacement of the wave relative to the baseline Fibonacci level. Higher values increase the height of oscillations, while lower values reduce the height, Negative values will invert the wave’s initial direction. The amplitude is dynamically applied in relation to the A-B swing direction, ensuring that an upward swing results in upward oscillations and a downward swing results in downward oscillations. 3️⃣ Phase Shifts the wave’s starting position along its cycle, adjusting alignment relative to the swing points. A phase of 0 aligns with a sine wave, where the cycle starts at zero and rises. A phase of 25 aligns with a cosine wave, starting at a peak and descending. A phase of 50 inverts the sine wave, beginning at zero but falling first, while a phase of 75 aligns with an inverted cosine , starting at a trough and rising. Intermediate values between these phases create gradual shifts in wave positioning, allowing for finer alignment with observed market structures. By fine-tuning these parameters, users can adapt Fibonacci waves to better reflect observed market behaviors. The wave structure integrates with price movements rather than simply overlaying static levels, allowing for a more dynamic representation of cyclical price tendencies. This indicator serves as an exploratory tool for understanding potential market rhythms, encouraging traders to test and visualize how Fibonacci principles extend beyond their traditional applications. 🖼️ CHART EXAMPLES 🖼️ Following this downtrend, price interacts with curved Fibonacci levels, highlighting resistance at the 0.236 and 0.382 levels, where price stalls before pulling back. Support emerges at the 0.5, 0.618, and 0.786 levels, where price finds stability and rebounds In this Fibonacci retracement, price initially finds support at the 1.0 level, following the natural curvature of the cycle. Resistance forms at 0.786, leading to a pullback before price breaks through and tests 0.618 as resistance. Once 0.618 is breached, price moves upward to test 0.5, illustrating how Fibonacci-based cycles may align with evolving market structure beyond static, horizontal retracements. Following this uptrend, price retraces downward and interacts with the Fibonacci levels, demonstrating both support and resistance at key levels such as 0.236, 0.382, 0.5, and 0.618. With only the 0.5 and 1.0 levels enabled, this chart remains uncluttered while still highlighting key price interactions. The short cycle length results in a mild curvature, aligning smoothly with market movement. Price finds resistance at the 0.5 level while showing strong support at 1.0, which follows the natural flow of the market. Keeping the focus on fewer levels helps maintain clarity while still capturing how price reacts within the cycle. 🛠️ CONFIGURATION AND SETTINGS 🛠️ Wave Parameters Wavelength : Stretches or compresses the wave along the time axis, determining the length of one full cycle. Higher values extend the wave across more bars, while lower values compress it into a shorter time frame. Amplitude : Expands or contracts the wave along the price axis, determining the height of oscillations relative to Fibonacci levels. Higher values increase the vertical range, while negative values invert the wave’s initial direction. Phase : Offsets the wave along the time axis, adjusting where the cycle begins. Higher values shift the starting position forward within the wave pattern. Fibonacci Levels Levels : Enable or disable specific Fibonacci levels (0.0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0) to focus on relevant price zones. Color : Modify level colors for enhanced visual clarity. Visibility Trend Line/Color : Toggle and customize the trend line connecting swing points A and B. Setup Lines : Show or hide lines linking Fibonacci levels to projected waves. A/B Labels Visibility : Control the visibility of swing point labels. Left/Right Labels : Manage the display of Fibonacci level labels on both sides of the chart. Fill % : Adjust shading intensity between Fibonacci levels (0% = no fill, 100% = maximum fill). A and B Points (Time/Price): These user-defined anchor points serve as the basis for Fibonacci wave calculations and can be manually set. A and B points can also be adjusted directly on the chart, with automatic synchronization to the settings panel, allowing for seamless modifications without needing to manually input values. ⚠️ DISCLAIMER ⚠️ The Fibonacci Cycle Finder is a visual analysis tool designed to illustrate Fibonacci relationships and serve as a supplement to traditional Fibonacci tools. While the indicator employs mathematical and geometric principles, no guarantee is made that its calculations will align with other Fibonacci tools or proprietary methods. Like all technical and visual indicators, the Fibonacci levels generated by this tool may appear to visually align with key price zones in hindsight. However, these levels are not intended as standalone signals for trading decisions. This indicator is intended for educational and analytical purposes, complementing other tools and methods of market analysis. 🧠 BEYOND THE CODE 🧠 Fibonacci Cycle Finder is the latest indicator in the Fibonacci Geometry Series. Building on the concepts of the Fibonacci Time-Price Zones and the Fibonacci 3-D indicators, this tool introduces a trigonometric approach to market structure analysis. The Fibonacci Cycle Finder indicator, like other xxattaxx indicators , is designed to encourage both education and community engagement. Your feedback and insights are invaluable to refining and enhancing the Fibonacci Cycle Finder indicator. We look forward to the creative applications, observations, and discussions this tool inspires within the trading community.
Pine Script® 인디케이터
xxattaxx의
9