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Lorentzian Harmonic Flow - Temporal Market Dynamic Lorentzian Harmonic Flow - Temporal Market Dynamic (⚡LHF) By: DskyzInvestments What this is LHF Pro is a research‑grade analytical instrument that models market time as a compressible medium , extracts directional flow in curved time using heavy‑tailed kernels, and consults a history‑based memory bank for context before synthesizing a final, bounded probabilistic score . It is not a mashup; each subsystem is mathematically coupled to a single clock (time dilation via gamma) and a single lens (Lorentzian heavy‑tailed weighting). This script is dense in logic (and therefore heavy) because it prioritizes rigor, interpretability, and visual clarity. Intended use Education and research. This tool expresses state recognition and regime context—not guarantees. It does not place orders. It is fully functional as published and contains no placeholders. Nothing herein is financial advice. Why this is original and useful Curved time: Markets do not move at a constant pace. LHF Pro computes a Lorentz‑style gamma (γ) from relative speed so its analytical windows contract when the tape accelerates and relax when it slows. Heavy‑tailed lens: Lorentzian kernels weight information with fat tails to respect rare but consequential extremes (unlike Gaussian decay). Memory of regimes: A K‑nearest‑neighbors engine works in a multi‑feature space using Lorentz kernels per dimension and exponential age fade , returning a memory bias (directional expectation) and assurance (confidence mass). One ecosystem: Squeeze, TCI, flow, acceleration, and memory live on the same clock and blend into a single final_score —visualized and documented on the dashboard. Cognitive map: A 2D heat map projects memory resonance by age and flow regime, making “where the past is speaking” visible. Shadow portfolio metaphor: Neighbor outcomes act like tiny hypothetical positions whose weighted average forms an educational pressure gauge (no execution, purely didactic). Mathematical framework (full transparency) 1) Returns, volatility, and speed‑of‑market Log return: rₜ = ln(closeₜ / closeₜ₋₁) Realized vol: rv = stdev(r, vol_len); vol‑of‑vol: burst = |rv − rv | Speed‑of‑market (analog to c): c = c_multiplier × (EMA(rv) + 0.5 × EMA(burst) + ε) 2) Trend velocity and Lorentz gamma (time dilation) Trend velocity: v = |close − close | / (vel_len × ATR) Relative speed: v_rel = v / c Gamma: γ = 1 / √(1 − v_rel²), stabilized by caps (e.g., ≤10) Interpretation: γ > 1 compresses market time → use shorter effective windows. 3) Adaptive temporal scale Adaptive length: L = base_len / γ^power (bounded for safety) Harmonic horizons: Lₛ = L × short_ratio, Lₘ = L × mid_ratio, Lₗ = L × long_ratio 4) Lorentzian smoothing and Harmonic Flow Kernel weight per lag i: wᵢ = 1 / (1 + (d/γ)²), d = i/L Horizon baselines: lw_h = Σ wᵢ·price / Σ wᵢ Z‑deviation: z_h = (close − lw_h)/ATR Harmonic Flow (HFL): HFL = (w_short·zₛ + w_mid·zₘ + w_long·zₗ) / (w_short + w_mid + w_long) 5) Flow kinematics Velocity: HFL_vel = HFL − HFL Acceleration (curvature): HFL_acc = HFL − 2·HFL + HFL 6) Squeeze and temporal compression Bollinger width vs Keltner width using L Squeeze: BB_width < KC_width × squeeze_mult Temporal Compression Index: TCI = base_len / L; TCI > 1 ⇒ compressed time 7) Entropy (regime complexity) Shannon‑inspired proxy on |log returns| with numerical safeguards and smoothing. Higher entropy → more chaotic regime. 8) Memory bank and Lorentzian k‑NN Feature vector (5D): Outcomes stored: forward returns at H5, H13, H34 Per‑dimension similarity: k(Δ) = 1 / (1 + Δ²), weighted by user’s feature weights Age fading: weight_age = mem_fade^age_bars Neighbor score: sᵢ = similarityᵢ × weight_ageᵢ Memory bias: mem_bias = Σ sᵢ·outcomeᵢ / Σ sᵢ Assurance: mem_assurance = Σ sᵢ (confidence mass) Normalization: mem_bias normalized by ATR and clamped into band Shadow portfolio metaphor: neighbors behave like micro‑positions; their weighted net forward return becomes a continuous, adaptive expectation. 9) Blended score and breakout proxy Blend factor: α_mem = 0.45 + 0.15 × (γ − 1) Final score: final_score = (1−α_mem)·tanh(HFL / (flow_thr·1.5)) + α_mem·tanh(mem_bias_norm) Breakout probability (bounded): energy = cap(TCI−1) + |HFL_acc|×k + cap(γ−1)×k + cap(mem_assurance)×k; breakout_prob = sigmoid(energy). Caps avoid runaway “100%” readings. Inputs — every control, purpose, mechanics, and tuning 🔮 Lorentz Core Auto‑Adapt (Vol/Entropy): On = L responds to γ and entropy (breathes with regime), Off = static testing. Base Length: Calm‑market anchor horizon. Lower (21–28) for fast tapes; higher (55–89+) for slow. Velocity Window (vel_len): Bars used in v. Shorter = more reactive γ; longer = steadier. Volatility Window (vol_len): Bars used for rv/burst (c). Shorter = more sensitive c. Speed‑of‑Market Multiplier (c_multiplier): Raises/lowers c. Lower values → easier γ spikes (more adaptation). Aim for strong trends to peak around γ ≈ 2–4. Gamma Compression Power: Exponent of γ in L. <1 softens; >1 amplifies adaptation swings. Max Kernel Span: Upper bound on smoothing loop (quality vs CPU). 🎼 Harmonic Flow Short/Mid/Long Horizon Ratios: Partition L into fast/medium/slow views. Smaller short_ratio → faster reaction; larger long_ratio → sturdier bias. Weights (w_short/w_mid/w_long): Governs HFL blend. Higher w_short → nimble; higher w_long → stable. 📈 Signals Squeeze Strictness: Threshold for BB1 = compressed (coiled spring); <1 = dilated. v/c: Relative speed; near 1 denotes extreme pacing. Diagnostic only. Entropy: Regime complexity; high entropy suggests caution, smaller size, or waiting for order to return. HFL: Curved‑time directional flow; sign and magnitude are the instantaneous bias. HFL_acc: Curvature; spikes often accompany regime ignition post‑squeeze. Mem Bias: Directional expectation from historical analogs (ATR‑normalized, bounded). Aligns or conflicts with HFL. Assurance: Confidence mass from neighbors; higher → more reliable memory bias. Squeeze: ON/RELEASE/OFF from BB
Индикатор Pine Script®
от DskyzInvestments
3
Savitzky-Golay Hampel Filter | AlphaNattSavitzky-Golay Hampel Filter | AlphaNatt A revolutionary indicator combining NASA's satellite data processing algorithms with robust statistical outlier detection to create the most scientifically advanced trend filter available on TradingView. "This is the same mathematics that processes signals from the Hubble Space Telescope and analyzes data from the Large Hadron Collider - now applied to financial markets." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🚀 SCIENTIFIC PEDIGREE Savitzky-Golay Filter Applications: NASA: Satellite telemetry and space probe data processing CERN: Particle physics data analysis at the LHC Pharmaceutical: Chromatography and spectroscopy analysis Astronomy: Processing signals from radio telescopes Medical: ECG and EEG signal processing Hampel Filter Usage: Aerospace: Cleaning sensor data from aircraft and spacecraft Manufacturing: Quality control in precision engineering Seismology: Earthquake detection and analysis Robotics: Sensor fusion and noise reduction ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🧬 THE MATHEMATICS 1. Savitzky-Golay Filter The SG filter performs local polynomial regression on data points: Fits a polynomial of degree n to a sliding window of data Evaluates the polynomial at the center point Preserves higher moments (peaks, valleys) unlike moving averages Maintains derivative information for true momentum analysis Originally published in Analytical Chemistry (1964) Mathematical Properties: Optimal smoothing in the least-squares sense Preserves statistical moments up to polynomial order Exact derivative calculation without additional lag Superior frequency response vs traditional filters 2. Hampel Filter A robust outlier detector based on Median Absolute Deviation (MAD): Identifies outliers using robust statistics Replaces spurious values with polynomial-fitted estimates Resistant to up to 50% contaminated data MAD is 1.4826 times more robust than standard deviation Outlier Detection Formula: |x - median| > k × 1.4826 × MAD Where k is the threshold parameter (typically 3 for 99.7% confidence) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💎 WHY THIS IS SUPERIOR vs Moving Averages: Preserves peaks and valleys (critical for catching tops/bottoms) No lag penalty for smoothness Maintains derivative information Polynomial fitting > simple averaging vs Other Filters: Outlier immunity (Hampel component) Scientifically optimal smoothing Preserves higher-order features Used in billion-dollar research projects Unique Advantages: Feature Preservation: Maintains market structure while smoothing Spike Immunity: Ignores false breakouts and stop hunts Derivative Accuracy: True momentum without additional indicators Scientific Validation: 60+ years of academic research ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ PARAMETER OPTIMIZATION 1. Polynomial Order (2-5) 2 (Quadratic): Maximum smoothing, gentle curves 3 (Cubic): Balanced smoothing and responsiveness (recommended) 4-5 (Higher): More responsive, preserves more features 2. Window Size (7-51) Must be odd number Larger = smoother but more lag Formula: 2×(desired smoothing period) + 1 Default 21 = analyzes 10 bars each side 3. Hampel Threshold (1.0-5.0) 1.0: Aggressive outlier removal (68% confidence) 2.0: Moderate outlier removal (95% confidence) 3.0: Conservative outlier removal (99.7% confidence) (default) 4.0+: Only extreme outliers removed 4. Final Smoothing (1-7) Additional WMA smoothing after filtering 1 = No additional smoothing 3-5 = Recommended for most timeframes 7 = Ultra-smooth for position trading ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 TRADING STRATEGIES Signal Recognition: Cyan Line: Bullish trend with positive derivative Pink Line: Bearish trend with negative derivative Color Change: Trend reversal with polynomial confirmation 1. Trend Following Strategy Enter when price crosses above cyan filter Exit when filter turns pink Use filter as dynamic stop loss Best in trending markets 2. Mean Reversion Strategy Enter long when price touches filter from below in uptrend Enter short when price touches filter from above in downtrend Exit at opposite band or filter color change Excellent for range-bound markets 3. Derivative Strategy (Advanced) The SG filter preserves derivative information Acceleration = second derivative > 0 Enter on positive first derivative + positive acceleration Exit on negative second derivative (momentum slowing) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📈 PERFORMANCE CHARACTERISTICS Strengths: Outlier Immunity: Ignores stop hunts and flash crashes Feature Preservation: Catches tops/bottoms better than MAs Smooth Output: Reduces whipsaws significantly Scientific Basis: Not curve-fitted or optimized to markets Considerations: Slight lag in extreme volatility (all filters have this) Requires odd window sizes (mathematical requirement) More complex than simple moving averages Best with liquid instruments ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔬 SCIENTIFIC BACKGROUND Savitzky-Golay Publication: "Smoothing and Differentiation of Data by Simplified Least Squares Procedures" - Abraham Savitzky & Marcel Golay - Analytical Chemistry, Vol. 36, No. 8, 1964 Hampel Filter Origin: "Robust Statistics: The Approach Based on Influence Functions" - Frank Hampel et al., 1986 - Princeton University Press These techniques have been validated in thousands of scientific papers and are standard tools in: NASA's Jet Propulsion Laboratory European Space Agency CERN (Large Hadron Collider) MIT Lincoln Laboratory Max Planck Institutes ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 ADVANCED TIPS News Trading: Lower Hampel threshold before major events to catch spikes Scalping: Use Order=2 for maximum smoothness, Window=11 for responsiveness Position Trading: Increase Window to 31+ for long-term trends Combine with Volume: Strong trends need volume confirmation Multiple Timeframes: Use daily for trend, hourly for entry Watch the Derivative: Filter color changes when first derivative changes sign ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ IMPORTANT NOTICES Not financial advice - educational purposes only Past performance does not guarantee future results Always use proper risk management Test settings on your specific instrument and timeframe No indicator is perfect - part of complete trading system ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏆 CONCLUSION The Savitzky-Golay Hampel Filter represents the pinnacle of scientific signal processing applied to financial markets. By combining polynomial regression with robust outlier detection, traders gain access to the same mathematical tools that: Guide spacecraft to other planets Detect gravitational waves from black holes Analyze particle collisions at near light-speed Process signals from deep space This isn't just another indicator - it's rocket science for trading . "When NASA needs to separate signal from noise in billion-dollar missions, they use these exact algorithms. Now you can too." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Developed by AlphaNatt Version: 1.0 Release: 2025 Pine Script: v6 "Where Space Technology Meets Market Analysis" Not financial advice. Always DYOR
Индикатор Pine Script®
от AlphaNatt
3
Global Bond Yields Monitor [MarktQuant]Global Bond Yields Monitor The Global Bond Yields Monitor is designed to help users track and compare government bond yields across major economies. It provides an at-a-glance view of short- and long-term interest rates for multiple countries, enabling users to observe shifts in global fixed-income markets. Key Features: Multi-Country Coverage: Includes major advanced and emerging economies such as the United States, China, Japan, Germany, United Kingdom, Canada, Australia, and more. Multiple Maturities: Displays yields for the 2-year, 5-year, 10-year, and 30-year maturities (20-year for Russia). Dynamic Yield Data: Plots real-time yields for the selected country directly from TradingView’s data sources. Weekly Change Tracking: Calculates and displays the yield change from one week ago ( ) for each maturity. Table Visualization: Option to display a compact data table showing current yields and weekly changes, color-coded for easier interpretation. Visual Yield Curve Comparison: Plots yield lines for short- and long-term maturities, with shaded areas between curves for visual clarity. Customizable Display: Choose table placement and whether to show or hide the weekly change table. Use Cases This script is intended for analysts, traders, and investors who want to monitor shifts in sovereign bond markets. Changes in yields can reflect adjustments in monetary policy expectations, inflation outlook, or broader macroeconomic trends. ❗Important Note❗ This indicator is for market monitoring and educational purposes only. It does not generate trading signals, and it should not be interpreted as financial advice. All data is sourced from TradingView’s available market feeds, and accuracy may depend on the source data.
Индикатор Pine Script®
от MarktQuant
Lorentzian Theory Classifier🧮 Lorentzian Theory Classifier: An Observatory for Market Spacetime Transcend the flat plane of traditional charting. Enter the curved, dynamic reality of market spacetime. The Lorentzian Theory Classifier (LTC) is not an indicator; it is a computational observatory. It is an instrument engineered to decode the geometry of market behavior, revealing the hidden curvatures and resonant frequencies that precede significant turning points. We discard the outdated tools of Euclidean simplicity and embrace a more profound truth: financial markets, much like the cosmos described by general relativity, are governed by a fabric that is warped by the mass of participation and the energy of volatility. The LTC is your lens to perceive this fabric, to move beyond predicting lines on a chart and begin reading the very architecture of probability. The Resonance Manifold: Standard Euclidean models search for historical analogues within a rigid sphere, missing the crucial outliers that define market extremes. The LTC's Lorentzian Resonance engine operates in a curved, non-Euclidean space, allowing it to connect with these "fat-tail" events—the true genesis points of major reversals. 🌌 THE THEORETICAL FRAMEWORK: A new Grand Unified Theory of Market Analysis The LTC is built upon a revolutionary synthesis of concepts from special relativity, quantum mechanics, and information theory. It reframes market analysis not as a problem of forecasting, but as a problem of state recognition in a non-Euclidean manifold. 1. The Lorentzian Kernel: The Mathematics of Reality Financial markets are not Gaussian. Their reality is one of "fat tails"—sudden, high-impact events that standard models dismiss as anomalies. The LTC acknowledges this reality by using the mathematically pure and robust Lorentzian kernel as its core engine: Similarity(x, y) = 1 / (1 + (||x − y||² / γ²)) ||x − y||²: The squared distance between the current market state (x) and a historical state (y) in our 8-dimensional feature space. γ (Gamma): A dynamic bandwidth parameter, our "Lorentz factor," which adapts to market entropy (chaos). In calm markets, gamma is small, demanding precise resonance. In chaotic markets, gamma expands, intelligently seeking broader patterns. This heavy-tailed function is revolutionary. It correctly assigns profound significance to the rare, extreme events that truly define market structure, while gracefully tuning out the noise of mundane price action. It doesn't just calculate; it understands context. 2. The 8-Dimensional State Vector: The Market's Quantum Fingerprint To achieve a holistic view, the LTC projects the market onto an 8-dimensional Hilbert space, where each dimension represents a critical "observable": Momentum & Acceleration (f_rsi, f_roc): The market's velocity and its rate of change. Cyclical Position (f_stoch, f_cci): The market's location within its recent oscillation cycles. Energy & Participation (f_vol, f_cor): The force of capital flow and its harmony with price. Chaos & Uncertainty (f_ent, f_mom): The degree of randomness and the standardized force of price changes. These are not eight separate indicators. They are entangled properties of a single "market wavefunction." The LTC's genius lies in measuring the geometric distance between these complete quantum states. 3. The k-NN Oracle: A Council of Past Universes The LTC employs a k-Nearest Neighbors algorithm, but in our curved Lorentzian spacetime. It poses a constant, profound question: " Which moments in history are most geometrically congruent to the present moment across all eight dimensions? " It then summons a "council" of these historical neighbors. Each neighbor's future outcome (did price ascend or descend?) casts a vote, weighted by its resonant similarity. The result is a probabilistic forecast of stunning clarity: Prognosis: The final weighted consensus on future direction. Assurance: The degree of unanimity within the council—a direct measure of the prediction's confidence. The Funnel of Conviction: The LTC's process is a rigorous distillation of information. Raw, chaotic market data is resolved into a clean 8-dimensional state vector. The Lorentzian Kernel filters these states for resonance, which are then passed to the k-NN Oracle for a vote. Noise is eliminated at each stage, resulting in a single, validated, high-conviction signal. ⚙️ THE COMMAND CONSOLE: A Guide to Calibrating Your Observatory Mastering the LTC's inputs is to become an architect of your own analytical universe. Each parameter is a dial that tunes the observatory's focus, from galactic structures to subatomic fluctuations. The tooltips in-script—over 6,000 words of documentation—provide immediate reference; this guide provides the philosophy. A summarized guide to the Core, Signal, Supreme, and Visual controls is included directly in the indicator's code and tooltips. We encourage all users to explore these settings to tune the LTC to their unique analytical style. 🏆 THE SUPREME DASHBOARD: Your Mission Control The dashboard is not a data table; it is your command interface with market reality. It translates the intricate dance of probabilities and vectors into clear, actionable intelligence. ⚡ ORACLE STATUS Prognosis: The primary directional vector. Its color, magnitude, and emoji (⚡) reveal the strength and conviction of the Oracle's forward guidance. Assurance: A real-time gauge of prediction quality, from "LOW" (high uncertainty) to "ELITE" (overwhelming statistical consensus). Interpret this as your core risk metric: trade with conviction when Assurance is ELITE; trade with caution when it is LOW. 🔮 RESONANCE ANALYSIS Chaos: A direct measurement of market entropy. "LOW CHAOS" signifies a predictable, orderly regime. "HIGH CHAOS" is a warning of randomness and unpredictability, where trend-following logic may fail. Turbulence: A measure of raw volatility. When the market is "TURBULENT," expect wider price swings and increased risk. Use this metric to adjust stop-loss distances and profit targets dynamically. 🏆 PERFORMANCE & ⚔️ GUARD METRICS These sections provide illustrative statistics on the script's recent historical behavior. Metrics like Yield Ratio and Guard Index offer a quick heuristic on the prevailing risk-reward environment. Crucially, these are for observational context only and are not a substitute for your own rigorous testing and analysis. 🎨 THE VISUAL MANIFESTATION: Charting the Unseen The LTC's visuals are designed to transform your chart from a 2D price graph into a 4D informational battlespace. The Dynamic Aura (Background Color): This is the ambient energy field of the market. A luminous green (Ascend) signifies a bullish resonance field; a deep red (Descend) indicates bearish pressure. The Assurance Shroud (Blue Bands): A visualization of confidence. When the shroud is wide and expansive , the Oracle's vision is clear and its predictions are robust. The Prognosis Arc (Curved Line): A geodesic projection of the market's most likely path, based on the current Prognosis. The Turbulence Cloud (Orange Mist): A visual warning system for market chaos. When this entropic mist expands , it is a clear sign that you are navigating a nebula of high unpredictability. Oracle Markers (▲▼): The final, validated signals. These are not merely pivot points. They are moments in spacetime where a structural pivot has been confirmed and then ratified by a high-conviction vote from the Lorentzian Oracle. They are the pinnacles of confluence. The Analyst's Observatory: The LTC transforms your chart into a command center for market analysis, providing a complete, at-a-glance view of market state, risk, and probabilistic trajectory. 🔧 THE ARCHITECT'S VISION: From a Blank Slate to a New Cosmos The LTC was not assembled; it was derived. It began not with code, but with first principles, asking: "If we were to build an instrument to measure the market today, unbound by the technical dogmas of the 20th century, what would it look like?" The answer was clear: it must be multi-dimensional, it must be adaptive, and it must be built on a mathematical framework that respects the "fat-tailed" nature of reality. The decision to use a pure Lorentzian kernel was non-negotiable. It represented a commitment to intellectual honesty over computational ease. The development of the Supreme Dashboard was driven by the philosophy of the "glass cockpit"—a belief that a trader's greatest asset is not a black box signal, but a transparent and intuitive flow of high-quality information. This script is the result of that unwavering vision: to create not just another indicator, but a new lens through which to perceive the market. ⚠️ RISK DISCLOSURE & PHILOSOPHY OF USE The Lorentzian Theory Classifier is an instrument of profound analytical power, intended for the serious, discerning trader. It does not generate infallible signals. It generates high-probability, data-driven hypotheses based on a rigorous and transparent methodology. All trading involves substantial risk, and the future is fundamentally unknowable. Past performance, whether real or simulated, is no guarantee of future results. Use this tool to augment your own skill, to confirm your own analysis, and to manage your own risk within a well-defined trading plan. "The effort to understand the universe is one of the very few things that lifts human life a little above the level of farce, and gives it some of the grace of tragedy." — Steven Weinberg, Nobel Laureate in Physics Trade with rigor. Trade with perspective. Trade with enlightenment. Trade with insight. Trade with anticipation. — Dskyz, for DAFE Trading Systems
Индикатор Pine Script®
от DskyzInvestments
5
RSI-Adaptive T3 [ChartPrime]The RSI-Adaptive T3 is a precision trend-following tool built around the legendary T3 smoothing algorithm developed by Tim Tillson , designed to enhance responsiveness while reducing lag compared to traditional moving averages. Current implementation takes it a step further by dynamically adapting the smoothing length based on real-time RSI conditions — allowing the T3 to “breathe” with market volatility. This dynamic length makes the curve faster in trending moves and smoother during consolidations. To help traders visualize volatility and directional momentum, adaptive volatility bands are plotted around the T3 line, with visual crossover markers and a dynamic info panel on the chart. It’s ideal for identifying trend shifts, spotting momentum surges, and adapting strategy execution to the pace of the market. HOIW IT WORKS At its core, this indicator fuses two ideas: The T3 Moving Average — a 6-stage recursively smoothed exponential average created by Tim Tillson , designed to reduce lag without sacrificing smoothness. It uses a volume factor to control curvature. A Dynamic Length Engine — powered by the RSI. When RSI is low (market oversold), the T3 becomes shorter and more reactive. When RSI is high (overbought), the T3 becomes longer and smoother. This creates a feedback loop between price momentum and trend sensitivity. // Step 1: Adaptive length via RSI rsi = ta.rsi(src, rsiLen) rsi_scale = 1 - rsi / 100 len = math.round(minLen + (maxLen - minLen) * rsi_scale) pine_ema(src, length) => alpha = 2 / (length + 1) sum = 0.0 sum := na(sum ) ? src : alpha * src + (1 - alpha) * nz(sum ) sum // Step 2: T3 with adaptive length e1 = pine_ema(src, len) e2 = pine_ema(e1, len) e3 = pine_ema(e2, len) e4 = pine_ema(e3, len) e5 = pine_ema(e4, len) e6 = pine_ema(e5, len) c1 = -v * v * v c2 = 3 * v * v + 3 * v * v * v c3 = -6 * v * v - 3 * v - 3 * v * v * v c4 = 1 + 3 * v + v * v * v + 3 * v * v t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3 The result: an evolving trend line that adapts to market tempo in real-time. KEY FEATURES ⯁ RSI-Based Adaptive Smoothing The length of the T3 calculation dynamically adjusts between a Min Length and Max Length , based on the current RSI. When RSI is low → the T3 shortens, tracking reversals faster. When RSI is high → the T3 stretches, filtering out noise during euphoria phases. Displayed length is shown in a floating table, colored on a gradient between min/max values. ⯁ T3 Calculation (Tim Tillson Method) The script uses a 6-stage EMA cascade with a customizable Volume Factor (v) , as designed by Tillson (1998) . Formula: T3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3 This technique gives smoother yet faster curves than EMAs or DEMA/Triple EMA. ⯁ Visual Trend Direction & Transitions The T3 line changes color dynamically: Color Up (default: blue) → bullish curvature Color Down (default: orange) → bearish curvature Plot fill between T3 and delayed T3 creates a gradient ribbon to show momentum expansion/contraction. Directional shift markers (“🞛”) are plotted when T3 crosses its own delayed value — helping traders spot trend flips or pullback entries. ⯁ Adaptive Volatility Bands Optional upper/lower bands are plotted around the T3 line using a user-defined volatility window (default: 100). Bands widen when volatility rises, and contract during compression — similar to Bollinger logic but centered on the adaptive T3. Shaded band zones help frame breakout setups or mean-reversion zones. ⯁ Dynamic Info Table A live stats panel shows: Current adaptive length Maximum smoothing (▲ MaxLen) Minimum smoothing (▼ MinLen) All values update in real time and are color-coded to match trend direction. HOW TO USE Use T3 crossovers to detect trend transitions, especially during periods of volatility compression. Watch for volatility contraction in the bands — breakouts from narrow band periods often precede trend bursts. The adaptive smoothing length can also be used to assess current market tempo — tighter = faster; wider = slower. CONCLUSION RSI-Adaptive T3 modernizes one of the most elegant smoothing algorithms in technical analysis with intelligent RSI responsiveness and built-in volatility bands. It gives traders a cleaner read on trend health, directional shifts, and expansion dynamics — all in a visually efficient package. Perfect for scalpers, swing traders, and algorithmic modelers alike, it delivers advanced logic in a plug-and-play format.
Индикатор Pine Script®
от ChartPrime
12
Volumetric Tensegrity🧮 Volumetric Tensegrity unifies two of the Leading Indicator suite's critical engines —  ZVOL  ( volume anomaly detection ) and  OBVX  ( directional conviction ). Originally designed as a structural economizer for traders navigating strict indicator limits (e.g. < 10 slots per chart), it was forced to evolve beyond that constraint simply to fulfill it, albeit with a difference. The fatal flaw of traditional fusion, where two metrics are blended mathematically, is that they lose scale integrity (i.e. meaning). VTense encodes  optical tensegrity  to scale the amplitude of the ZVOL histogram and the slope of the OBVX spread independently, so that expansion and direction may coexist without either dominating the frame. 🧬 Tensegrity , by definition, is an intelligent design principle where elements in compression are suspended within a network of continuous tension, forming a stable, self-supporting structure . Originally conceived in esoteric biomorphology (c.f. Da Vinci, Snelson, Casteneda), tensegrity balances force through opposition, not rigidity. Applied to financial markets,  Volumetric Tensegrity  captures this same principle: price compresses, volume expands, conviction builds or fades — yet structure holds through the interplay. The result is not a prediction engine, but a pressure field — one that visualizes where structure might bend, break, or rebound based on how volume breathes. 🗜️ Rather than layering multiple indicators and consuming precious chart space, VTense frees up room for complementary overlays like momentum mapping, liquidity tiers, or volatility phase detection — making it ideal for modular traders operating in tight technical real estate. 🧠 Core Logic - VTense separates and preserves two essential structural forces: • ZVOL Histogram : A Z-score-based expansion map that measures current volume deviation from its historical average. It reveals buildup zones, dormant stretches, and breakout pressure — regardless of price behavior. • OBVX Spread : A directional conviction curve that tracks the difference between On-Balance Volume and its volume-weighted fast trend. It shows whether the crowd is leaning in (accumulation/distribution) or backing off. 🔊 ZVOL controls the amplitude of the histogram, while OBVX controls the curvature and slope of the spread. Without sacrificing breathing behavior or analytical depth, VTense provides a compact yet dynamic lens to track both expansion pressure and directional bias within a single footprint. 🌊 Volumetric Tensegrity forecasts breakout readiness, trend fatigue, and compression zones by measuring the volatility within volume . Unlike traditional tools that track volatility of price, this indicator reveals when effort becomes unstable — signaling inflection points  before  price reacts. Designed to decode rhythm shifts at the volume level, it operates as a pre-ignition scanner that thrives on low-timeframe charts (15m and under) while scaling effectively to 1H for validation. 🪖 From Generals to Scouts 👀 When used jointly,  ZVOL + OBVX  act as the general : deep-field analysts confirming stress, commitment, or exhaustion. VTense , by contrast, functions as a  scout  — capturing subtle buildup and alignment before structure fully reveals itself. The indicator aims to be a literal vanguard, establishing a position that can be confirmed or flexibly abandoned when the higher authority arrives to evaluate. 🥂 Use the ZVOL + OBVX pair when : • You need independent axis control and manual dissection • You’re building long-form confluence setups • You have more indicator slots than you need 🔎 Use VTense when : • You need compact clarity across multiple instruments • You’re prioritizing confluence _detection_ over granular separation • You’re building efficient multi-layered systems under slot constraints 🏗️ Structural Behavior and Interpretation 🫁 Z VOL Respiration Histogram : Structural Effort vs Baseline 🔵  Compression Coil  – volume volatility is low and stable; the market is coiling 🟢  Steady Rhythm  – volume is healthy but unremarkable; balanced participation 🟡  Passive/Absorbed Effort  – expansion failing to manifest; watch for reversal 🟠  Clean Expansion  – actionable volatility rise backed by structure 🔴  Volatile Blowout  – chaos, climax; likely end-phase or fakeout ⚖️ ZVOL Respiration measures how hard the crowd is pressing — not just that volume is rising, but how statistically  abnormal the surge is. Because it is rescaled proportionally to OBVX, the amplitude of the histogram reflects structural urgency without overwhelming the visual field. 🖐️ OBVX Spread : Real-Time Directional Conviction Behind Price Moves 🔑 The curvature of the spread reveals not just directional bias but  crowd temp o: sharp slopes = urgent transitions; gradual slopes = building structural shifts. Curvature is key: sharp OBVX slope = urgency; gentle arcs = controlled drift or indecision. • Green Rising : Accumulation — upward pressure from real buyers • Red Falling : Distribution — sell pressure, downward slope • Flat Curves : Transitional → uncertainty, microstructure digestion 🎭 Synchronized vs Divergent Behavior ⏱️ Synchronized (high-confluence) : often precedes structural breakouts, with internal conviction clearly visible before price resolves. • ZVOL expands (yellow/orange/red) and OBVX climbs steeply green = strong bullish pressure • ZVOL expands while OBVX steepens red = growing sell-side intent 🪤 Divergent (conflict tension) : flags potential traps, fakeouts, and liquidity sweeps. • ZVOL expands sharply, but OBVX flattens or opposes → reactive expansion without crowd commitment ⛔️ Latent Drift + Structural Holding Patterns : tensegrity in action — the market holds tension without directional release. • ZVOL compresses (blue) + OBVX meanders near zero → structure is resting, building up energy • After prolonged drift, expect violent asymmetry when balance finally breaks 📚 Phase Interpretation: Dynamic Structural Read • 1️⃣ Quiet Coil : Histogram flat, OBVX flat → no urgency • 2️⃣ Initial Pulse : Yellow bars, OBVX slope builds → actionable tension • 3️⃣ Structural Breath : Synchronized expansion and slope → directional commitment • 4️⃣ Disagreement : Spike in ZVOL, flattening OBVX → exhaustion risk or false signal 💡 Suggested Use • Run on 15m charts for breakout anticipation and 1H for validation • Pair with ZVOL + OBVX to confirm crowd conviction behind the tension phase • Use as a rhythm filter for the suite's trend indicators (e.g., RDI , SUPeR TReND 2.718 , et. al.) • Ideal during low-volume regimes to detect pressure buildup before triggers 🧏🏻 Volumetric Tensegrity doesn’t signal. It  breathes , and listens to pressure shifts before they speak in price. As a scout, it lets you see structural posture before signals align — helping you front-run resolution with clarity, not prediction.
Индикатор Pine Script®
от TheLeadingIndicator
1
Smart Adaptive MACDAn advanced MACD variant that dynamically adapts to market volatility using ATR-based scaling. Key Features: Volatility-sensitive MACD and Signal lengths Optional smoothed MACD line Dynamic histogram heatmap (strong vs. weak momentum) Built-in Regular and Hidden Divergence detection Clear visual signals via solid (regular) and dashed (hidden) divergence lines What makes this different: Unlike traditional MACD indicators with fixed-length settings, this version adapts in real time to changing volatility conditions. It shortens during high-momentum environments for faster reaction, and lengthens during low-volatility phases to reduce noise. This allows better alignment with market behavior and cleaner momentum signals. Divergence Detection – How It Works The Smart Adaptive MACD detects both regular and hidden divergences by comparing price action with the smoothed MACD line. It uses recent pivot highs and lows to evaluate divergence and draws lines on the chart when conditions are met. Regular Divergence Detection This type of divergence signals potential reversals. It occurs when the price moves in one direction while the MACD moves in the opposite. Bullish Regular Divergence: Price makes lower lows, but MACD makes higher lows. Result: A solid green line is plotted beneath the MACD curve. Bearish Regular Divergence: Price makes higher highs, but MACD makes lower highs. Result: A solid red line is plotted above the MACD curve. Hidden Divergence Detection This type of divergence signals trend continuation. It occurs when price pulls back slightly, but the MACD shows deeper movement in the opposite direction. Bullish Hidden Divergence: Price makes higher lows, but MACD makes lower lows. Result: A dashed green line is plotted below the MACD curve. Bearish Hidden Divergence: Price makes lower highs, but MACD makes higher highs. Result: A dashed red line is plotted above the MACD curve. How to Use: This tool is best used alongside price structure, key support/resistance levels, or as a secondary confirmation for your trend or reversal strategy. It is designed to enhance your interpretation of market momentum and divergence without needing extra chart clutter. Disclaimer: This script is provided for educational and informational purposes only. It is not intended as financial advice or a recommendation to buy or sell any asset. Always conduct your own research and consult with a licensed financial advisor before making trading decisions. Use at your own risk. License: This script is published under the Mozilla Public License 2.0 and is fully open-source. Built by AresIQ | 2025
Индикатор Pine Script®
от AresIQ
10-Year Yields Table for Major CurrenciesThe "10-Year Yields Table for Major Currencies" indicator provides a visual representation of the 10-year government bond yields for several major global economies, alongside their corresponding Rate of Change (ROC) values. This indicator is designed to help traders and analysts monitor the yields of key currencies—such as the US Dollar (USD), British Pound (GBP), Japanese Yen (JPY), and others—on a daily timeframe. The 10-year yield is a crucial economic indicator, often used to gauge investor sentiment, inflation expectations, and the overall health of a country's economy (Higgins, 2021). Key Components: 10-Year Government Bond Yields: The indicator displays the daily closing values of 10-year government bond yields for major economies. These yields represent the return on investment for holding government bonds with a 10-year maturity and are often considered a benchmark for long-term interest rates. A rise in bond yields generally indicates that investors expect higher inflation and/or interest rates, while falling yields may signal deflationary pressures or lower expectations for future economic growth (Aizenman & Marion, 2020). Rate of Change (ROC): The ROC for each bond yield is calculated using the formula: ROC=Current Yield−Previous YieldPrevious Yield×100 ROC=Previous YieldCurrent Yield−Previous Yield​×100 This percentage change over a one-day period helps to identify the momentum or trend of the bond yields. A positive ROC indicates an increase in yields, often linked to expectations of stronger economic performance or rising inflation, while a negative ROC suggests a decrease in yields, which could signal concerns about economic slowdown or deflation (Valls et al., 2019). Table Format: The indicator presents the 10-year yields and their corresponding ROC values in a table format for easy comparison. The table is color-coded to differentiate between countries, enhancing readability. This structure is designed to provide a quick snapshot of global yield trends, aiding decision-making in currency and bond market strategies. Plotting Yield Trends: In addition to the table, the indicator plots the 10-year yields as lines on the chart, allowing for immediate visual reference of yield movements across different currencies. The plotted lines provide a dynamic view of the yield curve, which is a vital tool for economic analysis and forecasting (Campbell et al., 2017). Applications: This indicator is particularly useful for currency traders, bond investors, and economic analysts who need to monitor the relationship between bond yields and currency strength. The 10-year yield can be a leading indicator of economic health and interest rate expectations, which often impact currency valuations. For instance, higher yields in the US tend to attract foreign investment, strengthening the USD, while declining yields in the Eurozone might signal economic weakness, leading to a depreciating Euro. Conclusion: The "10-Year Yields Table for Major Currencies" indicator combines essential economic data—10-year government bond yields and their rate of change—into a single, accessible tool. By tracking these yields, traders can better understand global economic trends, anticipate currency movements, and refine their trading strategies. References: Aizenman, J., & Marion, N. (2020). The High-Frequency Data of Global Bond Markets: An Analysis of Bond Yields. Journal of International Economics, 115, 26-45. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2017). The Econometrics of Financial Markets. Princeton University Press. Higgins, M. (2021). Macroeconomic Analysis: Bond Markets and Inflation. Harvard Business Review, 99(5), 45-60. Valls, A., Ferreira, M., & Lopes, M. (2019). Understanding Yield Curves and Economic Indicators. Financial Markets Review, 32(4), 72-91.
Индикатор Pine Script®
от EdgeTools
2
True Amplitude Envelopes (TAE)The True Envelopes indicator is an adaptation of the True Amplitude Envelope (TAE) method, based on the research paper " Improved Estimation of the Amplitude Envelope of Time Domain Signals Using True Envelope Cepstral Smoothing " by Caetano and Rodet. This indicator aims to create an asymmetric price envelope with strong predictive power, closely following the methodology outlined in the paper. Due to the inherent limitations of Pine Script, the indicator utilizes a Kernel Density Estimator (KDE) in place of the original Cepstral Smoothing technique described in the paper. While this approach was chosen out of necessity rather than superiority, the resulting method is designed to be as effective as possible within the constraints of the Pine environment. This indicator is ideal for traders seeking an advanced tool to analyze price dynamics, offering insights into potential price movements while working within the practical constraints of Pine Script. Whether used in dynamic mode or with a static setting, the True Envelopes indicator helps in identifying key support and resistance levels, making it a valuable asset in any trading strategy. Key Features: Dynamic Mode: The indicator dynamically estimates the fundamental frequency of the price, optimizing the envelope generation process in real-time to capture critical price movements. High-Pass Filtering: Uses a high-pass filtered signal to identify and smoothly interpolate price peaks, ensuring that the envelope accurately reflects significant price changes. Kernel Density Estimation: Although implemented as a workaround, the KDE technique allows for flexible and adaptive smoothing of the envelope, aimed at achieving results comparable to the more sophisticated methods described in the original research. Symmetric and Asymmetric Envelopes: Provides options to select between symmetric and asymmetric envelopes, accommodating various trading strategies and market conditions. Smoothness Control: Features adjustable smoothness settings, enabling users to balance between responsiveness and the overall smoothness of the envelopes. The True Envelopes indicator comes with a variety of input settings that allow traders to customize the behavior of the envelopes to match their specific trading needs and market conditions. Understanding each of these settings is crucial for optimizing the indicator's performance. Main Settings Source: This is the data series on which the indicator is applied, typically the closing price (close). You can select other price data like open, high, low, or a custom series to base the envelope calculations. History: This setting determines how much historical data the indicator should consider when calculating the envelopes. A value of 0 will make the indicator process all available data, while a higher value restricts it to the most recent n bars. This can be useful for reducing the computational load or focusing the analysis on recent market behavior. Iterations: This parameter controls the number of iterations used in the envelope generation algorithm. More iterations will typically result in a smoother envelope, but can also increase computation time. The optimal number of iterations depends on the desired balance between smoothness and responsiveness. Kernel Style: The smoothing kernel used in the Kernel Density Estimator (KDE). Available options include Sinc, Gaussian, Epanechnikov, Logistic, and Triangular. Each kernel has different properties, affecting how the smoothing is applied. For example, Gaussian provides a smooth, bell-shaped curve, while Epanechnikov is more efficient computationally with a parabolic shape. Envelope Style: This setting determines whether the envelope should be Static or Dynamic. The Static mode applies a fixed period for the envelope, while the Dynamic mode automatically adjusts the period based on the fundamental frequency of the price data. Dynamic mode is typically more responsive to changing market conditions. High Q: This option controls the quality factor (Q) of the high-pass filter. Enabling this will increase the Q factor, leading to a sharper cutoff and more precise isolation of high-frequency components, which can help in better identifying significant price peaks. Symmetric: This setting allows you to choose between symmetric and asymmetric envelopes. Symmetric envelopes maintain an equal distance from the central price line on both sides, while asymmetric envelopes can adjust differently above and below the price line, which might better capture market conditions where upside and downside volatility are not equal. Smooth Envelopes: When enabled, this setting applies additional smoothing to the envelopes. While this can reduce noise and make the envelopes more visually appealing, it may also decrease their responsiveness to sudden market changes. Dynamic Settings Extra Detrend: This setting toggles an additional high-pass filter that can be applied when using a long filter period. The purpose is to further detrend the data, ensuring that the envelope focuses solely on the most recent price oscillations. Filter Period Multiplier: This multiplier adjusts the period of the high-pass filter dynamically based on the detected fundamental frequency. Increasing this multiplier will lengthen the period, making the filter less sensitive to short-term price fluctuations. Filter Period (Min) and Filter Period (Max): These settings define the minimum and maximum bounds for the high-pass filter period. They ensure that the filter period stays within a reasonable range, preventing it from becoming too short (and overly sensitive) or too long (and too sluggish). Envelope Period Multiplier: Similar to the filter period multiplier, this adjusts the period for the envelope generation. It scales the period dynamically to match the detected price cycles, allowing for more precise envelope adjustments. Envelope Period (Min) and Envelope Period (Max): These settings establish the minimum and maximum bounds for the envelope period, ensuring the envelopes remain adaptive without becoming too reactive or too slow. Static Settings Filter Period: In static mode, this setting determines the fixed period for the high-pass filter. A shorter period will make the filter more responsive to price changes, while a longer period will smooth out more of the price data. Envelope Period: This setting specifies the fixed period used for generating the envelopes in static mode. It directly influences how tightly or loosely the envelopes follow the price action. TAE Smoothing: This controls the degree of smoothing applied during the TAE process in static mode. Higher smoothing values result in more gradual envelope curves, which can be useful in reducing noise but may also delay the envelope’s response to rapid price movements. Visual Settings Top Band Color: This setting allows you to choose the color for the upper band of the envelope. This band represents the resistance level in the price action. Bottom Band Color: Similar to the top band color, this setting controls the color of the lower band, which represents the support level. Center Line Color: This is the color of the central price line, often referred to as the carrier. It represents the detrended price around which the envelopes are constructed. Line Width: This determines the thickness of the plotted lines for the top band, bottom band, and center line. Thicker lines can make the envelopes more visible, especially when overlaid on price data. Fill Alpha: This controls the transparency level of the shaded area between the top and bottom bands. A lower alpha value will make the fill more transparent, while a higher value will make it more opaque, helping to highlight the envelope more clearly. The envelopes generated by the True Envelopes indicator are designed to provide a more precise and responsive representation of price action compared to traditional methods like Bollinger Bands or Keltner Channels. The core idea behind this indicator is to create a price envelope that smoothly interpolates the significant peaks in price action, offering a more accurate depiction of support and resistance levels. One of the critical aspects of this approach is the use of a high-pass filtered signal to identify these peaks. The high-pass filter serves as an effective method of detrending the price data, isolating the rapid fluctuations in price that are often lost in standard trend-following indicators. By filtering out the lower frequency components (i.e., the trend), the high-pass filter reveals the underlying oscillations in the price, which correspond to significant peaks and troughs. These oscillations are crucial for accurately constructing the envelope, as they represent the most responsive elements of the price movement. The algorithm works by first applying the high-pass filter to the source price data, effectively detrending the series and isolating the high-frequency price changes. This filtered signal is then used to estimate the fundamental frequency of the price movement, which is essential for dynamically adjusting the envelope to current market conditions. By focusing on the peaks identified in the high-pass filtered signal, the algorithm generates an envelope that is both smooth and adaptive, closely following the most significant price changes without overfitting to transient noise. Compared to traditional envelopes and bands, such as Bollinger Bands and Keltner Channels, the True Envelopes indicator offers several advantages. Bollinger Bands, which are based on standard deviations, and Keltner Channels, which use the average true range (ATR), both tend to react to price volatility but do not necessarily follow the peaks and troughs of the price with precision. As a result, these traditional methods can sometimes lag behind or fail to capture sudden shifts in price momentum, leading to either false signals or missed opportunities. In contrast, the True Envelopes indicator, by using a high-pass filtered signal and a dynamic period estimation, adapts more quickly to changes in price behavior. The envelopes generated by this method are less prone to the lag that often affects standard deviation or ATR-based bands, and they provide a more accurate representation of the price's immediate oscillations. This can result in better predictive power and more reliable identification of support and resistance levels, making the True Envelopes indicator a valuable tool for traders looking for a more responsive and precise approach to market analysis. In conclusion, the True Envelopes indicator is a powerful tool that blends advanced theoretical concepts with practical implementation, offering traders a precise and responsive way to analyze price dynamics. By adapting the True Amplitude Envelope (TAE) method through the use of a Kernel Density Estimator (KDE) and high-pass filtering, this indicator effectively captures the most significant price movements, providing a more accurate depiction of support and resistance levels compared to traditional methods like Bollinger Bands and Keltner Channels. The flexible settings allow for extensive customization, ensuring the indicator can be tailored to suit various trading strategies and market conditions.
Индикатор Pine Script®
от The_Peaceful_Lizard
2
TrendLine ScythesTrendline Scythes is a script designed to automatically detect and draw special curved trendlines, resembling scythes or blades, based on pivotal points in price action. These trendlines adapt to the volatility of the market, providing a unique perspective on trend dynamics. 🔲 Methodology Traditional trendlines connect consecutive pivot points on a price chart, providing a linear representation of trend direction. However, this script employs a distinctive methodology by automatically detecting price pivots and then calculating special curved trendlines based on the Average True Range (ATR) of the price. This introduces a curvature to the trendlines, resembling scythes, offering a unique way to interpret market trends. 🔲 Auto Breakout and Target Detection Trendline Scythes includes features for automatic breakout detection, signaling potential trend changes. Additionally, the script assists in target detection, helping traders set realistic and data-driven profit-taking levels based on market volatility and user adjustment. 🔲 Utility Trend Confirmation - Use Trendline Scythes to confirm existing trends by observing how price interacts with the curved trendlines. Breakout Signals - Auto-detection of breakouts adds a proactive element to your trading strategy, helping you stay ahead of potential trend reversals. Target Setting - Utilize the script to set profit-taking targets based on volatility, aligning with the current market conditions. 🔲 Settings Pivot Length - Swing detection length Scythe Length - Adjusts the length of the scythes blade Sensitivity - Controls how restrained the target calculation is, higher values will result in tighter targets. 🔲 Alerts Breakout Breakdown Target Reached Target Invalidated As well as the option to trigger 'any alert' call. Trendline Scythes is a versatile tool combining the benefits of traditional trendlines with the dynamic adaptability of curved lines for a unique approach to trend analysis.
Индикатор Pine Script®
от EliCobra
15
"The Stocashi" - Stochastic RSI + Heikin-AshiWhat up guys and welcome to the coffee shop. I have a special little tool for you today to throw in your toolbox. This one is a freebie. This is the Stochastic RS-Heiken-Ashi "The Stocashi" This is the stochastic RSI built to look like Heikin-Ashi candles. a lot of people have trouble using the stochastic indicator because of its ability to look very choppy at its edges instead of having nice curves or arcs to its form when you use it on scalping time frames it ends up being very pointed and you can't really tell when the bands turn over if you're using a stochastic Ribbon or you can't tell when it's actually moving in a particular direction if you're just using the K and the D line. This new format of Presentation seeks to get you to have a better visual representation of what the stochastic is actually doing. It's long been noted that Heikin-Ashi do a very good job of representing momentum in a price so using it on something that is erratic as the stochastic indicator seems like a plausible idea. The strategy is simple because you use it exactly the same way you've always used the stochastic indicator except now you can look for the full color of the candle. this one uses a gradient color setup for the candle so when the candle is fully red then you have a confirmed downtrend and when the candle is fully green you have a confirmed up trend of the stochastic however if, you a combination of the two colors inside of one candle then you do not have a confirmed direction of the stochastic. the strategy is simple for the stochastic and that you need to know your overall trend. if you are in an uptrend you are waiting for the stochastic to reach bottom and start curving up. if you are in a downtrend you are waiting for the stochastic to reach its top or its peak and curve down. In an uptrend you want to make sure that the stochastic is making consistently higher lows just like price should be. if at any moment it makes a lower low then you know you have a problem with your Trend and you should consider exiting. The opposite is true for a downtrend. In a downtrend you want to make sure you have lower highs. if at any given moment you end up with a higher high than you know you have a problem with your Trend and it's probably ending so you should consider exiting. The stochastic indicator done as he can actually candles also does a very good job of telling you when there is a change of character. In that moment when the change of character shows up you simply wait until your trend and your price start to match up. You can also use the stochastic indicator in this format to find divergences the same way you would on the relative strength index against your price highs and price lows so Divergence trading is visually a little bit easier with this tool. The settings for the K percent D percent RSI length and stochastic length can be adjusted at will so be sure to study the history of the stochastic and find the good settings for your trading strategy.
Индикатор Pine Script®
от CoffeeshopCrypto
26
McDonald's Pattern [LuxAlgo]Tradingview asked, we delivered. This script fits a cubic Bezier curve using tops/bottoms in order to approximate a McDonalds pattern, a popular meme pattern in the crypto trading community. Traditionally the McDonalds pattern is described by an M pattern with deep retracement (> 50%), forming a McDonalds logo. Please note that this indicator is a meme & should not be taken seriously. Some aspects of this indicator are not real-time and meant for descriptive analysis alongside other components of this script, in this case, for entertainment purposes. We suggest looking through our other open-source scripts if you’re looking for more serious tools. 🔶 USAGE The script fits Bezier curves using specific tops/bottoms as control points. When the distance between tops and bottoms values is relatively small, the user can more easily identify the pattern. A score is shown on the top right of the chart, aiming to return how close the returned pattern is to the original logo. A regular Mcdonalds pattern would return a red background, while an inverted pattern would return a green one. 🔶 SETTINGS Length: Sensitivity of tops/bottoms detection. The method does not make use of pivot points, using rolling maximums/minimums instead. Use First Bar As Vertex: Use the price and bar index of the last bar as vertex.
Индикатор Pine Script®
от LuxAlgo
90
T3 Velocity "This technical indicator is a kind of rate of change oscillator made of 2 T3 moving average. It compares the momentum of the 2 curves between the current and the previous period. The first curve is calculated upon the period and the complete factor ("vFactor" parameter) and the second one with an half of this factor. It results a smoothed oscillator that can be traded almost like a MACD. An optional signal line could be added to give clear entries when the oscillator cross it."
Индикатор Pine Script®
от wpatte15
2
Advanced Speedometer Gauge [PhenLabs]Advanced Speedometer Gauge Version: PineScript™v6 📌 Description The Advanced Speedometer Gauge is a revolutionary multi-metric visualization tool that consolidates 13 distinct trading indicators into a single, intuitive speedometer display. Instead of cluttering your workspace with multiple oscillators and panels, this gauge provides a unified interface where you can switch between different metrics while maintaining consistent visual interpretation. Built on PineScript™ v6, the indicator transforms complex technical calculations into an easy-to-read semi-circular gauge with color-coded zones and a precision needle indicator. Each of the 13 available metrics has been carefully normalized to a 0-100 scale, ensuring that whether you’re analyzing RSI, volume trends, or volatility extremes, the visual interpretation remains consistent and intuitive. The gauge is designed for traders who value efficiency and clarity. By consolidating multiple analytical perspectives into one compact display, you can quickly assess market conditions without the visual noise of traditional multi-indicator setups. All metrics are non-overlapping, meaning each provides unique insights into different aspects of market behavior. 🚀 Points of Innovation 13 selectable metrics covering momentum, volume, volatility, trend, and statistical analysis, all accessible through a single dropdown menu Universal 0-100 normalization system that standardizes different indicator scales for consistent visual interpretation across all metrics Semi-circular gauge design with 21 arc segments providing smooth precision and clear visual feedback through color-coded zones Non-redundant metric selection ensuring each indicator provides unique market insights without analytical overlap Advanced metrics including MFI (volume-weighted momentum), CCI (statistical deviation), Volatility Rank (extended lookback), Trend Strength (ADX-style), Choppiness Index, Volume Trend, and Price Distance from MA Flexible positioning system with 5 chart locations, 3 size options, and fully customizable color schemes for optimal workspace integration 🔧 Core Components Metric Selection Engine: Dropdown interface allowing instant switching between 13 different technical indicators, each with independent parameter controls Normalization System: All metrics converted to 0-100 scale using indicator-specific algorithms that preserve the statistical significance of each measurement Semi-Circular Gauge: Visual display using 21 arc segments arranged in curved formation with two-row thickness for enhanced visibility Color Zone System: Three distinct zones (0-40 green, 40-70 yellow, 70-100 red) providing instant visual feedback on metric extremes Needle Indicator: Dynamic pointer that positions across the gauge arc based on precise current metric value Table Implementation: Professional table structure ensuring consistent positioning and rendering across different chart configurations 🔥 Key Features RSI (Relative Strength Index): Classic momentum oscillator measuring overbought/oversold conditions with adjustable period length (default 14) Stochastic Oscillator: Compares closing price to price range over specified period with smoothing, ideal for identifying momentum shifts MFI (Money Flow Index): Volume-weighted RSI that combines price movement with volume to measure buying and selling pressure intensity CCI (Commodity Channel Index): Measures statistical deviation from average price, normalized from typical -200 to +200 range to 0-100 scale Williams %R: Alternative overbought/oversold indicator using high-low range analysis, inverted to match 0-100 scale conventions Volume %: Current volume relative to moving average expressed as percentage, capped at 100 for extreme spikes Volume Trend: Cumulative directional volume flow showing whether volume is flowing into up moves or down moves over specified period ATR Percentile: Current Average True Range position within historical range using specified lookback period (default 100 bars) Volatility Rank: Close-to-close volatility measured against extended historical range (default 252 days), differs from ATR in calculation method Momentum: Rate of change calculation showing price movement speed, centered at 50 and normalized to 0-100 range Trend Strength: ADX-style calculation using directional movement to quantify trend intensity regardless of direction Choppiness Index: Measures market choppiness versus trending behavior, where high values indicate ranging markets and low values indicate strong trends Price Distance from MA: Measures current price over-extension from moving average using standard deviation calculations 🎨 Visualization Semi-Circular Arc Display: Curved gauge spanning from 0 (left) to 100 (right) with smooth progression and two-row thickness for visibility Color-Coded Zones: Green zone (0-40) for low/oversold conditions, yellow zone (40-70) for neutral readings, red zone (70-100) for high/overbought conditions Needle Indicator: Downward-pointing triangle (▼) positioned precisely at current metric value along the gauge arc Scale Markers: Vertical line markers at 0, 25, 50, 75, and 100 positions with corresponding numerical labels below Title Display: Merged cell showing “𓄀 PhenLabs” branding plus currently selected metric name in monospace font Large Value Display: Current metric value shown with two decimal precision in large text directly below title Table Structure: Professional table with customizable background color, text color, and transparency for minimal chart obstruction 📖 Usage Guidelines Metric Selection Select Metric: Default: RSI | Options: RSI, Stochastic, Volume %, ATR Percentile, Momentum, MFI (Money Flow), CCI (Commodity Channel), Williams %R, Volatility Rank, Trend Strength, Choppiness Index, Volume Trend, Price Distance | Choose the technical indicator you want to display on the gauge based on your current analytical needs RSI Settings RSI Length: Default: 14 | Range: 1+ | Controls the lookback period for RSI calculation, shorter periods increase sensitivity to recent price changes Stochastic Settings Stochastic Length: Default: 14 | Range: 1+ | Lookback period for stochastic calculation comparing close to high-low range Stochastic Smooth: Default: 3 | Range: 1+ | Smoothing period applied to raw stochastic value to reduce noise and false signals Volume Settings Volume MA Length: Default: 20 | Range: 1+ | Moving average period used to calculate average volume for comparison with current volume Volume Trend Length: Default: 20 | Range: 5+ | Period for calculating cumulative directional volume flow trend ATR and Volatility Settings ATR Length: Default: 14 | Range: 1+ | Period for Average True Range calculation used in ATR Percentile metric ATR Percentile Lookback: Default: 100 | Range: 20+ | Historical range used to determine current ATR position as percentile Volatility Rank Lookback (Days): Default: 252 | Range: 50+ | Extended lookback period for Volatility Rank metric using close-to-close volatility Momentum and Trend Settings Momentum Length: Default: 10 | Range: 1+ | Lookback period for rate of change calculation in Momentum metric Trend Strength Length: Default: 20 | Range: 5+ | Period for directional movement calculations in ADX-style Trend Strength metric Advanced Metric Settings MFI Length: Default: 14 | Range: 1+ | Lookback period for Money Flow Index calculation combining price and volume CCI Length: Default: 20 | Range: 1+ | Period for Commodity Channel Index statistical deviation calculation Williams %R Length: Default: 14 | Range: 1+ | Lookback period for Williams %R high-low range analysis Choppiness Index Length: Default: 14 | Range: 5+ | Period for calculating market choppiness versus trending behavior Price Distance MA Length: Default: 50 | Range: 10+ | Moving average period used for Price Distance standard deviation calculation Visual Customization Position: Default: Top Right | Options: Top Left, Top Right, Bottom Left, Bottom Right, Middle Right | Controls gauge placement on chart for optimal workspace organization Size: Default: Normal | Options: Small, Normal, Large | Adjusts overall gauge dimensions and text size for different monitor resolutions and preferences Low Zone Color (0-40): Default: Green (#00FF00) | Customize color for low/oversold zone of gauge arc Medium Zone Color (40-70): Default: Yellow (#FFFF00) | Customize color for neutral/medium zone of gauge arc High Zone Color (70-100): Default: Red (#FF0000) | Customize color for high/overbought zone of gauge arc Background Color: Default: Semi-transparent dark gray | Customize gauge background for contrast and chart integration Text Color: Default: White (#FFFFFF) | Customize all text elements including title, value, and scale labels ✅ Best Use Cases Quick visual assessment of market conditions when you need instant feedback on whether an asset is in extreme territory across multiple analytical dimensions Workspace organization for traders who monitor multiple indicators but want to reduce chart clutter and visual complexity Metric comparison by switching between different indicators while maintaining consistent visual interpretation through the 0-100 normalization Overbought/oversold identification using RSI, Stochastic, Williams %R, or MFI depending on whether you prefer price-only or volume-weighted analysis Volume analysis through Volume %, Volume Trend, or MFI to confirm price movements with corresponding volume characteristics Volatility monitoring using ATR Percentile or Volatility Rank to identify expansion/contraction cycles and adjust position sizing Trend vs range identification by comparing Trend Strength (high values = trending) against Choppiness Index (high values = ranging) Statistical over-extension detection using CCI or Price Distance to identify when price has deviated significantly from normal behavior Multi-timeframe analysis by duplicating the gauge on different timeframe charts to compare metric readings across time horizons Educational purposes for new traders learning to interpret technical indicators through consistent visual representation ⚠️ Limitations The gauge displays only one metric at a time, requiring manual switching to compare different indicators rather than simultaneous multi-metric viewing The 0-100 normalization, while providing consistency, may obscure the raw values and specific nuances of each underlying indicator Table-based visualization cannot be exported or saved as an image separately from the full chart screenshot Optimal parameter settings vary by asset type, timeframe, and market conditions, requiring user experimentation for best results 💡 What Makes This Unique Unified Multi-Metric Interface: The only gauge-style indicator offering 13 distinct metrics through a single interface, eliminating the need for multiple oscillator panels Non-Overlapping Analytics: Each metric provides genuinely unique insights—MFI combines volume with price, CCI measures statistical deviation, Volatility Rank uses extended lookback, Trend Strength quantifies directional movement, and Choppiness Index measures ranging behavior Universal Normalization System: All metrics standardized to 0-100 scale using indicator-appropriate algorithms that preserve statistical meaning while enabling consistent visual interpretation Professional Visual Design: Semi-circular gauge with 21 arc segments, precision needle positioning, color-coded zones, and clean table implementation that maintains clarity across all chart configurations Extensive Customization: Independent parameter controls for each metric, five position options, three size presets, and full color customization for seamless workspace integration 🔬 How It Works 1. Metric Calculation Phase: All 13 metrics are calculated simultaneously on every bar using their respective algorithms with user-defined parameters Each metric applies its own specific calculation method—RSI uses average gains vs losses, Stochastic compares close to high-low range, MFI incorporates typical price and volume, CCI measures deviation from statistical mean, ATR calculates true range, directional indicators measure up/down movement, and statistical metrics analyze price relationships 2. Normalization Process: Each calculated metric is converted to a standardized 0-100 scale using indicator-appropriate transformations Some metrics are naturally 0-100 (RSI, Stochastic, MFI, Williams %R), while others require scaling—CCI transforms from ±200 range, Momentum centers around 50, Volume ratio caps at 2x for 100, ATR and Volatility Rank calculate percentile positions, and Price Distance scales by standard deviations 3. Gauge Rendering: The selected metric’s normalized value determines the needle position across 21 arc segments spanning 0-100 Each arc segment receives its color based on position—segments 0-8 are green zone, segments 9-14 are yellow zone, segments 15-20 are red zone The needle indicator (▼) appears in row 5 at the column corresponding to the current metric value, providing precise visual feedback 4. Table Construction: The gauge uses TradingView’s table system with merged cells for title and value display, ensuring consistent positioning regardless of chart configuration Rows are allocated as follows: Row 0 merged for title, Row 1 merged for large value display, Row 2 for spacing, Rows 3-4 for the semi-circular arc with curved shaping, Row 5 for needle indicator, Row 6 for scale markers, Row 7 for numerical labels at 0/25/50/75/100 All visual elements update on every bar when barstate.islast is true, ensuring real-time accuracy without performance impact 💡 Note: This indicator is designed for visual analysis and market condition assessment, not as a standalone trading system. For best results, combine gauge readings with price action analysis, support and resistance levels, and broader market context. Parameter optimization is recommended based on your specific trading timeframe and asset class. The gauge works on all timeframes but may require different parameter settings for intraday versus daily/weekly analysis. Consider using multiple instances of the gauge set to different metrics for comprehensive market analysis without switching between settings.
Индикатор Pine Script®
от PhenLabs
1
On-Chain Signals [LuxAlgo]The On-Chain Signals indicator uses fundamental blockchain metrics to provide traders with an objective technical view of their favorite cryptocurrencies. It uses IntoTheBlock datasets integrated within TradingView to generate four key signals: Net Network Growth, In the Money, Concentration, and Large Transactions. Together, these four signals provide traders with an overall directional bias of the market. All of the data can be visualized as a gauge, table, historical plot, or average. 🔶 USAGE The main goal of this tool is to provide an overall directional bias based on four blockchain signals, each with three possible biases: bearish, neutral, or bullish. The thresholds for each signal bias can be adjusted on the settings panel. These signals are based on IntoTheBlock's On-Chain Signals. Net network growth: Change in the total number of addresses over the last seven periods; i.e., how many new addresses are being created. In the Money: Change in the seven-period moving average of the total supply in the money. This shows how many addresses are profitable. Concentration: Change in the aggregate addresses of whales and investors from the previous period. These are addresses holding at least 0.1% of the supply. This shows how many addresses are in the hands of a few. Large Transactions: Changes in the number of transactions over $100,000. This metric tracks convergence or divergence from the 21- and 30-day EMAs and indicates the momentum of large transactions. All of these signals together form the blockchain's overall directional bias. Bearish: The number of bearish individual signals is greater than the number of bullish individual signals. Neutral: The number of bearish individual signals is equal to the number of bullish individual signals. Bullish: The number of bullish individual signals is greater than the number of bearish individual signals. If the overall directional bias is bullish, we can expect the price of the observed cryptocurrency to increase. If the bias is bearish, we can expect the price to decrease. If the signal is neutral, the price may be more likely to stay the same. Traders should be aware of two things. First, the signals provide optimal results when the chart is set to the daily timeframe. Second, the tool uses IntoTheBlock data, which is available on TradingView. Therefore, some cryptocurrencies may not be available. 🔹 Display Mode Traders have three different display modes at their disposal. These modes can be easily selected from the settings panel. The gauge is set by default. 🔹 Gauge The gauge will appear in the center of the visible space. Traders can adjust its size using the Scale parameter in the Settings panel. They can also give it a curved effect. The number of bars displayed directly affects the gauge's resolution: More bars result in better resolution. The chart above shows the effect that different scale configurations have on the gauge. 🔹 Historical Data The chart above shows the historical data for each of the four signals. Traders can use this mode to adjust the thresholds for each signal on the settings panel to fit the behavior of each cryptocurrency. They can also analyze how each metric impacts price behavior over time. 🔹 Average This display mode provides an easy way to see the overall bias of past prices in order to analyze price behavior in relation to the underlying blockchain's directional bias. The average is calculated by taking the values of the overall bias as -1 for bearish, 0 for neutral, and +1 for bullish, and then applying a triangular moving average over 20 periods by default. Simple and exponential moving averages are available, and traders can select the period length from the settings panel. 🔶 DETAILS The four signals are based on IntoTheBlock's On-Chain Signals. We gather the data, manipulate it, and build the signals depending on each threshold. Net network growth float netNetworkGrowthData = customData('_TOTALADDRESSES') float netNetworkGrowth = 100*(netNetworkGrowthData /netNetworkGrowthData - 1) In the Money float inTheMoneyData = customData('_INOUTMONEYIN') float averageBalance = customData('_AVGBALANCE') float inTheMoneyBalance = inTheMoneyData*averageBalance float sma = ta.sma(inTheMoneyBalance,7) float inTheMoney = ta.roc(sma,1) Concentration float whalesData = customData('_WHALESPERCENTAGE') float inverstorsData = customData('_INVESTORSPERCENTAGE') float bigHands = whalesData+inverstorsData float concentration = ta.change(bigHands )*100 Large Transactions float largeTransacionsData = customData('_LARGETXCOUNT') float largeTX21 = ta.ema(largeTransacionsData,21) float largeTX30 = ta.ema(largeTransacionsData,30) float largeTransacions = ((largeTX21 - largeTX30)/largeTX30)*100 🔶 SETTINGS Display mode: Select between gauge, historical data and average. Average: Select a smoothing method and length period. 🔹 Thresholds Net Network Growth : Bullish and bearish thresholds for this signal. In The Money : Bullish and bearish thresholds for this signal. Concentration : Bullish and bearish thresholds for this signal. Transactions : Bullish and bearish thresholds for this signal. 🔹 Dashboard Dashboard : Enable/disable dashboard display Position : Select dashboard location Size : Select dashboard size 🔹 Gauge Scale : Select the size of the gauge Curved : Enable/disable curved mode Select Gauge colors for bearish, neutral and bullish bias 🔹 Style Net Network Growth : Enable/disable historical plot and choose color In The Money : Enable/disable historical plot and choose color Concentration : Enable/disable historical plot and choose color Large Transacions : Enable/disable historical plot and choose color
Индикатор Pine Script®
от LuxAlgo
8
AWR R & LR Oscillator with plots & tableHello trading viewers ! I'm glad to share with you one of my favorite indicator. It's the aggregate of many things. It is partly based on an indicator designed by gentleman goat. Many thanks to him. 1. Oscillator and Correlation Calculations Overview and Functionality: This part of the indicator computes up to 10 Pearson correlation coefficients between a chosen source (typically the close price, though this is user-configurable) and the bar index over various periods. Starting with an initial period defined by the startPeriod parameter and increasing by a set increment (periodIncrement), each correlation coefficient is calculated using the built-in ta.correlation function over successive ranges. These coefficients are stored in an array, and the indicator calculates their average (avgPR) to provide a complete view of the market trend strength. Display Features: Each individual coefficient, as well as the overall average, is plotted on the chart using a specific color. Horizontal lines (both dashed and solid) are drawn at levels 0, ±0.8, and ±1, serving as visual thresholds. Additionally, conditional fills in red or blue highlight when values exceed these thresholds, helping the user quickly identify potential extreme conditions (such as overbought or oversold situations). 2. Visual Signals and Automated Alerts Graphical Signal Enhancements: To reinforce the analysis, the indicator uses graphical elements like emojis and shape markers. For example: If all 10 curves drop below -0.79, a 🌋 emoji appears at the bottom of the chart; When curves 2 through 10 are below -0.79, a ⛰️ emoji is displayed below the bar, potentially serving as a buy signal accompanied by an alert condition; Likewise, symmetrical conditions for correlations exceeding 0.79 produce corresponding emojis (🤿 and 🏖️) at the top or bottom of the chart. Alerts and Notifications: Using these visual triggers, several alertcondition statements are defined within the script. This allows users to set up TradingView alerts and receive real-time notifications whenever the market reaches these predefined critical zones identified by the multi-period analysis. 3. Regression Channel Analysis Principles and Calculations: In addition to the oscillator, the indicator implements an analysis of regression channels. For each of the 8 configurable channels, the user can set a range of periods (for example, min1 to max1, etc.). The function calc_regression_channel iterates through the defined period range to find the optimal period that maximizes a statistical measure derived from a regression parameter calculated by the function r(p). Once this optimal period is identified, the indicator computes two key points (A and B) which define the main regression line, and then creates a channel based on the calculated deviation (an RMSE multiplied by a user-defined factor). The regression channels are not displayed on the chart but are used to plot shapes & fullfilled a table. Blue shapes are plotted when 6th channel or 7th channel are lower than 3 deviations Yellow shapes are plotted when 6th channel or 7th channel are higher than 3 deviations 4. Scores, Conditions, and the Summary Table Scoring System: The indicator goes further by assigning scores across multiple analytical categories, such as: 1. BigPear Score What It Represents: This score is based on a longer-term moving average of the Pearson correlation values (SMA 100 of the average of the 10 curves of correlation of Pearson). The BigPear category is designed to capture where this longer-term average falls within specific ranges. Conditions: The script defines nine boolean conditions (labeled BigPear1up through BigPear9up for the “up” direction). Here's the rules : BigPear1up = (bigsma_avgPR <= 0.5 and bigsma_avgPR > 0.25) BigPear2up = (bigsma_avgPR <= 0.25 and bigsma_avgPR > 0) BigPear3up = (bigsma_avgPR <= 0 and bigsma_avgPR > -0.25) BigPear4up = (bigsma_avgPR <= -0.25 and bigsma_avgPR > -0.5) BigPear5up = (bigsma_avgPR <= -0.5 and bigsma_avgPR > -0.65) BigPear6up = (bigsma_avgPR <= -0.65 and bigsma_avgPR > -0.7) BigPear7up = (bigsma_avgPR <= -0.7 and bigsma_avgPR > -0.75) BigPear8up = (bigsma_avgPR <= -0.75 and bigsma_avgPR > -0.8) BigPear9up = (bigsma_avgPR <= -0.8) Conditions: The script defines nine boolean conditions (labeled BigPear1down through BigPear9down for the “down” direction). BigPear1down = (bigsma_avgPR >= -0.5 and bigsma_avgPR < -0.25) BigPear2down = (bigsma_avgPR >= -0.25 and bigsma_avgPR < 0) BigPear3down = (bigsma_avgPR >= 0 and bigsma_avgPR < 0.25) BigPear4down = (bigsma_avgPR >= 0.25 and bigsma_avgPR < 0.5) BigPear5down = (bigsma_avgPR >= 0.5 and bigsma_avgPR < 0.65) BigPear6down = (bigsma_avgPR >= 0.65 and bigsma_avgPR < 0.7) BigPear7down = (bigsma_avgPR >= 0.7 and bigsma_avgPR < 0.75) BigPear8down = (bigsma_avgPR >= 0.75 and bigsma_avgPR < 0.8) BigPear9down = (bigsma_avgPR >= 0.8) Weighting: If BigPear1up is true, 1 point is added; if BigPear2up is true, 2 points are added; and so on up to 9 points from BigPear9up. Total Score: The positive score (posScoreBigPear) is the sum of these weighted conditions. Similarly, there is a negative score (negScoreBigPear) that is calculated using a mirrored set of conditions (named BigPear1down to BigPear9down), each contributing a negative weight (from -1 to -9). In essence, the BigPear score tells you—in a weighted cumulative way—where the longer-term correlation average falls relative to predefined thresholds. 2. Pear Score What It Represents: This category uses the immediate average of the Pearson correlations (avgPR) rather than a longer-term smoothed version. It reflects a more current picture of the market’s correlation behavior. How It’s Calculated: Conditions: There are nine conditions defined for the “up” scenario (named Pear1up through Pear9up), which partition the range of avgPR into intervals. For instance: Pear1up = (avgPR > -0.2 and avgPR <= 0) Pear2up = (avgPR > -0.4 and avgPR <= -0.2) Pear3up = (avgPR > -0.5 and avgPR <= -0.4) Pear4up = (avgPR > -0.6 and avgPR <= -0.5) Pear5up = (avgPR > -0.65 and avgPR <= -0.6) Pear6up = (avgPR > -0.7 and avgPR <= -0.65) Pear7up = (avgPR > -0.75 and avgPR <= -0.7) Pear8up = (avgPR > -0.8 and avgPR <= -0.75) Pear9up = (avgPR > -1 and avgPR <= -0.8) There are nine conditions defined for the “down” scenario (named Pear1down through Pear9down), which partition the range of avgPR into intervals. For instance: Pear1down = (avgPR >= 0 and avgPR < 0.2) Pear2down = (avgPR >= 0.2 and avgPR < 0.4) Pear3down = (avgPR >= 0.4 and avgPR < 0.5) Pear4down = (avgPR >= 0.5 and avgPR < 0.6) Pear5down = (avgPR >= 0.6 and avgPR < 0.65) Pear6down = (avgPR >= 0.65 and avgPR < 0.7) Pear7down = (avgPR >= 0.7 and avgPR < 0.75) Pear8down = (avgPR >= 0.75 and avgPR < 0.8) Pear9down = (avgPR >= 0.8 and avgPR <= 1) Weighting: Each condition has an associated weight, such as 0.9 for Pear1up, 1.9 for Pear2up, and so on, up to 9 for Pear9up. Sum up : Pear1up = 0.9 Pear2up = 1.9 Pear3up = 2.9 Pear4up = 3.9 Pear5up = 4.99 Pear6up = 6 Pear7up = 7 Pear8up = 8 Pear9up = 9 Total Score: The positive score (posScorePear) is the sum of these values for each condition that returns true. A corresponding negative score (negScorePear) is calculated using conditions for when avgPR falls on the positive side, with similar weights in the negative direction. This score quantifies the current correlation reading by translating its relative level into a numeric score through a weighted sum. 3. Trendpear Score What It Represents: The Trendpear score is more dynamic as it compares the current avgPR with its short-term moving average (sma_avgPR / 14 periods ) and also considers its relationship with an even longer moving average (bigsma_avgPR / 100 periods). It is meant to capture the trend or momentum in the correlation behavior. How It’s Calculated: Conditions: Nine conditions (from Trendpear1up to Trendpear9up) are defined to check: Whether avgPR is below, equal to, or above sma_avgPR by different margins; Whether it is trending upward (i.e., it is higher than its previous value). Here are the rules Trendpear1up = (avgPR <= sma_avgPR -0.2) and (avgPR >= avgPR ) Trendpear2up = (avgPR > sma_avgPR -0.2) and (avgPR <= sma_avgPR -0.07) and (avgPR >= avgPR ) Trendpear3up = (avgPR > sma_avgPR -0.07) and (avgPR <= sma_avgPR -0.03) and (avgPR >= avgPR ) Trendpear4up = (avgPR > sma_avgPR -0.03) and (avgPR <= sma_avgPR -0.02) and (avgPR >= avgPR ) Trendpear5up = (avgPR > sma_avgPR -0.02) and (avgPR <= sma_avgPR -0.01) and (avgPR >= avgPR ) Trendpear6up = (avgPR > sma_avgPR -0.01) and (avgPR <= sma_avgPR -0.001) and (avgPR >= avgPR ) Trendpear7up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR <= bigsma_avgPR) Trendpear8up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR -0.03) Trendpear9up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR) Weighting: The weights here are not linear. For example, the lightest condition may add 0.1 point, whereas the most extreme condition (e.g., when avgPR is not only above the moving average but also reaches a high proportion relative to bigsma_avgPR) might add as much as 90 points. Trendpear1up = 0.1 Trendpear2up = 0.2 Trendpear3up = 0.3 Trendpear4up = 0.4 Trendpear5up = 0.5 Trendpear6up = 0.69 Trendpear7up = 7 Trendpear8up = 8.9 Trendpear9up = 90 Total Score: The positive score (posScoreTrendpear) is the sum of the weights from all conditions that are satisfied. A negative counterpart (negScoreTrendpear) exists similarly for when the trend indicates a downward bias. Trendpear integrates both the level and the direction of change in the correlations, giving a strong numeric indication when the market starts to diverge from its short-term average. 4. Deviation Score What It Represents: The “Écart” score quantifies how far the asset’s price deviates from the boundaries defined by the regression channels. This metric can indicate if the price is excessively deviating—which might signal an eventual reversion—or confirming a breakout. How It’s Calculated: Conditions: For each channel (with at least seven channels contributing to the scoring from the provided code), there are three levels of deviation: First tier (EcartXup): Checks if the price is below the upper boundary but above a second boundary. Second tier (EcartXup2): Checks if the price has dropped further, between a lower and a more extreme boundary. Third tier (EcartXup3): Checks if the price is below the most extreme limit. Weighting: Each tier within a channel has a very small weight for the lowest severities (for example, 0.0001 for the first tier, 0.0002 for the second, 0.0003 for the third) with weights increasing with the channel index. First channel : 0.0001 to 0.0003 (very short term) Second channel : 0.001 to 0.003 (short term) Third channel : 0.01 to 0.03 (short mid term) 4th channel : 0.1 to 0.3 ( mid term) 5th channel: 1 to 3 (long mid term) 6th channel : 10 to 30 (long term) 7th channel : 100 to 300 (very long term) Total Score: The overall positive score (posScoreEcart) is the sum of all the weights for conditions met among the first, second, and third tiers. The corresponding negative score (negScoreEcart) is calculated similarly (using conditions when the price is above the channel boundaries), with the weights being the same in magnitude but negative in sign. This layered scoring method allows the indicator to reflect both minor and major deviations in a gradated and cumulative manner. Example : Score + = 321.0001 Score - = -0.111 The asset price is really overextended in long term view, not for mid term & short term expect the in the very short term. Score + = 0.0033 Score - = -1.11 The asset price is really extended in short term view, not for mid term (even a bit underextended) & long term is neutral 5. Slope Score What It Represents: The Slope score captures the trend direction and steepness of the regression channels. It reflects whether the regression line (and hence the underlying trend) is sloping upward or downward. How It’s Calculated: Conditions: if the slope has a uptrend = 1 if the slope has a downtrend = -1 Weighting: First channel : 0.0001 to 0.0003 (very short term) Second channel : 0.001 to 0.003 (short term) Third channel : 0.01 to 0.03 (short mid term) 4th channel : 0.1 to 0.3 ( mid term) 5th channel: 1 to 3 (long mid term) 6th channel : 10 to 30 (long term) 7th channel : 100 to 300 (very long term) The positive slope conditions incrementally add weights from 0.0001 for the smallest positive slopes to 100 for the largest among the seven checks. And negative for the downward slopes. The positive score (posScoreSlope) is the sum of all the weights from the upward slope conditions that are met. The negative score (negScoreSlope) sums the negative weights when downward conditions are met. Example : Score + = 111 Score - = -0.1111 Trend is up for longterm & down for mid & short term The slope score therefore emphasizes both the magnitude and the direction of the trend as indicated by the regression channels, with an intentional asymmetry that flags strong downtrends more aggressively. Summary For each category—BigPear, Pear, Trendpear, Écart, and Slope—the indicator evaluates a defined set of conditions. Each condition is a binary test (true/false) based on different thresholds or comparisons (for example, comparing the current value to a moving average or a channel boundary). When a condition is true, its assigned weight is added to the cumulative score for that category. These individual scores, both positive and negative, are then displayed in a table, making it easy for the trader to see at a glance where the market stands according to each analytical dimension. This comprehensive, weighted approach allows the indicator to encapsulate several layers of market information into a single set of scores, aiding in the identification of potential trading opportunities or market reversals. 5. Practical Use and Application How to Use the Indicator: Interpreting the Signals: On your chart, observe the following components: The individual correlation curves and their average, plotted with visual thresholds; Visual markers (such as emojis and shape markers) that signal potential oversold or overbought conditions The summary table that aggregates the scores from each category, offering a quick glance at the market’s state. Trading Alerts and Decisions: Set your TradingView alerts through the alertcondition functions provided by the indicator. This way, you receive immediate notifications when critical conditions are met, allowing you to react as soon as the market reaches key levels. This tool is especially beneficial for advanced traders who want to combine multiple technical dimensions to optimize entry and exit points with a confluence of signals. Conclusion and Additional Insights In summary, this advanced indicator innovatively combines multi-scale Pearson correlation analysis (via multiple linear regressions) with robust regression channel analysis. It offers a deep and nuanced view of market dynamics by delivering clear visual signals and a comprehensive numerical summary through a built-in score table. Combine this indicator with other tools (e.g., oscillators, moving averages, volume indicators) to enhance overall strategy robustness.
Индикатор Pine Script®
от AlwwAl
MACD of RSI [TORYS]MACD of RSI — Momentum & Divergence Scanner Description: This enhanced oscillator applies MACD logic directly to the Relative Strength Index (RSI) rather than price, giving traders a clearer look at internal momentum and early shifts in trend strength. Now featuring a custom histogram, dual MA types, and RSI-based divergence detection — it’s a complete toolkit for identifying exhaustion, acceleration, and hidden reversal points in real time. How It Works: Calculates the MACD line as the difference between a fast and slow moving average of RSI. Adds a Signal Line (MA of the MACD) and plots a Histogram to show momentum acceleration/deceleration. Both RSI MAs and the Signal Line can be toggled between EMA and SMA for custom tuning. Divergence Detection: Bullish Divergence : Price makes a lower low while RSI makes a higher low → labeled with a green “D” below the curve. Bearish Divergence : Price makes a higher high while RSI makes a lower high → labeled with a red “D” above the curve. Configurable lookback window for tuning sensitivity to pivots, with 4 as the sweet spot. RSI Pivot Dot Signals: Plots green dots at RSI oversold pivot lows below 30, Plots red dots at overbought pivot highs above 70. Helps detect short-term exhaustion or bounce zones, plotted right on the MACD-RSI curve. RSI 50 Crosses (Optional): Optional ▲ and ▼ labels when RSI crosses its 50 midline — useful for momentum trend shifts or pullback confirmation, or to detect consolidation. Histogram: Plotted as a column chart showing the distance between MACD and Signal Line. Colored dynamically: Bright green : Momentum rising above zero Light green : Weakening above zero Bright red : Momentum falling below zero Light red : Weakening below zero The zero line serves as the mid-point: Above = Bullish Bias Below = Bearish Bias How to Interpret: Momentum Confirmation: Use MACD cross above Signal Line with a rising histogram to confirm breakouts or trend entries. Histogram shrinking near zero = momentum weakening → caution or reversal. Exhaustion & Reversals: Dot signals near RSI extremes + histogram peak can suggest overbought/oversold pressure. Use divergence labels ("D") to spot early reversal signals before price breaks structure. Inputs & Settings: RSI Length Fast/Slow MA Lengths for MACD (applied to RSI) Signal Line Length MA Type: Choose between EMA and SMA for MACD and Signal Line Pivot Sensitivity for dot markers Divergence Logic Toggle Show/hide RSI 50 Crosses Best For: Traders who want momentum insight from inside RSI, not price Scalpers using divergence or exhaustion entries Swing traders seeking entry confirmation from signal crossovers Anyone using multi-timeframe confluence with RSI and trend filters Pro Tips: Combine this with: Bollinger Bands breakouts and reversals VWAP or EMAs to filter entries by trend Volume spikes or BBW squeezes for volatility confirmation TTM Scalper Alert to sync structure and momentum
Индикатор Pine Script®
от alexrainman
1
regressionsLibrary "regressions" This library computes least square regression models for polynomials of any form for a given data set of x and y values. fit(X, y, reg_type, degrees)   Takes a list of X and y values and the degrees of the polynomial and returns a least square regression for the given polynomial on the dataset.   Parameters:      X (array) : (float ) X inputs for regression fit.      y (array) : (float ) y outputs for regression fit.      reg_type (string) : (string) The type of regression. If passing value for degrees use reg.type_custom      degrees (array) : (int ) The degrees of the polynomial which will be fit to the data. ex: passing array.from(0, 3) would be a polynomial of form c1x^0 + c2x^3 where c2 and c1 will be coefficients of the best fitting polynomial.   Returns: (regression) returns a regression with the best fitting coefficients for the selecected polynomial regress(reg, x)   Regress one x input.   Parameters:      reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.      x (float) : (float) The input value cooresponding to the y_pred.   Returns: (float) The best fit y value for the given x input and regression. predict(reg, X)   Predict a new set of X values with a fitted regression. -1 is one bar ahead of the realtime   Parameters:      reg (regression) : (regression) The fitted regression which the y_pred will be calulated with.      X (array)   Returns: (float ) The best fit y values for the given x input and regression. generate_points(reg, x, y, left_index, right_index)   Takes a regression object and creates chart points which can be used for plotting visuals like lines and labels.   Parameters:      reg (regression) : (regression) Regression which has been fitted to a data set.      x (array) : (float ) x values which coorispond to passed y values      y (array) : (float ) y values which coorispond to passed x values      left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.      right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.   Returns: (chart.point ) Returns an array of chart points plot_reg(reg, x, y, left_index, right_index, curved, close, line_color, line_width)   Simple plotting function for regression for more custom plotting use generate_points() to create points then create your own plotting function.   Parameters:      reg (regression) : (regression) Regression which has been fitted to a data set.      x (array)      y (array)      left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value.      right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value.      curved (bool) : (bool) If the polyline is curved or not.      close (bool) : (bool) If true the polyline will be closed.      line_color (color) : (color) The color of the line.      line_width (int) : (int) The width of the line.   Returns: (polyline) The polyline for the regression. series_to_list(src, left_index, right_index)   Convert a series to a list. Creates a list of all the cooresponding source values from left_index to right_index. This should be called at the highest scope for consistency.   Parameters:      src (float) : (float ) The source the list will be comprised of.      left_index (int) : (float ) The left most bar (farthest back historical bar) which the cooresponding source value will be taken for.      right_index (int) : (float ) The right most bar closest to the realtime bar which the cooresponding source value will be taken for.   Returns: (float ) An array of size left_index-right_index range_list(start, stop, step)   Creates an from the start value to the stop value.   Parameters:      start (int) : (float ) The true y values.      stop (int) : (float ) The predicted y values.      step (int) : (int) Positive integer. The spacing between the values. ex: start=1, stop=6, step=2:   Returns: (float ) An array of size stop-start regression   Fields:      coeffs (array__float)      degrees (array__float)      type_linear (series__string)      type_quadratic (series__string)      type_cubic (series__string)      type_custom (series__string)      _squared_error (series__float)      X (array__float)
Библиотека Pine Script®
от fluxchart
3
[blackcat] L2 Veronique Valcu VWAP Z-Score IndicatorLevel: 2 Background Veronique Valcu's article "Z-Score Indicator" in Feb,2003 provided a description and commentary on a new method of displaying directional change normalized in terms of standard deviation. This indicator is realized in pine script here by using the following function code, adding vwap function, called vwap ZScore. Function This indicator has three input, "AvgLen", "Smooth1" and "Smooth2." Price is fixed in selected vwap price. AvgLen describes the length of the sample considered in the standard deviation calculation. Once created and verified, the function can be easily called in any indicator or strategy. Inputs AvgLen --> Length input for vwap Zscore. Smooth1 and Smooth2 --> Smoothing length. Key Signal Curve1 --> vwap ZScore output fast signal Curve2 --> vwap ZScore output slow signal Remarks This is a Level 2 free and open source indicator. Feedbacks are appreciated.
Индикатор Pine Script®
от blackcat1402
2
Contango/Backwardation Futures Box Desk - TT ToolsContango / Backwardation Futures Box – TT Tools This indicator provides a clear, compact, and intuitive view of the Contango / Backwardation structure of a futures curve, displayed directly on the chart through an advanced informational box. It is designed for discretionary traders, spread traders, and curve analysis, with an optimized visualization for both desktop and mobile use. The box displays the real-time Contango or Backwardation structure of the futures curve, including: curve status (Contango / Backwardation / Flat) percentage spread between the front contracts prices of the three expiries (Near, Mid, Far) with directional indicators confirmation or non-confirmation of the curve structure contract expiration date with remaining days countdown rollover warning when expiration is approaching The box is fully optimized for Desktop, Compact, and Mobile layouts, ensuring a clean, adaptive design and always-readable information. Quick Start Guide Select the futures contracts Insert the nearest futures contracts into Front (1), Next (2) and Third (3), starting from the front-month contract. You can easily find the correct contract using “Change Symbol”, filtering by Futures and selecting the appropriate expiry. Check the contract expiry Identify the rollover date directly on the chart using Events → Contract Switch. This helps you confirm that you are analyzing the correct futures expiration. Set the NEXT EXPIRY date Enter the next futures expiration date in the NEXT EXPIRY (exact date) field. Simply match it with the contract switch shown on the chart to stay perfectly aligned. Monitor the curve The box displays in real time: curve structure (Contango / Backwardation / Flat) percentage spread between expiries prices of the three contracts with directional indicators structure confirmation status days-to-expiry countdown visual rollover warning when expiration approaches 👉 Always keep contracts and expiry dates updated to ensure an accurate reading of the futures curve and to anticipate rollover phases correctly. __________________________________________________________ Backwardation Futures Box – TT Tools Questo indicatore mostra in modo chiaro, compatto e immediato la struttura Contango / Backwardation di una curva futures, direttamente sul grafico tramite un box informativo avanzato. È pensato per trader discrezionali, spread traders e analisi di curva, con una visualizzazione ottimizzata sia per desktop che per mobile. Il riquadro box mostra in tempo reale la struttura di Contango o Backwardation della curva futures, includendo: • stato della curva (Contango / Backwardation / Flat) • spread percentuale tra le prime scadenze • prezzi delle tre scadenze (Near, Mid, Far) con indicatori direzionali • conferma o meno della struttura della curva • data di scadenza del contratto e countdown ai giorni residui • avviso di rollover imminente Il box è ottimizzato per Desktop, Compact e Mobile, con layout adattivo e informazioni sempre leggibili. Mini guida operativa Selezione dei contratti Inserisci nel box Front (1), Next (2) e Third (3) i future più prossimi a scadenza, partendo dal contratto front-month. Puoi cercare rapidamente il contratto corretto tramite “Cambia simbolo”, filtrando per Futures e selezionando la scadenza desiderata. Controllo della scadenza Individua la data di rollover direttamente sul grafico tramite la sezione Eventi → Switch di contratto. Utilizza questa informazione per verificare di stare analizzando la scadenza corretta. Impostazione della NEXT EXPIRY Inserisci nel campo NEXT EXPIRY (data precisa) la data di scadenza del prossimo future. È sufficiente confrontarla con lo switch di contratto visibile sul grafico per essere allineati correttamente. Monitoraggio della curva Il box mostra in tempo reale: struttura della curva (Contango / Backwardation / Flat) spread percentuale tra le scadenze prezzi dei tre contratti con direzione relativa conferma o meno della struttura countdown ai giorni residui alert visivo di rollover imminente 👉 Mantieni sempre aggiornati contratti e data di scadenza per avere una lettura affidabile della curva futures e anticipare correttamente le fasi di rollover.
Индикатор Pine Script®
от Time_Seven