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Regressions

Short-Term Weekly Refuges (Shelters)## // Introduction // (Spanish Texts Below) ═══════════════════════════ Short-Term Weekly Refuges (Shelters) (WR or RS) is a structural analysis indicator designed to track price action during the current week. It combines a configurable ZigZag with Fibonacci retracements anchored to recent phases, using the Weekly Opening Price (W.O.P.) as a key reference level. This indicator is optimized for 4H timeframe but also works on 1H and 15min charts. ## // Theoretical Foundation of the Indicator // ═════════════════════════════════════════════════ The WR (RS) indicator provides a structural framework for following price action during the current trading week. The core concept: Recent ZigZag phases, combined with the Weekly Opening Price, create dynamic support and resistance levels that institutional traders often monitor and use for intraweek positioning. The indicator allows you to select which recent phase (1-10) serves as the Fibonacci anchor. ## // Indicator Objectives // ═════════════════════════════ 1) Display a configurable ZigZag showing recent price structure with numbered phases (1 = most recent). Users should configure the ZigZag parameters based on whether they are analyzing a Major Degree Pattern (larger swings, less noise) or a Minor Degree Pattern (smaller swings, more detail), following standard Elliott Wave terminology. Configure the ZigZag to match the degree of your analysis: use higher Depth values for Major Degree Patterns, or lower values for Minor Degree Patterns. 2) Draw Fibonacci retracements on a user-selected phase, with two modes: • "On ZigZag": Traditional Fibonacci on the selected phase. • "Relative to W.O.P.": Fibonacci from phase anchor (i0) to Weekly Opening Price. 3) Show Weekly Opening Price lines as horizontal references, with the current week's line extended into the future. 4) Provide Pivot Up/Down markers for additional confirmation of local highs and lows. 5) Support multiple simultaneous indicator loads with visual identifier labels to distinguish between different analysis degrees (e.g., "Major Degree Pattern" vs "Minor Degree Pattern"). 6) Optional Embedded Indicator: Enable Intraday Shelters (RID) - percentage-based support/resistance levels calculated from the Daily Opening Price, useful for 1H and 15min trading. ## // Key Features // ═════════════════════ • **Flexible ZigZag**: Adjustable Depth, Deviation, and Backstep parameters to adapt to any asset's volatility and degree pattern. • **Phase Selection**: Choose from the 10 most recent phases for Fibonacci anchoring. • **Dual Fibonacci Modes**: Trace on the ZigZag phase itself, or relative to the Weekly Opening Price. • **New Age Color Palette**: Professional Fibonacci color scheme used by old school experienced traders. • **Weekly Opening Price (W.O.P.)**: Historical weekly opens plus current week projection. • **"Show Only W.O.P." Mode**: Isolate just the Weekly Opening Price line for cleaner charts on non-4H timeframes. • **Optional Intraday Shelters (RID)**: 11 percentage levels (±0.382%, ±1%, ±1.5%, ±2%, ±2.5%) based on Daily Opening Price. • **Multi-Load Support**: Visual identifier tags and Large Label for running multiple indicator instances simultaneously. ## // Recommended Workflow // ═════════════════════════════ 1) Load the indicator on a 4H chart. 2) Adjust ZigZag parameters (Depth, Deviation) until the phases match your visual analysis of recent price structure. 3) Select the phase you want to use as Fibonacci anchor (typically Phase 2, 3 or higher). 4) Choose Fibonacci mode: "On ZigZag" for phase analysis, or "Relative to W.O.P." for analysis based on weekly opening price context. 5) Monitor how price interacts with the Fibonacci levels and Weekly Opening Price throughout the week. 6) Optionally enable RID for intraday high precision order placements on 1H or 15min charts. ## // Integration with Other Refuge Indicators // ═════════════════════════════════════════════════ This indicator WR (RS) is part of our complete refuge-based analysis ecosystem: • LTR (RLP) (Long-Term Refuges): For automatic determination of the predominant phase of a ZigZag, which institutional investors choose as the basis for a Fibo whose levels calculate the projection for order placement over the following months and years. • LTRS (RLPS) (Simple Long-Term Refuges): Simplified version of LTR in which the known coordinates of the predominant phases (obtained with the LTR indicator) of up to five assets are easily captured for permanent long-term operation. • WR (RS) (Short-Term Weekly Refuges): For short-term tactical analysis (4H, 1H) based on chosen phases of a ZigZag that define Fibo levels effective during the near past week(s). • IDR (RID) (Intra-Day Refuges): For daily operations relying on intraday levels on timeframes of 1H or less. Ideal for scalping traders. By combining LTR, LTRS, WR and IDR, you obtain a multi-level framework that allows you to operate with clarity at any time horizon, from intraday positions to investments spanning months and years. ## // Additional Notes // ═════════════════════════ 1) Default parameters are optimized for volatile assets (crypto, tech stocks). For forex or less volatile instruments, consider reducing Deviation to 3-8%. 2) The "Phase in Development" (dashed line) shows the tentative current ZigZag segment that may still change as new bars form. 3) Due to TradingView's English-only publication rules, the complete Spanish version of this indicator with all tooltips and documentation will be available soon in our GitHub repository: aj-poolom-maasewal. 4) Bug reports, improvement proposals for the ZigZag generator, pattern determination, or Fibo composition, etc., will be greatly appreciated and taken into account for a future version. Best regards and happy hunting. ════════════════════════════════════════════════════ ════════════════════════════════════════════════════ TEXTO EN ESPANIOL (Sin acentos ni enies para compatibilidad con TradingView) ════════════════════════════════════════════════════ ## // Introduccion // ═════════════════════ Refugios Semanales (RS o WR) es un indicador de analisis estructural diseniado para seguir la accion del precio durante la semana en curso. Combina un ZigZag configurable con retrocesos de Fibonacci anclados a fases recientes, utilizando el Precio de Apertura Semanal (P.A.S.) como nivel de referencia clave. Este indicador esta optimizado para temporalidad de 4H pero tambien funciona en graficos de 1H y 15min. ## // Fundamento Teorico del Indicador // ═════════════════════════════════════════ El indicador RS (WR) proporciona un marco estructural para seguir la accion del precio durante la semana de operacion actual. El concepto central: Las fases recientes del ZigZag, combinadas con el Precio de Apertura Semanal, crean niveles dinamicos de soporte y resistencia que los operadores institucionales frecuentemente monitorean para su posicionamiento intrasemanal. El indicador permite seleccionar cual fase reciente (1-10) sirve como ancla del Fibonacci. ## // Objetivos del Indicador // ════════════════════════════════ 1) Mostrar un ZigZag configurable con la estructura de precios reciente y fases numeradas (1 = mas reciente). Los usuarios deben configurar los parametros del ZigZag segun esten analizando una Pauta de Grado Mayor (oscilaciones mas amplias, menos ruido) o una Pauta de Grado Menor (oscilaciones mas pequenias, mas detalle), siguiendo la terminologia estandar de Ondas de Elliott. Configure el ZigZag para que coincida con el grado de su analisis: use valores de Profundidad mas altos para Pautas de Grado Mayor, o valores mas bajos para Pautas de Grado Menor. 2) Dibujar retrocesos de Fibonacci en una fase seleccionada por el usuario, con dos modos: • "En el ZigZag": Fibonacci tradicional sobre la fase seleccionada. • "Respecto al P.A.S.": Fibonacci desde el ancla de la fase (i0) hasta el Precio de Apertura Semanal. 3) Mostrar lineas del Precio de Apertura Semanal como referencias horizontales, con la linea de la semana actual extendida hacia el futuro. 4) Proporcionar marcadores de Pivote Arriba/Abajo para confirmacion adicional de maximos y minimos locales. 5) Soportar multiples cargas simultaneas del indicador con etiquetas identificadoras visuales para distinguir entre diferentes grados de analisis (ej: "Pauta de Grado Mayor" vs "Pauta de Grado Menor"). 6) Indicador Integrado Opcional: Habilitar Refugios Intra-Diarios (RID) - niveles de soporte/resistencia basados en porcentajes calculados desde el Precio de Apertura Diaria, util para operacion en 1H y 15min. ## // Caracteristicas Principales // ════════════════════════════════════ • **ZigZag Flexible**: Parametros ajustables de Profundidad, Desviacion y Retroceso para adaptarse a la volatilidad y grado de pauta de cualquier activo. • **Seleccion de Fase**: Elija entre las 10 fases mas recientes para el anclaje del Fibonacci. • **Modos Duales de Fibonacci**: Trace sobre la fase del ZigZag, o relativo al Precio de Apertura Semanal. • **Paleta de Colores New Age**: Esquema de colores profesional de Fibonacci usado por operadores institucionales de la vieja escuela. • **Precio de Apertura Semanal (P.A.S.)**: Aperturas semanales historicas mas proyeccion de la semana actual. • **Modo "Solo P.A.S."**: Aisla unicamente la linea del Precio de Apertura Semanal para graficos mas limpios en temporalidades distintas a 4H. • **Refugios Intra-Diarios Opcionales (RID)**: 11 niveles porcentuales (±0.382%, ±1%, ±1.5%, ±2%, ±2.5%) basados en el Precio de Apertura Diaria. • **Soporte Multi-Carga**: Etiquetas identificadoras visuales y Rotulo Grande para ejecutar multiples instancias del indicador simultaneamente. ## // Flujo de Trabajo Recomendado // ═════════════════════════════════════ 1) Cargue el indicador en un grafico de 4H. 2) Ajuste los parametros del ZigZag (Profundidad, Desviacion) hasta que las fases coincidan con su analisis visual de la estructura de precios reciente. 3) Seleccione la fase que desea usar como ancla del Fibonacci (tipicamente Fase 2, 3 o superior). 4) Elija el modo de Fibonacci: "En el ZigZag" para analisis de fase, o "Respecto al P.A.S." para analisis basado en el contexto del precio de apertura semanal. 5) Monitoree como el precio interactua con los niveles de Fibonacci y el Precio de Apertura Semanal durante la semana. 6) Opcionalmente habilite RID para precision intradiaria en graficos de 1H o 15min. ## // Integracion con Otros Indicadores de Refugios // ══════════════════════════════════════════ RS (WR) es parte de nuestro ecosistema completo de analisis basado en refugios: • RLP (LTR) (Refugios de Largo Plazo): Para determinacion automatica de la fase preponderante de un ZigZag, que los inversionistas institucionales eligen como base para un Fibo cuyos niveles calculan la proyeccion para colocacion de ordenes durante los meses y anios siguientes. • RLPS (LTRS) (Refugios de Largo Plazo Simplificado): Version simplificada de RLP en la cual las coordenadas conocidas de las fases preponderantes (obtenidas con el indicador RLP) de uno o hasta cinco activos se capturan facilmente para operacion permanente de largo plazo. • RS (WR) (Refugios Semanales de Corto Plazo): Para analisis tactico de corto plazo (4H, 1H) basado en fases elegidas de un ZigZag que definen niveles Fibo efectivos durante la(s) semana(s) pasada(s) reciente(s). • RID (IDR) (Refugios Intra-Diarios): Para operaciones diarias basadas en niveles intradiarios en temporalidades de 1H o menos. Ideal para operadores de scalping. Al combinar RLP, RLPS, RS y RID, obtiene un marco multinivel que le permite operar con claridad en cualquier horizonte temporal, desde posiciones intradiarias hasta inversiones que abarcan meses y anios. ## // Notas Adicionales // ══════════════════════════ 1) Los parametros por defecto estan optimizados para activos volatiles (cripto, acciones tecnologicas). Para forex o instrumentos menos volatiles, considere reducir la Desviacion a 3-8%. 2) La "Fase en Desarrollo" (linea discontinua) muestra el segmento tentativo actual del ZigZag que aun puede cambiar conforme se formen nuevas barras. 3) Debido a las reglas de publicacion de TradingView (solo ingles), la version completa en espaniol de este indicador con todos los tooltips y documentacion estara disponible proximamente en nuestro repositorio de GitHub: aj-poolom-maasewal. 4) El reporte de cualquier error encontrado, propuestas de mejoras al generador de Zigzags, determinacion de pautas, o composicion del Fibo, etc., seran ampliamente agradecidas y tomadas en cuenta para una proxima version. Saludos y buena caceria. ══════════════════════════════════════════════════
Pine Script® インジケーター
Masewal_aj_p_oolomの投稿
Universal Adaptive Tracking🙏🏻 Behold, this is UAT (Universal Adaptive Tracker) , with less words imma proceed how it compares with alternatives: ^^ comparison with non-adaptive quadratic regression (purple line), that has higher overshoots, less precision ^^ comparison with JMA and its adaptive gain. JMA’s gain is heavily limited, while UAT’s negative and positive gains are soft-saturated with p-order Möbius transform This drop is inspired by, dedicated to, and made will all love towards Jurik Research , who retired in October 2k21. When some1 steps out, some1 has to step in, and that time it’s me (again xd). But there’s some history u gotta know: Some history u gotta know: In ~2008 dudes from forexfactory reverse engineered Jurik Moving Average In late 1990s dudes from Jurik Research approximated the best possible adaptive tracking filter for evolution of prices via engineering miracles Today in 2k26, me I'm gonna present to you the real mathematical objects/entities behind JMA top-edge engineered approximates. You will prolly be even more happy now then all the dem together back then. Why all this? When we talk about object tracking stuff, e.g. air defense, drones, missiles, projectiles, prices, etc, it all comes down to adaptive control and (Position & Velocity & Acceleration) aka PVA state space models (the real stuff many of you count as DSP ). Why? Cuz while position (P) : (mean), or position & velocity (PV) : (linear regression) are stable enough in dem own ways, Position & Velocity & Acceleration (PVA) : (quadratic regression+) require adaptivity do be stable. And real world stuff needs PVA, due to non-linearity for starters. So that’s why. If your goal is Really smoothing and no lag, u gotta go there. I see a lot of folks are crazy with it and want it, so here is it, for y’all. And good news, this is perfect for your favorite Moving Windows. How to use it The upper study: The final filter (main state): just as you use other fast smoothers, MAs, etc, you know better than me here You can also turn in volatility bands in script’s style settings, these do not require any adjustments Finally, you can turn on, in the same place, separate trackers each based on negative and positive volatility exclusively. When both are almost equal, that indicates stability & persistence in markets. May sound like it’s nothing important, but I've never seen anything like it before. Also, if you'd allow your our inner mental gym hero gloriously arise, you can argue that these 2 separate trackers represent 2 fair prices (one for sellers, one for buyers). All better then 1 imaginary fair price for both (forget about it) The lower study: The lower study: you can analyze streams of upward of downward volatilities separately. This is incredibly powerful You can also turn these off and turn on neg & pos intensities, and use them as trend detector, when each or both cross 1.5 (naturally neutral) threshold. ^^ Upper study with expected typical and maximum volatility bands turned On ... The method explained What you got in the end is non-linear, adaptive, lighting fast when needed and slow when required price tracking. All built upon real math entities/objects, not a brilliantly engineered approximation of them. No parameters to optimize, data tells it all. ... It all starts from a process model, in our cause this is... MFPM (Mechanical Feedback Price Model) Doesn’t make gaussian assumptions like most quant mainstream tech, accepts that innovations are Laplace “at best”, relies in L inf and L0 spaces. I created this model neither trynna fit non-fitting ARMA / variants, nor trynna be silly assuming that price state evolution and markets are random. Theory behind it: if no new volume comes, then price evolution would be simply guided by the feedback based on previous trading activity, pushing prices towards the midrange between 2 latest datapoints, being the main force behind so called “pullbacks” and reason why most pullbacks end just a bit past 50% of a move. This is the Real mechanical feedback based mean reversion, that is always there in the markets no matter what, think of it as a background process that is always there, and fresh new volume deviates prices away from it. Btw, this can also be expressed as AR2 with both phis = 0.5 . Then I separate positive and negative innovations from this model and process them separately, reflecting the asymmetry between buy and sell forces, smth that most forget. Both of these follow exponential distribution . Each stream has its own memory so here we use recursive operators . We track maximum innovations (differences between real and expected datapoints) with exponentially decaying damping factor, and keep tracking typical innovation, with the same factor. Then we calculate what’s called in lovely audio engineering as “ crest factor ”, the difference is we don’t do RMS and stuff. But hey again we work with laplace innovations, so we keep things in L0 and L inf spirit. Then we go a couple of steps further, making this crest factor truly relative (resolution agnostic), and then, most importantly, we apply a natural saturation on it based on p-order Möbius transform, but not with arbitrary p and L, but guided by informational limits of the data. These final "intensity" parameters are what we need next to make our object tracking adaptive. Extended Beta(2, 2) Window This is imo the main part of this. Looking at tapering windows in DSP and how wavelets are made from derivatives of PDF functions of probability distributions, I figured that why use just one derivative? That made me come up with Universal Moving Average , that combines PDF and CDF of Beta(2, 2) distribution . And that is fine for P (position) tracking model. Here we need PVA (position & velocity & acceleration). We can realize that everything starts from PDF, and by adding derivatives and anti-derivatives of it as factors of final window weights, we can create smth truly unique, a weightset that is non-arbitrary and naturally provides response alike quadratic regression does, But, naturally smoothed. Why do I consider this a discovery, a primordial math object? Because x^2 itself and Beta(2, 2) based on it are the only primitives, esp out of all these dozens of DSP tapering windows, that provide you a finite amount of derivatives. You can keep differentiating Hann window until the kingdom f come, while Welch window aka Beta(2, 2) has a natural stopping point, because the 3rd derivative is 0, so we can’t use it. Symmetrically, we do 2 steps up from PDF, getting 1st and second anti-derivatives. What’s lovely, symmetrically, 3rd antiderivative even tho exist, it stops making any sense. 2nd one still makes sense, it’s smth like “potential” of probability distribution, not really discussed in mainstream open access sources. Finally, the last part is to introduce adaptivity using these intensity exponents we’ve calculated with MFPM. We do 2 separate trackers, one using the negative intensity exponent, another one uses positive intensity exponent. And at the end, even tho using both together is cool, the final state estimate is calculated simply as the state which intensity has higher. ^^ impulse response of our final kernel with fixed (non adaptive) intensity exponents: 1 (blue) and 2 (red). You see it's all about phase … And that’s all folks. … Actually no … Last, not least, is the ability to add additional innovation weight to the kernel: ^^ Weighting by innovations “On”. Provides incredible tracking precision, paid with smoothness. I think this screenshot, showing what happened after the gap, and how the tracker managed to react, explains it all. ... Live Long and Prosper, all good TradingView ∞
Pine Script® インジケーター
gorx1の投稿
Polynomial Trend Exhaustion & DivergencePolynomial Trend Exhaustion & Divergence Overview This indicator combines advanced polynomial regression analysis with momentum-based exhaustion detection and forecast-based divergence signals. It identifies potential trend reversals by analyzing when price momentum is fading (exhaustion) and when price direction conflicts with the mathematical trajectory projected by cubic polynomial forecasting (divergence). The system uses optional source smoothing (Linear Regression Blend or Kalman filtering) to reduce noise before analysis, then applies two independent detection methods to generate high-probability reversal warnings. Exhaustion Detection What it detects: Trend exhaustion occurs when price is still moving in one direction but the underlying momentum is weakening—a classic early warning of potential reversal. How it works: The indicator calculates either a cubic polynomial regression or Kalman filter trend, then monitors the slope of that trend line. Exhaustion is detected when: Bullish Exhaustion: The slope is positive (uptrend) but the rate of change of the slope is negative (momentum decelerating) Bearish Exhaustion: The slope is negative (downtrend) but the rate of change of the slope is positive (momentum decelerating) Signal filtering: Consecutive Bars Required: Exhaustion conditions must persist for a configurable number of bars before triggering Max Repeat Signals: Limits how many consecutive exhaustion signals can fire to prevent clustering Cooldown Period: After hitting the max signal limit, the indicator pauses before allowing new signals This produces clean, actionable warnings rather than noise during extended exhaustion phases. Divergence Detection What it detects: Divergence signals identify when the polynomial-projected future price path conflicts with current price direction—suggesting price may be overextended and due for a correction toward the forecast. How it works: The indicator fits a cubic polynomial to recent price data and extrapolates it forward by a configurable number of bars. It then compares: Current price direction (rising or falling over the lookback period) Forecast position (above or below current price) Divergence triggers when: Bullish Divergence: Price is falling but the polynomial forecast is above current price (suggesting upward reversion) Bearish Divergence: Price is rising but the polynomial forecast is below current price (suggesting downward reversion) Signal filtering: Minimum Divergence (ATR): The forecast must be at least X ATRs away from price Minimum Price Movement (ATR): Price must have moved at least X ATRs over the lookback period (filters out sideways noise) Consecutive Bars Required: Divergence conditions must persist for X bars before triggering Cooldown Period: Minimum bars between divergence signals of the same type Key Features Dual trend methods: Choose between Polynomial Regression or Kalman filtering for the base trend calculation Source smoothing options: None, LinReg Blend, or Kalman filter applied to OHLC data before analysis ATR-normalized thresholds: All filter thresholds adapt to current volatility Anti-clustering logic: Built-in repeat limits and cooldowns prevent signal spam during extended conditions Full alert support: All four signal types (Bull/Bear Exhaustion, Bullish/Bearish Divergence) have dedicated alert conditions
Pine Script® インジケーター
B3AR_Tradesの投稿
AHR999 Index (Renewed)AHR999 Indicator The AHR999 Indicator is created by a Weibo user named ahr999. It assists Bitcoin investors in making investment decisions based on a timing strategy. This indicator implies the short-term returns of Bitcoin accumulation and the deviation of Bitcoin price from its expected valuation. When the AHR999 index is < 0.45, it indicates a buying opportunity at a low price. When the AHR999 index is between 0.45 and 1.2, it is suitable for regular investment. When the AHR999 index is > 1.2, it suggests that the coin price is relatively high and not suitable for trading. In the long term, Bitcoin price exhibits a positive correlation with block height. By utilizing the advantage of regular investment, users can control their short-term investment costs, keeping them mostly below the Bitcoin price.
Pine Script® インジケーター
CryptoNE0の投稿
Regression Slope Oscillator [BigBeluga]🔵 OVERVIEW The Regression Slope Oscillator is a trend–momentum tool that applies multiple linear regression slope calculations over different lookback ranges, then averages them into a single oscillator line. This design helps traders visualize when price is extending beyond typical regression behavior, as well as when momentum is shifting up or down. 🔵 CONCEPTS Regression Slope – Measures the steepness and direction of price trends over a selected length. f_log_regression(src, length) => float sumX = 0.0 float sumY = 0.0 float sumXSqr = 0.0 float sumXY = 0.0 for i = 0 to length - 1 val = math.log(src ) per = i + 1.0 sumX += per sumY += val sumXSqr += per * per sumXY += val * per slope = (length * sumXY - sumX * sumY) / (length * sumXSqr - sumX * sumX) slope*-1 Multi–Sample Averaging – Instead of relying on one regression slope, the indicator loops through many lengths (from Min Range to Max Range with Step increments) and averages their slopes. multiSlope(length)=> // Get regression slope slope = f_log_regression(close, length) slopAvg.push(slope) for i = minRange to maxRange by step multiSlope(i) Color Gradient – The oscillator and candles are colored dynamically from oversold (orange) to overbought (aqua), based on slope extremes observed within the user–defined Color Range. Trend Oscillation – When the oscillator rises, price trend is strengthening; when it falls, momentum weakens. 🔵 FEATURES Calculates regression slopes across a user–defined range (e.g., 10–100 with steps of 5). Averages all sampled slopes into a single oscillator line. Dynamic coloring of oscillator and chart candles based on slope values. User–controlled Color Range : High values (e.g., 50–100) → interpret as overbought vs oversold zones. Low values (e.g., 2–5) → interpret as slope rising vs falling momentum shifts. Dashboard table (top–right) displaying number of slope samples and current averaged slope value. Candle coloring mode (optional) – candles take on the oscillator gradient color for at–a–glance reading of trend bias. Signal Line (SMA) – A moving average of the slope oscillator used to identify momentum reversals. Bullish Reversal Signal – Triggered when the oscillator crosses above the signal line while below zero, indicating downside momentum exhaustion and potential trend recovery. Bearish Reversal Signal – Triggered when the oscillator crosses below the signal line while above zero, indicating upside momentum exhaustion and potential trend rollover. Dual Placement Signals – Reversal signals are plotted both: On the oscillator pane (for momentum context) On the price chart (for execution alignment) Confirmation Logic – Signals are only printed on confirmed bars to reduce repainting and false triggers. 🔵 HOW TO USE Watch the oscillator cross above/below zero: signals shifts in regression slope direction. Use the signal line crossovers near zero to identify early trend reversals. Use high Color Range settings to identify potential overbought/oversold extremes in trend slope. Use low Color Range settings for a faster, momentum–driven color change that tracks slope rising/falling. Candle coloring highlights short–term trend pressure in sync with the oscillator. Combine reversal signals with structure, support/resistance, or volume for higher–probability entries. 🔵 CONCLUSION The Regression Slope Oscillator transforms raw regression slope data into a smooth, color–coded oscillator. By averaging across multiple regression lengths, it avoids the noise of single–range analysis while still capturing trend extensions and momentum shifts. With the addition of signal line crossovers and confirmed reversal markers, the indicator now provides both trend context and actionable momentum signals within a single regression-based framework.
Pine Script® インジケーター
BigBelugaの投稿
6
ETH - Log Regression BandsETH – Log Regression Bands: Detailed Description (Math + How to Use) Overview This indicator plots a long-term “fair value” growth curve for ETH and surrounds it with multiple upper and lower bands. The goal is to estimate where price sits relative to a long-term trend that is best interpreted in **logarithmic (percentage) terms**, not raw dollars. The bands create clear zones showing when ETH is historically cheap or expensive relative to that long-term curve. --- Why use logarithms? Price action is typically more meaningful in **percentage moves** than in absolute dollar moves. * A move from $100 → $200 is +100% * A move from $2000 → $2100 is only +5% By modelling the natural logarithm of price, multiplicative growth becomes additive. That makes long-term growth easier to model and band spacing more consistent across very different price regimes. So instead of modelling (P), the indicator models: --- The growth model: Power-law curve The indicator uses “time since inception” as the x-axis. However, rather than using time directly, it uses the logarithm of time: where (t) is the number of days (or bars) since the first data point. It then fits a straight-line model in log-log space: Substituting back in: Exponentiating both sides gives the curve in normal price units: This is a **power-law** trend curve. It naturally produces a smooth, slowly bending long-term curve similar to the “log regression” curves often seen in macro crypto reports. --- What “expanding regression” means The model uses all data available from the beginning of the chart up to the current bar. That means: * Early in the asset’s history the curve can change more because there are fewer points. * Over time the curve becomes more stable as more history is included. Important note: this does **not** repaint past bars. It simply means the current curve will update as new data comes in. --- Measuring “typical deviation” from the curve (residual volatility) Once the trend curve is fitted in log space, the indicator measures how far price typically wanders away from it. At any time point: * Actual log price is (y = \ln(P)) * Predicted log price from the curve is (\hat{y} = a + b\ln(t)) The **residual** is: The indicator computes the standard deviation of these residuals: This (\sigma) is a measure of typical “distance from trend” in log terms. --- Building the bands (the key idea) The bands are evenly spaced in **log space** using multiples of (\sigma). A band number (k) is created by shifting the log-trend up or down: Upper band (k): Lower band (k): Where: * (k) is the band number (1, 2, 3, …) * (s) is a user-chosen spacing factor (band spacing) * (\sigma) is the residual standard deviation Converting back to normal price: Upper band (k): Lower band (k): Why bands look like “translated copies” Because shifting by a constant in log space equals multiplying by a constant in price space: So the bands are the same underlying curve scaled up or down by fixed multipliers. That produces the smooth “stacked curve” look associated with macro log regression charts. --- Optional curve shift (manual adjustment) A manual offset can be applied in log space. This is useful if you want to align the entire structure slightly higher or lower. Because the shift is applied to (\ln(P)), this is not an additive dollar adjustment. It scales the entire curve by a constant factor: * Positive shift → multiplies all bands upward * Negative shift → multiplies all bands downward --- How to interpret the zones The base curve represents a long-term “trend center” in log-growth terms. * Price near the base curve → near long-term trend * Price in upper bands → expensive relative to long-term trend * Price in lower bands → cheap relative to long-term trend Because the bands are built using residual volatility in log space, “cheap/expensive” is measured in a way that remains meaningful across different eras and price levels. --- Long-term buy zones (Lower 1 and Lower 2) **Lower 1** and **Lower 2** are intended as **long-term accumulation zones**. When ETH trades in these zones, it is significantly below the long-term growth curve in log terms, which typically corresponds to: * deep bear markets, * high fear / capitulation phases, * long accumulation periods. A simple long-term framework many users apply: * **Accumulate gradually when price enters Lower 1** * **Accumulate more aggressively when price enters Lower 2** * Reduce risk / take profits progressively in higher upper bands These are not guarantees — they are **statistical “distance from trend” zones**, designed to help structure long-term decisions. --- ## Notes / limitations * This indicator is a **macro trend tool**, not an intraday trading system. * The curve is derived from historical behavior; it can shift slowly as new data arrives. * Extremely new market regimes or structural changes can reduce reliability. * Use alongside risk management and additional confirmation if trading. ---
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Giovanni_Ambrosioの投稿
LogTrend Retest EngineLogTrend Retest Engine (LTRE) LogTrend Retest Engine (LTRE) is an advanced trend-continuation overlay designed to identify high-probability breakout retests using logarithmic regression , volatility-adjusted deviation bands , and market regime filtering . Unlike traditional channels or moving averages, LTRE models price behavior in log space , allowing it to adapt naturally to exponential market moves common in crypto, indices, and long-term trends. 🔹 How It Works Logarithmic Regression Core Performs linear regression on log-transformed price and time Produces a structurally accurate trend midline that scales with price growth Volatility-Adjusted Deviation Bands Dynamic upper and lower zones based on statistical deviation ATR weighting expands or contracts bands as volatility changes Adaptive Lookback (Optional) Automatically adjusts regression length using volatility pressure Faster response in high-volatility environments, smoother in consolidation 🔹 Market Regime Detection LTRE actively filters conditions using: R² trend strength (trend quality, not just slope) Volatility compression vs expansion User-defined minimum trend strength threshold Signals are disabled during ranging or low-quality conditions . 🔹 Breakout → Retest Signal Logic LTRE does not chase breakouts. Signals trigger only when: 1. Price breaks cleanly outside the deviation band 2. Market regime is confirmed as trending 3. Price performs a controlled retest within a user-defined tolerance BUY Break above upper band → retest → trend confirmed SELL Break below lower band → retest → trend confirmed This structure is designed to reduce false breakouts and late entries. 🔹 Visual & Projection Tools Clean midline and deviation bands Optional filled zones Optional future trend projection for forward structure planning On-chart statistics for trend strength and volatility compression 🔹 Best Use Cases Trend continuation & pullback strategies Crypto, Forex, Indices, and equities Works best on 15m and higher timeframes ⚠️ Disclaimer LTRE is a decision-support tool , not a complete trading system. Always use proper risk management and confirm signals with additional structure, volume, or higher-timeframe context. Built for traders who wait for structure — not noise.
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Cryptobingbongの投稿
ULTIMATE SMC FUSION 💎 ULTIMATE SMC FUSION ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ A premier Smart Money Concepts (SMC) indicator that masterfully combines multi-dimensional structure analysis with precision momentum filtering. This edition is optimized for manual SMC traders looking for clarity and performance. 🚀 KEY FEATURES: • FULL SMC SUITE: Automated Break of Structure (BOS) and Change of Character (CHoCH) detection. • HTF ADAPTIVITY: Fine-tuned logic specifically for $30m$, $1h$, and $4h$ charts to catch the major institutional moves. • PRECISION REVERSAL ENGINE: Advanced detection for Pinbar and Engulfing patterns at key liquidity zones. • SMART SCORING SYSTEM: Integrated analysis of ADX (Trend Strength), RSI (Momentum), and Volume. • ZERO-API ARCHITECTURE: Streamlined for maximum efficiency on your local TradingView terminal. • 2026 V2026 VISUALS: Modern, premium interface with glassmorphic stats and high-contrast signals. BEST FOR: SMC Traders, Prop Firm Challenges, and High-Precision Analysis. Feel free to adjust the settings to your own needs. Do not put your full confidence into a script, make your own decisions allways. Trade at your own risk.
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Uncle_SamFxの投稿
ALPHA FUSION FIX - RSI Extreme Strategy [Webhook Ready]Overview: This indicator is a simplified, high-precision tool focused on RSI Overbought and Oversold extremes (95/5). It was designed for traders who seek exhaustion points in the market with surgical precision. Key Features: Pure RSI Logic: Signals are triggered strictly at RSI 95 (Short) and RSI 5 (Long), avoiding market noise. Automation Ready: Includes a dynamic JSON Webhook integration for automated trading on exchanges like Binance. Risk Management: Built-in inputs for Margin, Leverage, and Max Positions directly in the UI. Visual Aids: Includes a Trio of EMAs (28, 80, 200) for trend context. How to use: Attach to any chart (Optimized for 15m/1h timeframes). Configure your Webhook Secret and risk parameters. Set an alert using "Any alert() function call".
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MarechalKhanの投稿
1
Kernel Filter Histogram (RBF)The Kernel Filter Histogram (RBF) is a regime-detection and edge-confirmation tool built on Gaussian (RBF) kernel regression. It is designed to identify when market conditions are favorable for participation and when traders should stay defensive. Instead of reacting to price noise, this indicator measures the normalized slope of a smoothed kernel regression curve, converts it into a z-score, and displays it as a histogram representing directional edge pressure. What It Measures Underlying market regime (bullish, bearish, or neutral) Strength and quality of directional momentum Statistical edge expansion vs compression When trend continuation is more likely vs chop How It Works Applies Nadaraya–Watson kernel regression using a Gaussian (RBF) kernel Calculates the slope of the regression curve Normalizes slope using ATR for cross-instrument consistency Converts the result into a z-score to measure statistical deviation Smooths the output into a readable histogram + signal line Uses an optional threshold gate to filter low-quality conditions Reading the Histogram Green bars → Bullish regime / positive edge Red bars → Bearish regime / negative edge Gray bars → Neutral / low-edge environment Above zero → Bullish pressure dominates Below zero → Bearish pressure dominates Threshold gating allows you to require minimum edge strength before treating signals as actionable. Best Use Cases Trade filter (only take longs when bullish, shorts when bearish) Regime confirmation for existing strategies Momentum quality assessment Avoiding chop and low-probability setups Multi-timeframe alignment tool What This Is (and Is Not) ✔ IS: A high-quality regime and edge filter ✔ IS: Designed for professional trading systems ✔ IS: Instrument-agnostic and timeframe-agnostic ✖ NOT: A buy/sell signal generator ✖ NOT: A lagging moving average ✖ NOT: A beginner indicator Recommended Usage Use this indicator as a gatekeeper: Only execute setups when the histogram confirms favorable regime conditions Combine with your entry trigger, not instead of it Works exceptionally well with trend-following, momentum, and mean-expansion systems
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Marketscopeの投稿
Log Trend Channel Enhanced**Log Trend Channel Enhanced (LTC+)** A logarithmic regression channel with 11 deviation bands and comprehensive statistical metrics. **Features:** - Logarithmic regression trendline from customizable start date - 11 parallel bands at ±0.5σ, ±1σ, ±1.5σ, ±2σ, ±2.5σ standard deviations - Color-coded zones (green = undervalued, red = overvalued) **Metrics displayed:** - R² (goodness of fit) - Pearson correlation - Implied CAGR (annualized return from trendline) - Distance from trend (%) - Current σ position - Channel position (%) - Historical percentile rank **Usage:** Ideal for long-term trend analysis on assets with exponential growth patterns. Use on log-scale charts for best visualization. Green zones near -2σ historically indicate accumulation opportunities; red zones near +2σ suggest distribution phases. **Settings:** - Adjustable start date (default: 1 year ago) - Customizable colors and line widths - Optional deviation labels - Configurable future projection
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jsmith0111の投稿
15-Minute Squeeze Scalper (Traffic Light Edition)Overview This is a highly optimized version of the famous Squeeze Momentum Indicator, customized specifically for 15-minute scalping . While the original indicator is powerful, the default colors can be confusing for new traders. I have recoded this to function as a simple "Traffic Light" system to help you identify periods of inaction vs. periods of high-probability breakouts. How it Works This tool identifies when the market is "quiet" (low volatility) and getting ready to explode. It uses Bollinger Bands and Keltner Channels to measure this energy. The "Traffic Light" Visuals 🔴 RED Cross (Center Line): STOP / WAIT Meaning: The Squeeze is ON. The market is coiling tight. Action: Do not trade yet. Wait for the energy to release. The longer the line of red dots, the bigger the potential move. 🟢 GREEN Cross (Center Line): GO / ACTION Meaning: The Squeeze has FIRED. Volatility is expanding. Action: Look at the Histogram to determine the direction of the trade. 📊 Histogram Bars: Lime/Green: Bullish Momentum (Trade Long). Red/Maroon: Bearish Momentum (Trade Short). The 15-Minute Scalping Strategy Identify the Squeeze: Look for a series of Red Crosses on the zero line. Wait for the Fire: Wait for the first Green Cross to appear. Confirm Direction: If the Cross turns Green AND the Histogram is above zero: LONG. If the Cross turns Green AND the Histogram is below zero: SHORT. Alerts Included I have added custom alerts so you don't have to stare at the screen: "Squeeze Fired": Alerts you instantly when the Red Cross changes to Green. "Momentum Long/Short": Alerts you when momentum flips direction.
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billal-kashifの投稿
Linear Regression Blend Candles [Adaptive]Regression Blend Candles A hybrid candle system that blends standard OHLC candles with linear regression candles at a user-defined ratio. The result is a cleaner price representation that filters noise while preserving market structure. Adaptive modes automatically adjust the blend based on market conditions. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 𝗛𝗢𝗪 𝗜𝗧 𝗪𝗢𝗥𝗞𝗦 The indicator calculates linear regression values for each OHLC component over a lookback period, then blends them with regular candle values based on your blend percentage. At 0% you see pure price action; at 100% you see full regression candles; anything between gives you a mix. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 𝗙𝗘𝗔𝗧𝗨𝗥𝗘𝗦 ◽ Blend Control Set a fixed blend percentage or enable adaptive mode. The blend slider lets you dial in exactly how much smoothing you want—useful for finding the sweet spot between noise reduction and signal responsiveness. ◽ Adaptive Blend Modes Let the market decide the blend ratio: • ATR — Higher volatility increases LR blend to filter chop • StdDev — Similar concept using standard deviation • ATR + StdDev — Combines both volatility measures • R-Squared — Increases blend when price fits a linear trend well (high R² = clean trend = trust the regression more) • R² + ATR — Combines trend quality with volatility for a balanced approach ◽ R-Squared Thresholds Fine-tune when the R² adaptive mode kicks in. Below the low threshold, blend stays at minimum. Above the high threshold, blend reaches maximum. This prevents over-smoothing during choppy, non-linear price action. ◽ Post-LR Smoothing Apply additional smoothing to the regression values before blending: • ALMA — Arnaud Legoux Moving Average with offset/sigma control • Kalman — Adaptive filter that balances responsiveness and smoothness • KAMA — Kaufman Adaptive MA that adjusts to market efficiency ◽ Advanced LR Mode Enable weighted regression with exponential decay (emphasizes recent bars) and lag correction (extrapolates based on velocity to reduce inherent LR lag). ◽ Ghost Candles Display faded regular candles behind the blended candles to visualize the difference and spot divergences between raw price and the smoothed representation. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 𝗦𝗨𝗚𝗚𝗘𝗦𝗧𝗘𝗗 𝗦𝗘𝗧𝗨𝗣𝗦 𝟭. 𝗧𝗿𝗲𝗻𝗱 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 (𝗦𝘄𝗶𝗻𝗴 𝗧𝗿𝗮𝗱𝗶𝗻𝗴) • LR Lookback: 14 • Blend %: 60-70% • Smoothing: None • Ghost Candles: On Use for cleaner swing identification. The higher blend percentage filters out intrabar noise while ghost candles let you see when price deviates significantly from the smoothed trend—potential reversal or continuation signals. 𝟮. 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗡𝗼𝗶𝘀𝗲 𝗙𝗶𝗹𝘁𝗲𝗿 (𝗜𝗻𝘁𝗿𝗮𝗱𝗮𝘆) • LR Lookback: 10 • Adaptive Blend: On • Mode: R² + ATR • Min/Max Blend: 25% / 75% • R² Thresholds: 0.3 / 0.8 Ideal for intraday trading on volatile instruments. The blend automatically increases during clean trends (high R²) and volatile moves (high ATR), then backs off during choppy consolidation to keep you closer to raw price action when the regression isn't fitting well. 𝟯. 𝗨𝗹𝘁𝗿𝗮-𝗦𝗺𝗼𝗼𝘁𝗵 (𝗛𝗶𝗴𝗵𝗲𝗿 𝗧𝗶𝗺𝗲𝗳𝗿𝗮𝗺𝗲 𝗕𝗶𝗮𝘀) • LR Lookback: 20 • Blend %: 80% • Smoothing: ALMA (offset 0.85, sigma 6) • Advanced LR: On (decay 0.9, lag correction 1.5) Maximum smoothing for identifying higher timeframe directional bias. The combination of longer lookback, high blend, ALMA smoothing, and lag correction creates a highly filtered view that cuts through noise. Best used on 4H+ charts or as a trend filter for lower timeframe entries.
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B3AR_Tradesの投稿
1
Linear Regression ChannelsThis indicator dynamically identifies and plots the best-fit linear regression channels based on recent pivot points, optimizing for statistical strength across user-defined depths. How It Works (Technical Methodology) 1. Pivot Point Detection The indicator uses Pine Script's ta.pivothigh() and ta.pivotlow() functions with a configurable sensitivity length to detect swing highs and lows. All recent pivot indices are stored in an array (limited to avoid performance issues), providing potential starting points for regression calculations. 2. Multi-Depth Evaluation Users input comma-separated "Pivot History Depths" (e.g., "5,20,50"). For each depth: - The script evaluates regression fits starting from the most recent pivots, up to the specified depth count. - It calculates linear regression statistics for each possible channel originating from those pivot bars backward to the current bar. 3. Linear Regression Calculation For each candidate channel: - Slope (m) and intercept (b) are computed using least-squares method. - R-squared (R²) measures goodness of fit (how well price follows the trend line). - Standard error of the estimate is calculated to quantify volatility around the regression line. - A composite score = R² × log(length) prioritizes stronger fits on longer periods. 4. Best-Fit Selection and Validation - Only channels with R² ≥ user-defined minimum (default 0.5) are considered valid. - The channel with the highest score for each depth is selected and drawn. - This ensures the most statistically significant and relevant channels are displayed, avoiding weak or short-term noise. 5. Channel Construction - Mean Line: The regression trend line extended slightly into the future. - Inner Channels: ± user-configurable standard deviation multiplier (default 2.0σ) around the mean. - Outer Bands: ±1.5× the inner deviation for additional visual context. - Filled areas between mean and inner channels for better visibility. - Color: Green shades for upward slopes (bullish trend), red shades for downward slopes (bearish trend). 6. Dashboard and Statistics - Optional table in the top-right corner displays for each depth: - Depth value - R² (colored green if >0.7, orange otherwise) - Slope (Beta) – positive blue for uptrend, red for downtrend - Current Z-Score: How many standard deviations the latest close is from the expected regression value (yellow if |Z| > 2) How to Use Regression channels help identify trending markets, potential mean reversion, and overextension. - Upward Channels (Green): Price above the mean may indicate strength; pullbacks to the mean or lower band offer long opportunities. Overextension above upper band could signal exhaustion. - Downward Channels (Red): Price below the mean may indicate weakness; rallies to the mean or upper band offer short opportunities. Overextension below lower band could signal capitulation. - High R² (>0.7): Strong trending channel – trade in direction of slope. - Low R²: Choppy/range-bound market – avoid trend-following trades. - Z-Score: |Z| > 2 suggests price is statistically overextended from the trend (potential reversion setup). - Multi-Depth: Smaller depths catch short-term trends; larger depths capture major trends. Use multiple for confluence across timeframes. Combine with volume, support/resistance, or other indicators for confirmation. Disclaimer This indicator is a technical analysis tool and should be used in conjunction with other forms of analysis. Past performance does not guarantee future results. Always use proper risk management.
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s3u1lの投稿
Composite Index [Auto Signals]Composite Index Description (描述正文): Overview This is an enhanced version of the famous Composite Index (CI) developed by Connie Brown. While the traditional RSI is confined between 0 and 100, often masking true momentum in strong trends, the Composite Index is uncapped and incorporates a momentum component to reveal the market's true structural strength. I have engineered this script to include Automated Signal Markers based on the crossover of the Composite Index and its Slow Moving Average. This helps traders instantly identify momentum shifts and "Timing" entries/exits without manual guesswork. Key Features Uncapped Momentum: Unlike RSI, the CI can go anywhere, preventing the "flattening" effect seen in strong trending markets (e.g., TSLA, NVDA). Automated Signals: ▲ Green Triangle (Launch): Triggers when the Gray CI line crosses ABOVE the Red Slow MA. This indicates bearish momentum is exhausted and bulls are regaining control. ▼ Red Triangle (Warning): Triggers when the Gray CI line crosses BELOW the Red Slow MA. This indicates bullish momentum is failing, serving as an early warning for exits or tightening stops. Classic Formula: Uses the standard Connie Brown parameters (14, 9, 3) + SMA smoothing for reliable divergence detection. How to Use This Indicator This script is best used as a companion to trend indicators like TTM Squeeze or Moving Average Ribbons. For Entries (The "Dip Buy"): In an uptrend, wait for a pullback. When the Green Triangle (▲) appears, it confirms that the pullback is over and momentum has turned back up. For Exits (The "Top"): Look for Divergence. If Price makes a Higher High but the Composite Index makes a Lower High—followed by a Red Triangle (▼)—this is a high-probability sell signal. The "Slow MA" Filter: The signals are generated only when the CI crosses the Slow MA (Red Line). This filters out the noise of minor fluctuations (crossing the Green line) and focuses on significant momentum changes. Settings RSI Period: 14 (Default) Momentum Period: 9 (Default) Signal Logic: Crossover/Crossunder of the Slow MA (33 Period). Disclaimer This tool is for educational purposes only. Always combine momentum signals with price action and structure analysis.
Pine Script® インジケーター
liaorの投稿
Box Indicator - Auto Draw Previous Day's - High / Midline / LowThis indicator draws a box around the previous day’s high and low, calculates the midline, and displays them on the current day’s chart. It helps visualize key support/resistance levels from the prior trading day. This script gives you a static reference box from the prior day’s trading range, including a midpoint. It’s useful for spotting potential reversal zones, breakout levels, or intraday targets based on yesterday’s price action.
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alexh1166の投稿
Shiori TFGI Lite Technical Fear and Greed Index (Open Source)Shiori’s TFGI Lite Technical Fear & Greed Index (Open Source) --- English — Official Description Shiori’s TFGI Lite is an open-source Technical Fear & Greed Index designed to help traders and investors understand market emotion, not predict price. Instead of generating buy or sell signals, this indicator focuses on answering a calmer, more important question: > Is the market emotionally stretched away from its own historical balance? TFGI Lite combines three well-known technical dimensions — volatility, price deviation, and momentum — and normalizes them into a single, intuitive 0–100 sentiment scale. What This Indicator Is * A market context tool, not a trading signal * A way to observe emotional extremes and misalignment * Designed for any asset, any timeframe * Fully open source, transparent and adjustable Core Components * Fear Factor: Short-term vs long-term ATR ratio with logarithmic compression * Greed Factor: Price Z-score with tanh-based normalization * Momentum Factor: Classic RSI as emotional momentum These factors are blended and gently smoothed to form the current sentiment level. Historical Baseline & Deviation TFGI Lite introduces a historical baseline concept: * The baseline represents the market’s own emotional equilibrium * Deviation measures how far current sentiment has drifted from that equilibrium This allows the indicator to highlight conditions such as: * 🔥 Overheated: High sentiment + strong positive deviation * 💎 Undervalued: Low sentiment + strong negative deviation * ⚠️ Misaligned: Emotionally extreme, but inconsistent with historical behavior How to Use (Lite Philosophy) * Use TFGI Lite as a background compass, not a trigger * Combine it with price structure, risk management, and your own strategy * Extreme readings suggest emotional tension, not immediate reversal > Think of TFGI Lite as market weather — it tells you the climate, not when to open or close the door. About Parameters & Customization All parameters in TFGI Lite are fully adjustable. Markets have different personalities — volatility, sentiment range, and emotional extremes vary by asset and timeframe. You are encouraged to: * Adjust fear/greed thresholds based on the asset you trade * Tune smoothing and baseline lengths to match your timeframe * Treat sentiment levels as relative, not universal absolutes There is no single “correct” setting — TFGI Lite is designed to adapt to your market, not force the market into a fixed model. Important Notes * This is a technical sentiment indicator, not financial advice * No future performance is implied * Designed to reduce emotional decision-making, not replace it --- 🇹🇼 繁體中文 — 指標說明 Shiori’s TFGI Lite（技術型恐懼與貪婪指數） 是一款開源的市場情緒指標，目的不是預測價格，而是幫助你理解市場當下的「情緒狀態」。 與其問「現在該不該買或賣」，TFGI Lite 更關心的是： > 市場情緒是否已經偏離了它自己的歷史平衡？ 本指標整合三個常見但關鍵的技術面向，並統一轉換為 0–100 的情緒刻度，讓市場狀態一眼可讀。 這個指標是什麼 * 市場情緒與狀態觀察工具（非買賣訊號） * 用來辨識情緒極端與錯位狀態 * 適用於任何商品與任何週期 * 完全開源，可學習、可調整 核心構成 * 恐懼因子：短期 / 長期 ATR 比例（對數壓縮） * 貪婪因子：價格 Z-Score（tanh 正規化） * 動能因子：RSI 作為情緒動量 歷史基準與偏離 TFGI Lite 引入「歷史情緒基準」的概念： * 基準代表市場長期的情緒平衡 * 偏離值顯示當前情緒與自身歷史的距離 因此可以辨識： * 🔥 過熱（高情緒 + 正向偏離） * 💎 低估（低情緒 + 負向偏離） * ⚠️ 錯位（情緒極端，但不符合歷史行為） 使用建議（Lite 精神） * 將 TFGI Lite 作為「背景雷達」，而非進出場依據 * 搭配價格結構、風險控管與個人策略 * 情緒極端不等於立刻反轉 > 你可以把它想像成市場的天氣預報，而不是交易指令。 參數調整與個人化說明 本指標中的所有參數皆可調整。不同市場、不同商品，其波動特性與情緒區間並不相同。 建議你： * 依標的特性自行調整恐懼 / 貪婪門檻 * 依交易週期調整平滑與基準長度 * 將情緒數值視為「相對狀態」，而非固定答案 TFGI Lite 的設計初衷，是讓你定義市場，而不是被單一參數綁住。 溫馨提示 如果你在調整指標參數時遇到不熟悉的項目，請點擊參數旁邊的 「!」圖示，每個設定都有清楚的說明。 本指標設計為可慢慢探索，請依自己的節奏理解市場狀態。 --- 🇯🇵 日本語 — インジケーター説明 Shiori’s TFGI Lite は、価格を予測するための指標ではなく、 市場の「感情状態」を可視化するためのオープンソース指標です。 この指標が問いかけるのは、 > 現在の市場感情は、過去のバランスからどれだけ乖離しているのか？ という一点です。 特徴 * 売買シグナルではありません * 市場心理の極端さやズレを観察するためのツールです * すべての銘柄・時間軸に対応 * 学習・調整可能なオープンソース 構成要素 * 恐怖要素：ATR 比率（対数圧縮） * 強欲要素：価格 Z スコア（tanh 正規化） * モメンタム：RSI ベースラインと乖離 市場自身の感情的な基準点と、 現在の感情との距離を測定します。 過熱・割安・感情のズレを視覚的に把握できます。 パラメータ調整について TFGI Lite のすべてのパラメータは調整可能です。市場ごとにボラティリティや感情の振れ幅は異なります。 * 恐怖・強欲の閾値は銘柄に応じて調整してください * 時間軸に合わせて平滑化やベースライン期間を変更できます * 数値は絶対値ではなく、相対的な感情状態として捉えてください この指標は、市場に合わせて柔軟に使うことを前提に設計されています。 フレンドリーヒント 入力項目で分からない設定がある場合は、横に表示されている 「!」アイコン をクリックしてください。各パラメータには分かりやすい説明が用意されています。 このインジケーターは、落ち着いて市場の状態を理解するためのものです。 --- 🇰🇷 한국어 — 지표 설명 Shiori’s TFGI Lite는 매수·매도 신호를 제공하는 지표가 아니라, 시장 감정의 상태를 이해하기 위한 기술적 심리 지표입니다. 이 지표의 핵심 질문은 다음과 같습니다. > 현재 시장 감정은 과거의 균형 상태에서 얼마나 벗어나 있는가? 특징 * 거래 신호 아님 * 시장 심리의 과열·저평가·불일치를 관찰 * 모든 자산, 모든 타임프레임 지원 * 오픈소스 기반 구성 요소 * 공포 요인: ATR 비율 (로그 압축) * 탐욕 요인: Z-Score (tanh 정규화) * 모멘텀: RSI 활용 방법 TFGI Lite는 배경 지표로 사용하세요. 가격 구조와 리스크 관리와 함께 사용할 때 가장 효과적입니다. 파라미터 조정 안내 TFGI Lite의 모든 설정 값은 사용자가 직접 조정할 수 있습니다. 자산마다 변동성과 감정 범위는 서로 다릅니다. * 공포 / 탐욕 기준값은 종목 특성에 맞게 조정하세요 * 타임프레임에 따라 스무딩 및 기준 기간을 변경할 수 있습니다 * 감정 수치는 절대적인 값이 아닌 상대적 상태로 해석하세요 이 지표는 하나의 정답을 강요하지 않고, 시장에 맞춰 적응하도록 설계되었습니다. 친절한 안내 설정 값이 익숙하지 않다면, 항목 옆에 있는 "!" 아이콘을 클릭해 보세요. 각 입력값마다 설명이 제공됩니다. 이 지표는 천천히 시장의 맥락을 이해하도록 설계되었습니다. --- Educational purpose only. Not financial advice. --- #FearAndGreed #MarketSentiment #TradingPsychology #TechnicalAnalysis #OpenSourceIndicator #Volatility #RSI #ATR #ZScore #MultiAsset #TradingView #Shiori
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