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RLP V4.3 -Long Term Support/Resistance Levels (Refuges-Shelters)// Introduction // We have utilized the Zigzag library technology from ©Trendoscope Pty Ltd for Zigzag generation, allowing users the freedom to choose which of the different Zigzags calculated by Trendoscope as "Levels and Sub-Levels" is most suitable for generating ideal phases for evaluation and selection as "most preponderant phases" over long-term periods of any asset, according to its particular behavior based on its age, volatility, and price trend. // Theoretical Foundation of the Indicator // Many traditional institutional investors use the latest higher-degree market phase that stands out from others (longest duration and greatest price change on daily timeframe) to base a Fibonacci retracement on whose levels they open long-term positions. These positions can remain open to be activated in the future even years in advance. The phase is considered valid until a new, more preponderant phase develops over time, at which point the same strategy is repeated. // Indicator Objectives // 1) Automatically find the latest most preponderant long-term phase of an asset, analyzing it on daily timeframe while considering whether the long-term market trend is bullish or bearish. 2) Draw a Fibonacci Retracement over the preponderant phase (reversed if the phase is bullish). 3) The indicator automatically numbers and locates the 3 most preponderant phases, selecting Top-1 for initial Fibo drawing. 4) If the user disagrees with the indicator's automatic selection, they have the freedom to choose any of the other 2 Top phases for the Fibo drawing and its levels. 5) If the user disagrees with the amplitude or frequency of the initially drawn Zigzag phases, they can modify the Zigzag calculation algorithm parameters until one of the Top-3 matches the phase they had in mind. 6) As an experimental bonus, the indicator runs a popularity contest (CP) of "bullseye" daily price (OHLC) matches, subject to user-defined tolerance ranges, against all Fibo levels of the Top 3 selected phases, to verify which phase the market prices are validating as the most popular for placing trades. Contest results are displayed in the POP. CONTEST column of the Top-3 phases table. If the contest detects a change in the winning phase, a switch can be enabled to activate an alert that the user can utilize with TradingView's alert creator to display an alarm, send an email, etc. 7) This indicator was designed for users to find the preponderant long-term phase of their assets and manually record the date-price coordinates of the i0-i1 anchors of the preponderant phase. The Top-1 phase coordinates are shown in the Top-3 phases table where they can be captured. The date-price coordinates of all HH and LL pivots, from all Zigzag phases, can be displayed via a switch. With the pivots, the user can select a different phase than those automatically found by the indicator, according to the conclusions of their own research. Subsequently, the user can forget about this RLP indicator for a while and move on to apply in their normal trading our RLPS indicator (Simplified Long-Term Shelters), in which they can draw and simultaneously track the long-term shelters of up to 5 different assets, simply by entering their corresponding date-price coordinates, previously located with this RLP indicator or through their own observation. // Additional Notes // 1) As of the this V4.3 publication date (01/2026), the Zigzag generation parameters were adjusted by default to find the long-term preponderant phases for the following assets: Bitcoin, Ethereum, Bitcoin futures BTC1! (all generated due to the 2020-2021 pandemic). It also provides by default the confirmed preponderant phases for the following assets: Apple, Google, Amazon, Microsoft, PayPal, NQ1!, ES1! and SP500 Cash. 2) Prices, phases, and levels shown on the graphic chart correspond to results obtained using daily Bitcoin data from the Bitstamp exchange, BTCUSD:BITSTAMP (popular here in Europe). 3) Any error corrections or improvements that can be made to the phase selection algorithms or the CP phase popularity contest algorithm will be highly appreciated (statistics and mathematics, among many other sciences, are not particularly our strong suit). 4) We sincerely regret to inform you that we have not included the Spanish translation previously provided, due to our significant concern regarding the ambiguous rules on publication bans related to indicators. 4) Sharing motivates. Happy hunting in this great jungle!
Pine Script® indicator
by Masewal_aj_p_oolom
Regression ChannelAn enhanced version of TradingView's Linear Regression Channel that displays multiple upper and lower deviation channels with support for both linear and exponential regression models. Getting Started & Usage This indicator overlays a regression channel with up to 4 customizable standard deviation levels above and below the regression line. By default, it uses linear regression, but you can switch to an exponential regression model for curved price trends. For detailed explanations of the statistical concepts and additional usage examples, please visit the documentation .
Pine Script® indicator
by ZenithClown
1
Trend-cycle reversion (multi-timeframe)Trend-cycle reversion (multi-timeframe) is a mean-reversion “stretch” gauge built around a simple idea: price often deviates from its recent path (trend + dominant swing rhythm), and those deviations become more actionable when you scale them by volatility and express them as a standardized score. This script models the last N bars as: 1) a linear trend (to capture drift), plus 2) a single dominant cycle (to capture the most prominent oscillation inside the same window). It then measures how far current price is from the model’s next-bar projection, normalizes that distance by ATR (volatility), and finally converts the result into a rolling Z-score. The output is displayed as a multi-timeframe dashboard so you can see “stretch vs. fit” across several time compressions at once. ------------------------------------------------------------ What you see on the chart ------------------------------------------------------------ The indicator draws a table (overlay) with up to 12 rows (configurable), one per timeframe from your CSV list. Each row shows: • TF: The timeframe being evaluated (e.g., 1, 5, 15, 60, 240, D). • Z: The current Z-score of the volatility-scaled model gap on that timeframe. • State: A simple interpretation using your Z threshold: - “Short ▼” when Z > +threshold (price is extended above the model path) - “Long ▲” when Z < −threshold (price is extended below the model path) - “Hold •” when inside the band (not unusually stretched) Colors follow the same logic: red for high positive Z, green for high negative Z, gray when neutral or unavailable. Important: “Long/Short” here describes the direction of mean-reversion pressure (over/under the fitted path), not a complete trading system by itself. ------------------------------------------------------------ How it works (plain-English math) ------------------------------------------------------------ 1) Optional log transform If “Fit on log(price)” is enabled, the model runs on log(price) instead of raw price. This is often useful for markets that behave multiplicatively (large percentage moves, long-term exponential growth), because distances become closer to “percent-like” rather than absolute dollars. 2) Trend fit (linear regression in the window) Over the last Window Length bars, the script estimates a straight-line trend. Think of this as the baseline path that best explains the window if you ignore swings. 3) Cycle search (best period by least-squares error) After removing the linear trend, the script searches for a single sinusoidal cycle period between: • Min Period and Max Period (in bars), stepping by Period Step. For each candidate period, it computes the best-fitting sine+cosine components and measures the remaining error (SSE). The period with the smallest SSE is selected as the “best” cycle for that window. To reduce recalculation cost and to keep the chosen cycle from flapping every bar, the script re-runs this period search only every “Re-search best period every N bars”. Between searches, it keeps using the last best period. 4) Next-bar projection and “gap” Using the fitted trend + fitted cycle, the script projects the model value one bar ahead (relative to the window indexing). It then computes: gap = (current value) − (projected value) If “Invert sign” is enabled, the gap is multiplied by −1. This doesn’t change magnitude, it only flips interpretation (useful if you prefer the opposite sign convention). 5) Volatility scaling via ATR The raw gap is divided by ATR to make it comparable across symbols and regimes. If you are fitting on log(price), ATR is also computed in log space using a log-based true range, then smoothed similarly (so the scale is consistent). This produces a “gap in ATR units”. 6) Z-score standardization Finally, the script computes a rolling Z-score of the ATR-scaled gap over “Z-score length”: Z = (gapATR − mean(gapATR)) / stdev(gapATR) This is what appears in the table. The Z-score answers: “How unusual is today’s model deviation compared to the last Z-score length observations?” ------------------------------------------------------------ How to interpret the Z-score ------------------------------------------------------------ Z near 0: Price is close to the model path relative to recent volatility (nothing unusual). Z above +threshold: Price is meaningfully ABOVE the fitted path (stretched up). This can be read as elevated downside mean-reversion pressure — but it can also persist during strong trends. Z below −threshold: Price is meaningfully BELOW the fitted path (stretched down). This can be read as elevated upside mean-reversion pressure — but it can also persist during fast selloffs. A practical way to use this indicator is to treat it as a “context filter” or “risk tool”: • Fading extremes: look for mean-reversion setups when Z is beyond the threshold and price action confirms (e.g., momentum stalls, structure breaks, volatility contraction/expansion cues). • Trend-aware reversion: only take “reversion” signals in the direction permitted by your separate trend filter (higher-timeframe trend, moving average regime, market structure, etc.). • Take-profit / risk management: in a trend-following strategy, extremes can be used as partial profit zones or as “don’t chase here” warnings. ------------------------------------------------------------ Multi-timeframe (MTF) notes ------------------------------------------------------------ Each table row is computed with request.security() on that timeframe with no lookahead, so it is not using future bars to form the value. However, like any live indicator, the value for an actively forming bar can change until that bar closes (especially on the lower timeframes). Also, higher-timeframe rows update when that higher-timeframe bar updates/closes. ------------------------------------------------------------ Inputs (what to change first) ------------------------------------------------------------ If you only change a few settings, start here: • Window Length: Controls how much history the model uses. Larger = smoother/stabler, but slower to adapt. • Min/Max Period + Step: Controls the cycle search range and granularity. - Wider ranges can capture more possibilities but cost more computation. - Smaller steps can find a closer match but also cost more. • Re-search every N bars: Higher = faster performance and more stability; lower = more adaptive but can be noisier. • ATR length (scale gap): Controls the volatility scale. Shorter reacts faster to volatility changes; longer is steadier. • Z-score length: Controls how “rare” extremes are. Longer lengths make Z more stable, but require more history and adapt slower to regime shifts. • Z threshold: Defines when the table labels “Long/Short”. Common choices are 1.5–2.5 depending on how selective you want extremes to be. • Timeframes (CSV) + Max table rows: Controls what you see in the dashboard. ------------------------------------------------------------ Limitations and expectations ------------------------------------------------------------ This is a single-cycle, windowed model. Markets can be multi-cycle, non-sinusoidal, or structurally shifting; in those cases the “best period” is simply the best approximation inside the window, not a guarantee of a true underlying rhythm. Z-score extremes are not automatic reversal calls. In strong trends or during volatility shocks, Z can stay extreme longer than expected. Use this as a measurement tool, then combine it with your own confirmation and risk management. This indicator is for analysis/education and does not provide financial advice.
Pine Script® indicator
by Tradenometry
Kinetic Regression VectorKinetic Regression Vector (KRV) is a non-repainting direction and compression indicator designed for one job: help you avoid low-quality markets and catch high-quality expansion moves when the odds improve. Most “prediction” tools either repaint, lag, or pretend they can call exact future prices. KRV doesn’t do that. Instead, it focuses on what actually improves trading outcomes: regime quality, directional bias, and compression-to-expansion timing — all shown visually and locked on closed candles. What goes into it (what it’s built from) KRV fits a smooth model to the last N bars of price action and projects that structure forward as a “vector tunnel.” It uses three core ideas: Weighted Least Squares (WLS) regression Recent candles matter more than older ones. That means the model reacts faster when conditions change (important for sector shifts and fast ETF rotations), without using lagging moving averages. Quality gating with R² The indicator measures whether the market has been clean and structured recently. If structure is weak (chop/noise), KRV effectively turns itself “off” so you’re not trading randomness. Model-based uncertainty bands (SEE) with a volatility fallback Instead of sizing the tunnel only by volatility, KRV can size it by how consistent the model has been. When the model is unreliable, the tunnel widens. When it’s reliable, the tunnel tightens. If you prefer classic behavior, ATR-based band sizing is available as a fallback. What makes it different (why it stands out) KRV stands out because it combines features that are usually not together in one tool: Adaptive, model-driven tunnel width (based on model error when SEE is enabled), instead of a fixed volatility channel that can look “confident” even in messy regimes. Directional bias that is not a moving-average lag (it’s based on the fitted structure’s slope). A compression trigger that is self-relative (pinch compares current band width to its own historical baseline, not an arbitrary threshold). Strict non-repaint design (signals are computed from closed candles so the chart doesn’t lie after the fact). Forward visualization (the tunnel projects into the future as a reference map, with uncertainty naturally increasing forward). What you see on the chart Vector Tunnel: the projected path and the expected noise range around it. Color: bullish or bearish bias based on the current slope of the model. Pinch: compression detected (band width unusually tight versus its baseline). Bull/Bear Bullets: confirmed pinch signals aligned with directional bias. Target Marker: a forward reference point based on the current structure (not a guarantee, but a useful reference level). How to use it (simple, repeatable) Use it as a three-step decision tool: Gate (participate or stand down): If the model is not “on” (quality is weak), treat it as a “stay out” signal. This is the most important feature for avoiding bad trades. Direction (bias): When the model is on, follow the bias. Bull bias means your edge is on longs. Bear bias means you avoid longs (or only take bearish setups if you trade that way). Pinch + confirmation (timing): A pinch means pressure is building. The bullet marks “compression + bias.” For best results, act after you see expansion confirmation (breakout candle / range expansion / level break) rather than treating the bullet as a blind entry. Best features (why traders keep it) Non-repainting signals locked to closed bars Clear “stay out” logic during chop Direction bias that responds faster than classic lagging tools Compression detection designed to highlight expansion windows Forward tunnel for planning risk, entries, and exits visually Best markets and timeframes KRV performs best on liquid ETFs and liquid large-cap stocks, and on sector themes like energy where regime shifts matter. Recommended timeframes: 4H: best for timing entries and avoiding noise Daily: best for swing direction and higher-quality setups Weekly: best for big-picture regime filtering (stay out vs participate) Monthly can be used for macro regime, but not for timing. What to expect (honest expectations) KRV is not a guaranteed predictor of exact prices. Its edge comes from: filtering out weak/noisy regimes, identifying compression that often precedes expansion, and aligning that setup with a directional bias, without repainting.
Pine Script® indicator
by Finntech1
Short-Term Weekly Refuges (Shelters)## // Introduction // ══════════════ 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. • **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 precision on 1H or 15min charts. ## // Integration with Other Refuge Indicators // ════════════════════════════════ WR (RS) is part of a 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 one or up to five assets are easily captured for permanent long-term operation. • WR (RS) (Short-Term Weekly Refuges): (This indicator) For short-term tactical analysis (4H, 1H) based on chosen phases of a ZigZag that define Fibo levels generated during the near past week(s) and probably effective in the present week. • 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) 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. (Sorry: Spanish translation erased trying to avoid confusing publishing banning rules).
Pine Script® indicator
by Masewal_aj_p_oolom
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® indicator
by gorx1
5
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® indicator
by CryptoNE0
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® indicator
by B3AR_Trades
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® indicator
by BigBeluga
8
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. ---
Pine Script® indicator
by Giovanni_Ambrosio
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.
Pine Script® indicator
by Cryptobingbong
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.
Pine Script® indicator
by Uncle_SamFx
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".
Pine Script® indicator
by MarechalKhan
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
Pine Script® indicator
by Marketscope
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
Pine Script® indicator
by jsmith0111