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Hilbert Bandwidth [LB]🔬 Concept The Hilbert Bandwidth Index, derived from John Ehlers' analytic signal approach, measures the instantaneous stability of the dominant market cycle by computing the bandwidth — the absolute deviation between the raw instantaneous period and its smoothed counterpart. A narrow bandwidth indicates a clean, well-defined cycle suitable for trading. 📐 Mathematical Foundation The price median is transformed via a 7‑coefficient FIR Hilbert Transform to extract the analytic signal's real and imaginary components : real = 0.0962·P + 0.5769·P - 0.5769·P - 0.0962·P imag = 0.0962·P + 0.5769·P - 0.5769·P - 0.0962·P The instantaneous phase φ is obtained via the two‑argument arctangent of imag and real . After exponential smoothing, the phase difference Δφ between consecutive bars yields the instantaneous period : T = 2π / |Δφ| Finally, the bandwidth is defined as : B = |T - EMA(T, L) | where L is the period smoothing length. The result is expressed in bars. 🎯 What Problem Does It Solve ? Traditional cycle indicators assume a persistent dominant cycle, producing unreliable signals during chaotic or transitional markets — the Hilbert Bandwidth quantifies cycle cleanliness in real time, allowing traders to filter out low‑quality cyclic signals and act only when the market exhibits a stable, tradeable rhythm. 📊 How To Interpret Bandwidth below threshold (background colored) → the dominant cycle is narrow, well‑defined, and stable ; trend‑following and cycle‑based strategies have higher probability of success. Bandwidth above threshold → the cycle is broad and unstable ; the market is either noisy or in transition ; avoid cycle‑dependent entries. Bandwidth rapidly contracting → the market is shifting from chaos to order ; anticipate a breakout or the emergence of a clean trend. ⚙️ Parameters Phase Smoothing – exponential smoothing length applied to the instantaneous phase (default 50) ; higher values stabilize the phase estimate but introduce lag. Period Smoothing – EMA length applied to the instantaneous period (default 10) ; controls the responsiveness of the bandwidth calculation. Narrow Band Threshold – the bandwidth value in bars below which the cycle is considered "clean" (default 3.0) ; the background is highlighted when bandwidth falls below this level. 📚 Reference Ehlers J.F., "Rocket Science for Traders : Digital Signal Processing Applications", Chapter 7 – The Hilbert Transform, John Wiley & Sons, 2001. Ehlers J.F., "Cycle Analytics for Traders", Chapter 9 – Bandwidth Measurement, John Wiley & Sons, 2014.
مؤشر Pine Script®
من ‎luis_barlier‎
Trend Stability Ribbon [AGPro Series]Trend Stability Ribbon 🔹 OVERVIEW Most trend tools tell you WHICH WAY price is going. Trend Stability Ribbon tells you HOW WELL it is getting there. By pairing an ATR-normalized slope engine with a Kaufman path-efficiency score, it projects every bar into one of four rules-based states — Stable Up, Noisy Up, Stable Down, Noisy Down — and paints them onto a clean, adaptive ribbon that stays out of the candles' way. The result is a context layer that separates decisive trending from directional-but-choppy travel, without adding a second indicator pane. 🧭 UNIQUE EDGE — WHY THIS IS NOT "JUST ANOTHER TREND INDICATOR" Direction alone is cheap. Every moving-average cross, every supertrend, every slope color tells you "up" or "down" — and then leaves you holding the bag when the trend is technically up but structurally a mess. Trend Stability Ribbon adds the missing second dimension: path quality. The same 34-bar window that defines direction also feeds a Kaufman efficiency calculation (net travel divided by total path travel). An ER near 1.00 means price walked a near-straight line; an ER near 0.00 means it zig-zagged its way to the same point. Mapping that score against a calibrated threshold band produces the four composite states — and a visual language that finally distinguishes "trend worth trusting" from "trend worth fading". Additional design choices that set it apart: • Dual-layer event engine — direction flips, stability upgrades, and stability downgrades are tracked as independent transitions, each with its own alert. • Badge/alert separation — on-chart badges are throttled by a cooldown for visual hygiene, but alerts are always raw so automation pipelines never miss an event. • Reset state — when the slope-confirmation filter rejects a direction, the ribbon goes neutral instead of flipping false. Chop gets ignored, not misreported. 🧪 METHODOLOGY 1. TREND DIRECTION ENGINE • A slow EMA (default length 34) anchors the trend path and serves as the ribbon centerline. • Slope is measured over a 3-bar lookback and normalized by a 14-period ATR, making it instrument-agnostic across crypto, FX, equities, and futures. • With Slope Confirmation enabled (default), direction is only accepted when price position AND slope agree. Disagreement returns a Reset state. 2. PATH EFFICIENCY (STABILITY) ENGINE • ER = |close − close | ÷ Σ|close − close | over the same trend window. • Three classes: Stable (ER ≥ 0.45), Noisy (ER ≤ 0.25), Mixed (between). Thresholds scale with the Stability Sensitivity input. • The Mixed zone is a deliberate dead-band — during uncertain phases the previous state persists rather than flickering. 3. COMPOSITE STATE MACHINE • Direction × Stability yields five possible states: Stable Up, Noisy Up, Stable Down, Noisy Down, Reset. • Bars-in-state is tracked live, giving a simple persistence read on each state. 4. RIBBON RENDERING • Ribbon is anchored to the EMA centerline with height driven by ATR × a user-selected multiplier (Thin / ATR-Adaptive / Thick). • Fill opacity and border weight shift by state — Stable states are saturated, Noisy states are faded, Reset is a soft amber. 🔔 SIGNALS & ALERTS Three transition events are detected and exposed as separate, user-toggled alerts: • Trend State Flipped — direction changed (Up ↔ Down). Raw, never throttled. • Stability Improved — path upgraded from Noisy to Stable while direction held. Raw — delivered regardless of badge cooldown. • Stability Degraded — path downgraded from Stable to Noisy while direction held. Raw — delivered regardless of badge cooldown. Matching on-chart badges appear at the same moments, subject to the Stability Badge Cooldown for visual cleanliness. Direction-flip badges are never throttled. ⚙️ KEY INPUTS Engine • Trend Length (default 34) — lookback for both direction and path-efficiency windows. • Stability Sensitivity (default 1.0) — scales the Stable / Noisy thresholds. • Require Slope Confirmation (default on) — enforces price-and-slope agreement; rejects chop. Ribbon & Badge • Ribbon Height — Thin / ATR-Adaptive / Thick. • Show State Badge — toggle on-chart transition labels. • Label Font Size — tiny / small / normal / large (default normal). • Minimal Mode — hides panel and badges for pairing with other overlays. • Stability Badge Cooldown (default 5 bars) — visual throttle for stability transitions. Info Panel • Panel Position, Panel Font Size, Efficiency Ratio display, Active Thresholds display. Alerts • Independent toggles for each of the three transition events. 🧠 HOW TO USE • CONTEXT FILTER — use Stable states as a "green light" for continuation setups on your primary system; treat Noisy states as a headwind. • REGIME BREAKS — a Stability Degraded event mid-trend is often an early warning that the move is maturing, even before price has flipped. • CLEAN ENTRIES — pair a direction flip (Trend Up / Trend Down) with an immediate Stable classification to filter out whipsaw-prone breakouts. • CHOP AVOIDANCE — when the ribbon sits in a Reset or Mixed state, the script is telling you the underlying path is not tradeable as a trend. Stand aside or switch to range tactics. • PAIRING — with Minimal Mode on, the ribbon layers cleanly under structure tools, VWAPs, or S/R zones without visual conflict. ⚠️ LIMITATIONS & TRANSPARENCY • This is an indicator, NOT a strategy. It does not generate buy or sell orders, has no backtest, and makes no claim of performance. • Efficiency Ratio is a lagging measure — it describes the path already travelled. The ribbon should be read as context, not as a leading signal. • The Mixed zone is intentional persistence; expect the composite state to hold through brief chop rather than flipping on every bar. • Lower timeframes (<5m on thin-liquidity markets) can push ER values into erratic ranges. Start with the defaults on 15m–4h and tune from there. • All calculations are closed-bar. Intra-bar values may shift until the bar confirms. 🛡️ RISK DISCLOSURE This script is published for educational and analytical purposes only. It is not financial advice, not a signal service, and not a solicitation to buy or sell any instrument. Past behavior of markets does not predict future results. Always do your own research, apply proper risk management, and consult a licensed professional before making trading decisions. The author assumes no responsibility for losses incurred through use of this indicator.
مؤشر Pine Script®
من ‎AGProLabs‎
AG Pro KAMA Efficiency Zones [AGPro Series]AG Pro KAMA Efficiency Zones Overview KAMA stands for Kaufman’s Adaptive Moving Average. AG Pro KAMA Efficiency Zones is built around KAMA not as a simple trend-following line, but as an adaptive market reference for evaluating how efficiently price is moving. Instead of focusing only on direction, the script is designed to classify the quality of directional travel and separate cleaner movement from noisier, lower-clarity conditions. The core idea is straightforward: markets do not move with the same quality all the time. Some phases show relatively efficient directional travel where price stays organized around an adaptive path. Other phases become mixed, unstable, or reversion-prone, where direction weakens and noise becomes more dominant. This script is designed to map those changes visually through adaptive KAMA-based zones, state labels, and a compact panel that summarizes the current condition. This makes the tool structurally different from a basic moving average overlay. The objective is not to present KAMA as a one-line signal source. The objective is to use KAMA as the center of a state engine that helps users distinguish efficient trend phases from transitional or noisy environments. What this script does AG Pro KAMA Efficiency Zones evaluates price behavior around a Kaufman’s Adaptive Moving Average and organizes that behavior into visual market states. It does this by combining adaptive smoothing, slope behavior, distance from KAMA, and persistence around the KAMA path. The result is a chart framework that can help answer questions such as: • Is price moving in an efficient bullish or bearish path? • Is the market entering a mixed transition phase? • Has movement quality deteriorated into a noisier reversion-prone environment? • Is the adaptive path becoming stronger, weaker, or less stable? By turning those questions into zones and state-based chart feedback, the script aims to improve context rather than replace judgment. Unique edge The distinguishing feature of this script is that it does not treat KAMA as a standard moving average. Instead, it uses KAMA as the center of a layered efficiency model. That model focuses on the quality of movement, not just the existence of movement. Many tools emphasize momentum, volatility, volume pressure, or overbought/oversold conditions. This script is designed for a different purpose. It is a movement-quality map. It attempts to show whether price is traveling in a relatively efficient path or whether that path is degrading into a noisier condition where directional clarity may be weaker. This means the script is less about predicting a move and more about classifying the environment in which a move is taking place. How it works The script begins with KAMA, or Kaufman’s Adaptive Moving Average. KAMA is useful because it adapts its responsiveness according to market behavior. In cleaner directional phases it can respond more quickly, while in noisier phases it can become more conservative. That makes it a practical centerline for an efficiency-based state model. On top of KAMA, the script evaluates several components: 1. Efficiency behavior The script measures how directly price is moving relative to its recent path. This helps estimate whether price action is acting efficiently or becoming more erratic. 2. KAMA slope behavior The slope of KAMA is normalized so that directional angle can be evaluated in a more consistent way. Stronger and more persistent slope behavior supports higher-quality trend classifications. 3. Price-to-KAMA relationship Price position around KAMA helps determine whether movement is aligned with the adaptive path or drifting around it without clear structure. 4. Persistence The script also looks at how consistently price remains on one side of KAMA. That persistence can help distinguish a more stable move from a weaker and less durable one. These components are blended into a composite efficiency model that drives the active state and the corresponding visual zone. States and zones The script classifies market behavior into four main states: Efficient Bull Trend This state reflects a comparatively organized bullish environment where price and adaptive slope are aligned in a cleaner upward path. Efficient Bear Trend This state reflects a comparatively organized bearish environment where price and adaptive slope are aligned in a cleaner downward path. Transition This is a mixed condition. Direction may be weakening, changing, or failing to achieve the quality required for an efficient trend classification. Noise / Reversion This state reflects lower movement quality, weaker slope behavior, or a more unstable relationship between price and the adaptive path. The visual zone structure is designed to reinforce those classifications on the chart. Instead of using only one line, the script builds layered KAMA-centered bands so the user can read not only direction, but also how structured or fragile the current condition may be. How to read the chart The KAMA line is the adaptive spine of the script. The outer and inner bands represent zone structure around that adaptive path. In stronger trend states, the script increases the visual emphasis of the KAMA path and its supporting zone layers. In weaker or more mixed conditions, the script softens those visuals and allows the chart to communicate reduced clarity. State labels appear when the script confirms a meaningful shift in condition. These labels are intended to highlight a change in market state, not to promise a trade outcome. The on-chart panel summarizes the active reading using fields such as State, Efficiency, Score Band, Adaptive Bias, Active Zone, and Stability. This gives the user a compact interpretation layer without requiring every decision to be made directly from raw chart inspection. Key inputs KAMA Efficiency Length Controls the lookback used in the KAMA efficiency logic. Lower values react faster. Higher values smooth more noise. KAMA Fast Response and KAMA Slow Response Define the adaptive responsiveness range of the KAMA engine. ATR Length Used to normalize slope and distance so the tool behaves more consistently across different symbols and volatility conditions. KAMA Slope Lookback Controls how the script measures directional slope over time. Persistence Length Influences how much consistency price must show around KAMA before a move is treated as more structured. Efficient Trend Threshold and Noise Threshold These thresholds help determine when the model classifies a move as higher quality or lower quality. Zone Band ATR Width Adjusts the width of the adaptive visual zone. State Hold Bars Helps reduce rapid state flipping by requiring a condition to persist before the active state changes. Panel Font Size and Label Size Allow visual customization for different chart layouts and monitor sizes. Alerts The script includes state-oriented alerts intended to notify the user when market condition changes. These are designed around state transitions and movement-quality shifts rather than promotional “buy now” style messaging. Examples include bullish and bearish efficiency shifts, transition detection, noise-zone detection, efficiency recovery, efficiency breakdown, and trend strengthening. Alerts should be interpreted as contextual information. They are intended to support review and analysis, not to function as a standalone decision system. What this script is not This script is not a guarantee engine. It does not predict future price with certainty. It does not eliminate risk. It is not a substitute for broader market structure analysis, execution planning, or risk management. It should not be treated as a self-sufficient entry/exit system without additional confirmation and user judgment. Limitations and transparency All adaptive models are sensitive to parameter choices. Changing responsiveness, thresholds, smoothing, or persistence settings can materially affect the way states appear on the chart. Because the script is state-based, some shifts will naturally occur after the earliest turning point in price. That is part of the tradeoff involved in using confirmation and persistence to reduce noise. In highly erratic or news-driven conditions, classification can also become less stable. During those periods, transition or noise-oriented readings may occur more often, and users should interpret the visual output in that context. The script is best viewed as an analytical framework for movement quality and adaptive context, not as a promise of directional success. Practical use cases Users may find the script useful for: • separating cleaner trend phases from mixed or unstable phases • filtering chart environments before applying another workflow • evaluating whether direction is gaining or losing efficiency • adding adaptive context to discretionary analysis • comparing how different symbols behave around a KAMA-centered efficiency structure Risk disclosure This script is for analytical and educational use. It does not provide financial advice, investment advice, or guaranteed outcomes. Market conditions can change quickly, and any indicator can produce false, delayed, or incomplete signals. Users remain responsible for their own decisions, validation process, and risk management. In short, AG Pro KAMA Efficiency Zones is designed to help read the quality of movement, not just the direction of movement. It uses KAMA as an adaptive reference point and converts that reference into a structured zone and state model so users can assess whether price behavior appears efficient, transitional, or noisy.
مؤشر Pine Script®
من ‎AGProLabs‎
1
Fractal Retracement [Jamallo](2025) Intro FRAMA is a moving average that adapts its speed based on fractal geometry — specifically, the fractal dimension (D) of recent price action. When price is trending strongly (low fractal dimension), it moves fast. When price is choppy/ranging (high fractal dimension), it slows down. This makes it far more responsive than a standard EMA or SMA. Breakdown: The indicator wraps this with a continuous range logic layer: the filtered line = k only moves if price breaks beyond the FRAMA ± ATR-based range, creating a stepped/ratcheting effect that filters out noise. Two sets of bands are plotted around the filtered line, scaled by ATR multiplied by user-defined multipliers (tight at 0.5×, medium at 1.0×). They're smoothed with a short EMA to reduce jitter, and filled with gradient colors for visual clarity. Direction is simply determined by whether k is rising or falling, and colors everything green (uptrend) or pink/red (downtrend). END In short, it's a noise-filtered trend indicator useful for identifying trend direction, dynamic support/resistance , and gauging how far price has retraced from the trend baseline.
مؤشر Pine Script®
من ‎Jamallo22‎
1
Market Efficiency Ratio [Interakktive]The Market Efficiency Ratio decomposes price movement into two components: net progress vs wasted movement. This tool exposes the underlying math that most traders never see, helping you understand when price is moving efficiently versus chopping sideways. Unlike simple trend indicators, this shows you WHY price movement matters — not just whether it's up or down, but how much of that movement was useful directional progress versus noisy oscillation. █ WHAT IT DOES • Calculates Efficiency Ratio (0–1 or 0–100) measuring directional progress • Exposes Net Displacement (how far price actually moved) • Exposes Path Length (total distance price traveled) • Calculates Chop Cost (wasted movement) • Visual zones for high/mid/low efficiency states █ WHAT IT DOES NOT DO • NO signals, NO entries/exits, NO buy/sell • NO performance claims • NO predictions — purely diagnostic • This is a tool for understanding price behavior █ HOW IT WORKS The efficiency ratio answers one question: "Of all the movement price made, how much was useful progress?" 🔹 THE MATH Over a lookback period of N bars: Net Displacement = |Close - Close | Path Length = Σ |Close - Close | for all bars Efficiency Ratio = Net Displacement / Path Length 🔹 INTERPRETATION • Efficiency = 1.0 (100%): Price moved in a straight line — every tick was progress • Efficiency = 0.5 (50%): Half the movement was wasted in back-and-forth chop • Efficiency = 0.0 (0%): Price ended exactly where it started — all movement was noise 🔹 CHOP COST This is the "wasted movement" — how much price traveled without making progress: Chop Cost = Path Length - Net Displacement Chop % = Chop Cost / Path Length High chop cost means lots of effort for little result — a warning sign for trend traders. █ VISUAL GUIDE Three efficiency zones: • GREEN (≥70): High efficiency — strong directional movement • YELLOW (30-70): Mixed efficiency — some progress, some chop • RED (<30): Low efficiency — mostly noise, little progress █ INPUTS Lookback Length (default: 14) Number of bars to calculate efficiency over. Higher values produce smoother readings but respond slower to changes. Smoothing Length (default: 5) EMA smoothing applied to the output. Reduces noise in the efficiency reading. Apply Smoothing (default: true) Toggle EMA smoothing on/off. Scale Mode (default: 0–100) Display as percentage (0-100) or decimal ratio (0-1). Show Reference Bands (default: true) Display the high/low efficiency threshold lines. Low/High Efficiency Level (default: 30/70) Thresholds for classifying efficiency zones. Overlay Effect (default: None) • None: No overlay • Background Tint: Subtle chart background color in high/low zones • Bar Highlight: Color bars during low efficiency periods Show Data Window Values (default: true) Export all raw values (Net Displacement, Path Length, Efficiency, Chop Cost, Chop %) to the data window for analysis. █ USE CASES This indicator helps traders understand: • Why some trends are "clean" and others are "messy" • When price is consolidating vs trending (without using volume) • The relationship between movement and progress • Why high-chop environments are difficult to trade This is the foundational concept behind more advanced regime detection systems. █ SUITABLE MARKETS Works on: Stocks, Futures, Forex, Crypto Timeframes: All timeframes Note: This is a price-only indicator — no volume required █ DISCLAIMER This indicator is for informational and educational purposes only. It does not constitute financial advice. It does not generate trading signals. Past performance does not guarantee future results. Always conduct your own analysis.
مؤشر Pine Script®
من ‎Interakktive‎
1
Stochastic Ensembling of OutputsStochastic Ensembling of Outputs 🙏🏻 This is a simple tool/method that would solve naturally many well known problems: “Price reversed 1 tick before the actual level, not executing my limit order” “I consider intraday trend change by checking whether price is above/below VWAP, but is 1 tick enough? What to do, price is now whipsawing around vwap...”. “I want to gradually accumulate a position around a chosen anchor. But where exactly should I put my orders? And I want to automate it ofc.“ “All these DSP adepts are telling you about some kind of noise in the markets… But how can I actually see it?” The easy fix is to make things more analog less digital, by synthesizing numerous noise instances & adding it to any price-applied metric of yours. The ones who fw techno & psytrance, and other music, probably don’t need any more explanations. Then by checking not just 2 lines or 1 process against another one, you will be checking cloud vs cloud of lines, even allowing you to introduce proxies of probabilities. More crosses -> more confirmation to act. How-to use: The tool has 2 inputs: source and target: Sources should always be the underlying process. If you apply the tool to price based metric, leave it hlcc4 unless you have a better one point estimate for each bar; Target is your target, e.g if you want to apply it to VWAP, pick VWAP as target. You can thee on the chart above how trading activity recently never exactly touched VWAP, however noised instances of VWAP 'were' touched The code is clean and written in modular form, you can simply copy paste it to any script of yours if you don't want to have multiple study-on-study script pairs. ^^ applied to prev days highs and lows ^^ applied to MBAD extensions and basis ^^ applied to input series itself Here’s how it works, no ML, no “AI”, no 1k lines of code, just stats: The problem with metrics, even if they are time aware like WMA, is that they still do not directly gain information about “changes” between datapoints. If we pick noise characteristics to match these changes, we’d effectively introduce this info into our ops. ^^ this screenshot represents 2 very different processes: a sine wave and white noise, see how the noise instances learned from each process differ significantly. Changes can be represented as AR1 process . It’s dead simple, no PHD needed, it’s just how the current datapoint is related (or not) to the previous datapoint, no more than 1, and how this relationship holds/evolves over time. Unlike the mainstream approach like MLE, I estimate this relationship (phi parameter) via MoM but giving more weights to more recent datapoints via exponential smoothing over all the data available on your charts (so I encode temporal information), algocomplexity is O(1), lighting fast, just one pass. <- that gives phi , we’d use it as color for our noise generator Then we just need to estimate noise amplitude ( gamma ) via checking what AR1 model actually thought vs the reality, variance of these innovations. Same via exponential smoothing, time aware, O(1), one pass, it’s all it does. Then we generate white gaussian noise, and apply 2 estimated parameters (phi and gamma), and that’s all. Omg, I think I just made my first real DSP script xd Just like Monte Carlo for risk management, this is so simple and natural I can’t believe so many “pros” hide it and never talk about it in open access. Sharing it here on TradingView would’ve not done anything critical for em, but many would’ve benefited. ∞
مؤشر Pine Script®
من ‎gorx1‎
3
Volume Sampled Supertrend [BackQuant]Volume Sampled Supertrend A Supertrend that runs on a volume sampled price series instead of fixed time. New synthetic bars are only created after sufficient traded activity, which filters out low participation noise and makes the trend much easier to read and model. Original Script Link This indicator is built on top of my volume sampling engine. See the base implementation here: Why Volume Sampling Traditional charts print a bar every N minutes regardless of how active the tape is. During quiet periods you accumulate many small, low information bars that add noise and whipsaws to downstream signals. Volume sampling replaces the clock with participation. A new synthetic bar is created only when a pre-set amount of volume accumulates (or, in Dollar Bars mode, when pricevolume reaches a dollar threshold). The result is a non-uniform time series that stretches in busy regimes and compresses in quiet regimes. This naturally: filters dead time by skipping low volume chop; standardizes the information content per bar, improving comparability across regimes; stabilizes volatility estimates used inside banded indicators; gives trend and breakout logic cleaner state transitions with fewer micro flips. What this tool does It builds a synthetic OHLCV stream from volume based buckets and then applies a Supertrend to that synthetic price. You are effectively running Supertrend on a participation clock rather than a wall clock. Core Features Sampling Engine - Choose Volume buckets or Dollar Bars . Thresholds can be dynamic from a rolling mean or median, or fixed by the user. Synthetic Candles - Plots the volume sampled OHLC candles so you can visually compare against regular time candles. Supertrend on Synthetic Price - ATR bands and direction are computed on the sampled series, not on time bars. Adaptive Coloring - Candle colors can reflect side, intensity by volume, or a neutral scheme. Research Panels - Table shows total samples, current bucket fill, threshold, bars-per-sample, and synthetic return stats. Alerts - Long and Short triggers on Supertrend direction flips for the synthetic series. How it works Sampling Pick Sampling Method = Volume or Dollar Bars. Set the dynamic threshold via Rolling Lookback and Filter (Mean or Median), or enable Use Fixed and type a constant. The script accumulates volume (or pricevolume) each time bar. When the bucket reaches the threshold, it finalizes one or more synthetic candles and resets accumulation. Each synthetic candle stores its own OHLCV and is appended to the synthetic series used for all downstream logic. Supertrend on the sampled stream Choose Supertrend Source (Open, High, Low, Close, HLC3, HL2, OHLC4, HLCC4) derived from the synthetic candle. Compute ATR over the synthetic series with ATR Period , then form upperBand = src + factorATR and lowerBand = src - factorATR . Apply classic trailing band and direction rules to produce Supertrend and trend state. Because bars only come when there is sufficient participation, band touches and flips tend to align with meaningful pushes, not idle prints. Reading the display Synthetic Volume Bars - The non-uniform candles that represent equal information buckets. Expect more candles during active sessions and fewer during lulls. Volume Sampled Supertrend - The main line. Green when Trend is 1, red when Trend is -1. Markers - Small dots appear when a new synthetic sample is created, useful for aligning activity cycles. Time Bars Overlay (optional) - Plot regular time candles to compare how the synthetic stream compresses quiet chop. Settings you will use most Data Settings Sampling Method - Volume or Dollar Bars. Rolling Lookback and Filter - Controls the dynamic threshold. Median is robust to outliers, Mean is smoother. Use Fixed and Fixed Threshold - Force a constant bucket size for consistent sampling across regimes. Max Stored Samples - Ring buffer limit for performance. Indicator Settings SMA over last N samples - A moving average computed on the synthetic close series. Can be hidden for a cleaner layout. Supertrend Source - Price field from the synthetic candle. ATR Period and Factor - Standard Supertrend controls applied on the synthetic series. Visuals and UI Show Synthetic Bars - Turn synthetic candles on or off. Candle Color Mode - Green/Red, Volume Intensity, Neutral, or Adaptive. Mark new samples - Puts a dot when a bucket closes. Show Time Bars - Overlay regular candles for comparison. Paint candles according to Trend - Colors chart candles using current synthetic Supertrend direction. Line Width , Colors , and Stats Table toggles. Some workflow notes: Trend Following Set Sampling Method = Volume, Filter = Median, and a reasonable Rolling Lookback so busy regimes produce more samples. Trade in the direction of the Volume Sampled Supertrend. Because flips require real participation, you tend to avoid micro whipsaws seen on time bars. Use the synthetic SMA as a bias rail and trailing reference for partials or re-entries. Breakout and Continuation Watch for rapid clustering of new sample markers and a clean flip of the synthetic Supertrend. The compression of quiet time and expansion in busy bursts often makes breakouts more legible than on uniform time charts. Mean Reversion In instruments that oscillate, faded moves against the synthetic Supertrend are easier to time when the bucket cadence slows and Supertrend flattens. Combine with the synthetic SMA and return statistics in the table for sizing and expectation setting. Stats table (top right) Method and Total Samples - Sampling regime and current synthetic history length. Current Vol or Dollar and Threshold - Live bucket fill versus the trigger. Bars in Bucket and Avg Bars per Sample - How much time data each synthetic bar tends to compress. Avg Return and Return StdDev - Simple research metrics over synthetic close-to-close changes. Why this reduces noise Time based bars treat a 5 minute print with 1 percent of average participation the same as one with 300 percent. Volume sampling equalizes bar information content. By advancing the bar only when sufficient activity occurs, you skip low quality intervals that add variance but little signal. For banded systems like Supertrend, this often means fewer false flips and cleaner runs. Notes and tips Use Dollar Bars on assets where nominal price varies widely over time or across symbols. Median filter can resist single burst outliers when setting dynamic thresholds. If you need a stable research baseline, set Use Fixed and keep the threshold constant across tests. Enable Show Time Bars occasionally to sanity check what the synthetic stream is compressing or stretching. Link again for reference Original Volume Based Sampling engine: Bottom line When you let participation set the clock, your Supertrend reacts to meaningful flow instead of idle prints. The result is a cleaner state machine, fewer micro whipsaws, and a trend read that respects when the market is actually trading.
مؤشر Pine Script®
من ‎BackQuant‎
Quick scan for signal🙏🏻 Hey TV, this is QSFS, following: ^^ Quick scan for drift (QSFD) ^^ Quick scan for cycles (QSFC) As mentioned before, ML trading is all about spotting any kind of non-randomness, and this metric (along with 2 previously posted) gonna help ya'll do it fast. This one will show you whether your time series possibly exhibits mean-reverting / consistent / noisy behavior, that can be later confirmed or denied by more sophisticated tools. This metric is O(n) in windowed mode and O(1) if calculated incrementally on each data update, so you can scan Ks of datasets w/o worrying about melting da ice. ^^ windowed mode Now the post will be divided into several sections, and a couple of things I guess you’ve never seen or thought about in your life: 1) About Efficiency Ratios posted there on TV; Some of you might say this is the Efficiency Ratio you’ve seen in Perry's book. Firstly, I can assure you that neither me nor Perry, just as X amount of quants all over the world and who knows who else, would say smth like, "I invented it," lol. This is just a thing you R&D when you need it. Secondly, I invite you (and mods & admin as well) to take a lil glimpse at the following screenshot: ^^ not cool... So basically, all the Efficiency Ratios that were copypasted to our platform suffer the same bug: dudes don’t know how indexing works in Pine Script. I mean, it’s ok, I been doing the same mistakes as well, but loxx, cmon bro, you... If you guys ever read it, the lines 20 and 22 in da code are dedicated to you xD 2) About the metric; This supports both moving window mode when Length > 0 and all-data expanding window mode when Length < 1, calculating incrementally from the very first data point in the series: O(n) on history, O(1) on live updates. Now, why do I SQRT transform the result? This is a natural action since the metric (being a ratio in essence) is bounded between 0 and 1, so it can be modeled with a beta distribution. When you SQRT transform it, it still stays beta (think what happens when you apply a square root to 0.01 or 0.99), but it becomes symmetric around its typical value and starts to follow a bell-shaped curve. This can be easily checked with a normality test or by applying a set of percentiles and seeing the distances between them are almost equal. Then I noticed that on different moving window sizes, the typical value of the metric seems to slide: higher window sizes lead to lower typical values across the moving windows. Turned out this can be modeled the same way confidence intervals are made. Lines 34 and 35 explain it all, I guess. You can see smth alike on an autocorrelogram. These two match the mean & mean + 1 stdev applied to the metric. This way, we’ve just magically received data to estimate alpha and beta parameters of the beta distribution using the method of moments. Having alpha and beta, we can now estimate everything further. Btw, there’s an alternative parameterization for beta distributions based on data length. Now what you’ll see next is... u guys actually have no idea how deep and unrealistically minimalistic the underlying math principles are here. I’m sure I’m not the only one in the universe who figured it out, but the thing is, it’s nowhere online or offline. By calculating higher-order moments & combining them, you can find natural adaptive thresholds that can later be used for anomaly detection/control applications for any data. No hardcoded thresholds, purely data-driven. Imma come back to this in one of the next drops, but the truest ones can already see it in this code. This way we get dem thresholds. Your main thresholds are: basis, upper, and lower deviations. You can follow the common logic I’ve described in my previous scripts on how to use them. You just register an event when the metric goes higher/lower than a certain threshold based on what you’re looking for. Then you take the time series and confirm a certain behavior you were looking for by using an appropriate stat test. Or just run a certain strategy. To avoid numerous triggers when the metric jitters around a threshold, you can follow this logic: forget about one threshold if touched, until another threshold is touched. In general, when the metric gets higher than certain thresholds, like upper deviation, it means the signal is stronger than noise. You confirm it with a more sophisticated tool & run momentum strategies if drift is in place, or volatility strategies if there’s no drift in place. Otherwise, you confirm & run ~ mean-reverting strategies, regardless of whether there’s drift or not. Just don’t operate against the trend—hedge otherwise. 3) Flex; Extension and limit thresholds based on distribution moments gonna be discussed properly later, but now you can see this: ^^ magic Look at the thresholds—adaptive and dynamic. Do you see any optimizations? No ML, no DL, closed-form solution, but how? Just a formula based on a couple of variables? Maybe it’s just how the Universe works, but how can you know if you don’t understand how fundamentally numbers 3 and 15 are related to the normal distribution? Hm, why do they always say 3 sigmas but can’t say why? Maybe you can be different and say why? This is the primordial power of statistical modeling. 4) Thanks; I really wanna dedicate this to Charlotte de Witte & Marion Di Napoli, and their new track "Sanctum." It really gets you connected to the Source—I had it in my soul when I was doing all this ∞
مؤشر Pine Script®
من ‎gorx1‎
2
Noise Footprint ImbalanceNoise Footprint Imbalance Indicator The Noise Footprint Imbalance Indicator highlights areas of imbalance in price action, marking potential zones of support and resistance. This indicator helps traders visualize "footprints" of imbalance on the chart, allowing for better identification of areas where price moves significantly away from equilibrium. This can help traders pinpoint potential reversal points or zones where buyers or sellers may step in. Features Customizable Box Count: Choose the maximum number of imbalance zones displayed on the chart, keeping your workspace clear and focused. Imbalance Detection: Highlights both top and bottom imbalances, identifying them based on price discrepancies between open/close and high/low levels. Dynamic Zone Boxes: Draws boxes around imbalance zones with customizable colors and transparency, providing visual clarity without overwhelming the chart. Usage This indicator is beneficial for traders who: Use imbalance zones as potential areas of interest for entries or exits. Want to combine it with other indicators or price action analysis to improve trade setups. Customization Options Maximum Imbalance Zones: Adjusts the maximum number of imbalance boxes shown. Imbalance Box Color: Customize the color and transparency of the imbalance zones to suit your chart's theme. Add this script to your chart to enhance your technical analysis and bring more structure to your trading approach with the Noise Footprint Imbalance Indicator.
مؤشر Pine Script®
من ‎Random_Noise‎
2
Total Death and Golden Crosses Calculator The Indicator calculates the total number of the death and golden crosses in the total chart which can help the moving average user to compare the number of signals generated by the moving average pair in the given timeframe. All you need is to plot any two moving average then change the source of the indicator to get the total number of crosses. If Indicator is not plotting anything then right click on the indicator's scale and click on "Auto(data fits the screen" option.
مؤشر Pine Script®
من ‎Pro_Quantitative_Trader‎
1
White NoiseThe "White Noise" indicator is designed to visualize the dispersion of price movements around a moving average, providing insights into market noise and potential trend changes. It highlights periods of increased volatility or noise compared to the underlying trend. Code Explanation: Inputs: mlen: Input for the length of the noise calculation. hlen: Input for the length of the Hull moving average. col_up: Input for the color of the up movement. col_dn: Input for the color of the down movement. Calculations: ma: Calculate the simple moving average of the high, low, and close prices (hlc3) over the specified mlen period. dist: Calculate the percentage distance between the hlc3 and the moving average ma, then scale it by 850. This quantifies the deviation from the moving average as a value. sm: Smooth the calculated dist values using a weighted moving average (WMA) twice, with different weights, and subtract one from the other. This provides a smoothed representation of the dispersion. Coloring: col_wn: Determine the color of the bars based on whether dist is positive or negative and whether it's greater or less than the smoothed sm value. This creates color-coded columns indicating upward or downward movements with varying opacity. col_switch: Define the color for the current trend state. It switches color when the smoothed sm crosses above or below its previous value, indicating potential trend changes. col_switch2: Define the color for the horizontal line that separates the two trend states. It switches color based on the same crossover and crossunder conditions as col_switch. Plots: plot(dist): Plot the dispersion values as columns with color defined by col_wn. plot(sm): Plot the smoothed dispersion line with a white color and thicker linewidth. plot(sm ): Plot the previous smoothed dispersion value with a lighter white color to create a visual distinction. Usage: This indicator can help traders identify periods of increased market noise, visualize potential trend reversals, and assess the strength of price movements around the moving average. The colored columns and smoothed line offer insights into the ebb and flow of market sentiment, aiding in decision-making. ps. This can be used as a long-term TPI component if you dabble in Modern Portfolio Theory (MPT) Recommended for timeframes on the 1D or above:
مؤشر Pine Script®
من ‎IkkeOmar‎
Variety MA Cluster Filter Crosses [Loxx]What is a Cluster Filter? One of the approaches to determining a useful signal (trend) in stream data. Small filtering (smoothing) tests applied to market quotes demonstrate the potential for creating non-lagging digital filters (indicators) that are not redrawn on the last bars. Standard Approach This approach is based on classical time series smoothing methods. There are lots of articles devoted to this subject both on this and other websites. The results are also classical: 1. The changes in trends are displayed with latency; 2. Better indicator (digital filter) response achieved at the expense of smoothing quality decrease; 3. Attempts to implement non-lagging indicators lead to redrawing on the last samples (bars). And whereas traders have learned to cope with these things using persistence of economic processes and other tricks, this would be unacceptable in evaluating real-time experimental data, e.g. when testing aerostructures. The Main Problem It is a known fact that the majority of trading systems stop performing with the course of time, and that the indicators are only indicative over certain intervals. This can easily be explained: market quotes are not stationary. The definition of a stationary process is available in Wikipedia: A stationary process is a stochastic process whose joint probability distribution does not change when shifted in time. Judging by this definition, methods of analysis of stationary time series are not applicable in technical analysis. And this is understandable. A skillful market-maker entering the market will mess up all the calculations we may have made prior to that with regard to parameters of a known series of market quotes. Even though this seems obvious, a lot of indicators are based on the theory of stationary time series analysis. Examples of such indicators are moving averages and their modifications. However, there are some attempts to create adaptive indicators. They are supposed to take into account non-stationarity of market quotes to some extent, yet they do not seem to work wonders. The attempts to "punish" the market-maker using the currently known methods of analysis of non-stationary series (wavelets, empirical modes and others) are not successful either. It looks like a certain key factor is constantly being ignored or unidentified. The main reason for this is that the methods used are not designed for working with stream data. All (or almost all) of them were developed for analysis of the already known or, speaking in terms of technical analysis, historical data. These methods are convenient, e.g., in geophysics: you feel the earthquake, get a seismogram and then analyze it for few months. In other words, these methods are appropriate where uncertainties arising at the ends of a time series in the course of filtering affect the end result. When analyzing experimental stream data or market quotes, we are focused on the most recent data received, rather than history. These are data that cannot be dealt with using classical algorithms. Cluster Filter Cluster filter is a set of digital filters approximating the initial sequence. Cluster filters should not be confused with cluster indicators. Cluster filters are convenient when analyzing non-stationary time series in real time, in other words, stream data. It means that these filters are of principal interest not for smoothing the already known time series values, but for getting the most probable smoothed values of the new data received in real time. Unlike various decomposition methods or simply filters of desired frequency, cluster filters create a composition or a fan of probable values of initial series which are further analyzed for approximation of the initial sequence. The input sequence acts more as a reference than the target of the analysis. The main analysis concerns values calculated by a set of filters after processing the data received. In the general case, every filter included in the cluster has its own individual characteristics and is not related to others in any way. These filters are sometimes customized for the analysis of a stationary time series of their own which describes individual properties of the initial non-stationary time series. In the simplest case, if the initial non-stationary series changes its parameters, the filters "switch" over. Thus, a cluster filter tracks real time changes in characteristics. Cluster Filter Design Procedure Any cluster filter can be designed in three steps: 1. The first step is usually the most difficult one but this is where probabilistic models of stream data received are formed. The number of these models can be arbitrary large. They are not always related to physical processes that affect the approximable data. The more precisely models describe the approximable sequence, the higher the probability to get a non-lagging cluster filter. 2. At the second step, one or more digital filters are created for each model. The most general condition for joining filters together in a cluster is that they belong to the models describing the approximable sequence. 3. So, we can have one or more filters in a cluster. Consequently, with each new sample we have the sample value and one or more filter values. Thus, with each sample we have a vector or artificial noise made up of several (minimum two) values. All we need to do now is to select the most appropriate value. An Example of a Simple Cluster Filter For illustration, we will implement a simple cluster filter corresponding to the above diagram, using market quotes as input sequence. You can simply use closing prices of any time frame. 1. Model description. We will proceed on the assumption that: The aproximate sequence is non-stationary, i.e. its characteristics tend to change with the course of time. The closing price of a bar is not the actual bar price. In other words, the registered closing price of a bar is one of the noise movements, like other price movements on that bar. The actual price or the actual value of the approximable sequence is between the closing price of the current bar and the closing price of the previous bar. The approximable sequence tends to maintain its direction. That is, if it was growing on the previous bar, it will tend to keep on growing on the current bar. 2. Selecting digital filters. For the sake of simplicity, we take two filters: The first filter will be a variety filter calculated based on the last closing prices using the slow period. I believe this fits well in the third assumption we specified for our model. Since we have a non-stationary filter, we will try to also use an additional filter that will hopefully facilitate to identify changes in characteristics of the time series. I've chosen a variety filter using the fast period. 3. Selecting the appropriate value for the cluster filter. So, with each new sample we will have the sample value (closing price), as well as the value of MA and fast filter. The closing price will be ignored according to the second assumption specified for our model. Further, we select the МА or ЕМА value based on the last assumption, i.e. maintaining trend direction: For an uptrend, i.e. CF(i-1)>CF(i-2), we select one of the following four variants: if CF(i-1)fastfilter(i), then CF(i)=slowfilter(i); if CF(i-1)>slowfilter(i) and CF(i-1)slowfilter(i) and CF(i-1)>fastfilter(i), then CF(i)=MAX(slowfilter(i),fastfilter(i)). For a downtrend, i.e. CF(i-1)slowfilter(i) and CF(i-1)>fastfilter(i), then CF(i)=MAX(slowfilter(i),fastfilter(i)); if CF(i-1)>slowfilter(i) and CF(i-1)fastfilter(i), then CF(i)=fastfilter(i); if CF(i-1)
مؤشر Pine Script®
من ‎loxx‎
1
KERPD Noise Filter - Kaufman Efficiency Ratio and Price DensityThis indicator combines Kaufman Efficiency Ratio (KER) and Price Density theories to create a unique market noise filter that is 'right on time' compared to using KER or Price Density alone. All data is normalized and merged into a single output. Additionally, this indicator provides the ability to consider background noise and background noise buoyancy to allow dynamic observation of noise level and asset specific calibration of the indicator (if desired). The basic theory surrounding usage is that: higher values = lower noise, while lower values = higher noise in market. Notes: NON-DIRECTIONAL Kaufman Efficiency Ratio used. Threshold period of 30 to 40 applies to Kaufman Efficiency Ratio systems if standard length of 20 is applied; maintained despite incorporation of Price Density normalized data. TRADING USES: -Trend strategies, mean reversion/reversal/contrarian strategies, and identification/avoidance of ranging market conditions. -Trend strategy where KERPD is above a certain value; generally a trend is forming/continuing as noise levels fall in the market. -Mean reversion/reversal/contrarian strategies when KERPD exits a trending condition and falls below a certain value (additional signal confluence confirming for a strong reversal in price required); generally a reversal is forming as noise levels increase in the market. -A filter to screen out ranging/choppy conditions where breakouts are frequently fake-outs and or price fails to move significantly; noise level is high, in addition to the background buoyancy level. -In an adaptive trading systems to assist in determining whether to apply a trend following algorithm or a mean reversion algorithm. THEORY / THOUGHT SPACE: The market is a jungle. When apex predators are present it often goes quiet (institutions moving price), when absent the jungle is loud. There is always background noise that scales with the anticipation of the silence, which has features of buoyancy that act to calibrate the beginning of the silence and return to background noise conditions. Trend traders hunt in low noise conditions. Reversion traders hunt in the onset of low noise into static conditions. Ranges can be avoided during high noise and buoyant background noise conditions. Distance between the noise line and background noise can help inform decision making. CALIBRATION: - Set the Noise Threshold % color change line so that the color cut off is where your trend/reversion should begin. - Set the Background Noise Buoyancy Calibration Decimal % to match the beginning/end of the color change Noise Threshold % line. Match the Background Noise Baseline Decimal %' to the number set for buoyancy. - Additionally, create your own custom settings; 33/34 and 50 length also provides interesting results. - A color change tape option can be enabled by un-commenting the lines at the bottom of this script. Market Usage: Stock, Crypto, Forex, and Others Excellent for: NDQ, J225, US30, SPX Market Conditions: Trend, Reversal, Ranging
مؤشر Pine Script®
من ‎SensitiveSuit‎
17
Adaptive ATR Keltner Channels [Loxx]Adaptive ATR Channels are adaptive Keltner channels. ATR is calculated using a rolling signal-to-noise ratio making this indicator flex more to changes in price volatility than the fixed Keltner Channels. What is Average True Range (ATR)? The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.1 The true range is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges. What are Keltner Channel (ATR)? Keltner Channels are volatility-based bands that are placed on either side of an asset's price and can aid in determining the direction of a trend. The Keltner channel uses the average-true range (ATR) or volatility, with breaks above or below the top and bottom barriers signaling a continuation.
مؤشر Pine Script®
من ‎loxx‎
3
Parabolic SAR of KAMA [Loxx]Parabolic SAR of KAMA attempts to reduce noise and volatility from regular Parabolic SAR in order to derive more accurate trends. In addition, and to further reduce noise and enhance trend identification, PSAR of KAMA includes two calculations of efficiency ratio: 1) price change adjusted for the daily volatility; or, 2) Jurik Fractal Dimension Adaptive (explained below) What is PSAR? The parabolic SAR indicator, developed by J. Wells Wilder, is used by traders to determine trend direction and potential reversals in price. The indicator uses a trailing stop and reverse method called "SAR," or stop and reverse, to identify suitable exit and entry points. Traders also refer to the indicator as to the parabolic stop and reverse, parabolic SAR, or PSAR. What is KAMA? Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements. What is the efficiency ratio? In statistical terms, the Efficiency Ratio tells us the fractal efficiency of price changes. ER fluctuates between 1 and 0, but these extremes are the exception, not the norm. ER would be 1 if prices moved up 10 consecutive periods or down 10 consecutive periods. ER would be zero if price is unchanged over the 10 periods. What is Jurik Fractal Dimension? There is a weak and a strong way to measure the random quality of a time series. The weak way is to use the random walk index (RWI). You can download it from the Omega web site. It makes the assumption that the market is moving randomly with an average distance D per move and proposes an amount the market should have changed over N bars of time. If the market has traveled less, then the action is considered random, otherwise it's considered trending. The problem with this method is that taking the average distance is valid for a Normal (Gaussian) distribution of price activity. However, price action is rarely Normal, with large price jumps occuring much more frequently than a Normal distribution would expect. Consequently, big jumps throw the RWI way off, producing invalid results. The strong way is to not make any assumption regarding the distribution of price changes and, instead, measure the fractal dimension of the time series. Fractal Dimension requires a lot of data to be accurate. If you are trading 30 minute bars, use a multi-chart where this indicator is running on 5 minute bars and you are trading on 30 minute bars. Conclusion from the combined efforts explained above: -PSAR is a tool that identifies trends -To reduce noise and identify trends during periods of low volatility, we calculate a PSAR on KAMA -To enhance noise and reduction and trend identification, we attempt to derive an efficiency ratio that is less reliant on a Normal (Gaussian) distribution of price Included: -Customization of all variables -Select from two different ER calculation styles -Multiple timeframe enabled
مؤشر Pine Script®
من ‎loxx‎
2
Trend-Quality IndicatorBINANCE:BTCUSDT Open source version of the Trend-Quality Indicator as described by David Sepiashvili in [ Stocks & Commodities V. 22:4 (14-20) ] Q-Indicator and B-Indicator are available both separately or together █  OVERVIEW The Trend-Quality indicator is a trend detection and estimation tool that is based on a two-step filtering technique. It measures cumulative price changes over term-oriented semicycles and relates them to “noise”. The approach reveals congestion and trending periods of the price movement and focuses on the most important trends, evaluating their strength in the process. The indicator is presented in a centered oscillator (Q-Indicator) and banded oscillator format (B-Indicator). Semicycles are determined by using a short term and a longer term EMAs. The starting points for the cycles are determined by the moving averages crossover. Cumulative price change (CPC) indicator measures the amount that the price has changed from a fixed starting point within a given semicycle. The CPC indicator is calculated as a cumulative sum of differences between the current and previous prices over the period from the fixed starting point. The trend within the given semicycle can be found by calculating the moving average of the cumulative price change. The noise can be defined as the average deviation of the cumulative price change from the trend. To determine linear noise, we calculate the absolute value of the difference between CPC and trend, and then smooth it over the n-point period. The root mean square noise, similar to the conventional standard deviation, can be derived by summing the squares of the difference between CPC and trend over each of the preceding n-point periods, dividing the sum by n, and calculating the square root of the result. █  Q-INDICATOR The Q-Indicator is a centered oscillator that fluctuates around a zero line with no upper or lower limits, is calculated by dividing trend by noise. The Q-Indicator is intended to measure trend activity. The further the Q is from 0, the less the risk of trading with a trend, and the more reliable the trading opportunity. Values exceeding +2 or -2 can be qualified as promising Values: in the -1 to +1 range (GRAY) indicate that the trend is buried beneath noise. It is preferable to stay out of this zone in the +1 to +2 or -1 to -2 range (YELLOW) indicate weak trending in the +2 to +5 range (BLUE) or -2 to -5 range (ORANGE) indicate moderate trending above +5 range (GREEN) or below -5 (RED) indicate strong trending Readings exceeding strong trending levels can indicate overbought or oversold conditions and signal that price action should be monitored closely. █  B-INDICATOR The B-Indicator is a banded oscillator that fluctuates between 0 and 100, is calculated by dividing the absolute value of trend by noise added to absolute value of trend, and scaling the result appropriately. The B-indicator doesn’t show the direction of price movement, but only the existence of the trend and its strength. It requires additional tools for reversal manifestations. The indicator’s interpretation is simple. The central line suggests that the trend and noise are in equilibrium (trend is equal to noise). Values: below 50 (GRAY) indicate ranging market in the 50 to 65 range (YELLOW) indicate weak trending in the 65 to 80 range (BLUE) indicate moderate trending above 80 (GREEN) indicate strong trending The 65 level can be thought of as the demarcation line of trending and ranging markets and can help determine which type of technical analysis indicator (lagging or leading) is better suited to current market conditions. Readings exceeding strong trending levels can indicate overbought or oversold conditions.
مؤشر Pine Script®
من ‎Nick42_for_win‎
8
Cyclic RSI High Low With Noise Filter█ OVERVIEW This indicator displays Cyclic Relative Strength Index based on Decoding the Hidden Market Rhythm, Part 1 written by Lars von Thienen. To determine true or false for Overbought / Oversold are unnecessary, therefore these should be either strong or weak. Noise for weak Overbought / Oversold can be filtered, especially for smaller timeframe. █ FEATURES Display calculated Cyclic Relative Strength Index. Zigzag high low based on Cyclic Relative Strength Index. Able to filter noise for high low. █ LEGENDS ◍ Weak Overbought / Oversold OB ▼ = Strong Overbought OS ▲ = Strong Oversold █ USAGE / TIPS Recommend to be used for Harmonic Patterns such as XABCD and ABCD. Condition 1 (XABCD) : When ▼ and ▲ exist side by side, usually this outline XA, while the next two ◍ can be BC. Condition 2 (ABCD) : When ▼ and ▲ exist side by side, usually this outline AB, while the next one ◍ can be BC, strong ABCD. Condition 3 (ABCD) : When ▼ or ▲ exist at Point A, the next two ◍ can be Point B and Point C, medium ABCD. Condition 4 (ABCD) : When ◍ exist at Point a, the next two ◍ can be Point b and Point c, weak ABCD usually used as lower case as abcd. █ CREDITS LoneSomeTheBlue WhenToTrade
مؤشر Pine Script®
من ‎RozaniGhani-RG‎
57
Rebalance OscillatorRebalancing is a common strategy to reduce risk and achieve a constant portfolio ratio between two tokens. It shifts between two values in order to keep a static ratio between them as their value oscillates. However what is less known about rebalancing is that it provides a way to remove the noise from a signal, effectively showing us the points where to buy and to sell. This works best in highly volatile tickers, between two tokens that are not correlated and show a high std deviation between them (i.e. XTZUSD is a better signal for this than XTZBTC). The buying bars are marked in blue, and the selling bars are marked in red, in a similar fashion to Trading View strategy orders.
مؤشر Pine Script®
من ‎hugodanielcom‎
Efficiency RatioThe efficiency ratio (ER) is described by Perry Kaufman in his book, Trading Systems and Methods. It works by measuring the momentum of the market, that is, the absolute change from the current price to a past price, and divides it by the volatility, which is the sum of the absolute changes of each bar. That makes this a bounded indicator, going from 0 to 100, like an oscillator. Higher values mean less noise, while lower values mean more. Eg.: if the market moves from 10.0 to 15.0 in a directional manner, with every bar up, the ER is going to be at 100. However, if it moves up and down, and goes all over the place until finally reaching 15.0, the ER is going to be at around 20. It is very difficult for the ER to be at zero, because that would require 0 volatility, which is almost impossible to occur. This indicator is useful when planning for trades. If you notice the ER being higher than average, you may choose to increase the position size, because that would mean that the market is directional and has less chance of a whipsaw.
مؤشر Pine Script®
من ‎EduardoMattje‎
2
Ehlers Noise Elimination Technology [CC]The Noise Elimination Technology Indicator was created by John Ehlers (Stocks and Commodities Dec 2020 pg 17) and he created this indicator to be used with his version of RSI but I think it works well with any price data or any indicator really. I'm trying a new signal system due to a request from @luckyCamel58789 so let me know what you think. I now differentiate between a buy and a strong buy when the indicator increases over itself twice and vice versa. Dark green is a strong buy and light green is a regular buy. Dark red is a strong sell and light red is a regular sell. Let me know what indicators you would like to see me publish!
مؤشر Pine Script®
من ‎cheatcountry‎
12
Combo Backtest 123 Reversal & Signal To Noise This is combo strategies for get a cumulative signal. First strategy This System was created from the Book "How I Tripled My Money In The Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies. The strategy buys at market, if close price is higher than the previous close during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50. The strategy sells at market, if close price is lower than the previous close price during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50. Second strategy The signal-to-noise (S/N) ratio. And Simple Moving Average. WARNING: - For purpose educate only - This script to change bars colors.
استراتيجية Pine Script®
من ‎HPotter‎
4