Kyokutan-Ashi◈ Description
The Kyokutan-Ashi is a unique indicator designed to visualize the exact price deviation (noise) that standard Heikin-Ashi (HA) calculations normally smooth out and hide. By completely stripping away the trend component, it isolates pure market volatility and overextension.
◈ The Math Behind It (Why this calculation & Expected Output)
The core logic subtracts the Heikin-Ashi values from standard Japanese candlestick values. The purpose of this calculation is to extract the pure "difference" (noise) between the actual price and the smoothed trend, and then reconstruct it based on a chosen anchor point.
【 Calculation Example 】
Current actual candle: open = 100, high = 105, low = 95, close = 102
Previous HA candle: haOpen = 98, haClose = 100
Settings: Anchor Point = "Open", Multiplier = 1.0
haClose = (100 + 105 + 95 + 102) / 4.0 = 100.5
haOpen = (98 + 100) / 2.0 = 99
Subtract HA from standard values to isolate the noise.
rawOpen = 100 - 99 = 1
rawClose = 102 - 100.5 = 1.5
Add the isolated noise to the baseline (Anchor = Open: 100).
antiOpen = 100 + 1 = 101
antiClose = 100 + 1.5 = 101.5
【 Actual Output 】
A small bullish candle from 101 to 101.5 is plotted on the chart. Although the actual price moved from 100 to 102, removing the trend component reveals that the "pure overextension" (deviation) is only 0.5.
◈ Key Features
Pure Deviation Visualization: Shows only how far the actual price is stretching away from the Heikin-Ashi smoothed price.
Anchor & Base Settings: Choose where to project the deviation (Open, Close, or HL2).
Deviation Multiplier: Scale the noise up or down to easily spot extreme market extensions.
Chart-Type Independent: Built with robust data handling. Even if you change your main chart view to Heikin-Ashi, Renko, or Kagi, Kyokutan-Ashi always forcefully retrieves standard raw price data in the background to guarantee accurate deviation calculations.
◈ Why Use It
Use it in combination with other charts or indicators to extract your own unique noise and trading edges. When Kyokutan-Ashi prints unusually large candles, it signals that the actual price is violently snapping away from the smoothed trend — often indicating exhaustion, potential mean-reversion, or hidden volatility.
◈ Author's Note
While Kyokutan-Ashi was developed independently to address the loss of actual price data in Heikin-Ashi, I later discovered "BERLIN Candles" by lejmer. He had already recognized this critical issue and beautifully engineered a hybrid solution long before I did. I want to express my deepest respect for his foresight and pioneering work in tackling this specific problem.
Indicator
Wavelet Trend FilterWhat it is
A denoised price line whose smoothing is derived from measured noise, not a period you pick — plus the tool's signature number, the noise fraction (what share of the recent move is noise vs structure). Most published "wavelet denoising improves prediction" results denoise the whole series including future bars, which is lookahead-contaminated and impossible live. This tool refuses that: it runs a Haar wavelet transform over a trailing power-of-two window and keeps only the endpoint as today's value, fixed at close and never revised. Strictly causal, non-repainting, no forecast.
How it's built
The engine is a five-step Donoho-Johnstone wavelet shrinkage, run every bar over a trailing window (default 128 bars): a Haar DWT (shortest support, fewest boundary coefficients — the right choice for short causal series); noise scale σ = MAD(finest detail coefficients)/0.6745 (robust to outliers); the universal threshold λ = σ·√(2 ln n) with an optional multiplier; soft or hard shrinkage of the detail coefficients; and an inverse transform, keeping the endpoint. Optional symmetric reflection padding reduces edge distortion. Because the threshold self-scales from measured noise, a quiet tape is smoothed less and a noisy tape more — automatically, on any timeframe.
Derived reads: noise fraction = 1 − var(denoised)/var(raw) over the window (share the transform removed), ranked into a percentile; trend slope in noise-σ units; and a signal-to-noise strength = slope (σ) × structure share (1 − noise fraction), the single "is this trend real?" number.
How you read it — and the honest actionable signal
The noise fraction gates everything. A bold chop/structure ribbon along the bottom of the chart shows the whole regime spectrum (green = structure/low noise/trend trustworthy, red = chop/high noise/stand aside), and a ±1σ noise envelope around the denoised line makes the removed noise visible — so it's obviously not just a moving average.
The actionable markers are structure-gated: a green ▲ / red ▼ prints when the denoised slope turns up/down and the tape has structure (low noise); in chop the tool withholds and marks the turn with a small caution dot instead. A hysteresis on the structure state keeps the signals from re-firing as noise oscillates. This is not a blind crossover generator — it goes quiet exactly when the tape is too noisy to trust, which is when ordinary signals whipsaw. The panel's "Read" row states the current call in plain English ("up-trend · structure ✓", "chop — stand aside").
Exports & alerts
EXP_Denoised · EXP_NoiseSigma · EXP_NoiseFrac (0–1) · EXP_Slope (σ) · EXP_SNR (trend strength) · EXP_Stance (+1 up·structure / −1 down·structure / 0). Alerts: noise fraction entering top / bottom quartile (chop / structure) · denoised slope turning up / down · confirmed up-trend / down-trend with structure present. Indicator
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. Indicator
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. Indicator
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.
Indicator
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. Indicator
Library
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.
Indicator
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 PulseWire would’ve not done anything critical for em, but many would’ve benefited.
∞ Indicator
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.
Indicator
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 ∞ Indicator
Indicator
Indicator
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:
Indicator
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) Indicator
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 Indicator
Indicator
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 Indicator
Indicator
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. Indicator
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 Indicator
Indicator
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. Indicator
