Regression Trend [MiesOnCharts]Regression Trend - Mies
What it does
This indicator fits a linear regression line to price over a rolling window and draws a corridor around it based on the statistical error of that fit. The corridor is what decides the trend state. As long as price stays inside it, nothing changes. When price closes outside one side, the whole thing flips color and a triangle marks the bar.
The result is a trend line that carries its own tolerance band with it, so you can see at a glance both where the fitted trend sits and how much room price has before the state changes.
How it works
A least squares regression is fitted across the lookback window. That gives the center line.
Around it, the script computes the standard error of the estimate, which is the typical distance between actual price and the fitted line. It comes from the correlation between price and time:
r is the correlation of the source with bar index over the window
residual variance is the price variance scaled by (1 - r²)
the standard error is the square root of that, adjusted for the degrees of freedom of the fit.
This is the part that makes the corridor behave differently from a standard deviation band. The width responds to how well price is actually tracking the trend, not just to raw volatility. A strong, clean trend produces a high correlation, small residuals, and a narrow corridor, so the indicator stays sensitive.
Choppy price that wanders around the line produces a weak fit, a wide corridor, and a much higher bar for triggering a state change. The indicator effectively demands more evidence in exactly the conditions where evidence is thin.
The bands sit at the center line plus and minus a multiple of that standard error. A close above the upper band turns the state bullish, a close below the lower band turns it bearish, and everything in between leaves the previous state untouched. That hysteresis is intentional. It is what stops the indicator from flipping every time price crosses its own mean.
On the chart
Regression line, green when the state is bullish, red when bearish, gray before the first breakout
Upper and lower standard error bands with a light fill between them, colored to match the current state Triangle below the bar when the state flips bullish Triangle above the bar when the state flips bearish.
Display controls to hide the fill, or the bands entirely, if you want a bare trend line
Two alert conditions, one for each direction
Settings
Source sets which series gets fitted. Close is the standard choice. HL2 or a smoothed input will give a calmer line and fewer flips.
Regression Window sets how many bars the fit covers. Shorter windows follow recent structure and react fast. Longer windows describe the broader trend and produce fewer, slower signals. This is the main setting for matching the tool to your timeframe.
SE Band Multiplier controls how far price has to move from the fitted line before the state changes. Lower values tighten the corridor and generate more signals. Higher values require a more decisive break and filter more noise, at the cost of entering later.
Display group toggles the bands and the fill, and adjusts band opacity.
How to use it
The most direct use is as a trend filter. Trade only in the direction the line is colored and treat the opposite flip as your exit or your cue to step aside.
The corridor itself gives you two readable things. Its width tells you how well price is respecting the trend, so a corridor that has narrowed over recent bars means the fit is tightening and the move is orderly. A corridor that has ballooned means the fit has broken down and the state you are looking at is stale. The center line works as a dynamic reference within an established regime, since a pullback toward it is price returning to its own fitted mean rather than to an arbitrary level.
It pairs well with a volume or momentum check. A corridor break tells you the move is statistically unusual relative to the current fit, but it says nothing about whether there is participation behind it.
Behavior worth understanding
The regression is recalculated on every bar, and the corridor plotted on each bar is that bar's own fit. This is a running envelope, not a fixed channel anchored to a pivot, so the bands will look wavier than a manually drawn regression channel. The reference moves with price, which is what keeps the state stable through a sustained run.
Signals are evaluated on the live bar, so a flip can appear and then vanish before the bar closes. Wait for bar close if you need signals that hold.
Limitations
Linear regression assumes price is moving in a straight line across the window, which is never fully true. The fit degrades at sharp reversals and around gaps, and the corridor is slow to acknowledge a turn right after a strong move because that extension is still inside the window. Treat this as a description of current trend structure, not a forecast.
Disclaimer
The indicator provided is not financial advice. Always conduct your own research and consider multiple factors before making trading decisions. Trade at your own risk. Indicator
Adaptive Volumetric Reversion Channel Fade ValidatorAdaptive Volumetric Reversion Channel — Fade Validator (AVRC)
What it is
AVRC is an anchored, volume-weighted regression channel that frames mean-reversion ("fade") setups and then gates, scores and validates them — so you can see whether fading stretched price actually has an edge on your symbol and timeframe instead of taking it on faith. It is a study / analysis framework, not a strategy and not a signal service.
Why these components are combined (the mashup rationale)
Fading an extreme asks three different questions, and no single classic tool answers all three. AVRC coordinates several non-redundant lenses on one shared geometry (an anchored regression channel) and one shared volatility unit (residual σ), so each lens can check the others rather than echoing it:
WHERE is price stretched? A volume-weighted regression centerline with residual-σ bands is drawn against a slower macro trend-relative volume map — volume binned by σ-distance from a longer regression line. Whether the tactical band sits in a thin (fast-traversed) or thick (heavily-traded) macro node tells you if a fade is likely clean or absorbed. This cross-read is the connective tissue between the two layers.
Is a reversion ACTUALLY firing? Independent "tells" at the band — a close-back rejection, a band-confluent momentum divergence, an equal-high/low liquidity sweep, and the macro-density read. Because these tells are correlated, their agreement is shrunk by a design-effect correction so echoes can't masquerade as independent confirmation.
Is the market in a reverting STATE? A regime gate (variance-ratio test + a reversion-trust correlation) only passes fades when price increments offset rather than compound. An entry-time ride-risk score (macro-trend alignment, the two-centerline spread, an already-walking band, momentum, mean-reversion half-life, and multi-timeframe trend consensus) flags fades likely to be "walked" rather than reverted.
The components share one geometry and one volatility unit, and each can veto the others. The goal is to suppress low-quality fades more than to generate them.
How the validation layer works (what makes this more than a drawing)
Every fade is logged and, a fixed horizon later, resolved: its forward return is measured in ATR units and tabulated Gate ON vs Gate OFF — follow-through %, a Wilson 95% interval, whipsaw %, and mean R per fade. Outcomes are additionally split Reverted vs Rode, by macro node (thin/thick), and by ride-risk (low/high at the running median). The panel's Edge line synthesizes this into a single read: is Gate ON's follow-through interval clearing the ungated baseline with positive mean R and enough samples? Per-fade rows also export to the Data Window for your own analysis. Every filter has to earn its place against the ungated baseline.
How to use it
Set the Price source (top of settings). Defaults are tuned for an intraday index future; the source is user-selectable so the framework runs on any symbol or market. Volume-based parts (heatmap, profile, POC) need a real volume feed.
Read the panel top-down: Now (live setup) → State (regime + spread + compression) → the A/B scoreboard (Gate OFF, Gate ON, Revert, Ride) → Edge verdict.
A fade arms when price tags the outer band and at least one tell prints, then passes only if the regime (and optional ride-risk) gate agrees. Target is the centerline or the nearest untested POC.
If Gate ON does not beat Gate OFF on follow-through and mean R with non-overlapping intervals and enough samples, the edge isn't there on this symbol/timeframe — change them, don't force it. The signal is clearest on higher intraday timeframes; 1-minute is mostly noise.
What is original here
The original work is the coordination: a shared-σ, timeframe-adaptive regression channel used as a reversion frame; a trend-relative volume map cross-read against the band; decorrelated tells fused by a design-effect shrink; a statistical regime gate; an entry-time ride-risk score; and a built-in A/B + forward-return validation harness — combined so each lens can veto the others and the tool reports its own hit rate. It is not a re-skin of any single indicator.
Concept credits (techniques are standard; this implementation is original)
Volume-weighted least-squares & polynomial regression; residual-σ channels; anchored VWAP (all standard); Volume Profile / Value Area / Point of Control — Market Profile (Steidlmayer / CBOT); Variance-Ratio test — Lo & MacKinlay (1988); design effect / effective sample size — Kish (1965); proportion confidence interval — Wilson (1927); mean-reversion half-life — Ornstein–Uhlenbeck process; ATR, RSI, Parabolic SAR — Wilder; Stochastic — Lane; Supertrend (classic, MTF context). Builds on established open-source regression-channel and anchored-VWAP techniques.
Settings (all defaults are on; tuned for an intraday index future)
Data/Source · Volatility unit · Macro volume heatmap · Tactical channel & bands · Interrelation & band-walk · Ride-risk filter · Density cross-read · Reversion tells · Regime gate · POC targets · Fade signal · Validation & export · Dashboard & theme (auto light/dark) · MTF trend context. The two signal-suppression gates (walk-gate, ride-gate) ship off so the indicator shows its signals and lets the validation panel tell you whether enabling them helps.
Disclaimer
For research and education only. NOT financial advice, NOT a recommendation, and NOT a guarantee of future results. All statistics shown are in-sample on loaded history, close-to-close at the horizon, without costs or slippage — a study aid, not a backtest. Mean reversion fails in trends and during regime breaks. Do your own research and manage your own risk. Indicator
Dual Log Regression Channels [BigBeluga]Dual Log Regression Channels is a highly advanced multi-timeframe mathematical modeling terminal engineered for PulseWire. It maps, projects, and blends two independent logarithmic regression channels directly onto your asset layout screen to deliver an institutional-grade perspective on trend structure, market cycles, and structural volume distributions.
By separating price discovery parameters into a long-term Macro Channel and an execution-focused Short Term Channel, this tool effectively resolves the classic trader conflict of assessing structural trend directions while looking for immediate micro execution setups. Rather than treating market space as flat, standard geometric lines, this engine runs an advanced curve-fitting algorithm over your data to follow the exponential nature of capital expansion and distribution.
🔵 INTUITIVE SYSTEM ARCHITECTURE & ENGINE FEATURES
1. Logarithmic Regression Curve Optimization
Non-Linear Structural Tracking: Standard linear regression struggles with volatile crypto or high-growth equity trends over massive lookback structures. This script continuously converts incoming data matrices into mathematical log-space, computes a best-fit ordinary least squares (OLS) linear progression, and converts the output back into exponential value curves.
Dual Horizons Convergence Layer: Tracks an extensive trend anchor block (defaulting to 300 bars) simultaneously with a highly responsive, high-velocity swing lookback matrix (defaulting to 50 bars). This exposes localized micro contractions occurring right at major macro boundary extremes.
Visual Deviation Spacing Bands: Channels automatically map out distinct volatility boundaries based on real-time Standard Deviation multipliers. This defines predictable mathematical risk corridors where asset expansions typically exhaust and snap back toward the median baseline.
2. Predictive Channel Extension & Real-Time Trend Direction Arrows
Dynamic Origin Trend Arrows: The engine processes a dedicated directional diagnostic framework at the precise historical start (origin node) of each lookback channel. It generates sharp, high-visibility glyph trend arrows ( ⇗ for structural uptrends and ⇘ for structural downtrends). These arrows offer an instant, real-time assessment of the mathematically calculated baseline slope, entirely bypassing visual guesswork when channels run relatively flat.
Forward-Projected Space Models: When enabled, both the Macro and Short-Term structural bands project forward into the future chart space blank zone (e.g., 50 bars ahead for Macro, 20 bars for Short-Term). This lets you visually identify intercept locations and major trend crossroads long before price action arrives.
3. Adaptive Embedded Channel Volume Profiles (VP)
Integrated Block Volume Binning Matrix: Moving beyond basic fixed or visible range volume profiles, this module segments and collects transacted volume profiles exclusively inside the exact coordinate boundaries of each respective channel.
Dynamic Coordinate-Aligned Shading Bars: The volume profile rows scale and project outward utilizing advanced polyline geometry arrays, maintaining structural alignment with the slope of the moving channel boundaries.
Point of Control (POC) Trailing Baselines: Automatically tracks and renders a crisp, high-visibility solid horizontal baseline ( POC Line ) marking the exact price bin location that attracted the highest volume concentration throughout that lookback phase.
4. Volumetric Delta Tracking Panels
Buy vs. Sell Volume Accumulation Blocks: Aggregates total execution volume during the lookback period, classifying volume based on bar polarity.
Net Order Flow Delta Percentages: Computes and prints the precise net mathematical buying/selling pressure delta inside the channel. This reveals quiet accumulation behavior or hidden distribution trends directly alongside your spatial boundary drawings.
🔵 SYSTEMATIC EXECUTION STRATEGIES & RISK INTERPRETATION
Confluence Zone Intercept Trading: Look for setups where the Short Term Channel’s outer standard deviation boundaries align directly with the Macro Channel's major structural lines. When a high-velocity micro asset trend exhausts itself at a long-term macro floor or ceiling, it marks a highly efficient, asymmetric inflection zone for trend continuation entries or macro reversals.
Volume Profile POC Mean Reversion Matrix: The volume profile POC lines show where massive institutional blocks shifted hands inside that channel's lifespan. If the market stretches thin near an upper outer boundary but net volumetric volume indicators begin shifting toward seller control, look for a swift mean-reversion move down toward the high-liquidity POC baseline node.
Trend Acceleration vs. Overextended Breakouts: When an asset forces a candle close completely outside the projected log channel boundaries, it flags an exceptional shift in trend velocity. If the Volume Delta percentage prints an explosive spike in that direction, it supports a trend acceleration play. If volume is thin, it warns you of a predatory, overextended fakeout structure that is likely to snap back into the central channel values.
🔵 INTERFACE CONFIGURATION AND PARAMETERS
Lookback & Deviation Tuning Blocks: Customize historical calculation boundaries and volatility widths separately for both trend layers to match any asset class or time frame preference.
Volume Profile Customization: Control the precise resolution of the volume profile by adjusting row count bins and max bar widths to match your specific layout.
Clean Workspace Overrides: Toggle visibility filters to hide median baselines, remove raw background asset lines, or completely customize color theme hex codes to fit cleanly within your setup without causing visual clutter.
Transform your charting environment from basic straight lines into an exponential, volume-weighted structural map with the Dual Log Regression Channels terminal. Indicator
Structural Inefficiency Radar [JOAT]Structural Inefficiency Radar
Introduction
Structural Inefficiency Radar measures regression fair value, residual z-score, path asymmetry, volatility clustering, and repair/extension force.
This open-source indicator is designed as a context tool, not a standalone trading system. It focuses on explaining the current market state with restrained visuals and confirmed-bar logic where signals are used.
Core Concepts
1. Regression Fair Value
A linear regression spine represents statistical fair value.
2. Residual Z-Score
Distance from fair value is normalized by residual standard deviation.
3. Path Asymmetry
Upward and downward path movement are compared to detect directional distortion.
4. Repair and Extension Force
Deviation, slope, asymmetry, and clustering determine whether price is extending or repairing.
z = (close - fairValue) / residualDeviation
Features
Fair-value spine
Inner and outer inefficiency bands
Residual z-score
Repair and extension scoring
E+ and E- labels plus repair markers
Input Parameters
Regression and residual windows
Deviation gate
Repair quality gate
Cooldown
Bands, candles, and HUD toggles
How to Use This Script
Use the spine and bands to frame structural stretch. Extension labels indicate directional inefficiency; gold markers indicate fair-value repair.
Limitations
The script uses historical OHLCV data and cannot know future prices.
Signals and states can be late during fast reversals because confirmed-bar logic is used to reduce repainting.
Model outputs should be interpreted with market context, risk controls, and independent analysis.
No visual state should be treated as a certain trade outcome.
Originality Statement
SIR is original in combining regression residuals, path asymmetry, clustering, repair force, and extension force.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any financial instrument. All calculations are derived from historical market data and may produce inaccurate readings in some market conditions. No indicator can predict future market behavior. Use proper risk management and independent judgment.
-Made with passion by jackofalltrades
Indicator
Kinetic Inertia Field [JOAT]Kinetic Inertia Field
Introduction
Kinetic Inertia Field models price like a noisy particle using velocity, acceleration, jerk, kinetic energy, potential displacement, and equilibrium deviation.
This open-source indicator is designed as a context tool, not a standalone trading system. It focuses on explaining the current market state with restrained visuals and confirmed-bar logic where signals are used.
Core Concepts
1. Velocity and Acceleration
Log returns are normalized by volatility to create velocity, then differentiated into acceleration and jerk.
2. Kinetic Energy
Inverse volatility acts as a mass proxy and squared velocity creates energy context.
3. Equilibrium Displacement
A regression/VWAP blend creates a fair path and ATR-normalized displacement.
4. Inertia Field
Energy, acceleration, and displacement combine into inertial up, inertial down, or elastic state.
kineticEnergy = 0.5 * mass * velocity * velocity
Features
Velocity, acceleration, and jerk model
Kinetic and potential energy scoring
Regression/VWAP equilibrium
Energy rails and impulse trace
K+ and K- labels plus snapback markers
Input Parameters
Velocity smoothing
Volatility memory
Equilibrium horizon
Energy and inertia gates
Cooldown and display toggles
How to Use This Script
Use K+ and K- as confirmed high-energy state changes. Gold markers show elastic snapback conditions.
Limitations
The script uses historical OHLCV data and cannot know future prices.
Signals and states can be late during fast reversals because confirmed-bar logic is used to reduce repainting.
Model outputs should be interpreted with market context, risk controls, and independent analysis.
No visual state should be treated as a certain trade outcome.
Originality Statement
KIF is original in applying kinetic energy, potential displacement, and inertia scoring to price-state analysis.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any financial instrument. All calculations are derived from historical market data and may produce inaccurate readings in some market conditions. No indicator can predict future market behavior. Use proper risk management and independent judgment.
-Made with passion by jackofalltrades
Indicator
Orion Regression Field [JOAT]Orion Regression Field
Introduction
Orion Regression Field builds a weighted regression valuation field with standard error bands, curvature options, compression detection, and reprice signals.
This open-source indicator is designed as a context tool, not a standalone trading system. It focuses on explaining the current market state with restrained visuals and confirmed-bar logic where signals are used.
Core Concepts
1. Weighted Regression
Recent bars can receive more influence while still preserving a full model window.
2. Standard Error Bands
Inner and outer bands show statistical distance from the modeled path.
3. Curvature and Confidence
Optional curvature and R2-style confidence control when the field is considered reliable.
4. Pinch and Reprice
Compression and outer-band reactions create filtered reprice events.
mid = weightedRegression(close, len); band = standardError * multiplier
Features
Weighted regression midline
Inner and outer SEE bands
Optional curvature
Pinch shading and projection field
Filtered reprice labels
Input Parameters
Regression window and projection bars
Curvature toggle and weight floor
Minimum R2 confidence
Inner and outer band multipliers
Pinch ratio and cooldown
How to Use This Script
Use the field as statistical fair-value context. Outer band interaction means stretch, not automatic reversal.
Limitations
The script uses historical OHLCV data and cannot know future prices.
Signals and states can be late during fast reversals because confirmed-bar logic is used to reduce repainting.
Model outputs should be interpreted with market context, risk controls, and independent analysis.
No visual state should be treated as a certain trade outcome.
Originality Statement
Orion is original in combining weighted regression, curvature, standard error fields, compression context, and filtered reprice logic.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any financial instrument. All calculations are derived from historical market data and may produce inaccurate readings in some market conditions. No indicator can predict future market behavior. Use proper risk management and independent judgment.
-Made with passion by jackofalltrades
Indicator
SwingRegress Volatility Analytics [MarkitTick]💡 A comprehensive, multi-dimensional charting tool designed to fuse structural market analysis, statistically derived linear regression pathways, and volatility compression mechanics into a single, cohesive interface. By dynamically adapting its calculations to the latest shifts in market structure—specifically Change of Character (CHoCH) events—this script offers an adaptive mapping of price action, trend trajectory, and potential breakout zones directly on the primary chart.
● ✨ Originality and Utility
Traditional linear regression tools often require manual anchoring or rely on fixed lookback periods that fail to adapt to rapidly unfolding price dynamics. The distinct utility of this script lies in its self-adjusting structural anchoring mechanism. By automatically locking the regression baseline to the most recent significant pivot high or pivot low immediately following a structural break, the channel remains mathematically and contextually relevant to the current market regime.
Furthermore, this tool eliminates the need for separate sub-chart oscillators by integrating a sophisticated Smart Volatility Squeeze engine. This engine compares price variance against true range to identify periods of extreme price compression, overlaying these signals directly within the active regression pathway. The result is a unified, chart-centric view of both directional trend geometry and kinetic energy build-up, allowing for a more focused and uncluttered analytical process.
● 🔬 Methodology and Concepts
The underlying logic of this script is driven by three core mathematical engines operating in tandem:
• Pivot Discovery and Market Structure
The script continuously scans incoming price data to identify localized extremes, defined as Pivot Highs and Pivot Lows. A candidate bar is confirmed as a pivot only if it remains unbroken for a user-defined number of bars both prior to and following its occurrence. Once confirmed, these pivots establish the market structure. If the closing price breaks beyond the most recent opposing pivot, a Change of Character (CHoCH) is triggered, officially shifting the trend state.
• Anchored Linear Regression
Upon the confirmation of a new CHoCH, the script calculates a fresh Linear Regression Channel (LRC). The anchoring point is the origin pivot of the newly established trend. The script uses the Ordinary Least Squares (OLS) method to compute the slope and intercept of the best-fit line through the closing prices of the current regime. It then calculates the standard error of the estimate (standard deviation of the residuals) to project upper and lower variance bands parallel to the mid-line.
• Volatility Squeeze Mechanics
To identify volatility compression, the script employs a comparative analysis between standard deviation and Average True Range (ATR). It calculates a Bollinger Band (representing standard deviation) and a Keltner Channel (representing ATR) around a moving average baseline. A "squeeze" is structurally confirmed when the outer limits of the Bollinger Bands contract entirely within the boundaries of the Keltner Channels. This signifies that historical variance has dropped substantially below the average true range, often preceding a dynamic expansion in price movement.
● 🎨 Visual Guide
The visual interface is highly detailed and structurally color-coded to provide immediate contextual awareness without cluttering the chart.
• Current Anchored LRC
Mid Line: A solid Neon Cyan line representing the true mean of the current trend regime.
Band 1: A dashed Soft Cyan line mapping the first standard deviation threshold.
Band 2: A dotted Deep Azure line mapping the secondary, outer standard deviation extreme.
• Previous Anchored LRC
Mid Line: A solid Magenta line representing the historical mean of the preceding trend.
Band 1: A dashed Soft Magenta line for the historical inner variance.
Band 2: A dotted Blue-Violet line for the historical outer variance.
• Swing Point Zones
Swing High Boxes: Translucent red zones originating from a confirmed pivot high, drawing forward to act as dynamic resistance until broken by price action.
Swing Low Boxes: Translucent green zones originating from a confirmed pivot low, acting as dynamic support until structurally invalidated.
• Volatility Squeeze Candles
Cyber Gold Candles: When the market enters a state of extreme volatility compression (Bollinger Bands inside Keltner Channels) and is actively trading within the current or previous LRC pathway, the candles are painted a vibrant gold to highlight imminent kinetic release.
• Heads-Up Dashboard Display
Located in the top right corner, this self-updating data matrix provides critical real-time telemetry:
Structure Regime: Displays the active directional bias (Bullish, Bearish, or Neutral).
Last CHoCH: Indicates the direction and age (in bars) of the most recent structural shift.
Squeeze Intensity: A visual block-bar measuring the depth of the volatility compression.
ATR (14): The current absolute value of the Average True Range.
Dist to Swings: The percentage distance between the current price and the nearest Swing High/Low.
Risk/Reward Quality: A dynamic measurement of potential risk versus structural reward.
LRC Window Age: The duration of the current regression channel in bars.
LRC Position: Indicates whether price is currently trading inside the active regression channel, the previous channel, or is entirely unanchored.
● 📖 How to Use
The primary application of this tool is identifying high-probability continuation or mean-reversion setups following structural confirmation.
When a CHoCH event occurs, wait for the new Linear Regression Channel to populate. This channel defines your trading parameters. A high-probability setup manifests when price pulls back to the inner or mid-line of the active LRC, accompanied by the appearance of Cyber Gold squeeze candles. This visual confluence suggests that price is compressing directly at the statistical mean of the new trend, building energy for a move in the direction of the underlying structural regime.
Conversely, if price approaches the outer standard deviation bands (Deep Azure) without structural confirmation of a breakout, it suggests the market is statistically overextended, offering a potential mean-reversion opportunity back toward the Neon Cyan mid-line.
Note on Mechanics: Because the pivot discovery process requires a defined number of bars to confirm a swing high or low, there is an inherent lookback period. The swing zones will only appear after the pivot has been structurally verified. Furthermore, the linear regression channel recalculates its slope dynamically as new price data is added to the active regime, meaning the exact angle of the channel adapts in real-time until a new CHoCH locks it into history as the "Previous LRC."
● ⚙️ Inputs and Settings
The configuration panel is logically divided into primary analytical modules to allow for precise user calibration.
• Current Anchored CHoCH LRC
Adjust the sensitivity of the pivot discovery engine by modifying the Left and Right Pivot Bars. You can also customize the multipliers for the primary and secondary standard deviation bands, as well as toggle their visibility and modify line weights.
• Previous Anchored CHoCH LRC
Allows for the toggling of the historical channel, providing context on how the previous trend failed. Color and visibility settings are fully adjustable here.
• Swing Points & Zones Settings
Toggle the structural resistance and support boxes on or off, and customize their respective color opacities for a cleaner chart overlay.
• Smart Volatility Squeeze (BB vs KC)
Tune the underlying volatility engine. You can adjust the lookback length for the variance baseline, as well as the specific deviation multipliers for both the Bollinger Band boundaries and the Keltner Channel limits.
• Webhook Execution Configuration
Input exact JSON payload action names for algorithmic execution routing (Long, Short, Close Long, Close Short).
• Dashboard Settings
Customize the background and text colors of the heads-up data matrix to match your specific chart theme.
● 🔍 Deconstruction of the Underlying Scientific and Academic Framework
The mathematical foundation of this script is anchored heavily in econometrics and statistical probability theory.
At its core, the linear regression calculation utilizes the Ordinary Least Squares (OLS) estimator. This formula determines the line of best fit through a sequence of time-series data points by minimizing the sum of the squared differences (residuals) between the observed closing prices and the values predicted by the linear model. The slope of this line represents the average rate of change per unit of time, mathematically quantifying the drift of the active regime.
The parallel bands wrapping the regression line are derived by calculating the standard error of the estimate. Assuming the residuals are normally distributed (Gaussian distribution), one standard deviation captures approximately 68 percent of the price variance, while two standard deviations capture roughly 95 percent. When price moves beyond these outer bands, it represents a statistically significant deviation from the mean, inherently increasing the probabilistic likelihood of mean reversion.
The volatility squeeze mechanic operates on the principle of variance compression. Bollinger Bands are a derivative of standard deviation, making them highly reactive to short-term variance. Keltner Channels utilize the Average True Range (ATR), which measures absolute periodic volatility independent of a central mean. When the standard deviation of price contracts to such a degree that the Bollinger Bands fall entirely within the ATR-based Keltner Channels, it statistically confirms a state of anomalous energy compression. In financial academia, periods of artificially suppressed variance are overwhelmingly followed by periods of geometric expansion, providing the theoretical basis for breakout execution.
⚠️ Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion. Indicator
Regression Deviation Channel [JOAT]Regression Deviation Channel
Introduction
The Regression Deviation Channel is an institutional-style statistical trend and execution framework built around segmented regression, deviation envelopes, premium/discount zoning, breakout qualification, and risk mapping. Instead of acting like a plain moving-average channel, it models price through a best-fit regression path, measures dispersion with RMSE, then classifies where price is trading inside that structure: discount, equilibrium, or premium.
This version is designed to feel more like a desk-grade directional map than a simple overlay. It combines a frozen regression segment, internal band hierarchy, confidence scoring, Supertrend stack alignment, breakout detection, and ATR-based trade mapping into one visual structure. The goal is not just to show where price is, but whether the current move is balanced, compressed, expanding, or resolving.
Why This Indicator Exists
Most channels are too simple. They show boundaries but do not explain what price is doing inside those boundaries. This indicator was built to solve that by combining:
Segmented Regression: Tracks the current directional price path with a proper best-fit slope
Deviation Architecture: Uses RMSE to define statistically meaningful channel width
Premium / Discount Zoning: Splits the channel into expensive, fair value, and cheap territory
Breakout Qualification: Scores breakout quality using slope, participation, structure, and location
Trend Stack Context: Adds Supertrend alignment to distinguish strong directional pressure from noise
Trade Mapping: Builds clean ATR-based stop and multi-target projections after confirmed breaks
The result is a regression channel that does more than draw lines. It gives context, bias, execution framing, and visual hierarchy.
Core Components Explained
1. Segmented Regression Engine
= f_ols(winLen)
basisVal = intercept + slope * float(barsInSeg - 1)
upperVal = basisVal + rmse * multiplier
lowerVal = basisVal - rmse * multiplier
The core engine uses manual ordinary least squares regression to calculate the channel basis. Once the segment matures, the regression values are frozen and projected forward until price resolves beyond the envelope.
This “freeze and resolve” behavior keeps the channel visually stable instead of constantly shifting every bar.
2. RMSE Deviation Structure
Root mean squared error defines channel width, making the envelope responsive to how tightly price is hugging the trend.
Tight RMSE = cleaner trend structure
Wide RMSE = unstable or volatile structure
Internal bands split the envelope into inner, quarter, and outer zones
These nested bands create a true structure ladder instead of a single upper/lower shell.
3. Premium / Discount Channel Arrays
The channel is separated into three value areas:
Premium: Upper edge territory where price is extended and expensive relative to the current regression path
Equilibrium: The center band around fair value and neutral orderflow balance
Discount: Lower edge territory where price is cheap relative to the active path
This makes the indicator more useful for directional context:
Bull channels pressing premium signal strong continuation pressure
Bear channels pressing discount signal strong downside control
Repeated failure to hold premium/discount can signal exhaustion or rebalancing
4. Breakout Confidence Model
Breakouts are not treated equally. The indicator scores breakout quality using four ingredients:
Participation: Distance from the regression basis normalized by ATR
Slope Force: Strength of the normalized regression slope
Location: Whether price is already pressing the outer structure
Alignment: Whether price direction and Supertrend stack agree with the channel
breakoutConfidence = participation + slopeForce + location + alignment
This helps separate lazy drifts from high-quality channel resolution.
5. Supertrend Ribbon Stack
The Supertrend layer is not there as a generic add-on. It acts as a second-order directional filter.
Bull channel + bull Supertrend = higher-quality directional stack
Bear channel + bear Supertrend = stronger downside stack
When regression and Supertrend disagree, price is more likely in transition
The fill between regression basis and Supertrend visually shows whether pressure is aligned or conflicted.
6. ATR Risk Map
After a confirmed breakout, the indicator projects:
1 ATR-based stop level
3 reward targets using configurable risk-reward multiples
Auto-expiring lines so stale trade maps are removed
This gives the channel direct execution value instead of leaving the user to manually measure every move.
Visual Elements
Metallic Basis Line: Gold-toned centerline for the active regression basis
Outer Deviation Shell: Main channel boundaries with glow
Inner Structure Bands: Internal ladder for pressure staging
Premium / Discount Fills: Separate upper and lower value zones inside the channel
Equilibrium Fill: Neutral fair-value region
Supertrend Ribbon: Context layer showing secondary directional alignment
Iridescent Candles: Candle coloring that intensifies as control and confidence improve
Breakout Markers: Compact signals for confirmed resolves
Readiness Diamonds: Pre-break alignment markers when channel conditions are strong
The visual hierarchy is designed so you can read the channel at a glance without relying on heavy objects or clutter.
Dashboard
The dashboard is intentionally compact and fixed to the right side. It shows only the highest-signal metrics:
Bias
Regime
Flow
Channel Position
Confidence
Compression
Trend Stack
Trade Map
How to Use This Indicator
Step 1: Identify Channel Bias
Check whether the regression slope is bullish or bearish. That defines the primary directional path.
Step 2: Read Value Location
See whether price is trading in premium, equilibrium, or discount. This tells you whether price is extended or balanced inside the channel.
Step 3: Watch Trend Stack Alignment
When Supertrend and regression agree, directional pressure is cleaner. When they disagree, reduce conviction.
Step 4: Monitor Confidence
Use the breakout confidence score to judge whether price is merely drifting or building a meaningful resolution.
Step 5: Trade the Resolve, Not the Noise
Use breakout markers and ATR map levels when price exits the frozen envelope with qualified pressure.
Best Practices
Use higher timeframes for cleaner channel geometry
Treat equilibrium as fair value, not a signal by itself
Bull channels work best when premium holds and pullbacks respect the inner bands
Bear channels work best when discount holds and rallies fail at internal structure
High compression followed by rising confidence often precedes expansion
Use the risk map for framing, not blind automation
Indicator Limitations
Regression is still a model of recent price, not a guarantee of future direction
Sudden event-driven moves can invalidate the frozen segment quickly
Premium and discount are relative to the current channel, not absolute market value
High breakout confidence can still fail in thin or news-driven markets
Short segments increase responsiveness but also increase noise
Technical Implementation
Built in Pine Script v6 using:
Manual OLS regression
RMSE deviation envelopes
Segment freeze-and-resolve logic
Internal quarter and inner bands
Premium/discount channel zoning
Supertrend stack integration
Breakout confidence scoring
ATR-based stop and target map
Compact institutional dashboard
Originality Statement
This indicator is original in how it treats a regression channel as a full market-state framework instead of a static overlay. The value is not just in plotting upper and lower lines, but in combining:
Segment freezing
Internal value zoning
Directional stack confirmation
Breakout qualification
Execution mapping
Each layer contributes different information: regression defines path, RMSE defines structure, premium/discount defines value, Supertrend defines stack, and confidence defines quality.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Regression channels, premium/discount zones, and breakout scores are analytical tools, not guarantees of market outcome. All trading decisions remain the responsibility of the user.
-Made with passion by officialjackofalltrades
Indicator
Regression-Aligned Candlestick Architect [MarkitTick]💡 The Regression-Aligned Candlestick Architect is an advanced, institutional-grade technical analysis instrument engineered to seamlessly fuse structural market context with precise, deterministic candlestick morphology. Rather than presenting isolated, noisy signals, this indicator operates as a holistic market mapping system. It dynamically identifies changes in market character (CHoCH), anchors statistical regression channels to these pivotal structural nodes, and overlays a rigorously filtered, strength-tiered candlestick pattern recognition engine. This tool empowers analysts to visualize the exact mathematical relationship between micro-level price action anomalies and macro-level statistical deviations.
● ✨ Originality and Utility
Standard candlestick recognition tools often suffer from a fatal flaw: signal noise. By identifying every single pattern across the chart regardless of context, they overwhelm the analyst with false positives. This indicator revolutionizes pattern detection by introducing a multi-dimensional filtering matrix.
Contextual Awareness: Patterns are cross-referenced against a dynamic Simple Moving Average (SMA) baseline, ensuring that continuation patterns are only validated when aligned with the prevailing macroeconomic trend.
Hierarchical Strength Matrix: Patterns are not treated equally. They are mathematically scored and classified into five distinct strength tiers, from baseline indecision to highly reliable structural anomalies.
Anchored Statistical Modeling: Instead of static support and resistance lines, this tool maps volatility using an anchored Linear Regression Channel (LRC) that resets automatically upon validated structural breaks (CHoCH), providing an evolving map of fair value and extreme deviation.
Institutional Automation Ready: Built-in webhook templates format high-conviction signals directly into actionable JSON payloads, bridging the gap between discretionary charting and algorithmic execution.
● 🔬 Methodology and Concepts
The architecture of this script relies on a confluence of three primary mathematical and logical engines.
• Quantitative Pattern Recognition
The core engine deconstructs each individual candlestick into absolute mathematical variables: body size, total high-low range, upper shadow ratio, and lower shadow ratio. By applying rigid algorithmic tolerance thresholds (e.g., Dojis strictly requiring a body-to-range ratio of less than 5%), the script actively eliminates subjective interpretation.
• The Strength Stratification System
The indicator systematically grades market geometry into five actionable categories:
Strength 1 (Indecision): Identifies market equilibrium phases and compression (e.g., Doji, Spinning Tops).
Strength 2 (Weak Signals): Early signs of exhaustion that require further context (e.g., Hanging Man, Inverted Hammer).
Strength 3 (Moderate Confirmations): Standard two-candle reversal structures (e.g., Harami, Piercing Line, Dark Cloud Cover).
Strength 4 (Strong Confirmations): High-conviction multi-candle configurations (e.g., Engulfing setups, Morning/Evening Stars, Marubozu).
Strength 5 (Extreme Conviction): Rare, highly reliable setups signaling massive structural imbalances (e.g., Three White Soldiers, Breakaway Gaps).
• Structural CHoCH and LRC Anchoring
The script continuously scans for localized Pivot Highs and Pivot Lows using customizable look-left and look-right parameters. When the price closes beyond the most recent opposing pivot node, a Change of Character (CHoCH) is registered. This event immediately triggers the recalculation of the Linear Regression Channel, anchoring the starting point to the critical pivot and projecting statistical deviation bands forward to track the new trend's trajectory.
● 🎨 Visual Guide
The interface is meticulously designed with a 3D holographic aesthetic to ensure clarity without cluttering the primary price action.
• Candlestick Labels and Holographic Colors
Indigo Glass (Strength 1): Muted, translucent tones denoting indecision and market pauses without demanding immediate attention.
Cyber Blue & Violet (Strength 2 & 3): Intermediate colors highlighting developing reversals or moderate continuation patterns.
Neon Mint (Bullish Strength 4-5): Bright, high-contrast markers indicating strong bullish dominance (e.g., BE+, 3WS), plotted below the bar.
Hot Pink (Bearish Strength 4-5): Intense, high-visibility markers warning of severe bearish pressure (e.g., BE-, 3BC), plotted above the bar.
Cyber Gold (Special/Exhaustion): Reserved strictly for profound trend exhaustion signatures, such as the Three Line Strike configuration.
To learn more about Candlestick patterns, access the following link:
Quantitative Analysis of Algorithmic Candlestick Pattern
• Linear Regression Channel Bands
Active LRC: Displays a Neon Cyan midline representing the mean regression. Soft Cyan and Deep Azure dashed and dotted lines represent the first and second standard deviation bands respectively, filled with translucent gradient shading to represent volatility zones.
Historical LRC: Previous channels are preserved in deep Magenta and Blue-Violet hues. This allows the analyst to review past structural behavior, momentum shifts, and how price transitioned between volatility states.
● 📖 How to Use
This indicator is optimized for confluence trading. Discretionary traders should look for optimal alignment between the LRC boundaries and high-tier candlestick patterns.
Define the Structural Boundary: Observe the active Neon Cyan LRC. Determine if the current micro-trend is contained safely within the inner standard deviation bands or if it is stretching into extreme statistical anomaly (touching or piercing Band 2).
Wait for Signal Convergence: A standalone pattern is interesting, but confluence is key. A Strength 4 (Neon Mint) Bullish Engulfing pattern occurring exactly at the lower boundary (Band 2) of an ascending LRC presents an exceptionally high-probability mean-reversion or trend-continuation setup.
Automate Execution: Utilize the built-in alert system to capture the exact entry price alongside dynamically calculated ATR-based Stop Loss and Take Profit levels when a tier 4 or 5 pattern confirms.
Adapt to Trend Shifts: If a CHoCH occurs, the channel will instantly snap to the new trajectory. Immediately shift your directional bias and await new pattern formations that align with the updated regression mean.
● ⚙️ Inputs and Settings
The indicator provides granular control over internal parameters, allowing adaptation to varying asset classes and timeframes.
• General & Strength Filters
Max Patterns to Display: Limits historical label rendering to keep the chart performant and visually clean.
Show Only Trend-Appropriate Patterns: A critical toggle that forces the engine to ignore counter-trend signals by filtering outputs through the internal SMA logic.
Strength Toggles (S1 - S5): Allows the user to independently enable or disable specific tiers. Professional traders often disable tiers 1-3 to focus exclusively on high-probability tier 4 and 5 formations.
• Current Anchored CHoCH LRC
Pivot Left/Right Bars: Dictates the sensitivity of the Change of Character detection. Higher numbers require major macro swings to shift the channel, while lower numbers tightly track micro-structure fluctuations.
Band Multipliers (1 & 2): Adjusts the mathematical standard deviation widths of the regression channel. Defaulted to standard 1.0 and 2.0 deviations.
• Webhook Execution Configuration
Action Strings: Define custom text identifiers (e.g., 'long', 'closeshort') that will be injected into the automated JSON payload when high-strength signals or CHoCH events trigger on a confirmed bar close.
● 🔍 Deconstruction of the Underlying Scientific and Academic Framework
The analytical depth of this script is heavily grounded in established statistical mathematics and heuristic geometric modeling.
• Statistical Modeling via Ordinary Least Squares (OLS)
The dynamic Linear Regression Channel is derived using the Ordinary Least Squares method. The script iteratively loops through the dynamically anchored period (from the algorithmic CHoCH trigger to the current bar index) to calculate the line of best fit. It computes the summation of price coordinates, calculating the slope and y-intercept to minimize the sum of the squared residuals.
Furthermore, the indicator calculates the population standard deviation of these residuals (errors) to project the outer variance bands. In a normally distributed financial dataset, approximately 68% of price action should remain within Band 1, and 95% within Band 2. When price forcefully breaches Band 2, it statistically indicates an unsustainable momentum extreme, shifting the probability matrix heavily toward imminent mean-reversion.
• Quantitative Candlestick Heuristics
Traditional Japanese Candlestick theory relies heavily on qualitative visual assessment. This indicator transforms it into a rigorous quantitative science. By expressing wicks, shadows, and true bodies as strict fractional ratios of the total period variance, the algorithm entirely removes psychological bias. For instance, an Engulfing pattern is not merely determined by a visual overlap; the internal logic mathematically validates that the current open and close parameters completely eclipse the previous period's boundaries, while simultaneously verifying that the absolute body size explicitly exceeds the prior via comparative array lookbacks. Furthermore, advanced configurations like the 'Three Line Strike' necessitate the sequential tracking of four independent vector arrays to confirm precise exhaustion geometry and statistical anomaly.
⚠️ Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion. Indicator
AG Pro Regression Range Map [AGPro Series]AG Pro Regression Range Map
OVERVIEW
AG Pro Regression Range Map is a statistical corridor overlay built to answer one practical question as clearly as possible: what type of active movement corridor is price traveling in right now?
Instead of treating the market as a sequence of isolated signals, the script models the current price path as a rolling regression backbone surrounded by residual dispersion bands. This allows the chart to be read as a live structure: a directional corridor, a flat corridor, or a weakening corridor that is losing discipline.
The result is a clean visual framework that helps users judge whether price is progressing inside an organized range map or drifting without stable structure. The script is designed as an analytical overlay, not as a forecasting engine.
UNIQUE EDGE
The core idea here is different from indicators that measure simple distance from a moving average, fixed volatility envelopes, or breakout-style event detection.
This script does not ask, “How far is price from a reference?” It asks, “Given the current regression slope and the current residual dispersion, what movement corridor is active now?”
That distinction matters.
The center line is not a generic average. It is a rolling linear regression backbone. The bands are not ATR shells or standard deviation bands around price itself. They are built from the residual dispersion around the active regression backbone. In other words, the script maps drift and dispersion together.
This produces a different analytical lens:
- the backbone defines directional drift
- the corridor width reflects residual dispersion around that drift
- the containment rate shows whether price is respecting that corridor
- the quality score estimates how coherent the corridor currently is
This makes the tool suitable for users who want to evaluate market structure in a disciplined way without turning the chart into a signal-heavy dashboard.
WHAT THE SCRIPT DOES
The script plots:
- a rolling regression backbone
- an inner corridor around that backbone
- an outer corridor around that backbone
- subtle fill to make the active corridor readable without obscuring price
- a compact mini panel with corridor metrics
It also classifies the current corridor state into one of three modes:
- Uptrend Range
- Flat Range
- Downtrend Range
The intention is to show whether price is currently traveling inside an upward corridor, a neutral corridor, or a downward corridor, while also indicating how stable that corridor is.
METHODOLOGY
1) Regression backbone
The center line is a rolling linear regression calculated over the selected lookback window. This backbone is used as the active structural reference for the current chart state.
2) Residual dispersion corridor
After calculating the backbone, the script measures the residual distance between price and the regression line. The standard deviation of those residuals becomes the corridor unit.
The inner and outer bands are then built by multiplying that residual dispersion unit by user-defined multipliers.
This means the corridor is not based on absolute price volatility alone. It is based on how price is dispersing around the active regression path.
3) Normalized slope
The slope of the regression backbone is normalized relative to ATR so the directional reading is more comparable across instruments and conditions.
That normalized slope is then used to classify the corridor as upward, flat, or downward.
4) Containment
Containment measures how consistently price has remained inside the outer corridor over the selected lookback period.
A high containment reading suggests that price is respecting the active corridor. A lower reading suggests that the corridor is less representative of current behavior.
5) Range Width
Range Width expresses the outer corridor width relative to the current center value. This helps users quickly judge whether the active map is relatively tight or relatively wide.
6) Width Stability
Width Stability estimates how stable the corridor width has been over time. This helps distinguish between a corridor that is behaving consistently and one that is expanding or contracting too erratically.
7) Drift Quality
Drift Quality is a composite score derived from containment, normalized slope strength, width stability, and fit quality. It is not a prediction score. It is a structural quality score describing how coherent the active corridor currently is.
HOW TO USE IT
A practical way to read the script is to begin with the mode, then confirm the quality of the structure.
Mode
Start with the mode label:
- Uptrend Range suggests the active regression backbone is rising with enough normalized slope to avoid being treated as flat
- Flat Range suggests directional drift is weak relative to the selected threshold
- Downtrend Range suggests the active regression backbone is declining with enough normalized slope to define a downward corridor
Containment
Then check containment. High containment means price has been spending most of its recent time inside the outer corridor. This usually indicates that the displayed map is representative of the current market path.
Drift Quality
Use Drift Quality to judge whether the active corridor is coherent enough to be worth respecting as a structure. Higher values suggest cleaner organization. Lower values suggest weaker corridor integrity.
Range Width and Width Stability
Use these two together. A corridor can be narrow but unstable, or wide but orderly. The combination is often more informative than either metric alone.
VISUAL INTERPRETATION
In practice, the script is designed to help with questions such as:
- Is price traveling inside an orderly directional corridor or just moving noisily?
- Is the current range map still representative of behavior, or is it degrading?
- Is the structure flat, directional, tight, or loose?
- Is the current drift readable enough to justify a structure-based chart interpretation?
This makes the tool useful for context reading, corridor analysis, and chart organization. It is intentionally restrained in its presentation so price remains the primary object on the chart.
KEY INPUTS
Source
Selects the price source used to build the regression backbone.
Regression Length
Controls the lookback window used for the rolling linear regression center line. Shorter values make the map more reactive. Longer values make it smoother and more structural.
Containment Lookback
Defines the number of bars used to measure how consistently price remains inside the outer corridor.
Inner Band Multiplier
Controls the distance of the inner corridor around the regression backbone.
Outer Band Multiplier
Controls the distance of the outer corridor around the regression backbone.
Flat Threshold
Defines the normalized slope threshold below which the corridor is classified as flat.
Theme Preset
Provides a dark and light visual preset for better chart integration.
Mini Panel Controls
The panel can be shown or hidden and positioned in different chart corners depending on layout preference.
WHAT THIS SCRIPT IS NOT
This script is not a future path projection model.
It does not forecast a target.
It does not mark buy or sell entries.
It does not attempt to predict reversals.
It does not replace execution logic, confirmation logic, or risk management.
Its job is narrower and more disciplined: it maps the active regression corridor and summarizes how coherent that corridor currently is.
LIMITATIONS AND TRANSPARENCY
Like any rolling statistical model, this script is sensitive to lookback selection. Shorter lengths will react faster but may produce more frequent structural changes. Longer lengths will be smoother but slower to adapt.
Because the corridor is recalculated on a rolling basis, the map should be interpreted as a live description of current structure, not as a permanent historical truth.
The script also simplifies market behavior into a corridor framework. Strong news shocks, gap-like behavior, or abrupt volatility expansion can temporarily reduce corridor usefulness.
Drift Quality is a descriptive composite score, not an absolute truth metric. It should be used as context, not as a standalone trading decision.
HOW I THINK IT IS BEST USED
In my view, this tool works best when combined with discretionary chart reading or a broader structured workflow.
Examples:
- use it to decide whether a chart currently deserves trend-continuation thinking or range-neutral thinking
- use it to evaluate whether pullbacks are occurring inside a disciplined corridor or inside a deteriorating structure
- use it to compare the cleanliness of movement across symbols or timeframes
- use it as a chart-organization layer before applying separate execution logic
It is especially useful when the goal is not to chase events, but to understand the condition of the active movement map.
RISK DISCLOSURE
This script is an analytical indicator for chart interpretation. It does not provide financial advice, investment advice, or trading guarantees.
All trading decisions involve risk. Users should evaluate settings, market context, and risk management independently before using any indicator in live decision-making. Indicator
Retrograde Volatility Zones [PyraTime]The Problem: The "Invisible" Trend Killer
Most traders rely exclusively on lagging price indicators (Moving Averages, RSI, MACD). These tools are excellent at telling you what just happened, but they are notoriously blind to cyclic exhaustion. Markets don't just move in a vacuum; they undergo rhythmic shifts in volatility and psychology.
The "Invisible" problem is the Stationary Period. When a major planetary cycle slows down to a "Station," liquidity often thins, trends become erratic, and "Black Swan" volatility spikes occur. Without a way to quantify these cycles, traders frequently "buy the top" or "short the bottom" exactly as the underlying cyclic regime is shifting from green to red.
The Solution: PyraTime · RVZ
The Retrograde Volatility Zones indicator solves the problem of cyclic blindness by providing a real-time, quantitative map of planetary velocity. Unlike amateur scripts that use hardcoded (and expiring) date arrays,
PT·RVZ calculates the Geocentric Longitude Speed of the planets in real-time.Key Technical Pillars:Speed-Based Detection: If a planet’s speed is negative ( AMEX:SPD < 0$), it is retrograde. This is the professional standard for astronomical accuracy.
The Traffic Light Regime: It converts raw velocity into a weighted "Composite Score." It tells you not just that a planet is moving, but how much "thrust" the current cyclic environment has.Shadow Zone
Identification: It highlights the "Storm before the Storm" the period where a planet is still moving forward but has decelerated to less than 20% of its normal speed.
Volatility Sync: It overlays ATR spikes during these windows, filtering out noise and highlighting the specific moments where cyclic pressure translates into actual price expansion.
Tutorial: How to Master the Cycle
1. Reading the "Traffic Light"
The background and dashboard provide an instant read on the market "weather":
🟢 Green (Bullish): The weighted composite of planets is moving at high forward velocity. This is your "Trend is Friend" regime. Focus on long entries and trend-following.
🟡 Yellow (Mixed/Shadow): Major planets are decelerating or "Stationing." Expect chop, mean-reversion, and "fake-out" breakouts. Tighten stops and reduce position sizes.
🔴 Red (Bearish/Retrograde): The cycle is in retreat. Historically, these are windows of high-volatility corrections. Look for short opportunities or stay in cash.
2. Trading the "Stations" (℞ & D)
The glowing neon vertical lines mark the Station Point—the exact bar where velocity hits zero.
℞ (Station Retrograde): Often marks the "Blow-off Top" or the start of a deep corrective phase.
D (Station Direct): Often marks the "Final Flush" or the moment where the market regains its forward momentum.
3. The Dashboard Strategy
Watch the Speed°/d column in the dashboard. If you see Mercury ($\☿$) speed drop rapidly while price is hitting new highs, a "Mercury Retrograde" volatility spike is likely loading. Use the Composite Score at the bottom of the table to gauge the total "cyclic weight" of the market.
4. Setting High-Signal Alerts
Don't stare at the screen. Set the "High Vol + Retro" alert. This fires only when price range (ATR) expands significantly during a retrograde window—this is the signature of a major cyclic pivot. Indicator
Squeeze Momentum Regression Clouds [SciQua]╭──────────────────────────────────────────────╮
☁️ Squeeze Momentum Regression Clouds
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🔍 Overview
The Squeeze Momentum Regression Clouds (SMRC) indicator is a powerful visual tool for identifying price compression , trend strength , and slope momentum using multiple layers of linear regression Clouds. Designed to extend the classic squeeze framework, this indicator captures the behavior of price through dynamic slope detection, percentile-based spread analytics, and an optional UI for trend inspection — across up to four customizable regression Clouds .
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⚙️ Core Features
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Up to 4 Regression Clouds – Each Cloud is created from a top and bottom linear regression line over a configurable lookback window.
Slope Detection Engine – Identifies whether each band is rising, falling, or flat based on slope-to-ATR thresholds.
Spread Compression Heatmap – Highlights compressed zones using yellow intensity, derived from historical spread analysis.
Composite Trend Scoring – Aggregates directional signals from each Cloud using your chosen weighting model.
Color-Coded Candles – Optional candle coloring reflects the real-time composite score.
UI Table – A toggleable info table shows slopes, compression levels, percentile ranks, and direction scores for each Cloud.
Gradient Cloud Styling – Apply gradient coloring from Cloud 1 to Cloud 4 for visual slope intensity.
Weight Aggregation Options – Use equal weighting, inverse-length weighting, or max pooling across Clouds to determine composite trend strength.
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🧪 How to Use the Indicator
1. Understand Trend Bias with Cloud Colors
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Each Cloud changes color based on its current slope:
Green indicates a rising trend.
Red indicates a falling trend.
Gray indicates a flat slope — often seen during chop or transitions.
Cloud 1 typically reflects short-term structure, while Cloud 4 represents long-term directional bias. Watch for multi-Cloud alignment — when all Clouds are green or red, the trend is strong. Divergence among Clouds often signals a potential shift.
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2. Use Compression Heat to Anticipate Breakouts
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The space between each Cloud’s top and bottom regression lines is measured, normalized, and analyzed over time. When this spread tightens relative to its history, the script highlights the band with a yellow compression glow .
This visual cue helps identify squeeze zones before volatility expands. If you see compression paired with a changing slope color (e.g., gray to green), this may indicate an impending breakout.
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3. Leverage the Optional Table UI
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The indicator includes a dynamic, floating table that displays real-time metrics per Cloud. These include:
Slope direction and value , with historical Min/Max reference.
Top and Bottom percentile ranks , showing how price sits within the Cloud range.
Current spread width , compared to its historical norms.
Composite score , which blends trend, slope, and compression for that Cloud.
You can customize the table’s position, theme, transparency, and whether to show a combined summary score in the header.
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4. Analyze Candle Color for Composite Signals
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When enabled, the indicator colors candles based on a weighted composite score. This score factors in:
The signed slope of each Cloud (up, down, or flat)
The percentile pressure from the top and bottom bands
The degree of spread compression
Expect green candles in bullish trend phases, red candles during bearish regimes, and gray candles in mixed or low-conviction zones.
Candle coloring provides a visual shorthand for market conditions , useful for intraday scanning or historical backtesting.
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🧰 Configuration Guidance
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To tailor the indicator to your strategy:
Use Cloud lengths like 21, 34, 55, and 89 for a balanced multi-timeframe view.
Adjust the slope threshold (default 0.05) to control how sensitive the trend coloring is.
Set the spread floor (e.g., 0.15) to tune when compression is detected and visualized.
Choose your weighting style : Inverse Length (favor faster bands), Equal, or Max Pooling (most aggressive).
Set composite weights to emphasize trend slope, percentile bias, or compression—depending on your market edge.
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✅ Best Practices
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Use aligned Cloud colors across all bands to confirm trend conviction.
Combine slope direction with compression glow for early breakout entry setups.
In choppy markets, watch for Clouds 1 and 2 turning flat while Clouds 3 and 4 remain directional — a sign of potential trend exhaustion or consolidation.
Keep the table enabled during backtesting to manually evaluate how each Cloud behaved during price turns and consolidations.
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📌 License & Usage Terms
╰───────────────────────╯
This script is provided under the Creative Commons Attribution-NonCommercial 4.0 International License .
✅ You are allowed to:
Use this script for personal or educational purposes
Study, learn, and adapt it for your own non-commercial strategies
❌ You are not allowed to:
Resell or redistribute the script without permission
Use it inside any paid product or service
Republish without giving clear attribution to the original author
For commercial licensing , private customization, or collaborations, please contact Joshua Danford directly. Indicator
Weighted Regression Bands (Zeiierman)█ Overview
Weighted Regression Bands is a precision-engineered trend and volatility tool designed to adapt to the real market structure instead of reacting to price noise.
This indicator analyzes Weighted High/Low medians and applies user-selectable smoothing methods — including Kalman Filtering, ALMA, and custom Linear Regression — to generate a Fair Value line. Around this, it constructs dynamic standard deviation bands that adapt in real-time to market volatility.
The result is a visually clean and structurally intelligent trend framework suitable for breakout traders, mean reversion strategies, and trend-driven analysis.
█ How It Works
⚪ Structural High/Low Analysis
At the heart of this indicator is a custom high/low weighting system. Instead of using just the raw high or low values, it calculates a midline = (high + low) / 2, then applies one of three weighting methods to determine which price zones matter most.
Users can select the method using the “Weighted HL Method” setting:
Simple
Selects the single most dominant median (highest or lowest) in the lookback window. Ideal for fast, reactive signals.
Advanced
Ranks each bar based on a composite score: median × range × recency. This method highlights structurally meaningful bars that had both volatility and recency. A built-in Kalman filter is applied for extra stability.
Smooth
Blends multiple bars into a single weighted average using smoothed decay and range. This provides the softest and most stable structural response.
⚪ Smoothing Methods (ALMA / Linear Regression)
ALMA provides responsive, low-lag smoothing for fast trend reading.
Linear Regression projects the Fair Value forward, ideal for trend modeling.
⚪ Kalman Smoothing Filter
Before trend calculations, the indicator applies an optional Kalman-style smoothing filter. This helps:
Reduce choppy false shifts in trend,
Retain signal clarity during volatile periods,
Provide stability for long-term setups.
⚪ Deviation Bands (Dynamic Volatility Envelopes)
The indicator builds ±1, ±2, and ±3 standard deviation bands around the fair value line:
Calculated from the standard deviation of price,
Bands expand and contract based on recent volatility,
Visualizes potential overbought/oversold or trending conditions.
█ How to Use
⚪ Trend Trading & Filtering
Use the Fair Value line to identify the dominant direction.
Only trade in the direction of the slope for higher probability setups.
⚪ Volatility-Based Entries
Watch for price reaching outer bands (+2σ, +3σ) for possible exhaustion.
Mean reversion entries become higher quality when far from Fair Value.
█ Settings
Length – Lookback for Weighted HL and trend smoothing
Deviation Multiplier – Controls how wide the bands are from the fair value line
Method – Choose between ALMA or Linear Regression smoothing
Smoothing – Strength of Kalman Filter (1 = none, <1 = stronger smoothing)
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Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Indicator
Stochastic Overlay - Regression Channel (Zeiierman)█ Overview
The Stochastic Overlay – Regression Channel (Zeiierman) is a next-generation visualization tool that transforms the traditional Stochastic Oscillator into a dynamic price-based overlay.
Instead of leaving momentum trapped in a lower subwindow, this indicator projects the Stochastic oscialltor directly onto price itself — allowing traders to visually interpret momentum, overbought/oversold conditions, and market strength without ever taking their eyes off price action.
⚪ In simple terms:
▸ The Bands = The Stochastic Oscillator — but on price.
▸ The Midline = Stochastic 50 level
▸ Upper Band = Stochastic Overbought Threshold
▸ Lower Band = Stochastic Oversold Threshold
When the price moves above the midline → it’s the same as the oscillator moving above 50
When the price breaks above the upper band → it’s the same as Stochastic entering overbought.
When the price reaches the lower band →, think of it like Stochastic being oversold.
This makes market conditions visually intuitive. You’re literally watching the oscillator live on the price chart.
█ How It Works
The indicator layers 3 distinct technical elements into one clean view:
⚪ Stochastic Momentum Engine
Tracks overbought/oversold conditions and directional strength using:
%K Line → Momentum of price
%D Line → Smoothing filter of %K
Overbought/Oversold Bands → Highlight potential reversal zones
⚪ Volatility Adaptive Bands
Dynamic bands plotted above and below price using:
ATR * Stochastic Scaling → Creates wider bands during volatile periods & tighter bands in calm conditions
Basis → Moving average centerline (EMA, SMA, WMA, HMA, RMA selectable)
This means:
→ In strong trends: Bands expand
→ In consolidations: Bands contract
⚪ Regression Channel
Projects trend direction with different models:
Logarithmic → Captures non-linear growth (perfect for crypto or exponential stocks)
Linear → Classic regression fit
Adaptive → Dynamically adjusts sensitivity
Leading → Projects trend further ahead (aggressive mode)
Channels include:
Midline → Fair value trend
Upper/Lower Bounds → Deviation-based support/resistance
⚪ Heatmap - Bull & Bear Power Strength
Visual heatmeter showing:
% dominance of bulls vs bears (based on close > or < Band Basis)
Automatic normalization regardless of timeframe
Table display on-chart for quick visual insight
Dynamic highlighting when extreme levels are reached
⚪ Trend Candlestick Coloring
Bars auto-color based on trend filter:
Above Basis → Bullish Color
Below Basis → Bearish Color
█ How to Use
⚪ Trend Trading
→ Use Band direction + Regression Channel to identify trend alignment
→ Longs favored when price holds above the Basis
→ Shorts favored when price stays below the Basis
→ Use the Bull & Bear heatmap to asses if the bulls or the bears are in control.
⚪ Mean Reversion
→ Look for price to interact with Upper or Lower Band extremes
→ Stochastic reaching OB/OS zones further supports reversals
⚪ Momentum Confirmation
→ Crossovers between %K and %D can confirm continuation or divergence signals
→ Especially powerful when happening at band boundaries
⚪ Strength Heatmap
→ Quickly visualize current buyer vs seller control
→ Sharp spikes in Bull Power = Aggressive buying
→ Sharp spikes in Bear Power = Heavy selling pressure
█ Why It Useful
This is not a typical Stochastic or regression tool. The tool is designed for traders who want to:
React dynamically to price volatility
Map momentum into volatility context
Use adaptive regression channels across trend styles
Visualize bull vs bear power in real-time
Follow trends with built-in reversal logic
█ Settings
Stochastic Settings
Stochastic Length → Period of calculation. Higher = smoother, Lower = faster signals.
%K Smoothing → Smooths the Stochastic line itself.
%D Smoothing → Smooths the moving average of %K for slower signals.
Stochastic Band
Band Length → Length of the Moving Average Basis.
Volatility Multiplier → Controls band width via ATR scaling.
Band Type → Choose MA type (EMA, SMA, WMA, HMA, RMA).
Regression Channel
Regression Type → Logarithmic / Linear / Adaptive / Leading.
Regression Length → Number of bars for regression calculation.
Heatmap Settings
Heatmap Length → Number of bars to calculate bull/bear dominance.
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Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Indicator
Indicator
Indicator
Indicator
TF Segmented Polynomial Regression [LuxAlgo]This indicator displays polynomial regression channels fitted using data within a user selected time interval.
The model is fitted using the same method described in our previous script:
Settings
Degree: Degree of the fitted polynomial
Width: Multiplicative factor of the model RMSE. Controls the width of the polynomial regression's channels
Timeframe: Fits the polynomial regression using data within the selected timeframe interval
Show fit for new bars: If selected, will fit the regression model for newly generated bars, else the previous fitted value is displayed.
Src: Input source
Usage
Segmented (or piecewise) models yield multiple fits by first partitioning the data into multiple intervals from specific partitioning conditions. In this script this partitioning condition is for a user selected timeframe to change.
Segmented models can be particularly pertinent for market prices, which often describes a series of local trends.
Segmented polynomial regressions can describe the nature of underlying trends in the price from their fit, such as if an underlying trend is more linear (trending) or constant (ranging), and if a trend is monotonic.
The above chart shows a monthly partitioning on SPX 15m, using a polynomial regression of degree 3. Channel extremities allows highlighting local tops/bottoms.
For real time applications users can choose to fit a current model to incoming price data using the Show fit for new bars settings.
Details
The script does not make use of line.new to display the segmented linear regressions, which allows showing a higher number of historical fits. Each channel extremity as well as the model fit is displayed from the plot function, as such user can more easily set alerts on them.
It is important to note that achieving this requires accessing future price data, as such this script is subject to lookahead bias, historical results differ from the results one could have obtained in real-time. Indicator
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Indicator
Moving Regression Prediction BandsIntroducing the Moving Regression Prediction Bands indicator.
Here I aimed to combine the principles of traditional band indicators (such as Bollinger Bands), regression channel and outlier detection methods. Its upper and lower bands define an interval in which the current price was expected to fall with a prescribed probability, as predicted by the previous-step result of the local polynomial regression (for the original Moving Regression script, see link below).
Algorithm
1. At every time step, the script performs local polynomial regression of the sample data within the lookback window specified by the Length input parameter.
2. The fitted polynomial is used to construct the Moving Regression time series as well as to extrapolate data, that is, to predict the next data point ( MRPrediction ).
3. The accuracy of local interpolation is estimated by means of the root-mean-square error ( RMSE ), that is, the deviation between the fitted polynomial and the observed values.
4. The MRPrediction and RMSE values calculated for the previous bar are then used to build the upper and lower bands , which I define as follows:
Upper Band = MRPrediction_prev + Multiplier *( RMSE_prev )
Lower Band = MRPrediction_prev - Multiplier *( RMSE_prev )
Here the Multiplier is a user-defined parameter that should be interpreted as a quantile in the standard normal distribution (the default value of 2.0 roughly corresponds to the 95% prediction interval).
To visualize the central line , the script offers the following options:
Previous-Period MR Prediction: MRPrediction_prev time series from the above equation.
MR: Conventional Moving Regression time series.
Ribbon: “Previous-Period MR Prediction” and “MR” curves plotted together and colored according to their relative value (green if MR > Previous MR Prediction; red otherwise).
Usage
My original idea was to use the band breakouts as potential trading signals. For example, the price crossing above the upper band is a bullish signal , being a potential sign that price is gaining momentum and is out of a previously predicted trend. The exit signal could be the crossing under the lower band or under the central line.
However, be aware that it is an experimental indicator, so you might fin some better strategies.
Feel free to play around!
Indicator
GAURs Polynomial Regression ChannelsThanks to The Sweet Lord , here is the Gaur's Polynomial Regression Channel.
Its a Polynomial Regression Channel but applied a little differently. Wont go into technical details much. Overview of options is as follows-
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Channel Options
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1. Degree of Polynomial: 1/2/3
Default = 3
Defines the degree of polynomials - 1,2,3. Note here, degree 1 will not be a straight line since its applied differently.
Try different degrees for different fits and market conditions.
2. Channel Length:
Default 30 (candles)
You can go beyond 100 or 200 candle lengths but smaller is the usual preference of Poly-Reg-channel traders. It all depends on market conditions and your style of trading. Do your research. I am usually comfortable with a range of 20-50 (in crypto markets).
3. Basis of Channel height/boundries: ATR/Manual
Default: ATR
ATR provides a dynamically adjusted entry/exit bounds of the channels. As ATR changes, the channel bounds also changes its height. It can also be fixed manually. Manual heights wont change automatically.
4. Basis of Y-Value: open/close/ sma / ema / wma /hilow
Default: close
Y- value is the y value of the (x,y) coordinates used while calculating the regression coefficients. Dont worry about it, its nothing serious.
5. Apply channel smoothning using sma?: Yes/No
Default: Yes
Without smoothning, the channel does not "look" good.
6. Shaded Area Height Percentage:
Its the extra margin for the channel. Its in percentage of the total height (defined 3 above) of channels. The shaded area provides an extra allowance for your entries or exits beyond the ATR or manual heights.
7. Plot RSI?: Yes/No
Default: Yes
Plots RSI (orange line in between the channel - its different from the dotted center line) considering the downbound of channels as 0 (oversold) and upbound of channels as 100 (overbought)
8. Plot 200 sma?: Yes/No
Default: Yes
It plots a 200 period fast (green) and 225 period slow (red) sma . I usually use two MAs. Its visually very easy to understand.
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Sample Strategy
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You can develop your own strategy with the channels. But following is just one of the ways you can trade.
Best Application: Ranging markets. But can be happily used in volatile conditions, with a little experience.
1. SMA: -- (this condition is optional really)
If green (200) is above red (225) go only long. If red is above green go only short. Defines long term trend of the market.
2. Channel slope: -- (this stuff needs practice/experience)
Depending on the channel slope, like if its tending to go up or down, you can choose to take only short or long trades. It defines short term momentum of the market.
3. ATR based heights:
Since its ATR based, the channel height are our natural entry and exit points.
Long:
When price touches lower shaded area, consider possible long entry. Exit on price entering the upper shaded area.
Short:
Enter on upper bound shaded area, exit on lower.
4. RSI:
For additional conformations. Again note, the RSI considers the lower bound of channel as 0 and upper as 100. But since, the channel moves up and down, the RSI will also move not only as RSI but also with the channel. Meaning, say if the RSI is valued at 50, then it will be near the center of the channel but since the center changes as time and price changes, the RSI valued at 50 at different times will not be at the same horizontal level respect to the graph, although it will be at the same level (center) respect to the channel.
5. PRC Channel Percentage label:
This label is at the lower side a bit ahead of the current candle. Provides you info on what is the channel percentage. This is especially helpful in crypto markets to gauge your possible percentage profit where profits can be much higher than forex or other instruments. It can also helps you select a suitable market/instrument if the channels are based on ATR.
6. Extra indicators:
I usually use stochastic along with this setup for extra conformations.
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Donate
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Use freely and donate generously if you find value. Your help will really help.
I had earlier provided BTC addresses for donations but it seems to violate TV House rules.
Hope they make TV coins redeemable in future.
- Pranav Joshi
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Extra Info
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// © cpranavjoshi
// special thanks to the "Trading View" people for providing this great platform for free
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// MATH
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// special thanks to an article on the web that provided layman friendly explanation of the maths
// unfortunately i wont be able to provide the link to that article owing to TV restrictions, though i sincerely would have liked to credit the author.
// Google search this phrase, and you should be able to get it in one of the first results - "polynomialregression Mathematics of Polynomial Regression"
// my regression math calculation is a further resolution upon the generalized matrix formula given in the that article.
// the generalized matrix looks scary but in fact its much simpler than one may assume
// the summation sign things are just float numbers that can be easily found out
// so we get a matrix with number of equations equal to the number of unknowns.
// e.g. if its a 3rd degree poly, it has 4 unknowns (c0,c1,c2,c3) with 4 equations as in the generalized matrix
// it can be resolved by simple algebra
// Note: the results have been verified with excel using same input data points.
// pine was difficult for me so i coded it in python first to verify
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// WHY
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// this script was coded because Pranav badly needed Polynomial channels (had used them in mt4 earlier)
// and at the time of this coding, i could not find any readily available script in the trading view public library ( tnx public)
// the complex math was probably the hurdle
// i m not good in maths, but by the Will of the Lord, i could resolve the issue with simple algebra and logic
// ------------------------
// PINE
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// i am just an average (even poor probably) programmer and pine script is not my language
// this is a humble attempt to write my first pine with whatever i could do quickly
// experts - feel free to develop if needed. have used some workarounds in drawings/plottings. rectify them if possible
//
//
// - Pranav Joshi Indicator
