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Regression-trend-channel

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.
อินดิเคเตอร์ Pine Script®
โดย MarkitTick
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.
อินดิเคเตอร์ Pine Script®
โดย AGProLabs
1
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.
อินดิเคเตอร์ Pine Script®
โดย PyraTime
1
Squeeze Momentum Regression Clouds [SciQua]╭──────────────────────────────────────────────╮ ‎ ‎ ‎ ‎ ‎ ‎ ‎‎ ‎‎ ‎ ‎ ‎ ‎ ‎‎ ‎‎ ‎ ☁️ Squeeze Momentum Regression Clouds ╰──────────────────────────────────────────────╯ 🔍 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 . ──────────────────────────────────────────────────────────── ╭────────────────╮ ‎ ‎ ‎ ‎ ‎‎ ‎‎ ‎ ⚙️ Core Features ╰────────────────╯ 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. ──────────────────────────────────────────────────────────── ╭──────────────────────────────────────────╮ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎‎ ‎‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ 🧪 How to Use the Indicator ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎‎ ‎‎ ‎ ‎ ‎ ‎ ‎ ‎ 1. Understand Trend Bias with Cloud Colors ╰──────────────────────────────────────────╯ 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. ──────────────────────────────────────────────────────────── ╭───────────────────────────────────────────────╮ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎‎ ‎‎ ‎‎ ‎‎ ‎ ‎ ‎ ‎ 2. Use Compression Heat to Anticipate Breakouts ╰───────────────────────────────────────────────╯ 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. ──────────────────────────────────────────────────────────── ╭─────────────────────────────────╮ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎‎ ‎‎ ‎ ‎ ‎ 3. Leverage the Optional Table UI ╰─────────────────────────────────╯ 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. ──────────────────────────────────────────────────────────── ╭─────────────────────────────────────────────╮ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎‎ ‎‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ 4. Analyze Candle Color for Composite Signals ╰─────────────────────────────────────────────╯ 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. ──────────────────────────────────────────────────────────── ╭────────────────────────╮ ‎ ‎ ‎ ‎ ‎‎ ‎‎‎ ‎ ‎ ‎ 🧰 Configuration Guidance ╰────────────────────────╯ 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. ──────────────────────────────────────────────────────────── ╭────────────────╮ ‎ ‎ ‎ ‎‎ ‎‎‎ ‎ ‎ ‎ ✅ Best Practices ╰────────────────╯ 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. ──────────────────────────────────────────────────────────── ╭───────────────────────╮ ‎ ‎ ‎ ‎‎ ‎ ‎ ‎‎ ‎‎ ‎ 📌 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.
อินดิเคเตอร์ Pine Script®
โดย JoshuaDanford
1
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) ----------------- 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.
อินดิเคเตอร์ Pine Script®
โดย Zeiierman
11
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. ----------------- 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.
อินดิเคเตอร์ Pine Script®
โดย Zeiierman
27
Logarithmic Regression Channel-Trend [BigBeluga] This indicator utilizes logarithmic regression to track price trends and identify overbought and oversold conditions within a trend. It provides traders with a dynamic channel based on logarithmic regression, offering insights into trend strength and potential reversal zones. 🔵Key Features: Logarithmic Regression Trend Tracking: Uses log regression to model price trends and determine trend direction dynamically. f_log_regression(src, length) => float sumX = 0.0 float sumY = 0.0 float sumXSqr = 0.0 float sumXY = 0.0 for i = 0 to length - 1 val = math.log(src ) per = i + 1.0 sumX += per sumY += val sumXSqr += per * per sumXY += val * per slope = (length * sumXY - sumX * sumY) / (length * sumXSqr - sumX * sumX) average = sumY / length intercept = average - slope * sumX / length + slope Regression-Based Channel: Plots a log regression channel around the price to highlight overbought and oversold conditions. Adaptive Trend Colors: The color of the regression trend adjusts dynamically based on price movement. Trend Shift Signals: Marks trend reversals when the log regression line cross the log regression line 3 bars back. Dashboard for Key Insights: Displays: - The regression slope (multiplied by 100 for better scale). - The direction of the regression channel. - The trend status of the logarithmic regression band. 🔵Usage: Trend Identification: Observe the regression slope and channel direction to determine bullish or bearish trends. Overbought/Oversold Conditions: Use the channel boundaries to spot potential reversal zones when price deviates significantly. Breakout & Continuation Signals: Price breaking outside the channel may indicate strong trend continuation or exhaustion. Confirmation with Other Indicators: Combine with volume or momentum indicators to strengthen trend confirmation. Customizable Display: Users can modify the lookback period, channel width, midline visibility, and color preferences. Logarithmic Regression Channel-Trend is an essential tool for traders who want a dynamic, regression-based approach to market trends while monitoring potential price extremes.
อินดิเคเตอร์ Pine Script®
โดย BigBeluga
18
Machine Learning Regression Trend [LuxAlgo]The Machine Learning Regression Trend tool uses random sample consensus (RANSAC) to fit and extrapolate a linear model by discarding potential outliers, resulting in a more robust fit. 🔶 USAGE The proposed tool can be used like a regular linear regression, providing support/resistance as well as forecasting an estimated underlying trend. Using RANSAC allows filtering out outliers from the input data of our final fit, by outliers we are referring to values deviating from the underlying trend whose influence on a fitted model is undesired. For financial prices and under the assumptions of segmented linear trends, these outliers can be caused by volatile moves and/or periodic variations within an underlying trend. Adjusting the "Allowed Error" numerical setting will determine how sensitive the model is to outliers, with higher values returning a more sensitive model. The blue margin displayed shows the allowed error area. The number of outliers in the calculation window (represented by red dots) can also be indicative of the amount of noise added to an underlying linear trend in the price, with more outliers suggesting more noise. Compared to a regular linear regression which does not discriminate against any point in the calculation window, we see that the model using RANSAC is more conservative, giving more importance to detecting a higher number of inliners. 🔶 DETAILS RANSAC is a general approach to fitting more robust models in the presence of outliers in a dataset and as such does not limit itself to a linear regression model. This iterative approach can be summarized as follow for the case of our script: Step 1: Obtain a subset of our dataset by randomly selecting 2 unique samples Step 2: Fit a linear regression to our subset Step 3: Get the error between the value within our dataset and the fitted model at time t , if the absolute error is lower than our tolerance threshold then that value is an inlier Step 4: If the amount of detected inliers is greater than a user-set amount save the model Repeat steps 1 to 4 until the set number of iterations is reached and use the model that maximizes the number of inliers 🔶 SETTINGS Length: Calculation window of the linear regression. Width: Linear regression channel width. Source: Input data for the linear regression calculation. 🔹 RANSAC Minimum Inliers: Minimum number of inliers required to return an appropriate model. Allowed Error: Determine the tolerance threshold used to detect potential inliers. "Auto" will automatically determine the tolerance threshold and will allow the user to multiply it through the numerical input setting at the side. "Fixed" will use the user-set value as the tolerance threshold. Maximum Iterations Steps: Maximum number of allowed iterations.
อินดิเคเตอร์ Pine Script®
โดย LuxAlgo
12
Regression Channel Trend DetectionThis is a regression channel that uses ichimoku to determine trend. The sensitivity is customizable. The centerline will change color according to the trend detected by ichimoku, and each line can act as support/resistance. The bands of the channel also change colors according to how far price is getting away from them. If you notice in this example, the lower band is turning orange when the price is getting too far away from it, suggesting that it may have risen too fast and too soon. This is still in testing so feel free to comment with any suggestions or fixes.
อินดิเคเตอร์ Pine Script®
โดย grafman
1
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.
อินดิเคเตอร์ Pine Script®
โดย LuxAlgo
22
Linear Regression Channel - Auto Volume BasedBased on oryginal TV indicator BUT with a little twist. ;) I really like the regression channel - but the problem is that the length needs to be always manually adjusted. In this script I try to solve this issue. This is modified version on TV indicator - Linear Regression Channel. The main difference is that now you don't get static length - it is automatically adjuested to the recent price action (determined by highest volume in last 300 bars).
อินดิเคเตอร์ Pine Script®
โดย okhanLAB
10
Linear Regression Relative Strength[image/x/iZvwDWEY/ Relative Strength indicator comparing the current symbol to SPY (or any other benchmark). It may help to pick the right assets to complement the portfolio build around core ETFs such as SPY. The general idea is to show if the current symbol outperforms or underperforms the benchmark (SPY by default) when bought some certain time ago. Relative performance is displayed as percent and is calculated for three different time ranges - short (1 mo by default), mid (1 quarter), and long (half a year). To smooth the volatility, the script uses linear regression to estimate the trend and takes the start and the end points of the linear regression line to compute the relative strength. It is important to remember that the script shows the gain relative to SPY (or other selected benchmark), not the asset's gain. Therefore, it may indicate that the asset is profitable, but it still may lose value if SPY is in downtrend. Therefore, it is crucial to check other indicators before making a decision. In the example above, standard linear regression for one quarter is used to indicate the direction of the trend.
อินดิเคเตอร์ Pine Script®
โดย gaperton
2
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!
อินดิเคเตอร์ Pine Script®
โดย tbtkg
33
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Channel Options - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Sample Strategy - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Donate - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Extra Info - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - // © cpranavjoshi // special thanks to the "Trading View" people for providing this great platform for free // ------------------------ // MATH // ------------------------ // 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 // ------------------------ // WHY // ------------------------ // 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 // ------------------------ // 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
อินดิเคเตอร์ Pine Script®
โดย cpranavjoshi
2
Regression Channel [DW]This is an experimental study which calculates a linear regression channel over a specified period or interval using custom moving average types for its calculations. Linear regression is a linear approach to modeling the relationship between a dependent variable and one or more independent variables. In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. The regression channel in this study is modeled using the least squares approach with four base average types to choose from: -> Arnaud Legoux Moving Average (ALMA) -> Exponential Moving Average (EMA) -> Simple Moving Average (SMA) -> Volume Weighted Moving Average (VWMA) When using VWMA, if no volume is present, the calculation will automatically switch to tick volume, making it compatible with any cryptocurrency, stock, currency pair, or index you want to analyze. There are two window types for calculation in this script as well: -> Continuous, which generates a regression model over a fixed number of bars continuously. -> Interval, which generates a regression model that only moves its starting point when a new interval starts. The number of bars for calculation cumulatively increases until the end of the interval. The channel is generated by calculating standard deviation multiplied by the channel width coefficient, adding it to and subtracting it from the regression line, then dividing it into quartiles. To observe the path of the regression, I've included a tracer line, which follows the current point of the regression line. This is also referred to as a Least Squares Moving Average (LSMA). For added predictive capability, there is an option to extend the channel lines into the future. A custom bar color scheme based on channel direction and price proximity to the current regression value is included. I don't necessarily recommend using this tool as a standalone, but rather as a supplement to your analysis systems. Regression analysis is far from an exact science. However, with the right combination of tools and strategies in place, it can greatly enhance your analysis and trading.
อินดิเคเตอร์ Pine Script®
โดย DonovanWall
20