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Mean Reversion: BB + Stoch + ADX (Short Only)Strategy overview This strategy is a specialized tool for changing the average price, designed exclusively for short positions. It determines the overbought conditions in a non-trending market, allowing you to "smooth out" price extremes with high accuracy. How it works (logic): The script uses a triple confirmation filter to ensure high quality inputs: Bollinger Bands: defines price extremes. We are looking for when the price will cross the upper band. Stochastic Oscillator: Confirms that the asset is technically overbought (value > 80). ADX (Average Directivity Index): "The secret tool." It acts as a trend filter. The strategy only works if the ADX is below a certain threshold (default is 25), which ensures that we will not try to open short positions in case of a strong bullish breakout. Exit strategy: Risk management is automated using ATR (Average True Range). Stop Loss: Set above the entry price based on the current volatility. Take profit: Calculated as a multiple of the ATR value to record a return to the average value. Key functions: For short positions only: Optimized for bearish corrections and limited range markets. Trend protection: The ADX filter prevents "catching a falling knife" during strong rises. Visual feedback: The background turns green when the market is "flat" enough to trade, and red when the trend is too strong. It is best to use on: Time frame: 10 minutes, 15 minutes Assets: Crypto
Pine Script®策略
由Volatryx提供
Fractal Retracement [Jamallo](2025) Intro FRAMA is a moving average that adapts its speed based on fractal geometry — specifically, the fractal dimension (D) of recent price action. When price is trending strongly (low fractal dimension), it moves fast. When price is choppy/ranging (high fractal dimension), it slows down. This makes it far more responsive than a standard EMA or SMA. Breakdown: The indicator wraps this with a continuous range logic layer: the filtered line = k only moves if price breaks beyond the FRAMA ± ATR-based range, creating a stepped/ratcheting effect that filters out noise. Two sets of bands are plotted around the filtered line, scaled by ATR multiplied by user-defined multipliers (tight at 0.5×, medium at 1.0×). They're smoothed with a short EMA to reduce jitter, and filled with gradient colors for visual clarity. Direction is simply determined by whether k is rising or falling, and colors everything green (uptrend) or pink/red (downtrend). END In short, it's a noise-filtered trend indicator useful for identifying trend direction, dynamic support/resistance , and gauging how far price has retraced from the trend baseline.
Pine Script®指标
由Jamallo22提供
Keltner MTF Adaptive TargetsKeltner MTF Adaptive Targets Overview Keltner MTF Adaptive Targets is a multi-timeframe volatility framework designed to map potential price expansion zones directly on your chart. Instead of relying on a single timeframe Keltner Channel, this indicator calculates and plots Keltner extremes from multiple timeframes simultaneously. The result is a dynamic volatility map that highlights where price is statistically more likely to accelerate, stall, or pivot. For traders focused on momentum shifts or options pricing inefficiencies, these levels often represent areas where volatility expansion begins. Core Idea Traditional Keltner Channels create a volatility envelope around an EMA. This script extends that concept by calculating the same envelope across multiple timeframes and projecting them onto the current chart. When multiple timeframe bands cluster together, they frequently represent high-importance price zones where market participants react. You can think of it as a multi-timeframe volatility gravity map. Timeframes Included The indicator can display adaptive targets from the following timeframes: 30 Seconds 1 Minute 3 Minutes 5 Minutes 10 Minutes 15 Minutes 30 Minutes 45 Minutes 1 Hour 2 Hours 4 Hours 1 Day 1 Week Each timeframe can be enabled or disabled individually. How the Keltner Bands Are Calculated Middle Line EMA of HL2 Volatility Component ATR calculated from True Range Upper Band EMA + (ATR × Deviation) Lower Band EMA − (ATR × Deviation) An optional smoothing step can be applied to the source before the EMA calculation to reduce noise. Why This Is Useful Markets tend to move from one volatility boundary to another. By stacking multiple timeframes together, traders can visualize where price is most likely to encounter resistance, support, or momentum transitions. These zones often behave as: • volatility expansion targets • reaction levels • momentum pivots • consolidation boundaries Options Trading Perspective Options traders are often looking for moments where momentum is about to shift and volatility is still cheap. Multi-timeframe Keltner levels can help identify: • potential momentum pivots • volatility expansion zones • areas where price may move rapidly toward the next volatility boundary When several timeframe bands align, the probability of a significant move often increases. Information Table An optional info table displays current values for each active timeframe: • Upper band • Lower band • Channel width Channel width helps visualize the volatility size of each timeframe. Customization Users can adjust: EMA Period ATR Period Deviation Multiplier Source Smoothing You can also customize: Line colors Line width Visible timeframes Table position Practical Uses Traders commonly use the levels as: Volatility targets Price often moves from one timeframe band to the next. Momentum pivots Breaking multiple timeframe bands can signal a structural move. Compression detection Narrow channel widths may precede volatility expansion. Confluence zones Clusters of bands from different timeframes often act as high-reaction areas. Notes This indicator does not produce buy or sell signals. It is designed as a volatility structure tool to help traders visualize where price movement is most likely to expand or pivot.
Pine Script®指标
由Jysun提供
Trend Break + Structure + Range FilterThis strategy focuses on trading continuation moves after price breaks recent structure, while filtering out low-momentum conditions. The goal is to participate in directional markets and reduce entries during sideways activity. Concept Behind the Strategy Markets tend to alternate between consolidation and expansion. Instead of reacting to every price movement, this approach waits for a clear break of recent highs or lows, aligned with the prevailing trend. By combining structure, trend bias, and range filtering, the strategy aims to improve trade selection quality. How It Works 1. Trend Direction (EMA Filter) A moving average defines the primary market bias. Price above the EMA → bullish bias Price below the EMA → bearish bias Trades are only taken in the direction of this bias. 2. Structure Break The system monitors the highest high and lowest low over a defined lookback period. A trade is triggered when price breaks beyond that recent structure. 3. Range Filter (Bollinger Logic) If price is moving inside a compressed range, entries are avoided. This helps reduce signals during low-momentum conditions. 4. Risk Management Stop-loss and take-profit levels are based on ATR (Average True Range), which adapts to current volatility. A configurable risk-to-reward ratio determines the target distance. 5. Realistic Backtesting Setup Commission and slippage are included to simulate more practical conditions. Key Characteristics Non-repainting logic Breakout-based entries Trend-aligned trade direction Volatility-adjusted exits Designed for structured testing When It May Perform Better Markets with sustained directional movement Volatility expansion phases Instruments that respect breakout behavior It may underperform during prolonged sideways or low-volatility environments. Important Note This strategy is intended for research and testing purposes. Performance can vary across assets and timeframes. It should be evaluated across different market conditions before considering live use.
Pine Script®策略
由AIScripts提供
Multi-TF Keltner Heatmap# Multi-TF Keltner Heatmap A multi-timeframe volatility structure indicator designed to show where momentum pivots are forming across timeframes. Instead of plotting a single Keltner Channel, this script overlays Keltner envelopes from 12 timeframes simultaneously, allowing traders to see when lower timeframe volatility begins pivoting relative to higher timeframe structure. For options traders, these pivot points often represent the moments where momentum changes fastest while options are still relatively cheap. The goal is to identify the earliest structural shift in volatility expansion before the larger move becomes obvious. ## Core Idea Momentum rarely appears suddenly on higher timeframes. Instead, it typically builds from smaller timeframes upward. Lower timeframes begin expanding volatility until they interact with or surpass the volatility boundaries of larger timeframes. When this occurs, the script identifies it as a pivot event. A pivot means the shorter timeframe volatility envelope has reached or crossed the adjacent higher timeframe envelope, indicating that momentum pressure is shifting. As these pivots propagate upward through the timeframe ladder, a momentum chain forms. This chain represents how many layers of the market structure are currently shifting direction. ## Timeframes Included The script pulls Keltner Channel data from the following timeframes: - 1 Minute - 3 Minute - 5 Minute - 10 Minute - 15 Minute - 30 Minute - 45 Minute - 1 Hour - 2 Hour - 4 Hour - 1 Day - 1 Week These timeframes together create a stacked volatility structure showing how pressure builds through the market. ## Keltner Channel Construction Each timeframe uses the same parameters. Basis EMA (default length: 200) Volatility Envelope ATR (default length: 200) Bandwidth Multiplier ATR × 8 These intentionally large settings create structural volatility envelopes rather than short-term reactive channels. The focus is on major volatility shifts rather than micro fluctuations. ## Visual Structure The indicator uses color to separate layers of the timeframe hierarchy. ### White Bands (1m – 15m) These represent short-term market microstructure. They allow traders to see: - short-term compression - micro volatility expansion - early directional pressure Opacity is reduced so these bands remain informational rather than dominant. ### Intermediate Layer (30m / 45m) Upper bands are colored green. Lower bands are colored red. These timeframes often act as the bridge between intraday volatility and higher timeframe momentum. When price begins interacting strongly with these bands, it often signals that pressure is building toward a larger pivot. ### Higher Timeframe Bands (1H – 1W) Higher timeframe bands are hidden by default. They only appear when a pivot condition occurs. A pivot occurs when: Shorter timeframe upper band ≥ adjacent higher timeframe upper band or Shorter timeframe lower band ≤ adjacent higher timeframe lower band Example: 45m upper ≥ 1H upper When this happens, the 1H upper band becomes visible. This signals that short-term volatility is now interacting with higher timeframe structure. ## Pivot Chain Momentum shifts are tracked using adjacent timeframe pivots. Upper band pivots follow this sequence: - 45m → 1H - 1H → 2H - 2H → 4H - 4H → 1D - 1D → 1W Lower band pivots follow the same sequence. This adjacency logic reflects how momentum realistically propagates through the market rather than skipping timeframes. ## Pivot Chain Depth The indicator calculates two values shown in the status line and data window. Bull Chain Number of upward pivot steps currently active. Example: 45m pivoting above 1H 1H pivoting above 2H 2H pivoting above 4H Bull Chain = 3 Bear Chain Number of downward pivot steps currently active. Example: 45m pivoting below 1H 1H pivoting below 2H 2H pivoting below 4H Bear Chain = 3 ## Interpreting Chain Depth Lower chain values typically indicate: - localized volatility - range conditions - early momentum shifts Higher chain values indicate: - stronger structural alignment - expanding volatility - sustained directional momentum Deep pivot chains are relatively rare and often occur during: - breakouts - strong trend continuation - macro directional moves ## Why This Matters for Options Options traders benefit most when they can identify large momentum shifts early, before volatility expansion fully develops. When lower timeframes begin pivoting relative to higher timeframe envelopes, it often means: - directional pressure is building - volatility expansion may follow - option pricing has not fully reacted yet This creates the opportunity to enter positions before volatility and delta expansion make contracts expensive. ## Practical Uses This indicator can help traders: - identify early momentum pivots - visualize multi-timeframe volatility alignment - detect volatility expansion before breakouts - confirm trend continuation across timeframes It is particularly useful when looking for high momentum opportunities while options remain relatively inexpensive. ## Conceptual Summary Momentum builds from smaller timeframes upward. When lower timeframe volatility begins interacting with and pivoting against larger timeframe envelopes, the market is often entering a structural shift phase. This indicator visualizes that process so traders can see momentum transitions while they are still forming.
Pine Script®指标
由Jysun提供
Ornstein-Uhlenbeck Mean Reversion Probability Bands [UAlgo]Ornstein-Uhlenbeck Mean Reversion Probability Bands is a statistical mean reversion indicator that models price as a mean reverting process and projects dynamic probability style zones around an estimated equilibrium mean. The script uses a rolling lookback of closing prices, fits an Ornstein-Uhlenbeck inspired parameter set from recent behavior, and then converts that estimate into inner and outer deviation bands around the current mean. The indicator runs directly on price ( overlay=true ) and is built to help traders identify when price is stretched away from its estimated equilibrium. Instead of using a fixed moving average and static standard deviation, the script attempts to infer a mean reverting structure from the data itself. It estimates the long term mean, the speed of reversion, and an equilibrium style dispersion measure, then plots two upside and two downside mean reversion zones. When price pushes into the upper or lower band regions, the script calculates a standardized distance from the estimated mean and displays a probability style label with both the percentage score and the current z score. This gives the user a quick visual read of how statistically extended price is relative to the model. A key strength of this script is that it combines: A rolling Ornstein-Uhlenbeck style parameter estimation Adaptive mean reversion zones Probability style stretch labels at band events A clean overlay presentation with visible upper and lower probability regions Important note: The percentage label in this script is a normal distribution coverage style score derived from the current z score. It is best understood as a probabilistic stretch measure, not a literal exact OU first passage probability. 🔹 Features 🔸 1) Ornstein-Uhlenbeck Inspired Mean Reversion Model The script estimates a mean reverting process from recent closing prices instead of relying only on a moving average. It uses a rolling regression style approach on consecutive price observations, then converts those estimates into Ornstein-Uhlenbeck style parameters. This makes the indicator more model driven than a standard band tool. 🔸 2) Rolling Adaptive Mean Line The central mean line is not a fixed average only. It is the estimated equilibrium level ( mu ) of the fitted process. As the rolling price sample changes, the model updates and the mean shifts with changing market structure. The mean line also changes color depending on whether current price is above or below that estimated equilibrium. 🔸 3) Dual Mean Reversion Zones (Inner and Outer) The script builds two sets of reversion bands around the mean: Inner bands using the inner multiplier Outer bands using the outer multiplier This creates a layered framework where the inner zone marks an early stretch area and the outer zone marks a more extreme statistical extension. 🔸 4) Probability Style Stretch Labels When price crosses into the upper or lower band regions, the script calculates a z score based on current distance from the estimated mean and converts it into a percentage style probability score. The label shows: A directional marker The probability style percentage The current z score This gives the user both a visual event trigger and a numeric measure of extension. 🔸 5) Visual Zone Based Design The indicator uses filled upper and lower zones rather than emphasizing the band lines themselves. This creates a cleaner chart display where the mean line stays visible and the stretch regions are highlighted as colored areas above and below it. This makes the indicator easy to read during fast chart scanning. 🔸 6) Configurable Lookback, Time Step, and Band Width Users can customize: The rolling lookback period used for model estimation The time step parameter ( dt ) used in OU conversion The inner band multiplier The outer band multiplier This makes the script adaptable to different timeframes, instruments, and preferred sensitivity levels. 🔸 7) Built In Estimation Safeguards The parameter estimation logic includes fallback protections. If the inferred model parameters are unstable or unrealistic, the script falls back to simpler sample statistics. This helps prevent unusable outputs during difficult market regimes or low quality fits. 🔸 8) Directional Touch Event Logic The script tracks both upper side and lower side band interaction: Upper side events can signal statistically stretched bullish price movement Lower side events can signal statistically stretched bearish price movement Labels are only created on crossing events, which helps reduce repeated prints while price remains outside the band. 🔹 Calculations 1) Rolling Price Queue Management The script stores recent closing prices in an array with a fixed maximum length: price_array.update_queue(close, length_input) The queue update method behaves differently depending on bar state: On a new bar, it pushes the latest value On an updating live bar, it overwrites the last stored value This keeps the rolling sample aligned with the current chart state without duplicating the active bar. 2) Fallback Mean and Dispersion Estimates Before attempting the OU style fit, the script calculates simple fallback values: float fallback_mu = src_array.avg() float fallback_sigma = src_array.stdev() These act as safety defaults if the regression based OU estimate is not reliable. Important note: In this script, fallback_sigma is a simple sample standard deviation of price levels, not return volatility. 3) AR(1) Style Regression on Consecutive Prices The model estimation is built from consecutive price pairs: x = price y = price The script computes: Mean of x Mean of y Covariance between x and y Variance of x Then it estimates: float b = sum_cov / sum_var_x This creates an AR(1) style coefficient that is later translated into OU style parameters. 4) Conversion from AR(1) Form to OU Style Parameters If the estimated b is within a valid range: if b > 0.05 and b < 0.95 the script computes: float a = mean_y - b * mean_x float mu_exact = a / (1.0 - b) float theta_exact = -math.log(b) / dt Interpretation: mu_exact is the estimated long run mean. theta_exact is the implied mean reversion speed. The conversion assumes the AR(1) relation is a discrete time representation of a mean reverting process. 5) Residual Variance and Equilibrium Dispersion The script next measures residual error from the AR(1) fit: float err = y_i - (a + b * x_i) float var_err = sum_err_sq / (n - 1) Then it converts that residual variance into an equilibrium variance estimate: float var_eq = var_err / (1.0 - b * b) Finally: float calc_sigma = math.sqrt(var_eq) Important implementation note: The variable named sigma in this script is used as an equilibrium style standard deviation around the mean, not as the continuous time OU diffusion coefficient from the SDE form. 6) Stability Filter for the Estimated Sigma Even if the AR(1) fit is mathematically valid, the script only accepts the calculated sigma when it is reasonably close to the fallback sample standard deviation: if calc_sigma < fallback_sigma * 1.5 and calc_sigma > fallback_sigma * 0.5 If this test fails, the script keeps the fallback values instead. This helps avoid unstable band widths caused by bad short term fits. 7) Final Parameter Output The estimation method returns: OU_Params.new(theta, mu, sigma_eq) Where: theta is the estimated reversion speed mu is the estimated equilibrium mean sigma_eq is the accepted equilibrium dispersion measure These parameters are then used to build the bands. 8) Band Construction The script computes four band levels around the estimated mean: float up_out = mean_val + (dev_val * mult_outer) float up_in = mean_val + (dev_val * mult_inner) float dn_in = mean_val - (dev_val * mult_inner) float dn_out = mean_val - (dev_val * mult_outer) Interpretation: Inner bands represent a milder deviation from the mean. Outer bands represent a more extreme deviation from the mean. 9) Mean and Zone Visualization The mean line is explicitly plotted: p_mean = plot(ou_bands.mean, color=color_mean, linewidth=2, title="Mean") The inner and outer band plots are also created, but their colors are fully transparent: color color_inner_up = color.new(#ffb74d, 100) color color_outer_up = color.new(#ef5350, 100) ... This means the visible structure mainly comes from the zone fills: fill(p_ui, p_uo, ...) fill(p_li, p_lo, ...) So the user sees clean upper and lower probability zones rather than several bright boundary lines. 10) Touch and Crossing Logic The script first checks whether price is currently inside a stretch area: bool touch_upper = close >= ou_bands.upper_inner bool touch_lower = close <= ou_bands.lower_inner Then it checks for fresh crossings: bool cross_up_in = ta.crossover(close, ou_bands.upper_inner) bool cross_up_out = ta.crossover(close, ou_bands.upper_outer) bool cross_dn_in = ta.crossunder(close, ou_bands.lower_inner) bool cross_dn_out = ta.crossunder(close, ou_bands.lower_outer) Labels are only created when price is touching the region and a fresh crossing occurs. This avoids creating labels on every bar that remains outside the band. 11) Z Score Calculation When an event occurs, the script calculates the standardized distance from the mean: float current_z_score = dev_val != 0 ? math.abs(close - mean_val) / dev_val : 0.0 Interpretation: A z score of 1 means price is one equilibrium standard deviation away from the estimated mean. Higher values indicate a more statistically stretched condition. 12) Probability Style Score Calculation The script converts the z score into a percentage style score using an approximation of the error function: float x = math.abs(z_score) / math.sqrt(2.0) ... float prob = erf_approx * 100.0 Because erf(|z| / sqrt(2)) corresponds to the probability mass within plus or minus that z distance under a normal distribution, the output behaves like a confidence or coverage score. Important note: This is not a direct OU mean reversion probability in the strict stochastic process sense. It is a normal distribution style stretch score based on the current z distance. 13) Upper Event Label Logic When price crosses into the upper band region: if (touch_upper and cross_up_in) or (touch_upper and cross_up_out) the script prints a bearish styled label above the bar: "▼ %" + str.tostring(probability, "#.##") + " (Z:" + str.tostring(current_z_score, "#.##") + ")" This reflects the idea that price is statistically extended above the mean and may be vulnerable to reversion. 14) Lower Event Label Logic When price crosses into the lower band region: if (touch_lower and cross_dn_in) or (touch_lower and cross_dn_out) the script prints a bullish styled label below the bar: "▲ %" + str.tostring(probability, "#.##") + " (Z:" + str.tostring(current_z_score, "#.##") + ")" This reflects the idea that price is statistically extended below the mean and may be vulnerable to reversion. 15) Role of the Time Step Input The dt_input parameter affects the conversion from the AR(1) coefficient into the OU reversion speed: float theta_exact = -math.log(b) / dt A larger dt lowers the inferred theta for the same b . A smaller dt raises the inferred theta for the same b .
Pine Script®指标
由UAlgo提供
1
Improved EMA Trend + ATR Strategy with ArrowsThis strategy is built around a simple idea: strong directional moves often begin after periods of low volatility. Instead of chasing trends late, it waits for price compression and then trades the breakout when expansion begins. The approach combines volatility structure and trend alignment to reduce random entries. Core Concept Markets cycle between quiet phases and expansion phases. When volatility contracts, it often leads to a stronger move once price breaks out of that range. This strategy attempts to capture that transition. How It Works 1. Volatility Compression Detection A squeeze condition is identified when Bollinger Bands contract inside Keltner Channels. This signals reduced volatility. 2. Squeeze Release The strategy waits for volatility to expand again. Trades are only considered when the squeeze ends. 3. Breakout Confirmation Entries occur when price breaks above or below the Bollinger Bands after the squeeze release. 4. Trend Alignment A long-term EMA is used to filter direction: Above EMA → bullish bias Below EMA → bearish bias This helps reduce counter-trend trades. 5. Risk Management Stop-loss and take-profit levels are based on ATR. This allows exits to adapt to current market volatility. A configurable risk-to-reward ratio defines profit targets. Key Features Structured volatility-based entries Trend confirmation filter ATR-based dynamic risk control Commission and slippage included for realistic testing No repainting logic Best Conditions This type of strategy tends to perform better during: Volatility expansion phases Strong directional markets Assets that experience compression before breakouts It may underperform in slow, range-bound environments. Important Notes This strategy is designed for research and backtesting purposes. Performance will vary across different markets and timeframes. Always evaluate across multiple conditions before considering live deployment.
Pine Script®策略
由AIScripts提供
Liquidity Bands1. CONCEPT & PURPOSE Liquidity Bands is a volume-weighted volatility envelope indicator. Unlike standard Bollinger Bands that use a Simple Moving Average and equal-weighted standard deviation, this indicator weights every price observation by its liquidity (volume × true range). This means: High-volume, high-volatility bars have a stronger influence on the bands Low-volume, quiet bars contribute less to the calculation The bands naturally gravitate toward price levels where real trading activity occurred This makes the bands more responsive to institutional activity and genuine supply/demand zones rather than treating every bar equally. 2. CORE CALCULATIONS 2.1 Liquidity Proxy text liquidity = volume × true_range Each bar's "weight" is determined by multiplying its volume by its true range. This captures dollar-flow intensity — a bar with high volume AND wide range represents significant market participation. If volume is unavailable (e.g., some crypto pairs), it defaults to 1 so the indicator still functions. 2.2 Liquidity-Weighted Moving Average (LMA) text LMA = Σ(price × liquidity, n) / Σ(liquidity, n) Instead of a simple average where each bar counts equally, the LMA is a weighted mean where high-liquidity bars pull the average toward their price level more strongly. This is the center line (basis) of the bands. Interpretation: LMA represents the fair value based on where the most trading activity occurred Price above LMA → bullish bias Price below LMA → bearish bias 2.3 Liquidity-Weighted Standard Deviation text variance = Σ(price² × liquidity, n) / Σ(liquidity, n) − LMA² std_dev = √variance The standard deviation is also liquidity-weighted, meaning volatility is measured relative to where liquidity actually participated, not just raw price swings. 2.4 Band Construction Band Formula Purpose Upper Band LMA + (mult × std_dev) Overbought / resistance zone Lower Band LMA − (mult × std_dev) Oversold / support zone Inner Upper LMA + (inner_mult × std_dev) First standard deviation — early warning Inner Lower LMA − (inner_mult × std_dev) First standard deviation — early warning Default multipliers: Outer = 2.0, Inner = 1.0 This creates four zones: Above upper band → extreme overbought Upper band to inner upper → overbought zone Inner upper to inner lower → neutral / fair value zone Inner lower to lower band → oversold zone Below lower band → extreme oversold 3. SIGNAL MODES 3.1 Mean Reversion Mode Philosophy: Price tends to return to the mean after touching extremes. Signal Condition Logic LONG Previous close was at or below the lower band, current close is back above it Price was rejected at the lower extreme and is recovering — buy the bounce SHORT Previous close was at or above the upper band, current close is back below it Price was rejected at the upper extreme and is fading — sell the rejection Best used in: Ranging/sideways markets, mean-reverting instruments, when squeeze is active. 3.2 Breakout Mode Philosophy: When price breaks through the bands with conviction, momentum follows. Signal Condition Logic LONG Price crosses above the upper band Bullish momentum breakout — buy the strength SHORT Price crosses below the lower band Bearish momentum breakdown — sell the weakness Best used in: Trending markets, after a squeeze, high-momentum instruments. 3.3 Both Mode Fires signals from either mode. Useful for scanning all opportunities but requires additional context/filtering to avoid conflicting signals. 3.4 LMA Cross Signals (Optional) Signal Condition Cross Up Price crosses above the LMA basis line Cross Down Price crosses below the LMA basis line These are secondary/confirmation signals — a diamond shape appears. Useful for: Confirming a mean reversion signal (price bounced off band AND crossed back above LMA) Identifying trend direction shifts 4. BAND WIDTH & SQUEEZE DETECTION 4.1 Band Width text band_width = (upper_band − lower_band) / LMA × 100 Expressed as a percentage of the LMA, this normalizes volatility across different price levels and instruments. A 5% band width means the bands span 5% of the current price level. 4.2 Squeeze Detection text squeeze = band_width < lowest(band_width, 50) × 1.05 A squeeze is detected when the current band width is within 5% of the lowest band width in the last 50 bars. This means volatility has contracted to near-historic lows for the recent window. What it means: Energy is building — a large move is likely coming Direction is unknown — wait for breakout confirmation Displayed as a soft orange/yellow background tint across the chart 4.3 Expansion Detection text expansion = band_width > highest(band_width , 20) × 0.95 Bands are expanding when width is near the 20-bar high. This confirms an active trend/momentum move is underway. 4.4 Price Position Ratio text ratio = (close − lower_band) / (upper_band − lower_band) Gives a 0–100% reading of where price sits within the bands: 0% = at the lower band 50% = at the LMA 100% = at the upper band >100% = above upper band <0% = below lower band Displayed as a visual gauge bar ████░░░░░░ 40% in the dashboard. 5. VISUAL SYSTEM 5.1 Color Themes Theme Character Upper Lower Best For Cyber Futuristic neon Cyan #00e5ff Pink #ff006e Dark charts Ocean Cool aquatic Teal #00b4d8 Deep blue #0077b6 Clean look Lava Hot aggressive Orange #ff6b35 Red #d90429 High energy Frost Soft pastel Light blue #a2d2ff Pale blue #bde0fe Light charts Neon Maximum contrast Green #39ff14 Red #ff073a Visibility Each theme defines a coordinated 7-color palette (upper, lower, basis, bull, bear, squeeze, accent) so everything matches. 5.2 Glow Effect Three concentric layers are drawn behind each band line: Layer Opacity Width Effect Outer glow 92% transparent 6px Soft ambient halo Mid glow 82% transparent 4px Intermediate diffusion Inner glow 60% transparent 2px Concentrated glow Core line 0% transparent 1px Sharp band edge This creates a neon light tube effect around each band. 5.3 Zone Fills Five separate fill regions create depth: Zone Between Opacity Meaning Upper zone Upper band → Inner upper 88% Overbought territory Lower zone Lower band → Inner lower 88% Oversold territory Core zone Inner upper → Inner lower 95% Fair value area Upper half Upper band → LMA 94% Subtle upper bias tint Lower half Lower band → LMA 94% Subtle lower bias tint 5.4 Dynamic Basis Color The LMA line color shifts in real-time based on price position: When price is near the lower band → basis turns lower band color When price is near the upper band → basis turns upper band color This creates an instant visual read of where price sits 5.5 Signal Visualization (Triple-Layer) Each signal has three visual layers for maximum clarity: Layer Element Purpose Primary Large ▲/▼ triangle with bold "LONG"/"SHORT" text Unmissable directional signal Accent Small circle dot at the same location Adds visual weight and layering Context Dotted horizontal line spanning ±1 bar Marks the exact price level of the signal Background Full-bar bgcolor tint (green/red) Makes signal bars visible even when zoomed out 5.6 Band Touch Markers Small xcross shapes appear when price first touches a band without triggering a full signal. These serve as early warnings that price is testing an extreme. 5.7 LMA Dot Trail (Optional) When enabled, alternating dots • appear on the LMA line every other bar, creating a stylized beaded line effect instead of a solid line. 6. DASHBOARD A real-time information panel displayed in the corner with alternating dark row backgrounds: Row Left Column Right Column Color Logic Header ⚡ LIQUIDITY BANDS ━━━━━━ Theme accent Theme Theme Active theme name + ● Upper band color Mode Mode Active signal mode Accent color Width Width Band width as % Orange if squeeze, green if expanding, gray if normal Status Status ⊘ SQUEEZE / ⊕ EXPANDING / ◎ NORMAL Contextual color Price Price ▲ ABOVE / ▼ BELOW / ◈ INSIDE Green/red/white Ratio Ratio ████░░░░░░ 40% gauge bar Gradient from lower to upper band color 7. ALERTS Alert Fires When Long Signal Any long condition triggers (based on selected mode) Short Signal Any short condition triggers (based on selected mode) LMA Cross Up Price crosses above the LMA LMA Cross Down Price crosses below the LMA Squeeze Band width hits squeeze threshold All alert messages include the ticker symbol via {{ticker}}. 8. INPUT REFERENCE Input Default Range Description Lookback Length 20 1+ Number of bars for LMA and std dev calculation StdDev Multiplier 2.0 0.1+ Width of outer bands (higher = wider) Price Source Close Any Which price to use for calculations Inner Band Multiplier 1.0 0.1+ Width of inner bands Signal Mode Mean Reversion 3 options Which signal logic to use Show Signals ✓ Toggle Display signal markers Show LMA Cross ✗ Toggle Display LMA cross diamonds Color Theme Cyber 5 options Visual color scheme Band Glow Effect
Pine Script®指标
由ZakAlgoTrade提供
Premium Price Action [Alpha Extract]A sophisticated trend-following rail system that combines dual-lookback price averaging with adaptive ATR-based trailing levels for clean trend identification and momentum-sensitive visual feedback. Utilizing staircase rail logic with dynamic ribbon visualization and strength-modulated transparency, this indicator delivers institutional-grade trend detection with minimal whipsaw through intelligent rail adaptation. The system's multi-lookback baseline construction combined with ATR-scaled distance creates robust trend rails that only flip on genuine structural changes while maintaining visual clarity through glow effects and regime-based background coloring. 🔶 Advanced Dual-Lookback Baseline Framework Implements sophisticated baseline calculation using two configurable lookback periods to sample recent price history at different intervals, averaging results and applying dual smoothing for stable trend reference. The system retrieves close prices from first lookback (default 12 bars) and second lookback (default 27 bars), calculates their average, applies SMA smoothing, then EMA smoothing for ultra-clean baseline resistant to short-term noise while maintaining responsiveness to genuine trend shifts. // Dual-Lookback Baseline Construction Close_1 = close Close_2 = close Close_Avg = (Close_1 + Close_2) / 2.0 Baseline_Raw = ta.sma(Close_Avg, SMA_Length) Baseline = ta.ema(Baseline_Raw, Smooth_Length) 🔶 Adaptive Trailing Rail Architecture Features intelligent rail calculation that trails price using ATR-scaled distance from baseline with ratcheting logic preventing premature reversals. The system calculates upper rail (baseline + ATR × multiplier) and lower rail (baseline - ATR × multiplier), implements adaptive trailing where bullish rail only rises or holds while bearish rail only falls or holds, and transitions rails only when price violates opposite rail creating clean staircase pattern. // Adaptive Rail Logic Upper_Rail = Baseline + ATR * Rail_Multiplier Lower_Rail = Baseline - ATR * Rail_Multiplier // Ratcheting Behavior Bull_Rail = close > Bull_Rail ? max(Lower_Rail, Bull_Rail ) : Lower_Rail Bear_Rail = close < Bear_Rail ? min(Upper_Rail, Bear_Rail ) : Upper_Rail // Trend Determination Trend = close < Bull_Rail ? -1 : close > Bear_Rail ? 1 : Trend 🔶 Staircase Step-Line Visualization Implements stepline plot style creating distinctive staircase appearance that visually emphasizes trend persistence and makes rail level changes instantly recognizable. The system uses plot.style_stepline rendering that draws horizontal segments at each rail level with vertical connections only at transition points, producing clean geometric pattern that distinguishes this rail system from curved moving averages or bands. 🔶 Dynamic Strength-Based Transparency Provides sophisticated transparency modulation where background and ribbon opacity adjust based on momentum strength relative to ATR volatility. The system calculates momentum as price distance from baseline, normalizes by ATR to produce 0-1 strength score, and reduces transparency (increases intensity) as strength increases, creating visual feedback where strong trends display vivid colors and weak trends show muted tones. 🔶 Multi-Layer Glow Effect System Features triple-layer rail rendering with progressively wider and more transparent outer layers creating luminous glow effect emphasizing trend rail. The system plots core rail at specified width with full color intensity, adds inner glow layer at +2 width with moderate transparency, and outer glow at +4 width with higher transparency, producing visual depth and making rail instantly recognizable without cluttering chart space. 🔶 Adaptive Ribbon Visualization Creates ATR-scaled ribbon extending below bullish rail or above bearish rail with width proportional to current volatility and transparency modulated by trend strength. The system calculates ribbon size using ATR × ribbon multiplier, positions ribbon adjacent to active rail, and applies dynamic transparency that intensifies during strong momentum creating intuitive visual representation of trend conviction and volatility context. 🔶 Regime Background Highlighting Implements subtle background wash using trend color with strength-adjusted transparency providing full-chart regime awareness without obscuring price action. The system applies bullish or bearish background color with base transparency that decreases (color intensifies) as momentum strength increases, creating gradient effect where powerful trends display more prominent backgrounds while weak trends maintain subtle presence. 🔶 Intelligent Flip Detection Logic Generates trend reversal signals only when price violates opposite rail with confirmation, preventing false signals during normal retracements. The system detects bullish flip when previous trend bearish and close crosses above bearish rail, detects bearish flip when previous trend bullish and close crosses below bullish rail, and places compact BULL/BEAR labels at ribbon edges marking exact reversal bars for clear visual confirmation. 🔶 Comprehensive Visual Integration Provides multi-dimensional trend visualization through colored rail with glow effects, ATR-scaled ribbons, regime backgrounds, trend-synchronized candle plotting, and optional chart candle coloring. The system enables selective display toggling for each visual component while maintaining consistent color scheme and strength-based intensity across all elements, allowing customization from minimal (rail only) to comprehensive (all features) presentation. 🔶 Baseline Reference System Includes optional baseline plot showing underlying smoothed dual-lookback average serving as neutral reference level and trend bias indicator. The system displays baseline with neutral color at reduced opacity, enabling traders to assess whether price trades above baseline (inherent bullish bias) or below baseline (inherent bearish bias) independent of current rail trend state for confluence analysis. 🔶 Performance Optimization Framework Employs efficient calculation methods with optimized rail ratcheting logic, streamlined strength calculations, and intelligent plot rendering that only processes active visual elements. The system includes smart state tracking for trend persistence, minimal recalculation overhead through nz() functions and conditional logic, and smooth visual updates maintaining consistent performance across extended historical periods. 🔶 Why Choose Premium Price Action ? This indicator delivers sophisticated trend-following analysis through adaptive rail methodology with dual-lookback baseline construction and ATR-scaled distance. Unlike traditional moving average systems prone to whipsaw during choppy conditions, the ratcheting rail logic with opposite-rail violation requirements creates definitive trend states that persist through normal retracements. The system's staircase visualization instantly communicates trend persistence, strength-modulated transparency provides conviction feedback, and comprehensive visual integration enables complete trend assessment without switching between multiple indicators. Perfect for swing traders and position managers seeking clear trend identification with minimal false signals across cryptocurrency, forex, and equity markets where the adaptive rails naturally adjust to varying volatility regimes while maintaining consistent signal quality.
Pine Script®指标
由AlphaExtract提供
Momentum Cycle Sentry [LuxAlgo]The Momentum Cycle Sentry indicator is a comprehensive momentum visualization tool designed to identify trend cycles, measure volatility extremes, and precisely track retracement phases through a unique multi-layered symmetric architecture. 🔶 USAGE The indicator provides a high-fidelity view of market "flow" by mirroring price momentum across a zero line, creating a symmetric "cloud" whose width represents the intensity of the current move. It is primarily used to distinguish between strong trending expansion and temporary pullback cycles. 🔹 Trend Identification Bullish Momentum: The oscillator cloud appears above and below the zero line in a teal (bullish) color. Bearish Momentum: The oscillator cloud appears in a coral (bearish) color. Width: An expanding cloud indicates increasing velocity, while a thinning cloud suggests a squeeze or waning interest. 🔹 Momentum Cycle Tracing One of the core features of this tool is the Neon Path logic. When the indicator detects that momentum is cooling off against the prevailing trend (a retracement cycle): The background cloud and candle colors dim to signify a "resting" phase in the market. A Triple-Layer Glow activates directly on the oscillator's curve, making the retracement segments "light up" in a high-intensity version of the trend color. This glow uses graduated transparency to create a neon-like halo, visualizing the exact path of the pullback as it moves toward the zero line. Cross (X) markers appear on the zero line to provide a horizontal anchor for the duration of the retracement period. 🔹 Extreme Zones The indicator features dynamic Overbought (OB) and Oversold (OS) corridors. When the oscillator enters these shaded gradient zones, the trend cycle is considered overextended. Traders can look for the oscillator to curve back toward the zero line as a sign of potential exhaustion or mean reversion. 🔶 DETAILS 🔹 Layered Oscillator Architecture The script utilizes five distinct layers of Exponential Moving Average (EMA) smoothing applied to a base momentum calculation. This creates a "glow" effect where the inner core reacts to immediate price action while the outer layers represent the broader trend. The symmetric mirroring ensures that the visual weight of the momentum is balanced, making it easier to perceive the total "volume" of the move regardless of direction. 🔹 Dynamic Volatility Corridors Unlike traditional oscillators with fixed levels (e.g., 70/30), the Momentum Cycle Sentry uses standard deviation-based bands that adapt to current market volatility. This ensures that the overbought and oversold thresholds are relevant to the specific asset and timeframe being traded. 🔶 SETTINGS 🔹 Settings Base Length: Sets the lookback period for the underlying momentum calculation. Smoothing: Determines the base EMA smoothing for the multi-layered layers. Magnitude: A multiplier to scale the vertical height of the oscillator. Retracement Sensitivity: Adjusts how quickly the script detects a pullback/retracement cycle on the curve. 🔹 Extreme Zones OB/OS Lookback: The period used for the standard deviation calculation of the volatility bands. Inner Multiplier: Sets the threshold for the start of the OB/OS corridor. Outer Multiplier: Sets the threshold for the outer edge of the OB/OS corridor (historical extreme). 🔹 Visuals Bullish/Bearish Color: Customizes the colors for uptrends and downtrends. Base Transparency: Adjusts the transparency of the layered cloud effect. Color Candles: Toggles the synchronization of price chart candles with the oscillator's momentum state.
Pine Script®指标
由LuxAlgo提供
1
Donchian Ribbon [UAlgo]Donchian Ribbon is a chart-overlay Donchian Channel ribbon that visualizes multiple lookback lengths at the same time. Instead of plotting a single Donchian Channel, the script builds a fixed stack of channels that increase in length and blends them into a clean, layered ribbon above and below price using progressive fills. The goal is to make market structure and regime easier to read without clutter: - When the ribbon expands and stays orderly (fast boundaries leading, slow boundaries following), it often reflects sustained range expansion and more directional flow. - When the ribbon compresses and bands overlap frequently, it typically reflects consolidation, rotational behavior, and reduced clarity. - The slowest channel provides the structural “outer frame” of the market’s recent range, while shorter channels react first and show how quickly the range is shifting. This indicator is designed as a context tool. It does not attempt to “predict” direction by itself, but it gives a high-quality visual map of evolving highs/lows across multiple sensitivities so you can align entries, risk, and expectations with the current regime. 🔹 Features 1) Multi-Length Donchian Stack (Ribbon Engine) The script constructs several Donchian Channels from a Base Length and a Step Length. Each band represents a different sensitivity level: - Fast bands respond quickly to recent highs and lows. - Slow bands respond more conservatively and define broader containment. By stacking these lengths together, you can see short-term responsiveness and higher-level structure simultaneously. 2) Two-Sided Ribbon (Upper and Lower Envelopes) The indicator visualizes both sides of the Donchian framework: - Upper ribbon is built from stacked Donchian highs (highest highs per length). - Lower ribbon is built from stacked Donchian lows (lowest lows per length). This keeps interpretation intuitive: price pressing into the upper ribbon suggests pressure toward recent highs, while leaning into the lower ribbon suggests pressure toward recent lows. 3) Gradient Depth via Layered Fills (Clean Charts) Instead of drawing many lines, the script fills the space between consecutive bands. Transparency is gradually adjusted from the fast band to the slow band, producing a smooth depth effect that stays readable even on busy charts. Intermediate plots are intentionally hidden so the ribbon remains the main visual output. 4) Regime Readability (Expansion vs Compression) Because each band has a different lookback length, the ribbon naturally communicates volatility and state: - Expansion: spacing between fast and slow bands increases, commonly seen in stronger directional phases. - Compression: spacing collapses and bands cluster, commonly seen in ranges, pauses, or choppy rotation. This helps you quickly decide whether to treat price action as breakout-oriented, trend-continuation, or mean-reverting. 5) Trend Baseline Reference (Slow Midpoint) A baseline is plotted using the midpoint of the slowest channel. This provides a stable reference that helps you judge whether price is operating in the upper or lower half of the broader range structure. 🔹 Calculations 1) Donchian High, Low, and Midpoint Per Band Each Donchian band is computed from its own length: - High = highest high over the lookback length - Low = lowest low over the lookback length - Mid = average of High and Low id.high := ta.highest(id.length) id.low := ta.lowest(id.length) id.mid := math.avg(id.high, id.low) 2) Length Sequencing (Base Length + Step Length) The indicator creates a fixed number of bands. Lengths are built as: - Band 1: base_length - Band 2: base_length + step_length - Band 3: base_length + 2 * step_length - ... - Final band: base_length + (ribbon_count - 1) * step_length This yields a consistent progression from fast to slow sensitivity. int len = base_length + (i * step_length) channels.push(DonchianChannel.new(len)) 3) Iterative Updates with Arrays and Methods All bands are stored in an array and updated every bar using a unified method call. This ensures every band follows identical rules and makes the logic scalable and maintainable. for dc in channels dc.update() 4) Upper Ribbon Construction (Layered Fills Between Highs) The upper ribbon is created by filling between consecutive Donchian highs. Each layer uses the same upper tone with progressively stronger visibility toward the slow band. fill(p_fast_high, p_mid1_high, color.new(col_upper, 90), "Ribbon Upper 1") fill(p_mid1_high, p_mid2_high, color.new(col_upper, 80), "Ribbon Upper 2") fill(p_mid2_high, p_mid3_high, color.new(col_upper, 70), "Ribbon Upper 3") fill(p_mid3_high, p_slow_high, color.new(col_upper, 60), "Ribbon Upper 4") 5) Lower Ribbon Construction (Layered Fills Between Lows) The lower ribbon is created by filling between consecutive Donchian lows with the lower tone, again using progressive transparency. fill(p_fast_low, p_mid1_low, color.new(col_lower, 90), "Ribbon Lower 1") fill(p_mid1_low, p_mid2_low, color.new(col_lower, 80), "Ribbon Lower 2") fill(p_mid2_low, p_mid3_low, color.new(col_lower, 70), "Ribbon Lower 3") fill(p_mid3_low, p_slow_low, color.new(col_lower, 60), "Ribbon Lower 4") 6) Trend Baseline (Slow Midpoint) The baseline is the midpoint of the slowest Donchian band, plotted as a stable center reference for the broadest range framework. plot(dc_slow.mid, "Trend Baseline", color = color.from_gradient(0.5, 0, 1, col_lower, col_upper), linewidth = 2) 7) Visualization Choice (Hidden Internals, Visible Structure) To keep charts clean, most intermediate plots are hidden and the ribbon fills do the heavy lifting visually, while the slow boundaries remain visible as the outer frame. p_fast_high = plot(dc_fast.high, "Fast High", color = color.new(col_upper, 80), display = display.none) p_fast_low = plot(dc_fast.low, "Fast Low", color = color.new(col_lower, 80), display = display.none) p_slow_high = plot(dc_slow.high, "Slow High", color = color.new(col_upper, 50)) p_slow_low = plot(dc_slow.low, "Slow Low", color = color.new(col_lower, 50))
Pine Script®指标
由UAlgo提供
Neighboring Price Bands [LuxAlgo]The Neighboring Price Bands indicator provides dynamic support and resistance levels based on the local statistical distribution of historical prices relative to the current market position. Unlike traditional volatility bands that rely on fixed standard deviations, this tool identifies "price neighbors" within a sorted historical buffer to determine where the market has previously found friction. 🔶 USAGE The indicator helps traders identify potential reversal zones and breakout opportunities by analyzing the density of price action around the current level. 🔹 Support and Resistance The bands act as flexible zones of interest. The upper (green) band represents a bullish boundary derived from historical prices slightly higher than the current price, while the lower (red) band represents a bearish boundary from prices slightly lower. When the price interacts with these bands, it is entering a zone where historical price density suggests a potential reaction. 🔹 Price Discovery & Breakouts A unique feature of this tool is the "Discovery" mechanism. If the current price moves beyond the range of its historical "neighbors" (e.g., reaching a new multi-period high or low), the corresponding band will disappear, and a background highlight will appear. Bullish Discovery: A green background highlight indicates the price is entering uncharted territory relative to the historical buffer, suggesting a strong bullish breakout. Bearish Discovery: A red background highlight indicates the price is dropping below its local historical distribution, suggesting a strong bearish breakdown. 🔶 DETAILS The script maintains a historical buffer of prices, which it constantly sorts to create a price distribution. For every new bar, the algorithm performs the following: It locates the current price within the sorted distribution. It identifies a specific number of "neighbors" (K) above and below that position. It calculates a specific percentile within those neighbors to plot the bands. Because the bands are derived from actual price frequency rather than a calculation like standard deviation (Bollinger Bands) or Average True Range (Keltner Channels), they adapt more specifically to "sticky" price levels where the market has historically spent time. 🔶 SETTINGS Historical Buffer (Bars): The total number of past bars used to build the price distribution. A larger buffer includes more historical context, while a smaller buffer makes the bands more reactive to recent local ranges. Neighboring Range (K): Determines how many samples from the sorted distribution are used to calculate the bands. A smaller K makes the bands tighter and more sensitive to the immediate price position. Percentile: Controls the width of the bands within the neighbor groups. Higher values push the bands further away from the current price. Smoothing: Applies an SMA to the resulting bands to reduce noise and provide a cleaner visual output.
Pine Script®指标
由LuxAlgo提供
LOWESS Channel & Extrapolation [LuxAlgo]The LOWESS Channel & Extrapolation indicator calculates a Locally Weighted Scatterplot Smoothing (LOWESS) curve to define a non-linear trend and projects it into future bars using local regression slopes. It provides a dynamic channel based on the standard deviation of residuals, helping traders identify overextended price levels and potential mean-reversion points. The LOWESS Channel & Extrapolation indicator is subject to repainting and displayed retrospectively. 🔶 USAGE This tool is primarily designed for trend analysis and identifying exhaustion points. Because the LOWESS algorithm recalculates based on the most recent data window, the entire historical curve can adjust, making it a powerful tool for backtesting and analyzing past market structures rather than for real-time signal generation without confirmation. 🔹 Trend Identification The central fit line represents the smoothed local trend. When the curve is sloping upward, the local market sentiment is considered bullish; conversely, a downward slope indicates bearish sentiment. 🔹 Overbought/Oversold Conditions The dashed outer channels represent a volatility-adjusted boundary. When price moves outside these boundaries, it is statistically overextended relative to the local trend, often preceding a move back toward the mid-line. 🔹 Extrapolation The indicator extends the most recent local regression slope into the future. This provides a "path of least resistance" projection based on the current momentum of the smoothed curve. 🔶 DETAILS The LOWESS (Locally Weighted Scatterplot Smoothing) algorithm works by performing a separate weighted linear regression for every data point in the window. It uses a "tricube" weighting function, which ensures that data points closer to the focal point have a higher influence on the fit than points further away. This results in a curve that is much more flexible than a simple moving average and can adapt to complex price cycles without the lag associated with traditional filters. The channel width is determined by calculating the Standard Deviation of the residuals (the difference between the actual price and the LOWESS fit). This ensures the channel expands during high volatility and contracts during consolidation. 🔶 SETTINGS Length : Determines the number of historical observations used to fit the LOWESS curve. Larger values result in a smoother, more macro trend. Span : The fraction of data points used for each local regression. A higher span (closer to 1.0) creates a smoother line, while a lower span allows the curve to follow price more tightly. Channel Multiplier : Multiplier applied to the standard deviation of residuals to define the distance of the upper and lower bands from the mid-line. Extrapolation Bars : The number of bars to project the current trend into the future. Fit Color : Sets the color and transparency of the central LOWESS line. Channel Color : Sets the color of the dashed outer bands and the background fill. Line Width : Adjusts the thickness of the central fit line.
Pine Script®指标
由LuxAlgo提供
Rolling Trendline [LuxAlgo]The Rolling Trendline indicator provides a dynamic, self-adjusting trendline that tracks price action using linear regression slope projections and automatically resets when price deviates beyond a specific threshold. 🔶 USAGE The indicator is designed to provide a continuous trend bias without the "lag" often associated with static linear regression lines. It projects a line forward based on a calculated slope and only shifts its trajectory when the market demonstrates a significant change in momentum. The addition of ATR-based volatility zones allows traders to visualize a range of expected price action around the projected trend, providing a buffer that accounts for market volatility at the time of each trend reset. 🔹 Interpreting the Line and Zones Bullish Phase (Green): Indicates an upward-sloping trajectory. The trendline and its surrounding ATR zones will be colored green, suggesting a bullish bias. Bearish Phase (Red): Indicates a downward-sloping trajectory. The trendline and its surrounding ATR zones will be colored red, suggesting a bearish bias. ATR Zones: These shaded areas represent a volatility-adjusted range. As long as price remains within the deviation threshold, the zones follow the trendline's trajectory. Reset Points: Visualized by a small circle and a break in the line, these occur when price moves too far from the projection. At this moment, the indicator re-anchors to the current price and recalculates both the slope and the ATR zone width. 🔶 DETAILS The indicator follows a specific logic flow to maintain its "Rolling" characteristic: 1. Slope Calculation: It calculates the Linear Regression slope over a user-defined lookback period. This slope represents the average rate of change in price. 2. Projection: On every new bar, the indicator projects the next value of the trendline by adding the active slope to the previous trendline value. 3. Deviation Check: The indicator calculates a Standard Deviation threshold. If the distance between the current price and the projected trendline value exceeds this threshold, a reset is triggered. 4. Re-Anchoring: Upon a reset, the trendline "rolls" to the current price and adopts the most recent linear regression slope. Simultaneously, it captures the current ATR to set the width of the new trend zones. 🔶 SETTINGS 🔹 Trend Settings Slope Lookback: The period used to calculate the linear regression slope. Higher values result in a slope that considers more historical data. Deviation Multiplier: Determines how far price can deviate from the trendline before a reset occurs. Slope Divisor: This setting allows you to tame the trajectory of the line. Higher values divide the captured slope, resulting in flatter trendlines. Source: The price data used for all calculations (default is Close). 🔹 ATR Zones ATR Length: The lookback period used for the Average True Range calculation, which determines the width of the volatility bands. ATR Multiplier: Controls the width of the shaded zones around the trendline. 🔹 Visuals Bullish/Bearish Trend Colors: Customizes the colors for the trendline and zones based on the slope direction. Zone Color: Sets the base color for the ATR area fills. Line Width: Adjusts the thickness of the primary rolling trendline.
Pine Script®指标
由LuxAlgo提供
2
LOWESS Adaptive Envelope [BackQuant]LOWESS Adaptive Envelope Overview LOWESS Adaptive Envelope is a nonparametric trend-fit and volatility envelope tool built around LOWESS (Locally Weighted Scatterplot Smoothing). Instead of smoothing price with a fixed-form moving average, this indicator performs a rolling set of local weighted linear regressions across a chosen historical window and stitches those local fits into a single smooth curve that adapts to changing market structure. On top of the fitted curve, the script builds an adaptive envelope whose width is driven by the local magnitude of the model’s residuals (how far price deviates from the fit). That means the envelope automatically expands when the market is noisy or trending aggressively, and contracts when price is stable or mean-reverting cleanly. The output is a complete “structure map”: A LOWESS fitted centerline (trend estimate). Upper and lower adaptive bands derived from smoothed residual spread. A filled region that changes color based on where price sits relative to the fit. Optional extrapolation of the fit and envelope into the future using last slope, with widening uncertainty. An info label showing fit quality (R²), position inside the envelope, and direction. Where LOWESS comes from (and why it is different from moving averages) LOWESS (also written LOESS) is a classic statistical smoothing technique used in exploratory data analysis and robust curve fitting. It became popular because it can approximate complex shapes without assuming a single global model. Instead of forcing the entire window to follow one equation (like a single linear regression or a single moving average kernel), LOWESS fits many small local regressions , each one tailored to its neighborhood. Key distinction: A moving average is a fixed smoother, it applies the same weighting rule everywhere, regardless of whether the market is trending, chopping, or accelerating. LOWESS is a locally re-fitted model, it re-estimates slope and intercept at each point based on nearby data. In price terms: LOWESS is better at “hugging structure” when the market curves or transitions. It can follow gradual regime shifts without the same lag profile as long-window MAs. It does not assume the trend is constant across the whole lookback, it assumes trend can vary locally. What the indicator is modeling Think of the lookback window as a dataset of points: x = bar index (0..length-1 inside the window) y = price For every point i inside that window, the indicator estimates the best local line: y ≈ a + b * x But it does this using only nearby points, and it weights them by distance from i. So the fitted value at i is a locally weighted regression prediction. The final fitted curve is the collection of those predictions across i = 0..length-1. Core mechanics: local weighted linear regression 1) Neighborhood size (bandwidth) The “locality” is controlled by a bandwidth parameter. In this script: h = max(bandwidth * length / 2, 2) Interpretation: h acts like a radius measured in bars inside the fitting window. Lower bandwidth → smaller h → more local fit (more responsive, can track curvature, more sensitive to noise). Higher bandwidth → larger h → more global fit (smoother, more stable, more lag in transitions). So bandwidth controls the bias-variance tradeoff: Small bandwidth: low bias, high variance. Large bandwidth: higher bias, lower variance. 2) Tricube kernel weighting LOWESS requires a weight function that decays smoothly with distance. This script uses the classic tricube kernel : For each candidate point j around target i: u = |i - j| / h If u < 1: - w = (1 - u³)³ If u ≥ 1: - w = 0 Why tricube: Weights go to zero smoothly at the boundary (no sharp cutoff artifacts). Nearby points dominate the fit, distant points contribute little or nothing. It is a standard LOWESS choice because it produces stable smooth curves. 3) Weighted least squares fit For each i, the script accumulates weighted sums over j in the neighborhood: sumW, sumWX, sumWY, sumWXX, sumWXY These correspond to the normal equations for weighted linear regression. From those, it computes: denom = sumW * sumWXX - sumWX² a and b derived from sums (intercept and slope) fitted = a + b * i If denom is too small (numerical instability, insufficient variation), it falls back to the raw price at that i. This entire process is repeated for every i in the window, which is why it is done only on the last bar (performance). Why it fits inside the window rather than a single line A single regression across 200 bars assumes one slope b explains the whole move. Markets rarely do that. LOWESS allows the slope to drift through time, which is exactly what “trend structure” actually does in real price. Residuals: turning model error into volatility structure Once the LOWESS fitted curve is computed, the script measures the residual at each point: res = price - fitted Residuals are the model’s error. In trading terms, residual magnitude is a proxy for: Local noise level. Deviations from trend structure (overextension/underextension). Regime instability (trend is less “explanatory”). The script takes absolute residuals: absRes = |res | This is important because envelope width should reflect spread size regardless of direction. R²: fit quality and regime information The indicator also computes R² over the window: ssRes = Σ(res²) ssTot = Σ((price - meanPrice)²) R² = 1 - ssRes/ssTot Interpretation: Higher R² means the LOWESS fit explains more of the variation inside the window. Lower R² means price is behaving in a way the smooth trend model cannot explain well (chop, shocks, irregular volatility). In markets, R² can be read as “how trend-like vs how noisy” the recent environment is, but remember it depends on your chosen length and bandwidth. Adaptive envelope construction (what makes it “adaptive”) A normal envelope uses a constant width (like ±k*ATR or ±k*stdev). This script does something different: it estimates a local envelope width based on smoothed residual magnitude. 1) Smooth residual magnitude locally It computes a residual averaging window: rWin = max(3, int(h * 0.8)) So the residual smoothing window is linked to the LOWESS locality. If the fit is local, the envelope adapts locally. If the fit is global, the envelope adapts more slowly. Then for each i: envW = mean(absRes over ) * envMult Interpretation: The envelope width is proportional to how much price typically deviates from the fit around that region. envMult is your “how many spreads” multiplier. This creates an envelope that expands and contracts along the curve, not a single constant band. 2) Upper and lower envelopes For each i: upper = fitted + envW lower = fitted - envW This is a model-driven channel. It is not ATR-based directly, it is “error-based.” That makes it very effective at responding to the actual behavior of the market relative to the fitted structure. How to interpret the envelope The centerline is the best local structural estimate. The envelope is the expected deviation range around that structure. Typical readings: Price near centerline: balanced relative to structure. Price riding upper band: strong bullish pressure, trend continuation or overextension depending on context. Price riding lower band: strong bearish pressure, continuation or overextension. Repeated band rejections: mean-reversion regime around the structural fit. Envelope widening: instability rising, volatility expanding, structure less reliable. Envelope tightening: compression, cleaner trend or coiling behavior. Because the band width is based on residuals, widening often coincides with “trend breaks” and regime transitions, not just higher ATR. Color logic and visual encoding The envelope fill color is based on price relative to the most recent fitted value: If close > fitted , bullish color. Else bearish color. So color is a regime/bias cue, not a volatility cue. The bands themselves are drawn with translucent versions of the same regime color, while the fit line is a subtle white. The fill polygon is constructed by: Walking forward through upper points. Then walking backward through lower points. So the shape is closed and can be filled cleanly using polyline fills. Extrapolation: forward projection with widening uncertainty This script can project the fitted line into future bars. This is not forecasting in a statistical sense, it is a deterministic extension based on the current slope. How it extrapolates It takes: slope = fitted - fitted lastFit = fitted Then for i = 1..extrapBars: futureFit = lastFit + slope * i This is a linear continuation of the most recent fit direction. It is meant as a visual guide for “if the current local trend continues.” Why the forward envelope widens The script also grows the envelope slightly with each projected bar: envGrow = lastEnv * 0.01 futureEnv = lastEnv + envGrow * i This is a simple uncertainty widening mechanism. As you move further into the future, you should assume less confidence. The envelope expansion encodes that visually without claiming statistical rigor. Info label: what it reports and how to read it When enabled, the label shows: 1) Direction arrow It computes a slope over the last few fitted points: recentSlope = fitted - fitted (or closest valid index) ▲ if slope >= 0 ▼ if slope < 0 This gives a slightly more stable direction read than one-bar slope. 2) R² Displayed as R²: 0.xxx, representing how well the LOWESS curve explains window variation. 3) Envelope Position (Env Pos) It measures where the current close sits inside the latest envelope: 0% = at lower band 50% = at centerline 100% = at upper band This is extremely useful as a normalized “over/under extension” metric because it is scaled by the adaptive band width, not raw price units. How to use it properly Trend structure and regime filtering Use the fit line as structural trend direction. Use the fill color as quick bias context. Use R² as a “trend quality” read: high R² tends to mean cleaner structure, low R² tends to mean chop or instability. Mean reversion vs continuation This tool can support both styles, but interpretation differs: Mean reversion framing If market repeatedly returns to the fit line, the fit is acting like value. Upper band touches can be “overbought relative to structure.” Lower band touches can be “oversold relative to structure.” Envelope position becomes your normalized stretch gauge. Trend continuation framing In strong trends, price can ride a band rather than revert to centerline. Band riding plus rising fit slope suggests persistence. A sudden failure to hold the band plus falling R² can flag transition risk. Breakdown/transition identification Because the envelope width is residual-driven: If price starts producing large residuals, the envelope expands. That expansion is often a signature of regime change, not just volatility. Combine expansion with slope flattening to identify trend exhaustion. Parameter tuning (what each input really does) Length Defines how much historical data is used for the full fit. Larger length: More stable curve. More computational load. Tends to represent macro structure. Bandwidth Controls locality: Low bandwidth (0.10–0.25): more reactive, tracks curvature and micro-structure, more sensitive to noise. Higher bandwidth (0.30–0.50+): smoother, more stable, more lag in fast turns. Envelope Width (envMult) Scales how wide the adaptive band is relative to the local residual spread: Lower values create a tighter channel, more band interactions. Higher values create a wider channel, fewer touches, better for regime filtering. Extrapolation Bars Purely visual. More bars gives a longer projected structure line and uncertainty region. Limitations and correct expectations LOWESS is powerful, but it is not a magic predictor. LOWESS is descriptive, it fits what happened, then projects linearly if extrapolation is enabled. In sudden shocks or gaps, the fit will update only after the new data is inside the window. Very small bandwidth can overfit local noise, producing misleading curvature. Very large bandwidth can underfit, behaving like a slow regression and missing turning points. R² is window-dependent, a low value does not mean “bad indicator,” it often means “market is not smooth right now.” Summary LOWESS Adaptive Envelope applies locally weighted linear regression (LOWESS) with a tricube kernel to build a smooth, structure-following fitted price curve that adapts to regime changes without relying on a fixed moving-average form. It then converts the model’s local residual spread into a dynamic envelope that expands and contracts with real deviation behavior, provides fit quality via R², normalizes price position inside the band, and optionally extrapolates the latest structural slope forward with widening uncertainty. The result is a robust trend-structure and deviation framework that is equally useful for regime filtering, mean-reversion context, and trend persistence assessment.
Pine Script®指标
由BackQuant提供
Whittaker Envelope [LuxAlgo]The Whittaker Envelope indicator is a visualization tool that uses Asymmetric Least Squares (ALS) to create a smooth, non-linear envelope that adapts to price extremes and troughs. This indicator is for visualization purposes only and should not be used for direct trading signals without confirmation. It is important to note that this script is displayed retrospectively . Because it calculates the best fit over a fixed historical window (the last N bars) using an iterative optimization process, the entire shape of the envelope can change as new data arrives. This means the indicator repaints and should be used to analyze overall market structure and volatility rather than for real-time execution. 🔶 USAGE The Whittaker Envelope provides a unique way to visualize the "breathing" of the market. By applying different asymmetry parameters, the indicator generates a lower boundary that seeks out price troughs and an upper boundary that seeks out price peaks. 🔹 Trend and Volatility Analysis The area between the upper and lower bounds represents the smoothed price range. A widening envelope suggests increasing volatility, while a narrowing one indicates consolidation. The dashed midline acts as a smoothed average of these two extremes, providing a baseline for the current trend. 🔹 Extrapolation The tool includes a linear extrapolation feature that projects the current trajectory of the envelope into the future. This can help users visualize the potential direction of the trend if the current momentum persists. 🔶 DETAILS The script implements the Whittaker-Eilers smoothing algorithm, which balances two conflicting goals: fitting the data points closely and keeping the resulting curve smooth. By using Asymmetric Least Squares (ALS), we assign different weights to prices depending on whether they are above or below the curve. For the upper band, we use a high asymmetry value ( p ) so the curve is "pushed" toward the peaks. For the lower band, a very low p value is used to "pull" the curve toward the troughs. 🔶 SETTINGS Length : The number of recent bars included in the calculation window. Lambda (λ) : The smoothing factor. Higher values result in a stiffer, straighter envelope, while lower values allow the bands to follow price more closely. Lower Asymmetry (p) : Controls how the lower band reacts to prices. Typically set to a very low value (e.g., 0.001) to ensure it follows the bottom of the price action. Upper Asymmetry (p) : Controls how the upper band reacts to prices. Typically set to a very high value (e.g., 0.999) to ensure it follows the top of the price action. Iterations : The number of times the ALS algorithm runs to refine the fit. More iterations provide a more accurate envelope but require more computational power. Extrapolation : The number of bars to project the current slope of the bands into the future. Source : The price data used for the calculation (default is Close).
Pine Script®指标
由LuxAlgo提供
INAZUMA Bollinger Bands [v3.0]Overview This indicator detects Bollinger Band ±2σ breaks ("Band Walk") and visualizes trend initiation and continuation with "Lightning/Magma" effects and count labels. v3.0 Updates Pine Screener Support (MTF Filter): Added functionality to output Band Walk counts for a specified timeframe (e.g., Monthly) specifically for the Pine Screener. You can now screen for tickers that are band-walking on higher timeframes regardless of the chart's current timeframe. MTF Up Bandwalk Count: Consecutive upward band walk count for the specified timeframe. MTF Down Bandwalk Count: Consecutive downward band walk count for the specified 　timeframe. Note: Calculations are performed in real-time without waiting for the bar to close, allowing for the detection of the latest trends even on monthly charts. Code Optimization: Cleaned up internal logic and variable naming conventions. Features Visual Effects: Draws gradients based on price deviation when bands are broken. Consecutive Count Labels: Displays the number of consecutive band walks directly on the chart. Multi-Timeframe Settings: You can change the timeframe used for screener calculations (1D, 1W, 1M) via "Filter Timeframe" in the settings. How to use with Screener Select this indicator in the Pine Screener. Add "MTF Up Bandwalk Count" or "MTF Down Bandwalk Count" to your columns. Set the desired timeframe (default: "1M") in the "Filter Timeframe" setting of the indicator.
Pine Script®指标
由maru_jp提供
Multi-Sigma Bands [fmb]Multi-Sigma Bands plots standard deviation (sigma) bands around a selectable basis (SMA, EMA, RMA, or Linear Regression). It’s designed to help you spot when price is behaving “normally” versus when it’s stretching into statistically extended territory. What it shows - Basis: the central reference line (your chosen basis type) - ±1σ zone: the common range where price spends much of its time - ±2σ zone: extended range where moves often become more emotional or trend-driven - ±3σ zone: extreme range where price is statistically stretched (risk increases) How to read it - Inside ±1σ - Often normal behaviour and mean-reverting price action around the basis. - Between ±1σ and ±2σ - A meaningful extension. In trends, price can “walk” these areas for longer than expected. - Between ±2σ and ±3σ - Rare extension. Can signal exhaustion, blow-off behaviour, or capitulation depending on direction and context. How traders typically use it - Trend context - In strong uptrends, price may ride the upper bands (+1σ to +2σ) repeatedly. - In strong downtrends, the lower bands (-1σ to -2σ) can act the same way. - Bands show statistical stretch, not automatic reversal signals. - Extension and risk framing - The farther price is from the basis, the more “stretched” it is. - That usually means chasing becomes riskier and entries require tighter confirmation. - Range behaviour - Ranges often oscillate around the basis, with frequent returns toward the middle zone. Settings - Source: what price series to use (Close by default) - Length: lookback used for both basis and standard deviation - Basis: SMA, EMA, RMA, or LinReg - Stdev smoothing: optional smoothing on standard deviation for cleaner bands - σ multipliers: customise σ1, σ2, σ3 (defaults: 1, 2, 3) - Force Monthly Data (optional): calculate bands using a higher-timeframe source to reduce noise and focus on macro structure Disclaimer This indicator is for informational and educational purposes only and is not financial advice. Always use risk management and confirm with market structure and trend context.
Pine Script®指标
由Fullymobile提供
Universal Adaptive Tracking🙏🏻 Behold, this is UAT (Universal Adaptive Tracker) , with less words imma proceed how it compares with alternatives: ^^ comparison with non-adaptive quadratic regression (purple line), that has higher overshoots, less precision ^^ comparison with JMA and its adaptive gain. JMA’s gain is heavily limited, while UAT’s negative and positive gains are soft-saturated with p-order Möbius transform This drop is inspired by, dedicated to, and made will all love towards Jurik Research , who retired in October 2k21. When some1 steps out, some1 has to step in, and that time it’s me (again xd). But there’s some history u gotta know: Some history u gotta know: In ~2008 dudes from forexfactory reverse engineered Jurik Moving Average In late 1990s dudes from Jurik Research approximated the best possible adaptive tracking filter for evolution of prices via engineering miracles Today in 2k26, me I'm gonna present to you the real mathematical objects/entities behind JMA top-edge engineered approximates. You will prolly be even more happy now then all the dem together back then. Why all this? When we talk about object tracking stuff, e.g. air defense, drones, missiles, projectiles, prices, etc, it all comes down to adaptive control and (Position & Velocity & Acceleration) aka PVA state space models (the real stuff many of you count as DSP ). Why? Cuz while position (P) : (mean), or position & velocity (PV) : (linear regression) are stable enough in dem own ways, Position & Velocity & Acceleration (PVA) : (quadratic regression+) require adaptivity do be stable. And real world stuff needs PVA, due to non-linearity for starters. So that’s why. If your goal is Really smoothing and no lag, u gotta go there. I see a lot of folks are crazy with it and want it, so here is it, for y’all. And good news, this is perfect for your favorite Moving Windows. How to use it The upper study: The final filter (main state): just as you use other fast smoothers, MAs, etc, you know better than me here You can also turn in volatility bands in script’s style settings, these do not require any adjustments Finally, you can turn on, in the same place, separate trackers each based on negative and positive volatility exclusively. When both are almost equal, that indicates stability & persistence in markets. May sound like it’s nothing important, but I've never seen anything like it before. Also, if you'd allow your our inner mental gym hero gloriously arise, you can argue that these 2 separate trackers represent 2 fair prices (one for sellers, one for buyers). All better then 1 imaginary fair price for both (forget about it) The lower study: The lower study: you can analyze streams of upward of downward volatilities separately. This is incredibly powerful You can also turn these off and turn on neg & pos intensities, and use them as trend detector, when each or both cross 1.5 (naturally neutral) threshold. ^^ Upper study with expected typical and maximum volatility bands turned On ... The method explained What you got in the end is non-linear, adaptive, lighting fast when needed and slow when required price tracking. All built upon real math entities/objects, not a brilliantly engineered approximation of them. No parameters to optimize, data tells it all. ... It all starts from a process model, in our cause this is... MFPM (Mechanical Feedback Price Model) Doesn’t make gaussian assumptions like most quant mainstream tech, accepts that innovations are Laplace “at best”, relies in L inf and L0 spaces. I created this model neither trynna fit non-fitting ARMA / variants, nor trynna be silly assuming that price state evolution and markets are random. Theory behind it: if no new volume comes, then price evolution would be simply guided by the feedback based on previous trading activity, pushing prices towards the midrange between 2 latest datapoints, being the main force behind so called “pullbacks” and reason why most pullbacks end just a bit past 50% of a move. This is the Real mechanical feedback based mean reversion, that is always there in the markets no matter what, think of it as a background process that is always there, and fresh new volume deviates prices away from it. Btw, this can also be expressed as AR2 with both phis = 0.5 . Then I separate positive and negative innovations from this model and process them separately, reflecting the asymmetry between buy and sell forces, smth that most forget. Both of these follow exponential distribution . Each stream has its own memory so here we use recursive operators . We track maximum innovations (differences between real and expected datapoints) with exponentially decaying damping factor, and keep tracking typical innovation, with the same factor. Then we calculate what’s called in lovely audio engineering as “ crest factor ”, the difference is we don’t do RMS and stuff. But hey again we work with laplace innovations, so we keep things in L0 and L inf spirit. Then we go a couple of steps further, making this crest factor truly relative (resolution agnostic), and then, most importantly, we apply a natural saturation on it based on p-order Möbius transform, but not with arbitrary p and L, but guided by informational limits of the data. These final "intensity" parameters are what we need next to make our object tracking adaptive. Extended Beta(2, 2) Window This is imo the main part of this. Looking at tapering windows in DSP and how wavelets are made from derivatives of PDF functions of probability distributions, I figured that why use just one derivative? That made me come up with Universal Moving Average , that combines PDF and CDF of Beta(2, 2) distribution . And that is fine for P (position) tracking model. Here we need PVA (position & velocity & acceleration). We can realize that everything starts from PDF, and by adding derivatives and anti-derivatives of it as factors of final window weights, we can create smth truly unique, a weightset that is non-arbitrary and naturally provides response alike quadratic regression does, But, naturally smoothed. Why do I consider this a discovery, a primordial math object? Because x^2 itself and Beta(2, 2) based on it are the only primitives, esp out of all these dozens of DSP tapering windows, that provide you a finite amount of derivatives. You can keep differentiating Hann window until the kingdom f come, while Welch window aka Beta(2, 2) has a natural stopping point, because the 3rd derivative is 0, so we can’t use it. Symmetrically, we do 2 steps up from PDF, getting 1st and second anti-derivatives. What’s lovely, symmetrically, 3rd antiderivative even tho exist, it stops making any sense. 2nd one still makes sense, it’s smth like “potential” of probability distribution, not really discussed in mainstream open access sources. Finally, the last part is to introduce adaptivity using these intensity exponents we’ve calculated with MFPM. We do 2 separate trackers, one using the negative intensity exponent, another one uses positive intensity exponent. And at the end, even tho using both together is cool, the final state estimate is calculated simply as the state which intensity has higher. ^^ impulse response of our final kernel with fixed (non adaptive) intensity exponents: 1 (blue) and 2 (red). You see it's all about phase … And that’s all folks. … Actually no … Last, not least, is the ability to add additional innovation weight to the kernel: ^^ Weighting by innovations “On”. Provides incredible tracking precision, paid with smoothness. I think this screenshot, showing what happened after the gap, and how the tracker managed to react, explains it all. ... Live Long and Prosper, all good TradingView ∞
Pine Script®指标
由gorx1提供
5
MAD Supertrend [Alpha Extract]A sophisticated SuperTrend implementation that replaces traditional ATR calculations with Mean Absolute Deviation methodology for adaptive volatility measurement and band construction. Utilizing SMA baseline with MAD-based deviation bands and optional adaptive factor adjustments, this indicator delivers institutional-grade trend detection with strength-based filtering and dynamic visual feedback. The system's MAD approach provides superior noise reduction compared to ATR while maintaining responsiveness to genuine volatility changes, combined with momentum-based strength calculations for high-conviction signal generation. 🔶 Advanced MAD-Based Band Construction Implements Mean Absolute Deviation calculation as volatility proxy, measuring absolute price deviations from mean and smoothing for stable band generation without ATR dependency. The system calculates SMA baseline, computes MAD from configurable lookback period, applies factor multipliers to create upper and lower bands, then implements classic SuperTrend ratcheting logic where bands only adjust when price violates previous levels or calculations warrant updates. // Core MAD SuperTrend Framework SMA_Value = ta.sma(src, SMA_Length) Mean = ta.sma(src, MAD_Length) Abs_Deviation = abs(src - Mean) MAD_Value = ta.sma(Abs_Deviation, MAD_Length) // Band Construction with Ratcheting Upper_Band = SMA_Value + MAD_Factor * MAD_Value Lower_Band = SMA_Value - MAD_Factor * MAD_Value // Ratcheting logic prevents premature band adjustments 🔶 Adaptive Factor Adjustment Engine Features optional adaptive multiplier system that modulates MAD factor based on normalized MAD magnitude relative to recent extremes, creating bands that automatically expand during high-volatility regimes and contract during consolidation. The system applies min-max normalization to MAD values over configurable lookback, multiplies by adaptation parameter, and adds to base factor for dynamic volatility sensitivity without manual recalibration. 🔶 Momentum-Based Strength Filter Implements sophisticated strength calculation measuring price momentum relative to baseline divided by volatility-adjusted MAD bands, producing normalized 0-1 strength scores with exponential smoothing. The system calculates distance from SMA baseline, normalizes by MAD-derived band width, and applies configurable minimum threshold requiring sufficient momentum before trend signals activate, filtering weak or choppy market conditions. 🔶 SuperTrend Direction Logic Utilizes classic SuperTrend methodology adapted for MAD bands where trend direction flips on opposite band violations with state persistence until confirmation. The system tracks whether price closes above upper band (bearish flip to bullish) or below lower band (bullish flip to bearish), maintains directional state until opposing violation occurs, and generates binary +1/-1 trend signals suitable for systematic position management. 🔶 Intelligent Candle Sticking System Provides advanced line positioning option that anchors SuperTrend line to candle wicks or bodies rather than pure calculation values for enhanced visual clarity. The system supports two modes: Wick (positions at high/low extremes based on trend direction) and Body (constrains line between calculation and candle extremes), creating cleaner chart presentation while maintaining mathematical integrity of underlying signals. 🔶 Dynamic Gradient Visualization Framework Implements color intensity modulation based on smoothed strength calculations, transitioning from muted to vivid hues as momentum conviction increases. The system applies gradient interpolation using strength ratio, creating visual feedback where strong trending moves display intense colors while weak or consolidating conditions show faded tones across trend line, channel bands, and candle coloring for immediate regime assessment. 🔶 MAD Channel Architecture Features volatility-adjusted channel bands centered on baseline or candle-stuck line with configurable multiplier for support/resistance visualization. The system calculates upper and lower bounds using MAD values scaled by adaptive factors and channel multipliers, applies dynamic transparency based on trend strength, and creates filled regions that intensify during strong trends and fade during weak conditions. 🔶 Multi-Layer Glow Effect System Provides sophisticated line rendering with triple-layer plot system creating glow effect through progressively wider and more transparent outer layers. The system plots core trend line at specified width with full color intensity, adds inner glow layer at +2 width with moderate transparency, and outer glow at +4 width with higher transparency, creating visual depth and emphasis without cluttering chart space. 🔶 Strength-Based State Management Implements intelligent trend state logic requiring both directional signal and minimum strength threshold breach before confirming trend transitions. The system calculates raw SuperTrend direction, evaluates smoothed strength against configurable minimum, generates filtered trend state that can be bullish (+1), bearish (-1), or neutral (0), and maintains state persistence using hold logic that prevents oscillation during ambiguous conditions. 🔶 Comprehensive Alert Integration Generates trend flip alerts when filtered state transitions from bearish to bullish or bullish to bearish with full confirmation requirements satisfied. The system detects state changes through comparison with previous bar, triggers single alert per transition rather than continuous notifications, and provides customizable message templates for automated trading system integration or manual notification preferences. 🔶 Performance Optimization Architecture Utilizes efficient calculation methods with null value handling, nz() functions preventing errors during initialization bars, and optimized gradient calculations. The system includes intelligent state persistence minimizing recalculation overhead, streamlined MAD computation avoiding redundant mean calculations, and smooth visual updates maintaining consistent performance across extended historical periods. This indicator delivers sophisticated SuperTrend analysis through Mean Absolute Deviation methodology providing superior statistical properties compared to traditional ATR-based approaches. MAD calculations offer more robust volatility measurement resistant to extreme outliers while maintaining sensitivity to genuine market regime changes. The system's adaptive factor adjustment, momentum-based strength filtering, and dynamic visual feedback make it essential for traders seeking reliable trend-following signals with reduced false breakouts during choppy conditions. The combination of MAD bands, candle-sticking options, gradient strength visualization, and comprehensive filtering creates institutional-grade trend detection suitable for systematic approaches across cryptocurrency, forex, and equity markets with clear entry/exit signals and comprehensive alert capabilities.
Pine Script®指标
由AlphaExtract提供
Kinetic Flow [PyraTime]📊 INDICATOR OVERVIEW Kinetic Flow is a professional-grade momentum and trend-detection engine designed for traders who prioritize precision and clarity. By synthesizing Kinetic Flow Analysis with Fractal Efficiency Filtering, the V8 Flow edition provides a sophisticated, data-driven visualization of market regimes while systematically neutralizing noise through its proprietary "Chop Shield." 🎯 CORE TECHNOLOGIES 🔹 Kinetic Flow Engine Adaptive Equilibrium: A state-managed basis line that calculates the path of least resistance. Volatility-Scaled Ribbons: ATR-dynamic channels that expand and contract based on market energy. Iron-Clad Stability: Logic-locked to closed-bar calculations to eliminate intrabar flickering and "ghost" signals. 🔹 Chop Shield (Fractal Efficiency) Market Fragmentation Detection: Mathematically identifies when price action lacks directional efficiency. Regime Filtering: Automatically shifts the indicator into a "Neutral" state during low-efficiency phases to prevent whipsaws. Fibonacci Thresholding: Defaulted to 61.8% for optimal balance between speed and reliability. 🔹 Professional Signal System Transition Labels: High-contrast BUY and SELL markers at momentum pivot points. Overextension Logic: Strategic TP (Take-Profit) markers appear when the "Strain" on the kinetic ribbon reaches exhaustion levels. Visual Regime Mapping: Adaptive candle coloring provides an immediate heat-map of current market conditions (Bullish, Bearish, or Filtered). 🔹 PyraTime Dashboard (V8 HUD Standard) A specialized, monospace HUD positioned at the Bottom-Right for non-intrusive data monitoring: CONTEXT: Real-time regime status (BULLISH | BEARISH | FILTERED). EFFICIENCY: A percentage-based score of directional trend strength. VOL RATIO: Real-time volatility tracking via precision ATR. STATUS: Instant operational feedback (ACTIVE | FILTERED).
Pine Script®指标
由PyraTime提供
2