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LOBOWASS STOCHASTICThis script uses a DMI, Stochastic , and two stochastic RSI , when they are all overbought or oversold (also applying price action and looking for bounce points) we can obtain a greater probability that the price will go in the direction we expect This script is compact, which can be very useful for many traders Default values DMI: Lenght=10 Stolenght=3 Stochastic: K=14 D=3 Smooth=3 RSI Stochastic: K=3 D=3 Lenght=6 Lenght Stoch=6 RSI Stochastic 2: K=3 D=3 Lenght=14 Lenght Stoch=14 The indicators configured in this way can bring greater efficiency, do not confront only them, also use price action or other confirmat They are arranged in a graph, such that the DMI has the oversold at 10 and the overbought at 90, first stochastic RSI oversold at 120 and overbought at 180 the socond stochastic RSI at oversold at 220 and overbought at 280, and stochastic at oversold at 320 and overbought at 380, you can configure them your way taking into account that the DMI range is from 0-100, stochastic RSI 100-200, stochastic RSI 200-300 and stochastic from 300-400
Pine Script®指標
由Edrul_Alejandro提供
Linear Regression Trend Channel with Entries & AlertsPlease Use this Indicator If you understand the risk posed by linear regression trend channel Features Provides trend channel (best value for period is 40 on 5 minute timeframe Provides BUY/SELL entries based on current channel Provides custom color for channel Best used with MattyPips strategy indicators Risks : Please note, this script is the likes of Bollinger bands and poses a risk of falling in a trend range. Entries may keep running on the same direction while the market is moving.
Pine Script®指標
由MrTuanDoan提供
23
ANB AI Alert (my ANN)Hi guy This is a high level trend predicting study. It is modified from the strategy by sirlof. Feel free to use it as you like. ::USAGE only on 15 minutes 1. add the study in your chart 2. create an alert on the right 3. select ANB AI Alert (my ANN)(0,1D) 4. select the option you wish 5. select once per bar close alert 6. you can select email alert which i usually like 7. once the trade is alerted, execute your trade TP: DYNAMIC (read more) SL: null Setting TP and SL: this is in consideration with the daily volatility and sessions USDCAD TP 400 points, no stop loss. To maximize profit, use trailing stops. most trades are 500 to 1800 points
Pine Script®指標
由DevLucem提供
7
HFX321This indicator will provide the possibility of when trend reversals may happen on shorter time frames. It can work on any time frame and the use of Heiken Ashi candles can enhance it further. When used with other indicators such as the Stochastic RSI, support, resistance and trend lines, it can increase the possibility of a trend reversal being identified. On shorter time frames the alerts are much more frequent therefore can be less accurate so other indicators may be used. It will show an alert Arrow (green) pointing UP for the First Candle after a pivot LOW (LL, HL) that could indicate a trend reversal. It will show an alert Arrow (red) pointing DOWN for the First Candle after a pivot HIGH (HH, LH) that could indicate a trend reversal. The Colour changes on the Moving Average from Red to Green and green to red to support a trend change possibility. This has been designed to provide a visual confirmation that selected indicators have met certain criteria and that the trend has the possibility of reversing in the near future. It is NOT meant to be a trading system or offer trading advice. The indicator offers only possibilities of trend reversals when the above criteria is met. This is designed for Trend analysis ONLY. To gain access to this invite only script, please send me a private message on Trading View so I can assist you further. Thanks Les Gallagher
Pine Script®指標
由Lesgallagher247提供
Benchmark Above MA SignalBenchmark Above MA Signal (Configurable Visual) This tool provides a simple ON/OFF signal showing whether a selected benchmark asset (e.g., SPY, BTC, QQQ, etc.) is currently trading above a specified moving average. 🔧 Customizable Settings: Choose the benchmark symbol Set the timeframe (e.g., daily, 4H, weekly) Select SMA or EMA type Define the MA length (e.g., 21, 50, 200) Pick between two display modes: Stepline (default): plots a clean binary signal in the lower pane Background Only: visually highlights confluence periods without a line plot ✅ Ideal for macro filters, trend confirmation, or dashboard-style layouts 📊 Common use case: staying aware of the daily trend of SPY while trading lower intraday timeframes
Pine Script®指標
由kondor_trades提供
RS Triple MA Confluence Signal (Lower Pane)This indicator outputs a binary signal (1 or 0) based on triple moving average confluence of an asset’s relative strength vs a benchmark (e.g., SPY, BTC, etc). ✅ A value of 1 indicates full confluence, where the asset's relative strength is above three customizable moving averages (short, medium, and long). ❌ A value of 0 indicates confluence is off. This version is designed to be used in a lower pane for: Quick visual scanning Dashboard-style layouts Systematic filtering or alerting Pairs perfectly with the main overlay tool: 👉 Relative Strength Triple MA Confluence Use that version for candle coloring and price-level signals, and this version for clean signal tracking and screening support.
Pine Script®指標
由kondor_trades提供
AWR R & LR Oscillator with plots & tableHello trading viewers ! I'm glad to share with you one of my favorite indicator. It's the aggregate of many things. It is partly based on an indicator designed by gentleman goat. Many thanks to him. 1. Oscillator and Correlation Calculations Overview and Functionality: This part of the indicator computes up to 10 Pearson correlation coefficients between a chosen source (typically the close price, though this is user-configurable) and the bar index over various periods. Starting with an initial period defined by the startPeriod parameter and increasing by a set increment (periodIncrement), each correlation coefficient is calculated using the built-in ta.correlation function over successive ranges. These coefficients are stored in an array, and the indicator calculates their average (avgPR) to provide a complete view of the market trend strength. Display Features: Each individual coefficient, as well as the overall average, is plotted on the chart using a specific color. Horizontal lines (both dashed and solid) are drawn at levels 0, ±0.8, and ±1, serving as visual thresholds. Additionally, conditional fills in red or blue highlight when values exceed these thresholds, helping the user quickly identify potential extreme conditions (such as overbought or oversold situations). 2. Visual Signals and Automated Alerts Graphical Signal Enhancements: To reinforce the analysis, the indicator uses graphical elements like emojis and shape markers. For example: If all 10 curves drop below -0.79, a 🌋 emoji appears at the bottom of the chart; When curves 2 through 10 are below -0.79, a ⛰️ emoji is displayed below the bar, potentially serving as a buy signal accompanied by an alert condition; Likewise, symmetrical conditions for correlations exceeding 0.79 produce corresponding emojis (🤿 and 🏖️) at the top or bottom of the chart. Alerts and Notifications: Using these visual triggers, several alertcondition statements are defined within the script. This allows users to set up TradingView alerts and receive real-time notifications whenever the market reaches these predefined critical zones identified by the multi-period analysis. 3. Regression Channel Analysis Principles and Calculations: In addition to the oscillator, the indicator implements an analysis of regression channels. For each of the 8 configurable channels, the user can set a range of periods (for example, min1 to max1, etc.). The function calc_regression_channel iterates through the defined period range to find the optimal period that maximizes a statistical measure derived from a regression parameter calculated by the function r(p). Once this optimal period is identified, the indicator computes two key points (A and B) which define the main regression line, and then creates a channel based on the calculated deviation (an RMSE multiplied by a user-defined factor). The regression channels are not displayed on the chart but are used to plot shapes & fullfilled a table. Blue shapes are plotted when 6th channel or 7th channel are lower than 3 deviations Yellow shapes are plotted when 6th channel or 7th channel are higher than 3 deviations 4. Scores, Conditions, and the Summary Table Scoring System: The indicator goes further by assigning scores across multiple analytical categories, such as: 1. BigPear Score What It Represents: This score is based on a longer-term moving average of the Pearson correlation values (SMA 100 of the average of the 10 curves of correlation of Pearson). The BigPear category is designed to capture where this longer-term average falls within specific ranges. Conditions: The script defines nine boolean conditions (labeled BigPear1up through BigPear9up for the “up” direction). Here's the rules : BigPear1up = (bigsma_avgPR <= 0.5 and bigsma_avgPR > 0.25) BigPear2up = (bigsma_avgPR <= 0.25 and bigsma_avgPR > 0) BigPear3up = (bigsma_avgPR <= 0 and bigsma_avgPR > -0.25) BigPear4up = (bigsma_avgPR <= -0.25 and bigsma_avgPR > -0.5) BigPear5up = (bigsma_avgPR <= -0.5 and bigsma_avgPR > -0.65) BigPear6up = (bigsma_avgPR <= -0.65 and bigsma_avgPR > -0.7) BigPear7up = (bigsma_avgPR <= -0.7 and bigsma_avgPR > -0.75) BigPear8up = (bigsma_avgPR <= -0.75 and bigsma_avgPR > -0.8) BigPear9up = (bigsma_avgPR <= -0.8) Conditions: The script defines nine boolean conditions (labeled BigPear1down through BigPear9down for the “down” direction). BigPear1down = (bigsma_avgPR >= -0.5 and bigsma_avgPR < -0.25) BigPear2down = (bigsma_avgPR >= -0.25 and bigsma_avgPR < 0) BigPear3down = (bigsma_avgPR >= 0 and bigsma_avgPR < 0.25) BigPear4down = (bigsma_avgPR >= 0.25 and bigsma_avgPR < 0.5) BigPear5down = (bigsma_avgPR >= 0.5 and bigsma_avgPR < 0.65) BigPear6down = (bigsma_avgPR >= 0.65 and bigsma_avgPR < 0.7) BigPear7down = (bigsma_avgPR >= 0.7 and bigsma_avgPR < 0.75) BigPear8down = (bigsma_avgPR >= 0.75 and bigsma_avgPR < 0.8) BigPear9down = (bigsma_avgPR >= 0.8) Weighting: If BigPear1up is true, 1 point is added; if BigPear2up is true, 2 points are added; and so on up to 9 points from BigPear9up. Total Score: The positive score (posScoreBigPear) is the sum of these weighted conditions. Similarly, there is a negative score (negScoreBigPear) that is calculated using a mirrored set of conditions (named BigPear1down to BigPear9down), each contributing a negative weight (from -1 to -9). In essence, the BigPear score tells you—in a weighted cumulative way—where the longer-term correlation average falls relative to predefined thresholds. 2. Pear Score What It Represents: This category uses the immediate average of the Pearson correlations (avgPR) rather than a longer-term smoothed version. It reflects a more current picture of the market’s correlation behavior. How It’s Calculated: Conditions: There are nine conditions defined for the “up” scenario (named Pear1up through Pear9up), which partition the range of avgPR into intervals. For instance: Pear1up = (avgPR > -0.2 and avgPR <= 0) Pear2up = (avgPR > -0.4 and avgPR <= -0.2) Pear3up = (avgPR > -0.5 and avgPR <= -0.4) Pear4up = (avgPR > -0.6 and avgPR <= -0.5) Pear5up = (avgPR > -0.65 and avgPR <= -0.6) Pear6up = (avgPR > -0.7 and avgPR <= -0.65) Pear7up = (avgPR > -0.75 and avgPR <= -0.7) Pear8up = (avgPR > -0.8 and avgPR <= -0.75) Pear9up = (avgPR > -1 and avgPR <= -0.8) There are nine conditions defined for the “down” scenario (named Pear1down through Pear9down), which partition the range of avgPR into intervals. For instance: Pear1down = (avgPR >= 0 and avgPR < 0.2) Pear2down = (avgPR >= 0.2 and avgPR < 0.4) Pear3down = (avgPR >= 0.4 and avgPR < 0.5) Pear4down = (avgPR >= 0.5 and avgPR < 0.6) Pear5down = (avgPR >= 0.6 and avgPR < 0.65) Pear6down = (avgPR >= 0.65 and avgPR < 0.7) Pear7down = (avgPR >= 0.7 and avgPR < 0.75) Pear8down = (avgPR >= 0.75 and avgPR < 0.8) Pear9down = (avgPR >= 0.8 and avgPR <= 1) Weighting: Each condition has an associated weight, such as 0.9 for Pear1up, 1.9 for Pear2up, and so on, up to 9 for Pear9up. Sum up : Pear1up = 0.9 Pear2up = 1.9 Pear3up = 2.9 Pear4up = 3.9 Pear5up = 4.99 Pear6up = 6 Pear7up = 7 Pear8up = 8 Pear9up = 9 Total Score: The positive score (posScorePear) is the sum of these values for each condition that returns true. A corresponding negative score (negScorePear) is calculated using conditions for when avgPR falls on the positive side, with similar weights in the negative direction. This score quantifies the current correlation reading by translating its relative level into a numeric score through a weighted sum. 3. Trendpear Score What It Represents: The Trendpear score is more dynamic as it compares the current avgPR with its short-term moving average (sma_avgPR / 14 periods ) and also considers its relationship with an even longer moving average (bigsma_avgPR / 100 periods). It is meant to capture the trend or momentum in the correlation behavior. How It’s Calculated: Conditions: Nine conditions (from Trendpear1up to Trendpear9up) are defined to check: Whether avgPR is below, equal to, or above sma_avgPR by different margins; Whether it is trending upward (i.e., it is higher than its previous value). Here are the rules Trendpear1up = (avgPR <= sma_avgPR -0.2) and (avgPR >= avgPR ) Trendpear2up = (avgPR > sma_avgPR -0.2) and (avgPR <= sma_avgPR -0.07) and (avgPR >= avgPR ) Trendpear3up = (avgPR > sma_avgPR -0.07) and (avgPR <= sma_avgPR -0.03) and (avgPR >= avgPR ) Trendpear4up = (avgPR > sma_avgPR -0.03) and (avgPR <= sma_avgPR -0.02) and (avgPR >= avgPR ) Trendpear5up = (avgPR > sma_avgPR -0.02) and (avgPR <= sma_avgPR -0.01) and (avgPR >= avgPR ) Trendpear6up = (avgPR > sma_avgPR -0.01) and (avgPR <= sma_avgPR -0.001) and (avgPR >= avgPR ) Trendpear7up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR <= bigsma_avgPR) Trendpear8up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR -0.03) Trendpear9up = (avgPR >= sma_avgPR) and (avgPR >= avgPR ) and (avgPR >= bigsma_avgPR) Weighting: The weights here are not linear. For example, the lightest condition may add 0.1 point, whereas the most extreme condition (e.g., when avgPR is not only above the moving average but also reaches a high proportion relative to bigsma_avgPR) might add as much as 90 points. Trendpear1up = 0.1 Trendpear2up = 0.2 Trendpear3up = 0.3 Trendpear4up = 0.4 Trendpear5up = 0.5 Trendpear6up = 0.69 Trendpear7up = 7 Trendpear8up = 8.9 Trendpear9up = 90 Total Score: The positive score (posScoreTrendpear) is the sum of the weights from all conditions that are satisfied. A negative counterpart (negScoreTrendpear) exists similarly for when the trend indicates a downward bias. Trendpear integrates both the level and the direction of change in the correlations, giving a strong numeric indication when the market starts to diverge from its short-term average. 4. Deviation Score What It Represents: The “Écart” score quantifies how far the asset’s price deviates from the boundaries defined by the regression channels. This metric can indicate if the price is excessively deviating—which might signal an eventual reversion—or confirming a breakout. How It’s Calculated: Conditions: For each channel (with at least seven channels contributing to the scoring from the provided code), there are three levels of deviation: First tier (EcartXup): Checks if the price is below the upper boundary but above a second boundary. Second tier (EcartXup2): Checks if the price has dropped further, between a lower and a more extreme boundary. Third tier (EcartXup3): Checks if the price is below the most extreme limit. Weighting: Each tier within a channel has a very small weight for the lowest severities (for example, 0.0001 for the first tier, 0.0002 for the second, 0.0003 for the third) with weights increasing with the channel index. First channel : 0.0001 to 0.0003 (very short term) Second channel : 0.001 to 0.003 (short term) Third channel : 0.01 to 0.03 (short mid term) 4th channel : 0.1 to 0.3 ( mid term) 5th channel: 1 to 3 (long mid term) 6th channel : 10 to 30 (long term) 7th channel : 100 to 300 (very long term) Total Score: The overall positive score (posScoreEcart) is the sum of all the weights for conditions met among the first, second, and third tiers. The corresponding negative score (negScoreEcart) is calculated similarly (using conditions when the price is above the channel boundaries), with the weights being the same in magnitude but negative in sign. This layered scoring method allows the indicator to reflect both minor and major deviations in a gradated and cumulative manner. Example : Score + = 321.0001 Score - = -0.111 The asset price is really overextended in long term view, not for mid term & short term expect the in the very short term. Score + = 0.0033 Score - = -1.11 The asset price is really extended in short term view, not for mid term (even a bit underextended) & long term is neutral 5. Slope Score What It Represents: The Slope score captures the trend direction and steepness of the regression channels. It reflects whether the regression line (and hence the underlying trend) is sloping upward or downward. How It’s Calculated: Conditions: if the slope has a uptrend = 1 if the slope has a downtrend = -1 Weighting: First channel : 0.0001 to 0.0003 (very short term) Second channel : 0.001 to 0.003 (short term) Third channel : 0.01 to 0.03 (short mid term) 4th channel : 0.1 to 0.3 ( mid term) 5th channel: 1 to 3 (long mid term) 6th channel : 10 to 30 (long term) 7th channel : 100 to 300 (very long term) The positive slope conditions incrementally add weights from 0.0001 for the smallest positive slopes to 100 for the largest among the seven checks. And negative for the downward slopes. The positive score (posScoreSlope) is the sum of all the weights from the upward slope conditions that are met. The negative score (negScoreSlope) sums the negative weights when downward conditions are met. Example : Score + = 111 Score - = -0.1111 Trend is up for longterm & down for mid & short term The slope score therefore emphasizes both the magnitude and the direction of the trend as indicated by the regression channels, with an intentional asymmetry that flags strong downtrends more aggressively. Summary For each category—BigPear, Pear, Trendpear, Écart, and Slope—the indicator evaluates a defined set of conditions. Each condition is a binary test (true/false) based on different thresholds or comparisons (for example, comparing the current value to a moving average or a channel boundary). When a condition is true, its assigned weight is added to the cumulative score for that category. These individual scores, both positive and negative, are then displayed in a table, making it easy for the trader to see at a glance where the market stands according to each analytical dimension. This comprehensive, weighted approach allows the indicator to encapsulate several layers of market information into a single set of scores, aiding in the identification of potential trading opportunities or market reversals. 5. Practical Use and Application How to Use the Indicator: Interpreting the Signals: On your chart, observe the following components: The individual correlation curves and their average, plotted with visual thresholds; Visual markers (such as emojis and shape markers) that signal potential oversold or overbought conditions The summary table that aggregates the scores from each category, offering a quick glance at the market’s state. Trading Alerts and Decisions: Set your TradingView alerts through the alertcondition functions provided by the indicator. This way, you receive immediate notifications when critical conditions are met, allowing you to react as soon as the market reaches key levels. This tool is especially beneficial for advanced traders who want to combine multiple technical dimensions to optimize entry and exit points with a confluence of signals. Conclusion and Additional Insights In summary, this advanced indicator innovatively combines multi-scale Pearson correlation analysis (via multiple linear regressions) with robust regression channel analysis. It offers a deep and nuanced view of market dynamics by delivering clear visual signals and a comprehensive numerical summary through a built-in score table. Combine this indicator with other tools (e.g., oscillators, moving averages, volume indicators) to enhance overall strategy robustness.
Pine Script®指標
由AlwwAl提供
Bitcoin: Pi Cycle Top & Bottom | QuantumResearchBitcoin: Pi Cycle Top & Bottom | QuantumResearch Adaptive Deviation Model for Bitcoin Macro Extremes Bitcoin: Pi Cycle Top & Bottom by QuantumResearch is a proprietary interpretation of the famous Pi Cycle concept—enhanced with normalized deviation logic, adjustable thresholds, and visual clarity. Unlike traditional models that simply cross two moving averages, this tool calculates the dynamic spread between a short-term and amplified long-term exponential average, delivering a continuous score that adapts to Bitcoin's evolving volatility profile. 🧠 What Makes It Unique? 🔹 Pi Deviation Engine: This creates a centered, symmetric oscillator that better visualizes overextended conditions—something the original Pi Cycle model does not offer. 🔹 Dynamic Zoning via Thresholds: Users can set custom top and bottom thresholds to adjust sensitivity based on current market regimes, making it more flexible than static crossover models. 🔹 Gradient-Powered Area Fill: The oscillator plot is filled with directional gradients that react to the score's magnitude, creating an intuitive visual spectrum between bullish and bearish extremes. 🔹 Macro-Focused, Overlay-Free: The indicator runs in a clean subpanel, preserving chart space and allowing better integration into multi-layered macro dashboards. 🔹 Built for BTC’s Unique Structure: The moving average lengths and logic are specifically calibrated to Bitcoin’s halving-driven cycles, unlike generic Pi models applied across asset classes. 🔍 Key Features ✅ Continuous Cycle Score (not binary crosses) ✅ Custom upper/lower thresholds for signal flexibility ✅ Visual gradient fill and background shading ✅ Zero chart clutter (non-overlay) ✅ Fully customizable moving average lengths ✅ Designed for macro cycle top/bottom detection 📌 Ideal For: Long-term Bitcoin investors Macro traders and analysts Those seeking early warning signs of euphoria or despair Anyone using on-chain + cyclical tools to time large market pivots ⚠️ Disclaimer This indicator is for educational and research purposes only. It does not provide financial advice or guarantees. Past performance does not predict future behavior. Always confirm with additional tools and analysis.
Pine Script®指標
由QuantumResearch提供
ML: Lorentzian Classification Premium█ OVERVIEW Lorentzian Classification Premium represents the culmination of two years of collaborative development with over 1,000 beta testers from the TradingView community. Building upon the foundation of the open-source version, this premium edition introduces powerful enhancements that transform how machine-learning classification can be applied to market analysis. The premium version maintains the core Lorentzian distance-based classification algorithm while expanding its capabilities through triple the feature dimensionality (up to 15 features), sophisticated mean-reversion detection, first-pullback identification, and a comprehensive signal taxonomy that goes far beyond simple buy/sell signals. Whether you're building automated trading systems, conducting deep market research, or integrating proprietary indicators into ML workflows, this tool provides the advanced edge needed for professional-grade analysis. █ BACKGROUND Lorentzian Classification analyzes market structures, especially those exhibiting non-linear distortions under stress, by employing advanced distance metrics like the Lorentzian metric, prominent in fields such as relativity theory. Where traditional indicators assume flat space, we embrace the curve. The heart of this approach is the Lorentzian distance metric—a sophisticated mathematical tool. This framework adeptly navigates the complex curves and distortions of market space, aiming to provide insights that traditional analysis might miss, especially during moments of extreme volatility. It analyzes historical data from a multi-dimensional feature space consisting of various technical indicators of your choosing. Where traditional approaches fail, Lorentzian space reveals the true geometry of market dynamics. Neighborhoods in Different Geometries: In the above figure, the Lorentzian metric creates distinctive cross-patterns aligned with feature axes (RSI, CCI, ADX), capturing both local similarity and dimensional extremes. This unique geometry allows the algorithm to recognize similar market conditions that Euclidean spheres and Manhattan diamonds would miss entirely. In LC Premium, users can have up to 15 features -- you are not limited to 3-dimensions. Among the thousands of distance metrics discovered by mathematicians, each perceives data through its own geometric lens. The Lorentzian metric stands apart with its unique ability to capture market behavior during volatile events. █ COMMUNITY-DRIVEN EVOLUTION It has been profoundly humbling over the past 2 years to witness this indicator's evolution through the collaborative efforts of our incredible community. This journey has been shaped by thousands of user suggestions and validated through real-world application. A particularly amazing milestone was the development of a complete community-driven Python port, which meticulously matched even the most minute PineScript quirks. Building on this solid foundation, a new command-line interface (CLI) has opened up exciting possibilities for chart-specific parameter optimization: Early insights from parameter optimization research: Through grid-search testing across thousands of parameter combinations, the analysis identifies which parameters have the biggest effects on performance and maps regions of stability across different market regimes. This reveals that optimal neighbor counts vary significantly based on market conditions—opening up incredible potential for timeframe-specific optimization. This is just one of the insights gleaned so far from this ongoing investigation. The potential for chart-specific optimization for any given timeframe could transform how traders approach parameter selection. Demand from power users for extra capabilities—while keeping the open-source version simple—sparked this Premium release. The open-source branch remains maintained, but the premium tier adds unique features for those who need an analytical edge and to leverage their own custom indicators as feature series for the algorithm. █ KEY PREMIUM FEATURES 📈 First Pullback Detection System Automatically identifies high-probability trend-continuation entries after initial momentum moves. Detects when price retraces to optimal entry zones following breakouts or trend initiations. Green/red triangle signals often fire before main classification arrows. Dedicated alerts for both bullish and bearish pullback opportunities. Based on veryfid's extensive research into pullback mechanics and market structure. 🔄 Dynamic Kernel Regression Envelope Powerful, zero-setup confluence layer that immediately communicates trend shifts. Dual-kernel system creates a visual envelope between trend estimates. Color gradient dynamically represents prediction strength and market conviction. Crossovers provide additional confirmation without cluttering your chart. Professional visualization that rivals institutional-grade analysis tools. ✨ Massively Expanded Dimensionality: 10 Custom Sources, 5 Built-In Sources Transform the indicator from 5 built-in standard to 15 total total features—triple the analytical power. Integrate ANY TradingView indicator as a machine learning feature. Built-in normalization ensures all indicators contribute equally regardless of scale. Create theme-based systems: pure volume analysis, multi-timeframe momentum, or hybrid approaches. 📊 Tiered Mean Reversion Signals with Scalping Alerts Regular (🔄) and Strong (⬇️/⬆️) mean reversion signals based on statistical extremes. Opportunities often arise before candle close—perfect for scalping entries. Visual markers appear at high-probability reversal zones. Four specialized alert types: upward/downward for both regular and strong reversals. Pre-optimized probability thresholds, no fine-tuning required. 📅 Daily Kernel Trend Filter Instantly cleans up noisy intraday charts by aligning with higher timeframe trends. Swing traders report immediate signal quality improvement. Automatically deactivates on daily+ timeframes (intelligent context awareness). Reduces counter-trend signals by up to 60% on lower timeframes. Simple toggle—no complex multi-timeframe setup required. 📋 Professional Backtesting Stream (-6 to +6) Multiple distinct signal types (including pullbacks, mean reversions, and kernel deviations) vs. basic binary (buy/sell) output for nuanced analysis. Enables detailed walk-forward analysis and ML model training. Compatible with external backtesting frameworks via numeric stream. Rare precision for TradingView indicators—usually only found in institutional tools. Perfect for quants building sophisticated strategy layers. ⚡ Performance Optimizations Faster distance calculations through algorithmic improvements. Reduced indicator load time (measured via Pine Profiler). Handles 15 active features without timeouts—critical for multi-chart setups. Optimized for live auto-trading bots requiring minimal latency. 🎨 Full Visual Customization & Accessibility Complete color control for all visual elements. Colorblind-safe default palette with customization options. Dark mode optimization for extended trading sessions. Professional appearance matching your trading workspace. Accessibility features meeting modern UI standards. 🛠️ Advanced Training Modes Downsampling mode for training on diverse market conditions; Down-sampling and remote-fractals for exotic pattern discovery. Remote fractals option extends analysis to deep historical patterns. Reset factor control for fine-tuning neighbor diversity; Reset-factor tuning to control neighbor diversity. Appeals to systematic traders exploring exotic data approaches. Prevents temporal clustering bias in model training. █ HOW TO USE Understanding the Approach (Core Concept): Lorentzian Classification uses a k-Nearest Neighbors (k-NN) algorithm. It searches for historical price action "neighborhoods" similar to the current market state. Instead of a simple straight-line (Euclidean) distance, it primarily uses a Lorentzian distance metric, which can account for market "warping" or distortions often seen during high volatility or significant events. Each historical neighbor "votes" on what happened next in its context, and these votes aggregate into a classification score for the current bar. Interpreting Bar Scores & Signals (Interpreting the Chart): Bar Prediction Values: Numbers over each candle (e.g., ranging from -8 to +8 if Neighbors Count is 8) represent the aggregated vote from the nearest neighbors. Strong positive scores (e.g., +7, +8) indicate a strong bullish consensus among historical analogs. Strong negative scores (e.g., -7, -8) indicate a strong bearish consensus. Scores near zero suggest neutrality or conflicting signals from neighbors. The intensity of bar colors (if Use Confidence Gradient is on) often reflects these scores. Main Arrows (Main Buy/Sell Labels): Large ▲/▼ labels are the primary entry signals generated when the overall classification (after filters) is bullish or bearish. Pullback Triangles: Small green/red ▲/▼ identify potential trend continuation entries. These signals often appear after an initial price move and a subsequent minor retracement, suggesting the trend might resume. This is based on recognizing patterns where a brief counter-movement is followed by a continued advance in the initial trend direction. Mean-Reversion Symbols: 🔄 (Regular Reversion) appears when price has crossed the average band of the Dynamic Kernel Regression Envelope. ⬇️/⬆️ (Strong Reversion) means price has crossed the far band of the envelope, indicating a more extreme deviation and potentially a stronger reversion opportunity. Custom Mean Reversion Deviation Markers (Deviation Dots): If Enable Custom Mean Reversion Alerts is on, these dots appear when price deviates from the main kernel regression line by a user-defined ATR multiple, signaling a custom-defined reversion opportunity. Kernel Regression Lines & Envelope: The Main Kernel Estimate (thicker line) is an adaptive moving average that smooths price and helps identify trend direction. Its color indicates the current trend bias. The Envelope (outer bands and a midline) creates a channel around price, and its interaction with price generates mean reversion signals. Key Input Groups & Their Purpose: 🔧 GENERAL SETTINGS: Reduce Price-Time Warping : Toggles the distance metric. When enabled, it reduces the characteristic "warping" effect of the default Lorentzian metric, making the distance calculation more Euclidean in nature. This may be suited for periods exhibiting less pronounced price-time distortions. Source : Price data for calculations (default: close ). Neighbors Count : The 'k' in k-NN – number of historical analogs considered. Max Bars Back : How far back the indicator looks for historical patterns. Show Exits / Use Dynamic Exits : Controls visibility and logic for exit signals. Include Full History (Use Remote Fractals) : Allows model to pick "exotic" fractals from deep chart history. Use Downsampling / Reset Factor : Advanced training parameters affecting neighbor selection. Show Trade Stats / Use Worst Case Estimates : Displays a real-time performance table (for calibration only). 🎛️ DEFINE CUSTOM SOURCES (OPTIONAL): Integrate up to 10 external data series (e.g., from other indicators) as features. Each can be optionally normalized. Load the external indicator on your chart first for it to appear in the dropdown. 🧠 FEATURE ENGINEERING: Configure up to 15 features for the k-NN algorithm. Select type (RSI, WT, CCI, ADX, Custom Sources), parameters, and enable/disable. Start simple (3-5 features) and add complexity gradually. Normalize features with vastly different scales. 🖥️ DISPLAY SETTINGS: Controls visibility of chart elements: bar colors, prediction values/labels, envelope, etc. Align Signal with Current Bar : If true, pullback signals appear on the current bar (calculated on closed data). If false (default), they appear on the next bar. Use ATR Offset : Positions bar prediction values using ATR for visibility. 🧮 FILTERS SETTINGS: Refine raw classification signals: Volatility, Regime, ADX, EMA/SMA, and Daily Kernel filters. 🌀 KERNEL SETTINGS (Main Kernel): Adjust parameters for the primary Nadaraya-Watson Kernel Regression line. Lookback Window , Relative Weighting , Regression Level , Lag control sensitivity and smoothness. ✉️ ENVELOPE SETTINGS (for Mean Reversion): Configure the dynamic Kernel Regression Envelope. ATR Length , Near/Far ATR Factor define band width. 🎨 COLOR SETTINGS (Colors): Customize colors for all visual elements; override every palette element. General Approach to Using the Indicator (Suggested Workflow): Load defaults and observe behavior: Familiarize yourself with the indicator's behavior. Feature Engineering: Experiment with features, considering momentum, trend, and volatility. Add/replace features gradually. Apply Filters: Refine signals according to your trading style. Contextualize: Use kernels and envelope to understand broader trend and potential overbought/oversold areas. Observe Signals: Pay attention to the interplay of main signals, pullbacks, and mean reversions. Watch interplay of main, pullback & mean-reversion signals. Calibrate (Not Backtest): Use the "Trade Stats" table for real-time feedback on current settings. This is for calibration, *not a substitute for rigorous backtesting.* Iterate & refine: Adjust settings, observe outcomes, and refine your approach. █ ACKNOWLEDGMENTS This premium version wouldn't exist without the invaluable contributions of: veryfid for his groundbreaking ideas on unifying pullback detection with Lorentzian Classification, but most of all for always believing in and encouraging me and so many others. For being a mentor and, most importantly, a friend. We all miss you. RikkiTavi for his help in creating the settings optimization framework and for other invaluable theoretical discussions. The 1,000+ beta testers worldwide who provided continuous feedback over two years. The Python porting team who created the foundation for advanced optimization; for the cross-language clone. The broader TradingView community for making this one of the platform's most popular indicators. █ FUTURE DEVELOPMENT The Premium version will continue to evolve based on community feedback. Planned enhancements include: Specialized exit model trained independently from entry signals (ML-based exit model). Feature hub with pre-normalized, commonly requested indicators (Pre-normalized feature hub). Better risk-management options (Enhanced risk-management options). Fully automated settings optimization (Auto-settings optimization tool).
Pine Script®指標
由jdehorty提供
18
PhenLabs - Market Fluid Dynamics📊 Market Fluid Dynamics - Version: PineScript™ v6 📌 Description The Market Fluid Dynamics - Phen indicator is a new thinking regarding market analysis by modeling price action, volume, and volatility using a fluid system. It attempts to offer traders control over more profound market forces, such as momentum (speed), resistance (thickness), and buying/selling pressure. By visualizing such dynamics, the script allows the traders to decide on the prevailing market flow, its power, likely continuations, and zones of calmness and chaos, and thereby allows improved decision-making. This measure avoids the usual difficulty of reconciling multiple, often contradictory, market indications by including them within a single overarching model. It moves beyond traditional binary indicators by providing a multi-dimensional view of market behavior, employing fluid dynamic analogs to describe complex interactions in an accessible manner. 🚀 Points of Innovation Integrated Fluid Dynamics Model: Combines velocity, viscosity, pressure, and turbulence into a single indicator. Normalized Metrics: Uses ATR and other normalization techniques for consistent readings across different assets and timeframes. Dynamic Flow Visualization: Main flow line changes color and intensity based on direction and strength. Turbulence Background: Visually represents market stability with a gradient background, from calm to turbulent. Comprehensive Dashboard: Provides an at-a-glance summary of key fluid dynamic metrics. Multi-Layer Smoothing: Employs several layers of EMA smoothing for a clearer, more responsive main flow line. 🔧 Core Components Velocity Component: Measures price momentum (first derivative of price), normalized by ATR. It indicates the speed and direction of price changes. Viscosity Component: Represents market resistance to price changes, derived from ATR relative to its historical average. Higher viscosity suggests it’s harder for prices to move. Pressure Component: Quantifies the force created by volume and price range (close - open), normalized by ATR. It reflects buying or selling pressure. Turbulence Detection: Calculates a Reynolds number equivalent to identify market stability, ranging from laminar (stable) to turbulent (chaotic). Main Flow Indicator: Combines the above components, applying sensitivity and smoothing, to generate a primary signal of market direction and strength. 🔥 Key Features Advanced Smoothing Algorithm: Utilizes multiple EMA layers on the raw flow calculation for a fluid and responsive main flow line, reducing noise while maintaining sensitivity. Gradient Flow Coloring: The main flow line dynamically changes color from light to deep blue for bullish flow and light to deep red for bearish flow, with intensity reflecting flow strength. This provides an immediate visual cue of market sentiment and momentum. Turbulence Level Background: The chart background changes color based on calculated turbulence (from calm gray to vibrant orange), offering an intuitive understanding of market stability and potential for erratic price action. Informative Dashboard: A customizable on-screen table displays critical metrics like Flow State, Flow Strength, Market Viscosity, Turbulence, Pressure Force, Flow Acceleration, and Flow Continuity, allowing traders to quickly assess current market conditions. Configurable Lookback and Sensitivity: Users can adjust the base lookback period for calculations and the sensitivity of the flow to viscosity, tailoring the indicator to different trading styles and market conditions. Alert Conditions: Pre-defined alerts for flow direction changes (positive/negative crossover of zero line) and detection of high turbulence states. 🎨 Visualization Main Flow Line: A smoothed line plotted below the main chart, colored blue for bullish flow and red for bearish flow. The intensity of the color (light to dark) indicates the strength of the flow. This line crossing the zero line can signal a change in market direction. Zero Line: A dotted horizontal line at the zero level, serving as a baseline to gauge whether the market flow is positive (bullish) or negative (bearish). Turbulence Background: The indicator pane’s background color changes based on the calculated turbulence level. A calm, almost transparent gray indicates low turbulence (laminar flow), while a more vibrant, semi-transparent orange signifies high turbulence. This helps traders visually assess market stability. Dashboard Table: An optional table displayed on the chart, showing key metrics like ‘Flow State’, ‘Flow Strength’, ‘Market Viscosity’, ‘Turbulence’, ‘Pressure Force’, ‘Flow Acceleration’, and ‘Flow Continuity’ with their current values and qualitative descriptions (e.g., ‘Bullish Flow’, ‘Laminar (Stable)’). 📖 Usage Guidelines Setting Categories Show Dashboard - Default: true; Range: true/false; Description: Toggles the visibility of the Market Fluid Dynamics dashboard on the chart. Enable to see key metrics at a glance. Base Lookback Period - Default: 14; Range: 5 - (no upper limit, practical limits apply); Description: Sets the primary lookback period for core calculations like velocity, ATR, and volume SMA. Shorter periods make the indicator more sensitive to recent price action, while longer periods provide a smoother, slower signal. Flow Sensitivity - Default: 0.5; Range: 0.1 - 1.0 (step 0.1); Description: Adjusts how much the market viscosity dampens the raw flow. A lower value means viscosity has less impact (flow is more sensitive to raw velocity/pressure), while a higher value means viscosity has a greater dampening effect. Flow Smoothing - Default: 5; Range: 1 - 20; Description: Controls the length of the EMA smoothing applied to the main flow line. Higher values result in a smoother flow line but with more lag; lower values make it more responsive but potentially noisier. Dashboard Position - Default: ‘Top Right’; Range: ‘Top Right’, ‘Top Left’, ‘Bottom Right’, ‘Bottom Left’, ‘Middle Right’, ‘Middle Left’; Description: Determines the placement of the dashboard on the chart. Header Size - Default: ‘Normal’; Range: ‘Tiny’, ‘Small’, ‘Normal’, ‘Large’, ‘Huge’; Description: Sets the text size for the dashboard header. Values Size - Default: ‘Small’; Range: ‘Tiny’, ‘Small’, ‘Normal’, ‘Large’; Description: Sets the text size for the metric values in the dashboard. ✅ Best Use Cases Trend Identification: Identifying the dominant market flow (bullish or bearish) and its strength to trade in the direction of the prevailing trend. Momentum Confirmation: Using the flow strength and acceleration to confirm the conviction behind price movements. Volatility Assessment: Utilizing the turbulence metric to gauge market stability, helping to adjust position sizing or avoid choppy conditions. Reversal Spotting: Watching for divergences between price and flow, or crossovers of the main flow line above/below the zero line, as potential reversal signals, especially when combined with changes in pressure or viscosity. Swing Trading: Leveraging the smoothed flow line to capture medium-term market swings, entering when flow aligns with the desired trade direction and exiting when flow weakens or reverses. Intraday Scalping: Using shorter lookback periods and higher sensitivity to identify quick shifts in flow and turbulence for short-term trading opportunities, particularly in liquid markets. ⚠️ Limitations Lagging Nature: Like many indicators based on moving averages and lookback periods, the main flow line can lag behind rapid price changes, potentially leading to delayed signals. Whipsaws in Ranging Markets: During periods of low volatility or sideways price action (high viscosity, low flow strength), the indicator might produce frequent buy/sell signals (whipsaws) as the flow oscillates around the zero line. Not a Standalone System: While comprehensive, it should be used in conjunction with other forms of analysis (e.g., price action, support/resistance levels, other indicators) and not as a sole basis for trading decisions. Subjectivity in Interpretation: While the dashboard provides quantitative values, the interpretation of “strong” flow, “high” turbulence, or “significant” acceleration can still have a subjective element depending on the trader’s strategy and risk tolerance. 💡 What Makes This Unique Fluid Dynamics Analogy: Its core strength lies in translating complex market interactions into an intuitive fluid dynamics framework, making concepts like momentum, resistance, and pressure easier to visualize and understand. Market View: Instead of focusing on a single aspect (like just momentum or just volatility), it integrates multiple factors (velocity, viscosity, pressure, turbulence) to provide a more comprehensive picture of market conditions. Adaptive Visualization: The dynamic coloring of the flow line and the turbulence background provide immediate, adaptive visual feedback that changes with market conditions. 🔬 How It Works Price Velocity Calculation: The indicator first calculates price velocity by measuring the rate of change of the closing price over a given ‘lookback’ period. The raw velocity is then normalized by the Average True Range (ATR) of the same lookback period. Normalization enables comparison of momentum between assets or timeframes by scaling for volatility. This is the direction and speed of initial price movement. Viscosity Calculation: Market ‘viscosity’ or resistance to price movement is determined by looking at the current ATR relative to its longer-term average (SMA of ATR over lookback * 2). The further the current ATR is above its average, the lower the viscosity (less resistance to price movement), and vice-versa. The script inverts this relationship and bounds it so that rising viscosity means more resistance. Pressure Force Measurement: A ‘pressure’ variable is calculated as a function of the ratio of current volume to its simple moving average, multiplied by the price range (close - open) and normalized by ATR. This is designed to measure the force behind price movement created by volume and intraday price thrusts. This pressure is smoothed by an EMA. Turbulence State Evaluation: A equivalent ‘Reynolds number’ is calculated by dividing the absolute normalized velocity by the viscosity. This is the proclivity of the market to move in a chaotic or orderly fashion. This ‘reynoldsValue’ is smoothed with an EMA to get the ‘turbulenceState’, which indicates if the market is laminar (stable), transitional, or turbulent. Main Flow Derivation: The ‘rawFlow’ is calculated by taking the normalized velocity, dampening its impact based on the ‘viscosity’ and user-input ‘sensitivity’, and orienting it by the sign of the smoothed ‘pressureSmooth’. The ‘rawFlow’ is then put through multiple layers of exponential moving average (EMA) smoothing (with ‘smoothingLength’ and derived values) to reach the final ‘mainFlow’ line. The extensive smoothing is designed to give a smooth and clear visualization of the overall market direction and magnitude. Dashboard Metrics Compilation: Additional metrics like flow acceleration (derivative of mainFlow), and flow continuity (correlation between close and volume) are calculated. All primary components (Flow State, Strength, Viscosity, Turbulence, Pressure, Acceleration, Continuity) are then presented in a user-configurable dashboard for ease of monitoring. 💡 Note: The “Market Fluid Dynamics - Phen” indicator is designed to offer a unique perspective on market behavior by applying principles from fluid dynamics. It’s most effective when used to understand the underlying forces driving price rather than as a direct buy/sell signal generator in isolation. Experiment with the settings, particularly the ‘Base Lookback Period’, ‘Flow Sensitivity’, and ‘Flow Smoothing’, to find what best suits your trading style and the specific asset you are analyzing. Always combine its insights with robust risk management practices.
Pine Script®指標
由PhenLabs提供
2
MA Thrust Processor | QuantEdgeB⚡MA Thrust Processor | QuantEdgeB 🔭 What is the MA Thrust Processor? The MA Thrust Processor (MTP) is a dynamic and modular market momentum engine that specializes in thrust-based signal analysis derived from smoothed moving averages. It’s engineered to model directional commitment, detect signal imbalances, and visualize structural momentum in a range of market conditions. 🧬 Think of MTP as a precision-engineered motion sensor — decoding strength, follow-through, and structural imbalance in real time — it detects force, direction, velocity, and alignment, adapting based on your preferred calculation model (Wave, Thrust, Z-Score, or Normalized) and signal mode (Impulse, Trend, or HA Candles). _______________________ 1. 🔧 System Core: Customizability and Processing Engine 📊 Moving Average Types MA Thrust Processor supports a rich menu of 13+ moving average styles: • Standard: SMA, EMA, WMA • Advanced: HMA, LSMA, ALMA, JMA, TEMA, DEMA, SMMA • Volume-Based: VWMA • Adaptive Models: VIDYA (3 modes), FRAMA 💡 Each MA type acts as the backbone for signal smoothing and thrust deviation modeling, giving the user tight control over behavior and lag tradeoffs. ⚙️ Calculation Methods (MA Derivatives) You choose how the core value is calculated: 1️⃣ 𝓦𝓪𝓿𝓮 • Sine-wave modeled oscillator • Combines MA distance with standard deviation normalization • Ideal for detecting divergences and cyclical structure • Output includes center, smoothed line , and histogram. 2️⃣ 𝓣𝓱𝓻𝓾𝓼𝓽 • Calculates MA deviation vs. price and midpoint • Captures aggressive directional pushes relative to range center • Excellent for breakout/trend force analysis 3️⃣ 𝓩-𝓢𝓬𝓸𝓻𝓮 • Mean-reverting z-score over MA • Expresses statistical distance from norm • Used in reversion or probabilistic strategies 4️⃣ 𝓝𝓸𝓻𝓶𝓪𝓵𝓲𝔃𝓮𝓭 • Scales MA output to 0–1 (centered at 0.5) • Tracks where the signal lies in its own relative range • Great for flat vs. trending recognition _______________________ 2. 🧨 SIGNAL MODES – The Behavioral Core The system supports 3 powerful signal modes that define how the thrust logic is interpreted and visualized. 1️⃣ 𝓘𝓶𝓹𝓾𝓵𝓼𝓮 Mode 🔥 Use Case: Breakouts, aggressive reversals, divergences 🔍 Logic: • In Wave mode: compares Wave O (oscillator) and S (smoothed) • In Thrust/Z-Score/Normalized: applies thresholds (static, SD, or percentile) • Detects sharp departures or rejections from bounds 🎯 Ideal for: • Scalp or event trades • High-velocity moves • Leading edge of trend or compression breaks 2️⃣ 𝓣𝓻𝓮𝓷𝓭 Mode 🧭 Use Case: Stable continuation and trend following 🔍 Logic: • Signal triggers when value crosses a calculated midline or long-term average • Thresholds: midline or 365-SMA of smoothed value • Acts as a stable “bias direction” for trades 🎯 Ideal for: • Swing trading • Portfolio allocations • Holding directional exposure longer 3️⃣ 𝓗𝓐 𝓒𝓪𝓷𝓭𝓵𝓮𝓼 Mode 🎨 Use Case: Visual clarity + phase detection 🔍 Logic: • Converts signal to Heikin-Ashi candles • Adds logic for momentum, reversal, continuation, or chop • Highly discretionary and pattern-oriented 🎯 Ideal for: • Visual traders • Phase-based strategies • Avoiding false positives in chop _______________________ 3. 📊 System Sensor Table (Strength Meter) MA Thrust Processor includes a sophisticated sensor display with multi-layered insights. 🔁 Signal State • Long ⟹ bullish conviction or thrust • Short ⟹ bearish dominance or rejection • Cash ⟹ neutrality or low conviction Dynamically generated by logic mode and indicator thresholds. 📊 Strength Bars: Conviction + Potential Strength output is never binary — instead, it’s expressed via: 1️⃣ Conviction Strength (when signal is active): • Score from 0% to 100% • Based on: o Momentum velocity (Rate of Change) o Distance from key thresholds o Divergence signal (Wave mode) o Flat signal detection (for Normalized) 2️⃣ Potential Strength (when signal = neutral): • Displays both Bullish and Bearish readiness • Interprets which side is loading pressure • Helps traders spot “who has the edge” before breakout 👀 In Wave Mode, potential is calculated from oscillator vs. smoothed 👀 In Impulse/Trend, it blends distance + RoC + signal stability 🔸 HA Candle Phase (HA Mode Only) When HA mode is active, strength bars are replaced with a live phase classifier: • Momentum Up/Down: price above/below threshold + same color trend • Reversal Up/Down: price above/below bounds, opposite signal color • Continuation Up/Down: following a breakout/confirmation • Chop: indecision zone • Neutral: no clear trend This turns HA mode into a narrative engine, expressing what’s happening, why, and what might come next. _______________________ 4. 🧠 Smart Auto-Configuration Enabling Use Recommended Settings triggers optimized configurations: • Pre-set thresholds (static, percentile, SD) • Ideal lengths for each logic type • Proper bounds scaling • MA selections that match the logic For example: • Impulse + Thrust ⇒ shorter length + SD • Trend + Z-Score ⇒ long mean-based • Wave ⇒ balanced smoothing + SD blend _______________________ 5. 🧪 Summary of Use Cases Each mode and calculation method within the MA Thrust Processor is tailored for specific trading styles and market conditions. Here’s how to think about their best applications: 🔹 Signal Modes Impulse Mode is designed for speed and responsiveness. It excels in fast markets where breakouts or sharp reversals happen quickly. Ideal for scalpers, intraday traders, or anyone looking to catch momentum just as it ignites. It’s particularly effective around high-impact events like economic reports or news catalysts, as it picks up directional shifts rapidly. Trend Mode focuses on longer-term, stable price action. It identifies directional bias using midline or average-based thresholds, making it best for swing traders and trend followers. Because of its stability, it filters out minor fluctuations and helps you stay in trades longer when the directional move is sustained. HA Candles Mode is for traders who prefer a visual, phase-based approach. It smooths data using Heikin-Ashi logic and adds interpretive layers like "Momentum," "Reversal," or "Chop" to describe what’s happening structurally. This is excellent for discretionary traders, those who use price rhythm or structure, and those seeking cleaner entry points in noisy environments. 🔹 Calculation Methods Wave is an oscillator-based model. It detects momentum swings and divergence between price and the smoothed oscillator. Great for spotting early reversals, pullback continuations, or cyclical rhythm patterns. In Impulse mode, it shows histogram shifts that reflect internal thrust dynamics. Thrust quantifies directional pressure by comparing the signal’s distance from both the midpoint of price range and an SMA. This method is powerful when you want to assess how much true force is behind a move — especially useful during breakout scenarios or strong directional pushes. Z-Score mode normalizes the signal to its statistical distance from the mean. This makes it ideal for mean reversion strategies or situations where price has deviated too far from historical behavior. Traders can look for exhaustion zones or pullback opportunities with greater confidence. Normalized rescales the signal within a 0–1 range (centered at 0.5), helping traders understand where the price sits within its own context — whether it’s relatively extended or compressed. It’s great for range traders, flat market identification, or mapping gradual bias accumulation. _______________________ Each mode and method has been thoughtfully designed to align with different strategy frameworks — and switching between them completely reconfigures the way the system operates, giving traders unmatched flexibility across timeframes and asset classes _______________________ 🧭 Conclusion MA Thrust Processor isn’t just a tool - it’s a precision-calibrated thrust engine that gives market context form. It lets you define your logic, style, and MA behavior while delivering rich visual output and conviction-based strength insight. Whether you're reading momentum waves, modeling thrust deviation, or interpreting candle structure, MTP adapts to your strategy. 🚀 From short-term scalps to long-term rotations, MTP delivers signal clarity with the quantitative conviction needed in modern markets. 📌 Trade with Statistical Precision | Powered by QuantEdgeB 🔹 Disclaimer: Past performance is not indicative of future results. 🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Pine Script®指標
由QuantEdgeB提供