OPEN-SOURCE SCRIPT
Supertrend Laboratory
Supertrend Laboratory (ST-Lab)
A Multi-Algorithm Trend & Regime Analysis Engine
🎓 THEORETICAL FOUNDATION
The Supertrend Laboratory (ST-Lab) is not a single indicator; it is a comprehensive research and analysis environment built into a single tool. It was designed to solve a fundamental problem in trend trading: no single algorithm is perfect for all markets or all conditions. ST-Lab addresses this by providing an arsenal of 28 distinct Supertrend calculation engines, ranging from classic statistical methods to proprietary, advanced mathematical models.
The core philosophy is to empower the trader to become a researcher, allowing them to experiment, test, and discover the optimal trend-following algorithm for their specific instrument, timeframe, and market regime.
Architectural Pillars
Multi-Algorithm Core: At its heart is the ability to switch between 28 unique mathematical approaches for calculating the Supertrend's baseline and bands. This includes methods based on fractal geometry, information theory, state-space estimation (Kalman), and digital signal processing (DSP).
Advanced Noise Filtering: A sophisticated filtering module can be applied to either the price source or the final Supertrend line. It includes 13 different filter types, from classic MAs to proprietary adaptive models like Spectral Laguerre and Vortex Core, allowing for precise control over the smoothness-vs-lag trade-off.
Chaos & Regime Detection: Acknowledging that not all trends are equal, ST-Lab incorporates a powerful regime filter. Using metrics from chaos theory like the Hurst Exponent, Permutation Entropy, and the Lyapunov Exponent, it can quantify market predictability. When enabled, this system can block signals during periods of high chaos or randomness, focusing only on high-quality trending environments.
Integrated Performance Analysis: A full-featured backtesting and performance tracking dashboard is built directly into the indicator. This allows for immediate, objective feedback on how any combination of algorithm, filter, and settings performs, tracking metrics like Win Rate, Profit Factor, and Max Drawdown.
🔬 THE ALGORITHM COMPENDIUM
ST-Lab includes 28 distinct engines. Each calculates the Supertrend's baseline and/or band width using a different mathematical approach. Understanding their nature is key to selecting the right tool for the job.
Statistical & Adaptive Algos (1-14)
Gaussian Fractal Filter: A smooth, adaptive engine. It uses a SuperSmoother filter for the baseline and adjusts its bands based on the market's fractal dimension. Best for balancing smoothness and responsiveness.
Shannon Entropy Adaptive: Measures market information content or "surprise." In high-entropy (random, choppy) markets, bands widen to avoid noise. In low-entropy (trending, predictable) markets, bands tighten. Excellent for assets that switch between clean trends and messy consolidation.
Hilbert Cycle Adaptive: Employs a Hilbert Transform to find the dominant cycle period in the price data. It then phase-locks the baseline filter to this cycle, creating a highly responsive trendline for cyclical assets. Ideal for commodities and forex pairs with known cyclical behavior.
Volume-Imbalance Flow: A volume-centric model. The baseline adapts based on smoothed volume delta and money flow, while the bands react to volume-at-price imbalances. Powerful in markets where volume is a key leading indicator.
Kalman State Filter: A state-space model that treats the true trend as a hidden state to be estimated. It produces an exceptionally smooth and predictive baseline that filters out measurement noise. A top-tier choice for noisy instruments.
Wavelet Decomposition: Deconstructs price into multiple layers (approximations and details) to separate the underlying trend from high-frequency noise. The baseline is a reconstruction of the most significant layers. Useful for multi-layered analysis of market movement.
Kalman Position–Velocity: An advanced Kalman filter that models price not just as a position, but also its rate of change (velocity). The baseline is a prediction of the next bar's position. Offers a predictive quality for momentum analysis.
Adaptive Recursive Filter: A simplified machine-learning concept where the filter's weightings adapt based on its own recent error. It learns to track price more closely over time. An interesting model for exploring self-adapting systems.
Entropy-Weighted KAMA: A hybrid model that combines the efficiency ratio of a KAMA with the information entropy of the price stream, creating a doubly adaptive moving average. A sophisticated choice for highly variable markets.
Nadaraya–Watson Regression: A non-parametric kernel regression method. It calculates the baseline by taking a weighted average of past prices, where weights are determined by a Gaussian kernel. It's mathematically intensive and produces a smooth, regressive trendline.
Bayesian Information Filter: An alternative to the Kalman filter that operates on information content (inverse of variance). It provides a robust, statistically-grounded trend estimate.
Garman–Klass Realized Volatility: Uses the advanced G-K formula to calculate historical volatility for the bands, offering a more statistically complete volatility measure than simple ATR.
Efficiency Ratio Adaptive: The classic approach of using the Chande Efficiency Ratio to adapt the ATR multiplier. Bands widen in inefficient (choppy) markets and tighten in efficient (trending) markets.
Low-Lag Filtered: A modular engine that allows you to apply separate advanced filters (SuperSmoother, Kalman, AlphaBeta) to both the price source and the ATR calculation for maximum lag reduction.
DAFE Proprietary Architectures (15-28)
Dual Engine Gate: Runs two Supertrends (fast and slow) in parallel. It uses a consensus/disagreement logic to dynamically boost the multiplier during conflicting signals, effectively filtering whipsaws.
Path Curvature Flow: Measures the geometric curvature of the smoothed price path. Bands widen dramatically during sharp turns (high curvature) and tighten on straight, linear trends. Geometrically intuitive.
Acceleration Regime: A physics-based model that calculates the acceleration of price. Bands expand during periods of high acceleration (breakouts) and contract during constant velocity (stable trends).
Multi-Timeframe Coherence: Calculates the trend direction on the current chart and two higher timeframes. The bands adapt based on the degree of "coherence" or agreement across all three TFs. Bands tighten during full alignment.
Zero-Lag Ensemble: A consensus-based engine that uses three different zero-lag MAs (ZLEMA, Hull, Kalman). A trend is confirmed only when a user-defined number of them agree, providing a high-confidence baseline.
Jurik Velocity Adaptive: Employs the legendary JMA for its ultra-smooth, low-lag baseline, with band adaptation based on price velocity.
Cyber Regime Filter: Uses a DSP approach to determine if the market is in a "trend mode" or "cycle mode" and adjusts the baseline and bands accordingly.
VIDYA Momentum Gate: Uses a Volatility-Adjusted Dynamic Moving Average (VIDYA) where the smoothing is gated by the Efficiency Ratio, preventing signals in low-momentum chop.
Ultimate Consensus Engine: The most powerful "meta-algorithm." It runs the top N selected algorithms in the background, weighs their performance in real-time, and generates a final baseline from their weighted consensus. The best starting point for analysis.
Recursive Zero-Lag Engine: A pure error-correction model. It applies multiple orders of zero-lag compensation to an EMA, with each order correcting the lag of the previous one. This creates an extremely responsive, "over-corrected" trendline. For traders who prioritize speed above all else.
Phase-Locked Zero-Lag: Uses a Hilbert Transform to find the dominant cycle and subtracts exactly one quarter-cycle of lag. This is the theoretical optimal for lag removal in cyclical markets, creating a zero-phase filter that doesn't smear peaks or troughs.
Predictive Deviation Engine: A forward-looking model. It uses linear regression to forecast the next bar's price and centers the Supertrend on this predicted path. The bands adapt based on the model's predictive accuracy. Aims to be one step ahead of the market.
Zero-Lag Fractal Resonance: A hybrid engine combining a ZLEMA with fractal dimension. The bands "resonate" with the market's structure, shrinking dramatically in clean trends (D ≈ 1.0) and expanding to filter out pure noise (D ≈ 2.0).
Quantum Momentum Zero-Lag: A dual-ZLEMA system (fast and slow) that creates a momentum differential. A signal can only fire if the momentum is strong enough to pass through a dynamic "quantum gate," filtering out weak or indecisive moves. Includes an optional squeeze filter.
🌪️ THE NOISE & CHAOS FILTERING ENGINE
You can apply one of 13 filters to the Source price, the final Supertrend Line, or Both.
DAFE Proprietary Filters: Spectral Laguerre (adaptive step-response), Hybrid Adaptive (Kalman/Wilder fusion), and Vortex Core (fluid dynamics vorticity filter) offer unique and powerful smoothing characteristics.
Chaos Theory Filters: Hurst Exponent, Permutation Entropy, and Lyapunov Exponent are adaptive filters that automatically adjust their smoothing length based on market predictability. They become slower in choppy markets and faster in trending markets.
Classic DSP & Low-Lag Filters: Includes industry standards like the Hull MA (fast), ZLEMA (zero-lag), Adaptive KAMA, SuperSmoother (excellent smoothing), McGinley Dynamic (market-speed adaptive), and T3 (composite smoothed).
Chaos Regime Filter
When enabled, this system blocks all new buy/sell signals if the market is deemed too chaotic or unpredictable, based on thresholds you set for the Hurst, Permutation Entropy, and Lyapunov exponents. This is a powerful tool to enforce discipline and avoid trading during periods of pure randomness.
📊 PERFORMANCE TRACKING & DASHBOARD
When enabled, the built-in backtester simulates trades based on the selected algorithm and settings.
Customizable Strategy: Define your starting capital, position size, and exit strategy. The stop-loss can dynamically trail the Supertrend line or be a fixed ATR multiple/percentage. Take-profits can be set based on Risk:Reward, ATR, or percentage.
Comprehensive Dashboard: The on-chart dashboard provides a complete overview:
Algorithm & Filter: Confirms your selected configuration.
Trend Status: Displays the current trend direction.
Regime Analysis: Shows the real-time values for Hurst, Entropy, and Lyapunov, and the overall "Regime Quality" score.
Performance Stats: Displays key metrics like Total Trades, Win Rate %, Profit Factor, Net P&L, and Max Drawdown %.
🚀 PRACTICAL APPLICATION & OPTIMIZATION GUIDE
The power of ST-Lab lies in its flexibility, but this can be daunting. Follow this workflow for best results.
Step 1: Choose Your Battleground (Algorithm Selection)
Start Here: Begin with the Ultimate Consensus Engine or Gaussian Fractal Filter. They are robust generalists.
Observe Your Asset: Does your market have clean, long trends? Try a classic like Kalman State Filter. Is it highly cyclical? Use Hilbert Cycle Adaptive. Is it extremely fast and noisy? Experiment with the Recursive Zero-Lag or Phase-Locked engines.
The Goal: Find an algorithm whose baseline "fits" the personality of your market.
Step 2: Tame the Noise (Filter Selection)
Once you have an algorithm you like, apply a filter to the Supertrend Line.
For Speed: Use Hull MA or ZLEMA.
For Smoothness: Use SuperSmoother or DAFE Spectral Laguerre.
For Adaptability: Use Hurst Exponent or one of the other chaos-based filters.
Step 3: Avoid the Void (Chaos Filter)
If you are still getting whipsawed in directionless chop, enable the Block Signals in Chaos feature.
Tuning: Adjust the thresholds one by one. Lower the Hurst Threshold or raise the PE Threshold to make the filter more aggressive in blocking signals.
Step 4: Measure What Matters (Performance Tracking)
Enable the Performance Dashboard. Set the Stop Loss and Take Profit parameters to match your personal trading style.
Iterate: Make small adjustments to the algorithm, filter, and multiplier, and observe the impact on the Win Rate and Profit Factor. Let the data guide your optimization.
⚖️ RESPONSIBLE USAGE & LIMITATIONS
It's a Laboratory, Not a Holy Grail: The purpose of this tool is research and optimization. No single setting will be perfect forever. Continuous evaluation is required.
Curve-Fitting Risk: With so many parameters, it is possible to over-optimize the settings for past performance. Always validate your chosen settings on out-of-sample data.
Past Performance is Not Indicative of Future Results: The backtester is a guide, not a guarantee. Real-world trading involves slippage, commissions, and psychological factors not present in the simulation.
🔮 CONCLUSION
The Supertrend Laboratory is a definitive toolkit for the serious student of trend analysis. It provides an unparalleled collection of mathematical engines, filters, and analytical tools, transforming the chart into a dynamic research environment. By allowing you to dissect, compare, and quantify the performance of dozens of trend-following methodologies, ST-Lab empowers you to move beyond generic indicators and engineer a trend analysis system that is precisely calibrated to your market and your strategy.
This is the tool for those who aren't just looking for signals, but for a deeper understanding of the trend itself.
— Dskyz, Trade with insight. Trade with anticipation. (Again and again)
A Multi-Algorithm Trend & Regime Analysis Engine
🎓 THEORETICAL FOUNDATION
The Supertrend Laboratory (ST-Lab) is not a single indicator; it is a comprehensive research and analysis environment built into a single tool. It was designed to solve a fundamental problem in trend trading: no single algorithm is perfect for all markets or all conditions. ST-Lab addresses this by providing an arsenal of 28 distinct Supertrend calculation engines, ranging from classic statistical methods to proprietary, advanced mathematical models.
The core philosophy is to empower the trader to become a researcher, allowing them to experiment, test, and discover the optimal trend-following algorithm for their specific instrument, timeframe, and market regime.
Architectural Pillars
Multi-Algorithm Core: At its heart is the ability to switch between 28 unique mathematical approaches for calculating the Supertrend's baseline and bands. This includes methods based on fractal geometry, information theory, state-space estimation (Kalman), and digital signal processing (DSP).
Advanced Noise Filtering: A sophisticated filtering module can be applied to either the price source or the final Supertrend line. It includes 13 different filter types, from classic MAs to proprietary adaptive models like Spectral Laguerre and Vortex Core, allowing for precise control over the smoothness-vs-lag trade-off.
Chaos & Regime Detection: Acknowledging that not all trends are equal, ST-Lab incorporates a powerful regime filter. Using metrics from chaos theory like the Hurst Exponent, Permutation Entropy, and the Lyapunov Exponent, it can quantify market predictability. When enabled, this system can block signals during periods of high chaos or randomness, focusing only on high-quality trending environments.
Integrated Performance Analysis: A full-featured backtesting and performance tracking dashboard is built directly into the indicator. This allows for immediate, objective feedback on how any combination of algorithm, filter, and settings performs, tracking metrics like Win Rate, Profit Factor, and Max Drawdown.
🔬 THE ALGORITHM COMPENDIUM
ST-Lab includes 28 distinct engines. Each calculates the Supertrend's baseline and/or band width using a different mathematical approach. Understanding their nature is key to selecting the right tool for the job.
Statistical & Adaptive Algos (1-14)
Gaussian Fractal Filter: A smooth, adaptive engine. It uses a SuperSmoother filter for the baseline and adjusts its bands based on the market's fractal dimension. Best for balancing smoothness and responsiveness.
Shannon Entropy Adaptive: Measures market information content or "surprise." In high-entropy (random, choppy) markets, bands widen to avoid noise. In low-entropy (trending, predictable) markets, bands tighten. Excellent for assets that switch between clean trends and messy consolidation.
Hilbert Cycle Adaptive: Employs a Hilbert Transform to find the dominant cycle period in the price data. It then phase-locks the baseline filter to this cycle, creating a highly responsive trendline for cyclical assets. Ideal for commodities and forex pairs with known cyclical behavior.
Volume-Imbalance Flow: A volume-centric model. The baseline adapts based on smoothed volume delta and money flow, while the bands react to volume-at-price imbalances. Powerful in markets where volume is a key leading indicator.
Kalman State Filter: A state-space model that treats the true trend as a hidden state to be estimated. It produces an exceptionally smooth and predictive baseline that filters out measurement noise. A top-tier choice for noisy instruments.
Wavelet Decomposition: Deconstructs price into multiple layers (approximations and details) to separate the underlying trend from high-frequency noise. The baseline is a reconstruction of the most significant layers. Useful for multi-layered analysis of market movement.
Kalman Position–Velocity: An advanced Kalman filter that models price not just as a position, but also its rate of change (velocity). The baseline is a prediction of the next bar's position. Offers a predictive quality for momentum analysis.
Adaptive Recursive Filter: A simplified machine-learning concept where the filter's weightings adapt based on its own recent error. It learns to track price more closely over time. An interesting model for exploring self-adapting systems.
Entropy-Weighted KAMA: A hybrid model that combines the efficiency ratio of a KAMA with the information entropy of the price stream, creating a doubly adaptive moving average. A sophisticated choice for highly variable markets.
Nadaraya–Watson Regression: A non-parametric kernel regression method. It calculates the baseline by taking a weighted average of past prices, where weights are determined by a Gaussian kernel. It's mathematically intensive and produces a smooth, regressive trendline.
Bayesian Information Filter: An alternative to the Kalman filter that operates on information content (inverse of variance). It provides a robust, statistically-grounded trend estimate.
Garman–Klass Realized Volatility: Uses the advanced G-K formula to calculate historical volatility for the bands, offering a more statistically complete volatility measure than simple ATR.
Efficiency Ratio Adaptive: The classic approach of using the Chande Efficiency Ratio to adapt the ATR multiplier. Bands widen in inefficient (choppy) markets and tighten in efficient (trending) markets.
Low-Lag Filtered: A modular engine that allows you to apply separate advanced filters (SuperSmoother, Kalman, AlphaBeta) to both the price source and the ATR calculation for maximum lag reduction.
DAFE Proprietary Architectures (15-28)
Dual Engine Gate: Runs two Supertrends (fast and slow) in parallel. It uses a consensus/disagreement logic to dynamically boost the multiplier during conflicting signals, effectively filtering whipsaws.
Path Curvature Flow: Measures the geometric curvature of the smoothed price path. Bands widen dramatically during sharp turns (high curvature) and tighten on straight, linear trends. Geometrically intuitive.
Acceleration Regime: A physics-based model that calculates the acceleration of price. Bands expand during periods of high acceleration (breakouts) and contract during constant velocity (stable trends).
Multi-Timeframe Coherence: Calculates the trend direction on the current chart and two higher timeframes. The bands adapt based on the degree of "coherence" or agreement across all three TFs. Bands tighten during full alignment.
Zero-Lag Ensemble: A consensus-based engine that uses three different zero-lag MAs (ZLEMA, Hull, Kalman). A trend is confirmed only when a user-defined number of them agree, providing a high-confidence baseline.
Jurik Velocity Adaptive: Employs the legendary JMA for its ultra-smooth, low-lag baseline, with band adaptation based on price velocity.
Cyber Regime Filter: Uses a DSP approach to determine if the market is in a "trend mode" or "cycle mode" and adjusts the baseline and bands accordingly.
VIDYA Momentum Gate: Uses a Volatility-Adjusted Dynamic Moving Average (VIDYA) where the smoothing is gated by the Efficiency Ratio, preventing signals in low-momentum chop.
Ultimate Consensus Engine: The most powerful "meta-algorithm." It runs the top N selected algorithms in the background, weighs their performance in real-time, and generates a final baseline from their weighted consensus. The best starting point for analysis.
Recursive Zero-Lag Engine: A pure error-correction model. It applies multiple orders of zero-lag compensation to an EMA, with each order correcting the lag of the previous one. This creates an extremely responsive, "over-corrected" trendline. For traders who prioritize speed above all else.
Phase-Locked Zero-Lag: Uses a Hilbert Transform to find the dominant cycle and subtracts exactly one quarter-cycle of lag. This is the theoretical optimal for lag removal in cyclical markets, creating a zero-phase filter that doesn't smear peaks or troughs.
Predictive Deviation Engine: A forward-looking model. It uses linear regression to forecast the next bar's price and centers the Supertrend on this predicted path. The bands adapt based on the model's predictive accuracy. Aims to be one step ahead of the market.
Zero-Lag Fractal Resonance: A hybrid engine combining a ZLEMA with fractal dimension. The bands "resonate" with the market's structure, shrinking dramatically in clean trends (D ≈ 1.0) and expanding to filter out pure noise (D ≈ 2.0).
Quantum Momentum Zero-Lag: A dual-ZLEMA system (fast and slow) that creates a momentum differential. A signal can only fire if the momentum is strong enough to pass through a dynamic "quantum gate," filtering out weak or indecisive moves. Includes an optional squeeze filter.
🌪️ THE NOISE & CHAOS FILTERING ENGINE
You can apply one of 13 filters to the Source price, the final Supertrend Line, or Both.
DAFE Proprietary Filters: Spectral Laguerre (adaptive step-response), Hybrid Adaptive (Kalman/Wilder fusion), and Vortex Core (fluid dynamics vorticity filter) offer unique and powerful smoothing characteristics.
Chaos Theory Filters: Hurst Exponent, Permutation Entropy, and Lyapunov Exponent are adaptive filters that automatically adjust their smoothing length based on market predictability. They become slower in choppy markets and faster in trending markets.
Classic DSP & Low-Lag Filters: Includes industry standards like the Hull MA (fast), ZLEMA (zero-lag), Adaptive KAMA, SuperSmoother (excellent smoothing), McGinley Dynamic (market-speed adaptive), and T3 (composite smoothed).
Chaos Regime Filter
When enabled, this system blocks all new buy/sell signals if the market is deemed too chaotic or unpredictable, based on thresholds you set for the Hurst, Permutation Entropy, and Lyapunov exponents. This is a powerful tool to enforce discipline and avoid trading during periods of pure randomness.
📊 PERFORMANCE TRACKING & DASHBOARD
When enabled, the built-in backtester simulates trades based on the selected algorithm and settings.
Customizable Strategy: Define your starting capital, position size, and exit strategy. The stop-loss can dynamically trail the Supertrend line or be a fixed ATR multiple/percentage. Take-profits can be set based on Risk:Reward, ATR, or percentage.
Comprehensive Dashboard: The on-chart dashboard provides a complete overview:
Algorithm & Filter: Confirms your selected configuration.
Trend Status: Displays the current trend direction.
Regime Analysis: Shows the real-time values for Hurst, Entropy, and Lyapunov, and the overall "Regime Quality" score.
Performance Stats: Displays key metrics like Total Trades, Win Rate %, Profit Factor, Net P&L, and Max Drawdown %.
🚀 PRACTICAL APPLICATION & OPTIMIZATION GUIDE
The power of ST-Lab lies in its flexibility, but this can be daunting. Follow this workflow for best results.
Step 1: Choose Your Battleground (Algorithm Selection)
Start Here: Begin with the Ultimate Consensus Engine or Gaussian Fractal Filter. They are robust generalists.
Observe Your Asset: Does your market have clean, long trends? Try a classic like Kalman State Filter. Is it highly cyclical? Use Hilbert Cycle Adaptive. Is it extremely fast and noisy? Experiment with the Recursive Zero-Lag or Phase-Locked engines.
The Goal: Find an algorithm whose baseline "fits" the personality of your market.
Step 2: Tame the Noise (Filter Selection)
Once you have an algorithm you like, apply a filter to the Supertrend Line.
For Speed: Use Hull MA or ZLEMA.
For Smoothness: Use SuperSmoother or DAFE Spectral Laguerre.
For Adaptability: Use Hurst Exponent or one of the other chaos-based filters.
Step 3: Avoid the Void (Chaos Filter)
If you are still getting whipsawed in directionless chop, enable the Block Signals in Chaos feature.
Tuning: Adjust the thresholds one by one. Lower the Hurst Threshold or raise the PE Threshold to make the filter more aggressive in blocking signals.
Step 4: Measure What Matters (Performance Tracking)
Enable the Performance Dashboard. Set the Stop Loss and Take Profit parameters to match your personal trading style.
Iterate: Make small adjustments to the algorithm, filter, and multiplier, and observe the impact on the Win Rate and Profit Factor. Let the data guide your optimization.
⚖️ RESPONSIBLE USAGE & LIMITATIONS
It's a Laboratory, Not a Holy Grail: The purpose of this tool is research and optimization. No single setting will be perfect forever. Continuous evaluation is required.
Curve-Fitting Risk: With so many parameters, it is possible to over-optimize the settings for past performance. Always validate your chosen settings on out-of-sample data.
Past Performance is Not Indicative of Future Results: The backtester is a guide, not a guarantee. Real-world trading involves slippage, commissions, and psychological factors not present in the simulation.
🔮 CONCLUSION
The Supertrend Laboratory is a definitive toolkit for the serious student of trend analysis. It provides an unparalleled collection of mathematical engines, filters, and analytical tools, transforming the chart into a dynamic research environment. By allowing you to dissect, compare, and quantify the performance of dozens of trend-following methodologies, ST-Lab empowers you to move beyond generic indicators and engineer a trend analysis system that is precisely calibrated to your market and your strategy.
This is the tool for those who aren't just looking for signals, but for a deeper understanding of the trend itself.
— Dskyz, Trade with insight. Trade with anticipation. (Again and again)
סקריפט קוד פתוח
ברוח האמיתית של TradingView, יוצר הסקריפט הזה הפך אותו לקוד פתוח, כך שסוחרים יוכלו לעיין בו ולאמת את פעולתו. כל הכבוד למחבר! אמנם ניתן להשתמש בו בחינם, אך זכור כי פרסום חוזר של הקוד כפוף ל־כללי הבית שלנו.
כתב ויתור
המידע והפרסומים אינם מיועדים להיות, ואינם מהווים, ייעוץ או המלצה פיננסית, השקעתית, מסחרית או מכל סוג אחר המסופקת או מאושרת על ידי TradingView. קרא עוד ב־תנאי השימוש.
סקריפט קוד פתוח
ברוח האמיתית של TradingView, יוצר הסקריפט הזה הפך אותו לקוד פתוח, כך שסוחרים יוכלו לעיין בו ולאמת את פעולתו. כל הכבוד למחבר! אמנם ניתן להשתמש בו בחינם, אך זכור כי פרסום חוזר של הקוד כפוף ל־כללי הבית שלנו.
כתב ויתור
המידע והפרסומים אינם מיועדים להיות, ואינם מהווים, ייעוץ או המלצה פיננסית, השקעתית, מסחרית או מכל סוג אחר המסופקת או מאושרת על ידי TradingView. קרא עוד ב־תנאי השימוש.