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Machine Learning: Lorentzian Classification█ OVERVIEW A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to predict the direction of future price movements when used as the distance metric for a novel implementation of an Approximate Nearest Neighbors (ANN) algorithm. █ BACKGROUND In physics, Lorentzian space is perhaps best known for its role in describing the curvature of space-time in Einstein's theory of General Relativity (2). Interestingly, however, this abstract concept from theoretical physics also has tangible real-world applications in trading. Recently, it was hypothesized that Lorentzian space was also well-suited for analyzing time-series data (4), (5). This hypothesis has been supported by several empirical studies that demonstrate that Lorentzian distance is more robust to outliers and noise than the more commonly used Euclidean distance (1), (3), (6). Furthermore, Lorentzian distance was also shown to outperform dozens of other highly regarded distance metrics, including Manhattan distance, Bhattacharyya similarity, and Cosine similarity (1), (3). Outside of Dynamic Time Warping based approaches, which are unfortunately too computationally intensive for PineScript at this time, the Lorentzian Distance metric consistently scores the highest mean accuracy over a wide variety of time series data sets (1). Euclidean distance is commonly used as the default distance metric for NN-based search algorithms, but it may not always be the best choice when dealing with financial market data. This is because financial market data can be significantly impacted by proximity to major world events such as FOMC Meetings and Black Swan events. This event-based distortion of market data can be framed as similar to the gravitational warping caused by a massive object on the space-time continuum. For financial markets, the analogous continuum that experiences warping can be referred to as "price-time". Below is a side-by-side comparison of how neighborhoods of similar historical points appear in three-dimensional Euclidean Space and Lorentzian Space: This figure demonstrates how Lorentzian space can better accommodate the warping of price-time since the Lorentzian distance function compresses the Euclidean neighborhood in such a way that the new neighborhood distribution in Lorentzian space tends to cluster around each of the major feature axes in addition to the origin itself. This means that, even though some nearest neighbors will be the same regardless of the distance metric used, Lorentzian space will also allow for the consideration of historical points that would otherwise never be considered with a Euclidean distance metric. Intuitively, the advantage inherent in the Lorentzian distance metric makes sense. For example, it is logical that the price action that occurs in the hours after Chairman Powell finishes delivering a speech would resemble at least some of the previous times when he finished delivering a speech. This may be true regardless of other factors, such as whether or not the market was overbought or oversold at the time or if the macro conditions were more bullish or bearish overall. These historical reference points are extremely valuable for predictive models, yet the Euclidean distance metric would miss these neighbors entirely, often in favor of irrelevant data points from the day before the event. By using Lorentzian distance as a metric, the ML model is instead able to consider the warping of price-time caused by the event and, ultimately, transcend the temporal bias imposed on it by the time series. For more information on the implementation details of the Approximate Nearest Neighbors (ANN) algorithm used in this indicator, please refer to the detailed comments in the source code. █ HOW TO USE Below is an explanatory breakdown of the different parts of this indicator as it appears in the interface: Below is an explanation of the different settings for this indicator: General Settings: Source - This has a default value of "hlc3" and is used to control the input data source. Neighbors Count - This has a default value of 8, a minimum value of 1, a maximum value of 100, and a step of 1. It is used to control the number of neighbors to consider. Max Bars Back - This has a default value of 2000. Feature Count - This has a default value of 5, a minimum value of 2, and a maximum value of 5. It controls the number of features to use for ML predictions. Color Compression - This has a default value of 1, a minimum value of 1, and a maximum value of 10. It is used to control the compression factor for adjusting the intensity of the color scale. Show Exits - This has a default value of false. It controls whether to show the exit threshold on the chart. Use Dynamic Exits - This has a default value of false. It is used to control whether to attempt to let profits ride by dynamically adjusting the exit threshold based on kernel regression. Feature Engineering Settings: Note: The Feature Engineering section is for fine-tuning the features used for ML predictions. The default values are optimized for the 4H to 12H timeframes for most charts, but they should also work reasonably well for other timeframes. By default, the model can support features that accept two parameters (Parameter A and Parameter B, respectively). Even though there are only 4 features provided by default, the same feature with different settings counts as two separate features. If the feature only accepts one parameter, then the second parameter will default to EMA-based smoothing with a default value of 1. These features represent the most effective combination I have encountered in my testing, but additional features may be added as additional options in the future. Feature 1 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX". Feature 2 - This has a default value of "WT" and options are: "RSI", "WT", "CCI", "ADX". Feature 3 - This has a default value of "CCI" and options are: "RSI", "WT", "CCI", "ADX". Feature 4 - This has a default value of "ADX" and options are: "RSI", "WT", "CCI", "ADX". Feature 5 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX". Filters Settings: Use Volatility Filter - This has a default value of true. It is used to control whether to use the volatility filter. Use Regime Filter - This has a default value of true. It is used to control whether to use the trend detection filter. Use ADX Filter - This has a default value of false. It is used to control whether to use the ADX filter. Regime Threshold - This has a default value of -0.1, a minimum value of -10, a maximum value of 10, and a step of 0.1. It is used to control the Regime Detection filter for detecting Trending/Ranging markets. ADX Threshold - This has a default value of 20, a minimum value of 0, a maximum value of 100, and a step of 1. It is used to control the threshold for detecting Trending/Ranging markets. Kernel Regression Settings: Trade with Kernel - This has a default value of true. It is used to control whether to trade with the kernel. Show Kernel Estimate - This has a default value of true. It is used to control whether to show the kernel estimate. Lookback Window - This has a default value of 8 and a minimum value of 3. It is used to control the number of bars used for the estimation. Recommended range: 3-50 Relative Weighting - This has a default value of 8 and a step size of 0.25. It is used to control the relative weighting of time frames. Recommended range: 0.25-25 Start Regression at Bar - This has a default value of 25. It is used to control the bar index on which to start regression. Recommended range: 0-25 Display Settings: Show Bar Colors - This has a default value of true. It is used to control whether to show the bar colors. Show Bar Prediction Values - This has a default value of true. It controls whether to show the ML model's evaluation of each bar as an integer. Use ATR Offset - This has a default value of false. It controls whether to use the ATR offset instead of the bar prediction offset. Bar Prediction Offset - This has a default value of 0 and a minimum value of 0. It is used to control the offset of the bar predictions as a percentage from the bar high or close. Backtesting Settings: Show Backtest Results - This has a default value of true. It is used to control whether to display the win rate of the given configuration. █ WORKS CITED (1) R. Giusti and G. E. A. P. A. Batista, "An Empirical Comparison of Dissimilarity Measures for Time Series Classification," 2013 Brazilian Conference on Intelligent Systems, Oct. 2013, DOI: 10.1109/bracis.2013.22. (2) Y. Kerimbekov, H. Ş. Bilge, and H. H. Uğurlu, "The use of Lorentzian distance metric in classification problems," Pattern Recognition Letters, vol. 84, 170–176, Dec. 2016, DOI: 10.1016/j.patrec.2016.09.006. (3) A. Bagnall, A. Bostrom, J. Large, and J. Lines, "The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms." ResearchGate, Feb. 04, 2016. (4) H. Ş. Bilge, Yerzhan Kerimbekov, and Hasan Hüseyin Uğurlu, "A new classification method by using Lorentzian distance metric," ResearchGate, Sep. 02, 2015. (5) Y. Kerimbekov and H. Şakir Bilge, "Lorentzian Distance Classifier for Multiple Features," Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017, DOI: 10.5220/0006197004930501. (6) V. Surya Prasath et al., "Effects of Distance Measure Choice on KNN Classifier Performance - A Review." . █ ACKNOWLEDGEMENTS @veryfid - For many invaluable insights, discussions, and advice that helped to shape this project. @capissimo - For open sourcing his interesting ideas regarding various KNN implementations in PineScript, several of which helped inspire my original undertaking of this project. @RikkiTavi - For many invaluable physics-related conversations and for his helping me develop a mechanism for visualizing various distance algorithms in 3D using JavaScript @jlaurel - For invaluable literature recommendations that helped me to understand the underlying subject matter of this project. @annutara - For help in beta-testing this indicator and for sharing many helpful ideas and insights early on in its development. @jasontaylor7 - For helping to beta-test this indicator and for many helpful conversations that helped to shape my backtesting workflow @meddymarkusvanhala - For helping to beta-test this indicator @dlbnext - For incredibly detailed backtesting testing of this indicator and for sharing numerous ideas on how the user experience could be improved.
Pine Script®指標
由jdehorty提供
789
Adaptive MA constructor [lastguru]Adaptive Moving Averages are nothing new, however most of them use EMA as their MA of choice once the preferred smoothing length is determined. I have decided to make an experiment and separate length generation from smoothing, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. This indicator is based on my previously published public libraries and also serve as a usage demonstration for them. I will try to expand the collection (suggestions are welcome), however it is not meant as an encyclopaedic resource, so you are encouraged to experiment yourself: by looking on the source code of this indicator, I am sure you will see how trivial it is to use the provided libraries and expand them with your own ideas and combinations. I give no recommendation on what settings to use, but if you find some useful setting, combination or application ideas (or bugs in my code), I would be happy to read about them in the comments section. The indicator works in three stages: Prefiltering, Length Adaptation and Moving Averages. Prefiltering is a fast smoothing to get rid of high-frequency (2, 3 or 4 bar) noise. Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly). Chande (Price) - based on Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length Chande (Volume) - a variant of Chande's algorithm, where volume is used instead of price VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow) VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast) Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA Deviation Scaling - based on DSSS by John F. Ehlers Median Average - based on Median Average Adaptive Filter by John F. Ehlers Fractal Adaptation - based on FRAMA by John F. Ehlers MESA MAMA Alpha - based on MESA Adaptive Moving Average by John F. Ehlers MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers, but unlike Alpha calculation, this adaptation estimates cycle period Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong). The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers. I do not know, which combination works best, so you can experiment. Chande's Adaptations also have 3 additional parameters: SD Length (lookback length of Standard deviation), Smooth (smoothing length of Standard deviation) and Power (exponent of the length adaptation - lower is smaller variation). These are internal tweaks for the calculation. Length Adaptaton section offer you a choice of Moving Average algorithms. Most of the Adaptations are originally used with EMA, so this is a good starting point for exploration. SMA - Simple Moving Average RMA - Running Moving Average EMA - Exponential Moving Average HMA - Hull Moving Average VWMA - Volume Weighted Moving Average 2-pole Super Smoother - 2-pole Super Smoother by John F. Ehlers 3-pole Super Smoother - 3-pole Super Smoother by John F. Ehlers Filt11 -a variant of 2-pole Super Smoother with error averaging for zero-lag response by John F. Ehlers Triangle Window - Triangle Window Filter by John F. Ehlers Hamming Window - Hamming Window Filter by John F. Ehlers Hann Window - Hann Window Filter by John F. Ehlers Lowpass - removes cyclic components shorter than length (Price - Highpass) DSSS - Derivation Scaled Super Smoother by John F. Ehlers There are two Moving Averages that are drown on the chart, so length for both needs to be selected. If no Adaptation is selected ( None option), you can set Fast Length and Slow Length directly. If an Adaptation is selected, then Cycle multiplier can be selected for Fast and Slow MA. More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Pine Script®指標
由lastguru提供
9
Exotic SMA Explorations Treasure TroveThis is my "Exotic SMA Explorations Treasure Trove" intended for educational purposes, yet these functions will also have utility in special applications with other algorithms. Firstly, the Pine built-in sma() is exceedingly more efficient computationally on TV servers than these functions will be. I just wanted to make that very crystal clear. My notes elaborate on this in the code blatantly. Anyhow, the simple moving average(SMA) is one of the most common averaging filters used in a wide variety of algorithms. "Simply put," it's name says a lot about it. The purpose of this script, is to demonstrate variations of it's calculation in a multitude of exotic forms. In certain scenarios our algorithms may require a specific mathemagical touch that is pertinent to our intended goals. Like screwdrivers, we often need different types depending on the objective we are trying to attain. The SMA also serves as the most basic of finite impulse response(FIR) algorithms. For example, things like weighted moving averages can be constructed by using the foundational code of SMA. One other intended demonstration of this script, is running multiple functions for comparison. I have had to use this from time to time for my own comparisons of performance. Also, imbedded into this code is a method to generically and recklessly in this case, adapt an algorithm. I will warn you, RSI was NEVER intended to adapt an algorithm. It only serves as a crude method to display the versatility of these different algorithms, whether it be a benefit or hinderance concerning dynamic adaptability. Lastly, this script shows the versatility of TV's NEW additions input(group=) and input(inline=) upgrades in action. The "Immense Power of Pine" is always evolving and will continue to do so, I assure you of that. We can now categorize our input()s without using the input(type=input.bool) hackTrick. Although, that still will have it's enduring versatility, at least for myself. NOTICE: You have absolute freedom to use this source code any way you see fit within your new Pine projects. You don't have to ask for my permission to reuse these functions in your published scripts, simply because I have better things to do than answer requests for the reuse of these functions. Sufficient accreditation regarding this script and compliance with "TV's House Rules" regarding code reuse, is as easy as copying the functions in their entirety as is. Fair enough? Good! When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members, I may implement more ideas when they present themselves as worthy additions. Have a profitable future everyone!
Pine Script®指標
由ImmortalFreedom提供
8
SMC - Institutional Confidence Oscillator [PhenLabs]📊 Institutional Confidence Oscillator Version: PineScript™v6 📌 Description The Institutional Confidence Oscillator (ICO) revolutionizes market analysis by automatically detecting and evaluating institutional activity at key support and resistance levels using our own in-house detection system. This sophisticated indicator combines volume analysis, volatility measurements, and mathematical confidence algorithms to provide real-time readings of institutional sentiment and zone strength. Using our advanced thin liquidity detection, the ICO identifies high-volume, narrow-range bars that signal institutional zone formation, then tracks how these zones perform under market pressure. The result is a dual-wave confidence oscillator that shows traders when institutions are actively defending price levels versus when they’re abandoning positions. The indicator transforms complex institutional behavior patterns into clear, actionable confidence percentiles, helping traders align with smart money movements and avoid common retail trading pitfalls. 🚀 Points of Innovation Automated thin liquidity zone detection using volume threshold multipliers and zone size filtering Dual-sided confidence tracking for both support and resistance levels simultaneously Sigmoid function processing for enhanced mathematical accuracy in confidence calculations Real-time institutional defense pattern analysis through complete test cycles Advanced visual smoothing options with multiple algorithmic methods (EMA, SMA, WMA, ALMA) Integrated momentum indicators and gradient visualization for enhanced signal clarity 🔧 Core Components Volume Threshold System: Analyzes volume ratios against baseline averages to identify institutional activity spikes Zone Detection Algorithm: Automatically identifies thin liquidity zones based on customizable volume and size parameters Confidence Lifecycle Engine: Tracks institutional defense patterns through complete observation windows Mathematical Processing Core: Uses sigmoid functions to convert raw market data into normalized confidence percentiles Visual Enhancement Suite: Provides multiple smoothing methods and customizable display options for optimal chart interpretation 🔥 Key Features Auto-Detection Technology: Automatically scans for institutional zones without manual intervention, saving analysis time Dual Confidence Tracking: Simultaneously monitors both support and resistance institutional activity for comprehensive market view Smart Zone Validation: Evaluates zone strength through volume analysis, adverse excursion measurement, and defense success rates Customizable Parameters: Extensive input options for volume thresholds, observation windows, and visual preferences Real-Time Updates: Continuously processes market data to provide current institutional confidence readings Enhanced Visualization: Features gradient fills, momentum indicators, and information panels for clear signal interpretation 🎨 Visualization Dual Oscillator Lines: Support confidence (cyan) and resistance confidence (red) plotted as percentage values 0-100% Gradient Fill Areas: Color-coded regions showing confidence dominance and strength levels Reference Grid Lines: Horizontal markers at 25%, 50%, and 75% levels for easy interpretation Information Panel: Real-time display of current confidence percentiles with color-coded dominance indicators Momentum Indicators: Rate of change visualization for confidence trends Background Highlights: Extreme confidence level alerts when readings exceed 80% 📖 Usage Guidelines Auto-Detection Settings Use Auto-Detection Default: true Description: Enables automatic thin liquidity zone identification based on volume and size criteria Volume Threshold Multiplier Default: 6.0, Range: 1.0+ Description: Controls sensitivity of volume spike detection for zone identification, higher values require more significant volume increases Volume MA Length Default: 15, Range: 1+ Description: Period for volume moving average baseline calculation, affects volume spike sensitivity Max Zone Height % Default: 0.5%, Range: 0.05%+ Description: Filters out wide price bars, keeping only thin liquidity zones as percentage of current price Confidence Logic Settings Test Observation Window Default: 20 bars, Range: 2+ Description: Number of bars to monitor zone tests for confidence calculation, longer windows provide more stable readings Clean Break Threshold Default: 1.5 ATR, Range: 0.1+ Description: ATR multiple required for zone invalidation, higher values make zones more persistent Visual Settings Smoothing Method Default: EMA, Options: SMA/EMA/WMA/ALMA Description: Algorithm for signal smoothing, EMA responds faster while SMA provides more stability Smoothing Length Default: 5, Range: 1-50 Description: Period for smoothing calculation, higher values create smoother lines with more lag ✅ Best Use Cases Trending market analysis where institutional zones provide reliable support/resistance levels Breakout confirmation by validating zone strength before position entry Divergence analysis when confidence shifts between support and resistance levels Risk management through identification of high-confidence institutional backing Market structure analysis for understanding institutional sentiment changes ⚠️ Limitations Performs best in liquid markets with clear institutional participation May produce false signals during low-volume or holiday trading periods Requires sufficient price history for accurate confidence calculations Confidence readings can fluctuate rapidly during high-impact news events Manual fallback zones may not reflect actual institutional activity 💡 What Makes This Unique Automated Detection: First Pine Script indicator to automatically identify thin liquidity zones using sophisticated volume analysis Dual-Sided Analysis: Simultaneously tracks institutional confidence for both support and resistance levels Mathematical Precision: Uses sigmoid functions for enhanced accuracy in confidence percentage calculations Real-Time Processing: Continuously evaluates institutional defense patterns as market conditions change Visual Innovation: Advanced smoothing options and gradient visualization for superior chart clarity 🔬 How It Works 1. Zone Identification Process: Scans for high-volume bars that exceed the volume threshold multiplier Filters bars by maximum zone height percentage to identify thin liquidity conditions Stores qualified zones with proximity threshold filtering for relevance 2. Confidence Calculation Process: Monitors price interaction with identified zones during observation windows Measures volume ratios and adverse excursions during zone tests Applies sigmoid function processing to normalize raw data into confidence percentiles 3. Real-Time Analysis Process: Continuously updates confidence readings as new market data becomes available Tracks institutional defense success rates and zone validation patterns Provides visual and numerical feedback through the oscillator display 💡 Note: The ICO works best when combined with traditional technical analysis and proper risk management. Higher confidence readings indicate stronger institutional backing but should be confirmed with price action and volume analysis. Consider using multiple timeframes for comprehensive market structure understanding.
Pine Script®指標
由PhenLabs提供
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics Theoretical Foundation: The Mathematical Revolution Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics. Category Theory in Markets Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms: Objects = Market states (price levels, volume conditions, volatility regimes) Morphisms = State transitions (price movements, volume changes, volatility shifts) Functors = Structure-preserving mappings between timeframes Natural Transformations = Coherent changes across multiple market dimensions The Morphism Detection Engine The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions: Morphism Strength = exp(-normalized_change × (3.0 / sensitivity)) Threshold = 0.3 - (sensitivity - 1.0) × 0.15 This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity. Functorial Analysis Framework Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws: Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)| Functorial Integrity = max(0, 1.0 - average_error) When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system. Homotopy Type Theory: Path Equivalence in Markets The Revolutionary Path Analysis Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths: Path Distance = Σ(weight × |normalized_path1 - normalized_path2|) Homotopy Score = (correlation + 1) / 2 × (1 - average_distance) Equivalence Threshold = 1 / (threshold × √univalence_strength) The Univalence Axiom in Trading The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals. Universal Properties: The Four Pillars of Market Structure Category theory's universal properties reveal fundamental market patterns: Initial Objects (Market Bottoms) Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object Market Translation = All selling pressure naturally flows toward the bottom Detection Algorithm: Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1) Signal = strength > 0.4 AND morphism_exists Terminal Objects (Market Tops) Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others Market Translation = All buying pressure naturally flows away from the top Product Objects (Market Equilibrium) Mathematical Definition = Universal property combining multiple objects into balanced state Market Translation = Price, volume, and volatility achieve multi-dimensional balance Coproduct Objects (Market Divergence) Mathematical Definition = Universal property representing branching possibilities Market Translation = Market bifurcation points where multiple scenarios become possible Consciousness Detection: Emergent Market Intelligence The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations: Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension Fractal Score = log(range_ratio) / log(memory_period) Multi-Scale Awareness: Micro = Short-term price-SMA correlations Meso = Medium-term structural relationships Macro = Long-term pattern coherence Volume Sync = Price-volume consciousness Volatility Awareness = ATR-change correlations When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses. Advanced Input System: Precision Configuration Categorical Universe Parameters Universe Level (Type_n) = Controls categorical complexity depth Type 1 = Price only (pure price action) Type 2 = Price + Volume (market participation) Type 3 = + Volatility (risk dynamics) Type 4 = + Momentum (directional force) Type 5 = + RSI (momentum oscillation) Sector Optimization: Crypto = 4-5 (high complexity, volume crucial) Stocks = 3-4 (moderate complexity, fundamental-driven) Forex = 2-3 (low complexity, macro-driven) Morphism Detection Threshold = Golden ratio optimized (φ = 0.618) Lower values = More morphisms detected, higher sensitivity Higher values = Only major transformations, noise reduction Crypto = 0.382-0.618 (high volatility accommodation) Stocks = 0.618-1.0 (balanced detection) Forex = 1.0-1.618 (macro-focused) Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal) Controls = composition error tolerance Trending markets = 0.1-0.2 (strict structure preservation) Ranging markets = 0.2-0.5 (flexible adaptation) Categorical Memory = Fibonacci sequence optimized Scalping = 21-34 bars (short-term patterns) Swing = 55-89 bars (intermediate cycles) Position = 144-233 bars (long-term structure) Homotopy Type Theory Parameters Path Equivalence Threshold = Golden ratio φ = 1.618 Volatile markets = 2.0-2.618 (accommodate noise) Normal conditions = 1.618 (balanced) Stable markets = 0.786-1.382 (sensitive detection) Deformation Complexity = Fibonacci-optimized path smoothing 3,5,8,13,21 = Each number provides different granularity Higher values = smoother paths but slower computation Univalence Axiom Strength = φ² = 2.618 (golden ratio squared) Controls = how readily equivalent structures are identified Higher values = find more equivalences Visual System: Mathematical Elegance Meets Practical Clarity The Morphism Energy Fields (Red/Green Boxes) Purpose = Visualize categorical transformations in real-time Algorithm: Energy Range = ATR × flow_strength × 1.5 Transparency = max(10, base_transparency - 15) Interpretation: Green fields = Bullish morphism energy (buying transformations) Red fields = Bearish morphism energy (selling transformations) Size = Proportional to transformation strength Intensity = Reflects morphism confidence Consciousness Grid (Purple Pattern) Purpose = Display market self-awareness emergence Algorithm: Grid_size = adaptive(lookback_period / 8) Consciousness_range = ATR × consciousness_level × 1.2 Interpretation: Density = Higher consciousness = denser grid Extension = Cloud lookback controls historical depth Intensity = Transparency reflects awareness level Homotopy Paths (Blue Gradient Boxes) Purpose = Show path equivalence opportunities Algorithm: Path_range = ATR × homotopy_score × 1.2 Gradient_layers = 3 (increasing transparency) Interpretation: Blue boxes = Equivalent path opportunities Gradient effect = Confidence visualization Multiple layers = Different probability levels Functorial Lines (Green Horizontal) Purpose = Multi-timeframe structure preservation levels Innovation = Smart spacing prevents overcrowding Min_separation = price × 0.001 (0.1% minimum) Max_lines = 3 (clarity preservation) Features: Glow effect = Background + foreground lines Adaptive labels = Only show meaningful separations Color coding = Green (preserved), Orange (stressed), Red (broken) Signal System: Bull/Bear Precision 🐂 Initial Objects = Bottom formations with strength percentages 🐻 Terminal Objects = Top formations with confidence levels ⚪ Product/Coproduct = Equilibrium circles with glow effects Professional Dashboard System Main Analytics Dashboard (Top-Right) Market State = Real-time categorical classification INITIAL OBJECT = Bottom formation active TERMINAL OBJECT = Top formation active PRODUCT STATE = Market equilibrium COPRODUCT STATE = Divergence/bifurcation ANALYZING = Processing market structure Universe Type = Current complexity level and components Morphisms: ACTIVE (X%) = Transformations detected, percentage shows strength DORMANT = No significant categorical changes Functoriality: PRESERVED (X%) = Structure maintained across timeframes VIOLATED (X%) = Structure breakdown, instability warning Homotopy: DETECTED (X%) = Path equivalences found, arbitrage opportunities NONE = No equivalent paths currently available Consciousness: ACTIVE (X%) = Market self-awareness emerging, major moves possible EMERGING (X%) = Consciousness building DORMANT = Mechanical trading only Signal Monitor & Performance Metrics (Left Panel) Active Signals Tracking: INITIAL = Count and current strength of bottom signals TERMINAL = Count and current strength of top signals PRODUCT = Equilibrium state occurrences COPRODUCT = Divergence event tracking Advanced Performance Metrics: CCI (Categorical Coherence Index): CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5) STRONG (>0.7) = High structural coherence MODERATE (0.4-0.7) = Adequate coherence WEAK (<0.4) = Structural instability HPA (Homotopy Path Alignment): HPA = max_homotopy_score × functorial_integrity ALIGNED (>0.6) = Strong path equivalences PARTIAL (0.3-0.6) = Some equivalences WEAK (<0.3) = Limited path coherence UPRR (Universal Property Recognition Rate): UPRR = (active_objects / 4) × 100% Percentage of universal properties currently active TEPF (Transcendence Emergence Probability Factor): TEPF = homotopy_score × consciousness_level × φ Probability of consciousness emergence (golden ratio weighted) MSI (Morphological Stability Index): MSI = (universe_depth / 5) × functorial_integrity × consciousness_level Overall system stability assessment Overall Score = Composite rating (EXCELLENT/GOOD/POOR) Theory Guide (Bottom-Right) Educational reference panel explaining: Objects & Morphisms = Core categorical concepts Universal Properties = The four fundamental patterns Dynamic Advice = Context-sensitive trading suggestions based on current market state Trading Applications: From Theory to Practice Trend Following with Categorical Structure Monitor functorial integrity = only trade when structure preserved (>80%) Wait for morphism energy fields = red/green boxes confirm direction Use consciousness emergence = purple grids signal major move potential Exit on functorial breakdown = structure loss indicates trend end Mean Reversion via Universal Properties Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes Confirm with Product states = equilibrium circles show balance points Watch Coproduct divergence = bifurcation warnings Scale out at Functorial levels = green lines provide targets Arbitrage through Homotopy Detection Blue gradient boxes = indicate path equivalence opportunities HPA metric >0.6 = confirms strong equivalences Multiple timeframe convergence = strengthens signal Consciousness active = amplifies arbitrage potential Risk Management via Categorical Metrics Position sizing = Based on MSI (Morphological Stability Index) Stop placement = Tighter when functorial integrity low Leverage adjustment = Reduce when consciousness dormant Portfolio allocation = Increase when CCI strong Sector-Specific Optimization Strategies Cryptocurrency Markets Universe Level = 4-5 (full complexity needed) Morphism Sensitivity = 0.382-0.618 (accommodate volatility) Categorical Memory = 55-89 (rapid cycles) Field Transparency = 1-5 (high visibility needed) Focus Metrics = TEPF, consciousness emergence Stock Indices Universe Level = 3-4 (moderate complexity) Morphism Sensitivity = 0.618-1.0 (balanced) Categorical Memory = 89-144 (institutional cycles) Field Transparency = 5-10 (moderate visibility) Focus Metrics = CCI, functorial integrity Forex Markets Universe Level = 2-3 (macro-driven) Morphism Sensitivity = 1.0-1.618 (noise reduction) Categorical Memory = 144-233 (long cycles) Field Transparency = 10-15 (subtle signals) Focus Metrics = HPA, universal properties Commodities Universe Level = 3-4 (supply/demand dynamics) [/b Morphism Sensitivity = 0.618-1.0 (seasonal adaptation) Categorical Memory = 89-144 (seasonal cycles) Field Transparency = 5-10 (clear visualization) Focus Metrics = MSI, morphism strength Development Journey: Mathematical Innovation The Challenge Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets. The Breakthrough The key insight came from recognizing that market states form a category where: Price levels, volume conditions, and volatility regimes are objects Market movements between these states are morphisms The composition of movements must satisfy categorical laws This realization led to the morphism detection engine and functorial analysis framework . Implementation Challenges Computational Complexity = Category theory calculations are intensive Real-time Performance = Markets don't wait for mathematical perfection Visual Clarity = How to display abstract mathematics clearly Signal Quality = Balancing mathematical purity with practical utility User Accessibility = Making PhD-level math tradeable The Solution After months of optimization, we achieved: Efficient algorithms = using pre-calculated values and smart caching Real-time performance = through optimized Pine Script implementation Elegant visualization = that makes complex theory instantly comprehensible High-quality signals = with built-in noise reduction and cooldown systems Professional interface = that guides users through complexity Advanced Features: Beyond Traditional Analysis Adaptive Transparency System Two independent transparency controls: Field Transparency = Controls morphism fields, consciousness grids, homotopy paths Signal & Line Transparency = Controls signals and functorial lines independently This allows perfect visual balance for any market condition or user preference. Smart Functorial Line Management Prevents visual clutter through: Minimum separation logic = Only shows meaningfully separated levels Maximum line limit = Caps at 3 lines for clarity Dynamic spacing = Adapts to market volatility Intelligent labeling = Clear identification without overcrowding Consciousness Field Innovation Adaptive grid sizing = Adjusts to lookback period Gradient transparency = Fades with historical distance Volume amplification = Responds to market participation Fractal dimension integration = Shows complexity evolution Signal Cooldown System Prevents overtrading through: 20-bar default cooldown = Configurable 5-100 bars Signal-specific tracking = Independent cooldowns for each signal type Counter displays = Shows historical signal frequency Performance metrics = Track signal quality over time Performance Metrics: Quantifying Excellence Signal Quality Assessment Initial Object Accuracy = >78% in trending markets Terminal Object Precision = >74% in overbought/oversold conditions Product State Recognition = >82% in ranging markets Consciousness Prediction = >71% for major moves Computational Efficiency Real-time processing = <50ms calculation time Memory optimization = Efficient array management Visual performance = Smooth rendering at all timeframes Scalability = Handles multiple universes simultaneously User Experience Metrics Setup time = <5 minutes to productive use Learning curve = Accessible to intermediate+ traders Visual clarity = No information overload Configuration flexibility = 25+ customizable parameters Risk Disclosure and Best Practices Important Disclaimers The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure. Recommended Usage Never trade signals in isolation = always use confluence with other analysis Respect risk management = categorical analysis doesn't eliminate risk Understand the mathematics = study the theoretical foundation Start with paper trading = master the concepts before risking capital Adapt to market regimes = different markets need different parameters Position Sizing Guidelines High consciousness periods = Reduce position size (higher volatility) Strong functorial integrity = Standard position sizing Morphism dormancy = Consider reduced trading activity Universal property convergence = Opportunities for larger positions Educational Resources: Master the Mathematics Recommended Reading "Category Theory for the Sciences" = by David Spivak "Homotopy Type Theory" = by The Univalent Foundations Program "Fractal Market Analysis" = by Edgar Peters "The Misbehavior of Markets" = by Benoit Mandelbrot Key Concepts to Master Functors and Natural Transformations Universal Properties and Limits Homotopy Equivalence and Path Spaces Type Theory and Univalence Fractal Geometry in Markets The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis. This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide. Why CMM Dominates Mathematical Foundation = Built on proven mathematical frameworks Original Innovation = First application of category theory to markets Professional Quality = Institution-grade metrics and analysis Visual Excellence = Clear, elegant, actionable interface Educational Value = Teaches advanced mathematical concepts Practical Results = High-quality signals with risk management Continuous Evolution = Regular updates and enhancements The DAFE Trading Systems Difference At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines: PhD-level mathematical expertise Real-world trading experience Cutting-edge programming skills Artistic visual design Educational commitment The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics. "In mathematics you don't understand things. You just get used to them." - John von Neumann "The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems Trade with Mathematical Precision. Trade with Categorical Market Morphisms. Created with passion for mathematical excellence, and empowering traders through mathematical innovation. — Dskyz, Trade with insight. Trade with anticipation.
Pine Script®指標
由DskyzInvestments提供
9
Kijun Shifting Band Oscillator | QuantMAC🎯 Kijun Shifting Band Oscillator | QuantMAC 📊 **Revolutionary Technical Analysis Tool Combining Ancient Ichimoku Wisdom with Cutting-Edge Statistical Methods** 🌟 Overview The Kijun Shifting Band Oscillator represents a sophisticated fusion of traditional Japanese technical analysis and modern statistical theory. Built upon the foundational concepts of the Ichimoku Kinko Hyo system, this indicator transforms the classic Kijun-sen (base line) into a dynamic, multi-dimensional analysis tool that provides traders with unprecedented market insights. This advanced oscillator doesn't just show you where price has been – it reveals the underlying momentum dynamics and volatility patterns that drive market movements, giving you a statistical edge in your trading decisions. 🔥 Key Features & Innovations Dual Trading Modes for Maximum Flexibility: 🚀 Long/Short Mode: Full bidirectional trading capability for aggressive traders seeking to capitalize on both bullish and bearish market conditions Long/Cash Mode: Conservative approach perfect for risk-averse traders, taking long positions during uptrends and moving to cash during downtrends (avoiding short exposure) Advanced Visual Intelligence: 🎨 9 Professional Color Schemes: From classic blue/navy to vibrant orange/purple combinations, each optimized for different chart backgrounds and personal preferences Dynamic Gradient Histogram: Color intensity reflects oscillator strength, providing instant visual feedback on momentum magnitude Intelligent Overlay Bands: Semi-transparent fills create clear visual boundaries without cluttering your chart Smart Candle Coloring: Real-time color changes reflect current market state and trend direction Customizable Threshold Lines: Clearly marked entry and exit levels with contrasting colors Professional-Grade Analytics: 📊 Real-Time Performance Metrics: Live calculation of 9 key performance indicators Risk-Adjusted Returns: Sharpe, Sortino, and Omega ratios for comprehensive performance evaluation Position Sizing Guidance: Half-Kelly percentage for optimal risk management Drawdown Analysis: Maximum drawdown tracking for risk assessment 📈 Deep Technical Foundation Kijun-Based Mathematical Framework: 🧮 The indicator begins with the traditional Kijun-sen calculation but extends it significantly: Statistical Enhancements: 📉 Adaptive Volatility: Bands expand and contract based on market volatility Momentum Filtering: EMA smoothing of oscillator for trend confirmation State Management: Intelligent signal filtering prevents whipsaws and false signals Multi-Timeframe Compatibility: Optimized algorithms work across all timeframes ⚙️ Comprehensive Parameter Control Kijun Core Settings: 🎛️ Kijun Length (Default: 30): Controls the lookback period for the base calculation. Shorter periods = more responsive, longer periods = smoother signals Source Selection: Choose from Close, Open, High, Low, or HL2. Close price recommended for most applications Calculation Method: Uses traditional Ichimoku methodology ensuring compatibility with classic analysis Advanced Oscillator Configuration: 📊 Standard Deviation Length (Default: 36): Determines volatility measurement period. Affects band width and sensitivity SD Multiplier (Default: 2.1): Fine-tune band distance from basis line. Higher values = wider bands, lower values = tighter bands Oscillator Multiplier (Default: 100): Scales the final oscillator output. Useful for matching other indicators or personal preference Smoothing Algorithm: Built-in EMA smoothing prevents noise while maintaining responsiveness Signal Threshold Optimization: 🎯 Long Threshold (Default: 83): Oscillator level that triggers long entries. Higher values = fewer but stronger signals Short Threshold (Default: 42): Oscillator level that triggers short entries. Lower values = fewer but stronger signals Threshold Logic: Crossover-based system with state management prevents signal overlap Customization Range: Fully adjustable to match your trading style and risk tolerance Precision Date Control: 📅 Start Date/Month/Year: Precise backtesting control down to the day Historical Analysis: Test strategies on specific market periods or events Strategy Validation: Isolate performance during different market conditions 📊 Professional Metrics Dashboard Risk Assessment Metrics: 💼 Maximum Drawdown %: Largest peak-to-trough decline in portfolio value. Critical for understanding worst-case scenarios and position sizing Sortino Ratio: Risk-adjusted return measure focusing only on downside volatility. Superior to Sharpe ratio for asymmetric return distributions Sharpe Ratio: Classic risk-adjusted performance metric. Values above 1.0 considered good, above 2.0 excellent Omega Ratio: Probability-weighted ratio capturing all moments of return distribution. More comprehensive than Sharpe or Sortino Performance Analytics: 📈 Profit Factor: Gross Profit ÷ Gross Loss. Values above 1.0 indicate profitability, above 2.0 considered excellent Win Rate %: Percentage of profitable trades. Consider alongside average win/loss size for complete picture Net Profit %: Total return on initial capital. Accounts for compounding effects Total Trades: Sample size for statistical significance assessment Advanced Position Sizing: 🎯 Half Kelly %: Optimal position size based on Kelly Criterion, reduced by 50% for safety margin Risk Management: Helps determine appropriate position size relative to account equity Mathematical Foundation: Based on win probability and profit factor calculations Practical Application: Directly usable percentage for position sizing decisions 🎨 Advanced Display Options Flexible Interface Design: 🖥️ 6 Positioning Options: Top/Bottom/Middle × Left/Right combinations for optimal chart organization Toggle Functionality: Show/hide metrics table for clean chart presentation during analysis Color Coordination: Metrics table colors match selected oscillator color scheme Professional Styling: Clean, readable format with proper spacing and alignment Visual Hierarchy: 🎭 Oscillator Histogram: Primary focus with gradient intensity showing momentum strength Threshold Lines: Clear horizontal references for entry/exit levels Zero Line: Neutral reference point for trend bias determination Background Bands: Subtle overlay context without chart clutter 🚀 Advanced Signal Generation System Multi-Layer Signal Logic: ⚡ Primary Signal Generation: Oscillator crossover above Long Threshold (default 83) triggers long entries Exit Signal Processing: Oscillator crossunder below Short Threshold (default 42) triggers position exits State Management System: Prevents duplicate signals and ensures clean position transitions Mode-Specific Logic: Different behavior for Long/Short vs Long/Cash modes Date Range Filtering: Signals only generated within specified backtesting period Confirmation Requirements: Bar confirmation prevents false signals from intrabar price spikes Intelligent Position Management: 🧠 Entry Tracking: Precise entry price recording for accurate P&L calculations Position State Monitoring: Continuous tracking of long/short/cash positions Automatic Exit Logic: Seamless position closure and new position initiation Performance Calculation: Real-time P&L tracking with compounding effects 📉📈 Comprehensive Band Interpretation Guide Dynamic Band Analysis: 🔍 Upper Band Function: Represents dynamic resistance based on recent volatility. Price approaching upper band suggests potential reversal or breakout Lower Band Function: Represents dynamic support with volatility adjustment. Price near lower band indicates oversold conditions or support testing Middle Line (Basis): Trend direction indicator. Price above = bullish bias, price below = bearish bias Band Width Interpretation: Wide bands = high volatility, narrow bands = low volatility/potential breakout setup Band Slope Analysis: Rising bands = strengthening trend, falling bands = weakening trend Oscillator Interpretation: 📊 Values Above 50: Price in upper half of recent range, bullish momentum Values Below 50: Price in lower half of recent range, bearish momentum Extreme Values (>80 or <20): Overbought/oversold conditions, potential reversal zones Momentum Divergence: Oscillator direction vs price direction for early reversal signals Trend Confirmation: Oscillator direction confirming or contradicting price trends 💡 Strategic Trading Applications Primary Trading Strategies: 🎯 Trend Following: Use threshold crossovers to capture major directional moves. Best in trending markets with clear directional bias Mean Reversion: Identify extreme oscillator readings for counter-trend opportunities. Effective in range-bound markets Breakout Trading: Monitor band compressions followed by expansions for breakout signals Swing Trading: Combine oscillator signals with band interactions for swing position entries/exits Risk Management: Use metrics dashboard for position sizing and risk assessment Market Condition Optimization: 🌊 Trending Markets: Increase threshold separation for fewer, stronger signals Choppy Markets: Decrease threshold separation for more responsive signals High Volatility: Increase SD multiplier for wider bands Low Volatility: Decrease SD multiplier for tighter bands and earlier signals ⚙️ Advanced Configuration Tips Parameter Optimization Guidelines: 🔧 Kijun Length Adjustment: Shorter periods (10-20) for faster signals, longer periods (50-100) for smoother trends SD Length Tuning: Match to your trading timeframe - shorter for responsive, longer for stability Threshold Calibration: Backtest different levels to find optimal entry/exit points for your market Color Scheme Selection: Choose schemes that provide best contrast with your chart background and other indicators Integration with Other Indicators: 🔗 Volume Indicators: Confirm oscillator signals with volume spikes Support/Resistance: Use key levels to filter oscillator signals Momentum Indicators: RSI, MACD confirmation for signal strength Trend Indicators: Moving averages for overall trend bias confirmation ⚠️ Important Usage Notes & Limitations Indicator Characteristics: ⚡ Lagging Nature: Based on historical price data - signals occur after moves have begun Best Practice: Combine with leading indicators and price action analysis Market Dependency: Performance varies across different market conditions and instruments Backtesting Essential: Always validate parameters on historical data before live implementation Optimization Recommendations: 🎯 Parameter Testing: Systematically test different combinations on your preferred instruments Walk-Forward Analysis: Regularly re-optimize parameters to maintain effectiveness Market Regime Awareness: Adjust parameters for different market conditions (trending vs ranging) Risk Controls: Implement maximum drawdown limits and position size controls 🔧 Technical Specifications Performance Optimization: ⚡ Efficient Algorithms: Optimized calculations for smooth real-time operation Memory Management: Smart array handling for metrics calculations Visual Optimization: Balanced detail vs performance for responsive charts Multi-Symbol Ready: Consistent performance across different assets --- The Kijun Shifting Band Oscillator represents the evolution of technical analysis, bridging the gap between traditional methods and modern quantitative approaches. This indicator provides traders with a comprehensive toolkit for market analysis, combining the intuitive wisdom of Japanese candlestick analysis with the precision of statistical mathematics. 🎯 Designed for serious traders who demand professional-grade analysis tools with institutional-quality metrics and risk management capabilities. Whether you're a discretionary trader seeking visual confirmation or a systematic trader building quantitative strategies, this indicator provides the foundation for informed trading decisions. ⚠️ IMPORTANT DISCLAIMER Past Performance Warning: 📉⚠️ PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS. Historical backtesting results, while useful for strategy development and parameter optimization, do not guarantee similar performance in live trading conditions. Market conditions change continuously, and what worked in the past may not work in the future. Remember: Successful trading requires discipline, continuous learning, and adaptation to changing market conditions. No indicator or strategy guarantees profits, and all trading involves substantial risk of loss.
Pine Script®指標
由Quant_MAC提供
mrD Open InterestIntroduction "mrD Open Interest" is a technical analysis reference tool that can help investors monitor and analyze Open Interest data from various cryptocurrency exchanges. This indicator provides insights into Open Interest data through patterns, bursts, and money flow based on proprietary algorithms. Important Note Trading always involves risk and can lead to capital loss. This indicator should only be used as a supplementary tool in technical analysis and should not be considered as an accurate forecasting tool or the sole basis for trading decisions. Past results do not guarantee future results. Proprietary Features of the Indicator "mrD Open Interest" has been developed with several proprietary features, qualifying it for source code protection when published: - Unique Multi-Source Integration Algorithm: The indicator uses a smart aggregation method to combine OI data from multiple exchanges, creating a holistic view of market pressure that is not dependent on a single exchange. This method employs special weighting and noise filtering to ensure the aggregated data accurately reflects market conditions. - Proprietary OI-Price Correlation Analysis Algorithm: Unlike traditional OI indicators that simply display OI values, this indicator uses a complex algorithm to analyze the correlation between price movements and OI changes. This algorithm automatically identifies four money flow patterns (Buy Inflow, Sell Inflow, Buy Outflow, Sell Outflow) and ranks them by potential market impact. - Advanced Burst Detection Technology: The proprietary algorithm identifies "bursts" - sudden changes in OI that can lead to significant market volatility. This system relies not only on absolute change but also analyzes the rate of change, amplitude, and correlation with historical peaks/troughs to determine the significance of a burst. - Integrated Smart Alert System: The indicator features a smart alert algorithm, only sending notifications when patterns with high statistical significance are detected, reducing "alert noise" and helping users focus on the most potential opportunities. - Visual Representation Technology: The user interface design uses proprietary visual representation technology, allowing users to easily identify important patterns and signals through a special system of colors, icons, and display formats. Features That May Assist 1. Reference Data from Multiple Exchanges: The indicator can collect Open Interest information from various exchanges (Binance, BitMEX, Kraken) and different currency pairs (USDT, USD, BUSD), potentially providing investors with more information about the market. 2.Money Flow Pattern Analysis: The indicator suggests 4 patterns that may help identify market conditions: Buy Inflow: Potential opening of new long positions (price up, OI up) Buy Outflow: Potential closing of long positions (price down, OI down) Sell Inflow: Potential opening of new short positions (price down, OI up) Sell Outflow: Potential closing of short positions (price up, OI down) Burst Identification: The indicator attempts to detect "bursts" - notable changes in Open Interest that may signal changes in money flow. Bursts are divided into two types: Up Burst and Down Burst. 3. Price-OI Correlation Reference: The tool provides information about the relationship between price movement and OI changes, potentially helping to assess whether current price momentum is supported by new money flow. 4. Diverse Display Modes: The indicator offers 3 display modes (Columns, Candles, Columns, and Price Line) that may suit different analytical approaches. Setup and Usage Guide 1. Basic Setup Select Data Sources (Exchange Settings): By default, the indicator uses data from Binance USDT Perpetual. Depending on the coin pair and exchange you're interested in, you can enable/disable different data sources (Binance USD, BUSD, BitMEX USD, USDT, or Kraken). Recommendation: For popular coins like BTC or ETH, consider combining data from 2-3 major exchanges for a more comprehensive view. 2. Display Customization (Visuals Settings): OI Display Type: Choose a display type that suits your analysis style: "Columns": Column format, making it easy to identify OI changes. "Candles": Candle format, similar to price charts, helps identify candlestick patterns in OI. "Columns and Price Line": Combines OI columns and price line, helping directly compare OI with price movements. Show background: Enable to highlight burst periods with a colored background (recommended when using candle mode). Show signals: Enable to display of burst indicators on the chart (recommended to keep enabled). Text Color: Customize text color to match your chart background. 3. Alert Settings: hoose alert types that suit your trading strategy: "Inflows Only": Only alerts when new money flows into the market. "Outflows Only": Only alerts when money flows out of the market. "Bursts Only": Only alerts when there's a strong burst in OI. "All": Alerts for all the above events. Effective Usage Trend Analysis Based on Money Flow Patterns: Buy Inflow (Green): When the price increases along with OI, it may indicate new buying pressure. Can be considered as a supportive signal for an uptrend. Sell Inflow (Red): When price decreases along with increasing OI, it may indicate new selling pressure. Can be considered as a supportive signal for a downtrend. Buy Outflow (Teal): When price decreases but OI also decreases, it may indicate taking profit/cutting loss from long positions. Usually not strong selling pressure and may be ending soon. Sell Outflow (Dark Red): When the price increases but OI decreases, it may indicate closing of short positions. Usually not strong buying pressure and may be ending soon. Burst Analysis: Up Burst: Strong and positive change in OI, most notable when occurring in a Buy Inflow pattern, may signal strong buying money flow into the market. Down Burst: Strong and negative change in OI, most notable when occurring in a Sell Inflow pattern, may signal strong selling money flow into the market. Bursts are often signals that deserve special attention and may indicate strong changes in market sentiment. Using the Information Table: Monitor "Aggregated OI" to capture the total amount of open contracts. Pay attention to "OI Change (%)" to assess the degree of change compared to the previous candle. "Relative OI" provides information about the relative level of OI compared to the average. "Flow Type" indicates the current money flow pattern. "Burst Status" displays the burst status if any. Combining with Other Indicators: Use in combination with trend indicators (MA, MACD) to confirm trends. Combine with volume indicators for a more comprehensive view of market activity. Reference additional momentum indicators to assess trend strength. Customizing According to Timeframe: Short timeframes (1m-15m): May show more noise signals. Medium timeframes (30m-4h): Often provide a good balance between sensitivity and noise filtering. Long timeframes (D-W): Suitable for monitoring long-term OI trends.
Pine Script®指標
由mrDocC提供
1
Flux Charts - SFX Screener💎 GENERAL OVERVIEW The SFX Screener by Flux Charts is a multi-timeframe market scanner that extracts and visually organizes key conditions detected by the SFX Algo indicator across multiple assets in real-time. It does not perform independent analysis or generate new signals—instead, it pulls data directly from the SFX Algo’s calculations to ensure full alignment across different timeframes and tickers. The SFX Algo is a multi-factor trading indicator that integrates trend analysis, signal generation, market overlays, and take-profit/stop-loss levels into a single system. It evaluates multiple trend components, including EMA direction, momentum shifts, and volatility cycles, to determine market conditions. Signal generation is based on an Adjusted Weighted Majority Algorithm, filtering out weaker signals by prioritizing the most reliable market indicators. Market overlays, such as Volatility Bands and the Retracement Wave, provide dynamic support, resistance, exit points, and entry points. Its adaptable structure allows traders to customize settings based on strategy preferences, making it effective for scalping, swing trading, and long-term trend analysis. The SFX Screener’s purpose is to give traders a dashboard view of these SFX Algo signals across multiple tickers and timeframes in real-time. 📌 HOW DOES IT WORK ? The SFX Algo indicator employs an Adjusted Weighted Majority algorithm to generate "buy" and "sell" signals. It evaluates multiple market indicators ("experts"), including momentum, ATR trends, and EMA trends, and assigns weights based on their recent performance. The "Time Weighting" setting allows users to balance between using more historical data or prioritizing recent trends. Unlike traditional weighted majority methods, SFX also dynamically penalizes larger losses. Signals are confirmed based on the consensus of the most successful indicators within the selected time period, filtering out weaker signals during underperforming phases. The SFX Screener extracts these calculated outputs and visually organizes them into a real-time dashboard. Each signal, status, and volatility condition displayed in the screener is a direct output from the SFX Algo indicator. 🚩 UNIQUENESS Unlike traditional screeners that rely on preset filters or static conditions, the SFX Screener dynamically updates its dashboard based on live outputs from the SFX Algo’s adaptive algorithm. Traditional Screeners → Use predefined filters like “price above EMA” or “RSI overbought.” They do not adjust to market dynamics. SFX Screener → Displays outputs directly from an adaptive algorithm that continuously evaluates trends, volatility, and momentum changes. The SFX Screener can show SFX Algo's status on 8 different tickers on different timeframes. Key factors that make it unique include: ✅ Real-time sync with SFX Algo → Displays live conditions, not static filters. ✅ Comprehensive Dashboard – This screener provides a complete and customizable dashboard designed to enhance traders' decision-making by consolidating crucial SFX Algo insights into one user-friendly interface. ✅ Multi-Ticker & Multi-Timeframe Analysis – With support for up to 8 tickers and timeframes, traders can effortlessly analyze the bigger market picture, identifying trends and opportunities across different assets and timeframes. By combining multiple analytical elements in a single view, this screener empowers traders with the insights needed to navigate the market more effectively. 🎯 SFX SCREENER FEATURES: SFX Algo Signals : This tool can detect SFX Algo signals across different tickers & timeframes. Volatility Bands : Detection of Volatility Bands Status & Retests. Retracement Wave : Detection of Retracement Wave Status & Retests. Highly Configurable : Offers multiple parameters for fine-tuning detection settings. Up to 8 Tickers : Allows traders to analyze multiple tickers & timeframes simultaneously for enhanced accuracy. 📊 SFX SCREENER DATA BREAKDOWN Signal -> Buy -> The latest signal is a buy signal. Sell -> The latest signal is a sell signal. The rating of the signal is shown after the signal type. Δ⭐ -> Shows the rating change (delta) after the signal is triggered. Positive values mean that the rating is increased after the signal is given, negative values mean that it's decreased. Status -> Displays the amount of time passed after the signal is given. TP Targets -> Shows the Take-Profit targets of the signal, if a target was achieved, there is a ✅ symbol near it and the next target it displayed. V. Bands -> The Volatility Bands dynamically adjust to market conditions, expanding during high volatility and contracting during low volatility. When the volatility bands are tight, or the upper and lower bands are close to each other, the market is not volatile. During periods of low volatility, it’s common for price to consolidate or move sideways. An early indication of a large price move can occur when the bands widen or open up after being tight. When the volatility bands are wide, it reflects a period of increased volatility, typically during strong price trends or after a breakout. The volatility bands can also act as support and resistance areas. The upper band acts as resistance while the lower band acts as support. These mark out good areas for potential reversals. Breakouts can also occur when price moves beyond the bands, signaling a potential trend in the breakout direction. Outside -> The price is currently outside of the Volatility Bands. Inside | Upper -> The price is currently inside the Upper Volatility Band. Inside | Lower -> The price is currently inside the Lower Volatility Band. R. Wave -> The Retracement Wave is used to identify entry points during pullbacks in trending markets. It can also be used to find exit points for open trades. The wave is bullish when price is above it and bearish when the price is below it. The retracement wave can be used as an area to enter during a pullback in a trending market. The wave can also be helpful for managing risk and closing out positions. Outside | Bullish -> The Retracement Wave is currently Bullish, and the price is outside of it. Outside | Bearish -> The Retracement Wave is currently Bearish, and the price is outside of it. Inside | Bullish -> The Retracement Wave is currently Bullish, and the price is inside of it. Inside | Bearish -> The Retracement Wave is currently Bearish, and the price is inside of it. Profit & Loss (P&L) -> Shows the amount of profit or loss the position is currently in. All values are shown in terms of percentage, and positive values mean the position is in profit while negative values mean that the position is in loss. ⚠ Timeframe Restriction : The selected timeframes for analysis cannot be lower than the chart’s current timeframe to ensure proper data alignment. ⏰ ALERTS This screener supports alerts, so you never miss a key market move. You can choose to receive alerts when a buy or sell signal is given, helping you spot potential trading opportunities. Additionally, you can enable alerts for take-profit or stop-loss levels, which notify you when the price achieves those levels. The alerts will work for each enabled ticker in the settings. You can also toggle webhook format for alerts, and choose to include ticker metadata in it. ⚙️ SETTINGS 1. Algorithm Settings Sensitivity: The sensitivity setting is a key parameter that influences the frequency of signals the SFX Algo generates. By adjusting this parameter, you can control the frequency of signals produced by the algorithm. Using a lower sensitivity setting generates more frequent signals that are highly responsive to minor price fluctuations. Using a higher sensitivity setting reduces the frequency of signals, focusing on more significant price movements and filtering out minor fluctuations. Signal Strength: The Signal Strength setting filters signals based on their quality, allowing traders to focus on the most reliable opportunities. This feature helps traders balance the quantity and reliability of the algorithm’s signals to suit their trading strategy. Using a lower signal strength will display more signals, including those with lower signal ratings, for broader market coverage. Using a higher signal strength will display fewer signals by prioritizing those with higher signal ratings, reducing market noise. Time Weighting: The Time Weighting setting in the SFX Algo determines how historical market data is analyzed to generate signals. a) Recent Trends Focuses on the most recent movements for short-term analysis. This setting is good for scalpers and intraday traders who need to react quickly to market changes. b) Mixed Trends Balances recent and historical price movements for a comprehensive market view. This setting is well-suited for swing traders and those who want to capture medium-term opportunities by combining the benefits of short-term responsiveness with the reliability of long-term trends. c) Long-term Trends Relies on extended historical market data to identify broader market trends, making it an excellent choice for traders focused on long-term strategies. Minimum Star Rating : The Minimum Star Rating setting allows you to filter signals based on their strength, showing only those that meet or exceed your chosen threshold. For instance, setting the minimum star rating to 3 ensures you only receive signals with a rating of 3 stars or higher. 2. Take Profit / Stop Loss Methods Key Levels The Key Levels method uses pivot points to set take profit and stop-loss levels. The TP and SL levels are shown when a new signal is generated. Volatility Bands This TP/SL method uses the Volatility Bands overlay to set dynamic TP and SL levels. These levels are not predetermined so they will not be shown in advance when a signal is generated. Signal Rating Sets take profit and stop-loss levels based on changes in a signal's rating strength. These levels are not predetermined so they will not be shown in advance when a signal is generated. Auto Stop-Loss The auto method can only be applied to the SL. The auto method allows the algorithm to detect SL automatically when a momentum shift is detected. You can adjust the risk tolerance of the Auto SL by adjusting the ‘Auto Risk Tolerance’ setting. You can choose between Low, Medium, and High. A high-risk tolerance will result in stop losses being triggered less often. 3. Tickers You can set, then enable or disable up to 8 tickers in this section to get informed about their latest SFX Algo signal. ‼️ Important Notes TradingView has limitations when running advanced screeners, resulting in the following restrictions: Computation Errors: The computation of using MTF features and viewing several tickers is very intensive on TradingView. This can sometimes cause calculation timeouts. When this occurs simply force the recalculation by modifying one indicator’s settings or by removing the indicator and adding it to your chart again. Inconsistencies: You may notice inconsistencies when viewing the screener on a chart with a specific symbol because screener tickers originate from different markets. Since the cryptocurrency market operates 24/7, while stock markets have defined opening and closing hours, the screener may return varying information depending on whether you're currently viewing a cryptocurrency, stock, or currency pair.
Pine Script®指標
由fluxchart提供
Goertzel Adaptive JMA T3Hello Fellas, The Goertzel Adaptive JMA T3 is a powerful indicator that combines my own created Goertzel adaptive length with Jurik and T3 Moving Averages. The primary intention of the indicator is to demonstrate the new adaptive length algorithm by applying it on bleeding-edge MAs. It is useable like any moving average, and the new Goertzel adaptive length algorithm can be used to make own indicators Goertzel adaptive. Used Adaptive Length Algorithms Normalized Goertzel Power: This uses the normalized power of the Goertzel algorithm to compute an adaptive length without the special operations, like detrending, Ehlers uses for his DFT adaptive length. Ehlers Mod: This uses the Goertzel algorithm instead of the DFT, originally used by Ehlers, to compute a modified version of his original approach, which sticks as close as possible to the original approach. Scoring System The scoring system determines if bars are red or green and collects them. Then, it goes through all collected red and green bars and checks how big they are and if they are above or below the selected MA. It is positive when green bars are under MA or when red bars are above MA. Then, it accumulates the size for all positive green bars and for all positive red bars. The same happens for negative green and red bars. Finally, it calculates the score by ((positiveGreenBars + positiveRedBars) / (negativeGreenBars + negativeRedBars)) * 100 with the scale 0–100. Signals Is the price above MA? -> bullish market Is the price below MA? -> bearish market Usage Adjust the settings to reach the highest score, and enjoy an outstanding adaptive MA. It should be useable on all timeframes. It is recommended to use the indicator on the timeframe where you can get the highest score. Now, follows a bunch of knowledge for people who don't know about the concepts used here. T3 The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science. The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA. To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works: Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.' Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.' Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.' The formula for the T3 moving average is as follows: T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3) By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action. JMA The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data. JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility. In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action. Goertzel Algorithm The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of individual terms of the Discrete Fourier Transform (DFT). It's particularly useful when you need to compute a small number of selected frequency components. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. This makes it more numerically efficient when computing a small number of selected frequency components¹. Discrete Fourier Transform The Discrete Fourier Transform (DFT) is a mathematical technique used in signal processing to convert a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency . The DFT provides a frequency domain representation of the original input sequence . Usage of DFT/Goertzel In Adaptive Length Algorithms Adaptive length algorithms are automated trading systems that can dynamically adjust their parameters in response to real-time market data. This adaptability enables them to optimize their trading strategies as market conditions fluctuate. Both the Goertzel algorithm and DFT can be used in these algorithms to analyze market data and detect cycles or patterns, which can then be used to adjust the parameters of the trading strategy. The Goertzel algorithm is more efficient than the DFT when you need to compute a small number of selected frequency components. However, for covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms. I hope this can help you somehow. Thanks for reading, and keep it up. Best regards, simwai --- Credits to: @ClassicScott @yatrader2 @cheatcountry @loxx
Pine Script®指標
由simwai提供
3
All Support and Resistance Levels [PINESCRIPTLABS]First, we observe the Light Blue Macro Supports and the Pink Macro Resistances. These channels are automatically formed based on market data, identifying pivot points in price history and determining the strength of these levels based on the number of pivot points within these same channels. When the price interacts with the macro Supports, we have a strong reaction that we can take advantage of in two ways: 1. The first and most common, as we can see in the chart, is that these zones elicit a strong reaction, and the price respects the channel. For us, as traders, it signifies a pivot point where we can initiate a trade, either a buy at the macro Support or a sell at the macro Resistance. 2. The second way to use them, for which this algorithm is also prepared, is in case a movement occurs where the price breaks these Macro Supports or Macro Resistances. We have a special alert that will notify us because when these macro channels are broken, they tend to do so violently in a move that we can also capitalize on. Usually, when such a breakout occurs, we will visit the next support or resistance channel, which can bring us significant benefits. The following complex and highly accurate calculation provided by this indicator allows us to work with price supports and resistances within the internal structure of macro channels. As we can see in the chart, "boxes" are formed that represent the detected support and resistance areas. It also detects breakouts when the price crosses below the support "box" or above the resistance "box" and displays labels on the chart indicating when the breakout occurred, all in real-time. But here comes something very special: the algorithm also has a calculation that, as we see in the chart, there are occasions when the breakout occurs, but the price returns to the support or resistance "box" and is detected. At this moment, a label appears on the chart indicating a possible confirmation of the breakout. In other words, as the price initially broke out but returned to the "box," the algorithm will notify us with another label and a special alert when the price confirms the breakout. At the same time, we can see in the chart that the algorithm also provides us with a volume profile that allows us to see where the most trading activity has concentrated based on price levels. We can also use it to identify support and resistance levels based on the point of control (POC) and value area levels. As we can see in the chart, there are labels with the exact price where the highest volume was traded. The top label in the chart shows the highest price, and the last label we see is for the lowest price. These displayed labels are within the defined range of retrocession or Lookback Length, which we can configure in our indicator. As we observe, the algorithm shows a strong confluence between the Macro Support channels and the volume profile labels, confirming the strongest areas of the range. Finally, after calculating supports and resistances from three different perspectives, the algorithm provides us with a macro view of the price in the form of trend lines. In other words, it shows us supports and resistances in the form of diagonal channels where we can see trends in the market and areas where the price has historically encountered difficulties in advancing or retreating, which we can corroborate with the supports and resistances mentioned at the beginning. As we can see in the chart, the algorithm also shows us labels with the exact price where angular price supports and resistances are located. These calculations are very important as they provide a trend perspective, and we can get an idea of where the price is headed, combining these with the other support and resistance calculations. Remember that all the previous calculations have their own alerts for when supports or resistances are broken, or in the case of new channels being created, also when there is a breakout of a box or a confirmation of a breakout. The second type of alert from the indicator is configured to make our indicators work for us without the need to be present on the chart, thanks to special programming within the indicator's code. It will execute automatic buys and sells on our preferred exchange through an alert configured for the 3Commas bot. All you need to do is input your Bot ID, provided by 3Commas, into the alert. All premium indicators come with a configuration explanation that will guide you in detail on where to input your Bot ID. ESPAÑOL: En primer lugar, observamos los Macro Soportes en color azul claro y las Macro Resistencias en color rosa. Estos canales se forman automáticamente en función de los datos del mercado, identificando puntos de pivote en el historial de precios y determinando la fuerza de estos niveles según la cantidad de puntos de pivote dentro de estos mismos canales. Cuando el precio interactúa con los macro Soportes, tenemos una fuerte reacción que podemos aprovechar de dos formas: 1. La primera y más común, como observamos en el gráfico, es que estas zonas provocan una fuerte reacción, y el precio respeta el canal. Para nosotros, como traders, significa un punto de pivote donde podemos generar una entrada, ya sea de compra en el macro soporte o de venta en la macro resistencia. 2. La segunda forma de utilizarlos, para la cual este algoritmo también está preparado, es en caso de que se genere un movimiento en el que el precio rompa estos Macro Soportes o Macro Resistencias. Contamos con una alerta especial que nos avisará, ya que al romperse estos macro canales suelen hacerlo con violencia en un movimiento que también podemos aprovechar. Regularmente, cuando existe este rompimiento, visitaremos el siguiente canal de soporte o resistencia, lo que nos puede traer grandes beneficios. El siguiente cálculo complejo y muy preciso que nos ofrece este indicador nos permite trabajar con soportes y resistencias del precio dentro de la estructura interna de los canales macro. Como observamos en el gráfico, se producen "boxes" que representan las áreas de soporte y resistencia detectadas. Además, detecta breakouts cuando el precio cruza por debajo del "box" de soporte o por encima del "box" de resistencia y muestra etiquetas en el gráfico que nos indican cuándo ocurrió el breakout, todo esto en tiempo real. Pero aquí viene algo super especial: el algoritmo también tiene un cálculo que, como vemos en el gráfico, hay ocasiones en las que el breakout ocurre, pero el precio retorna al "box" de soporte o resistencia y es detectado. En este momento, aparece una etiqueta en el gráfico que nos muestra que estamos ante una posible confirmación del breakout. Es decir, como el precio había hecho en primer lugar el breakout pero regresó al "box", el algoritmo nos avisará con otra etiqueta y alerta especial cuando el precio confirme el breakout. Al mismo tiempo, observamos en el gráfico que el algoritmo también nos muestra un perfil de volumen que nos permite ver dónde se ha concentrado la mayor actividad de negociación en función de los niveles de precios. También podemos usarlo para identificar niveles de soporte y resistencia basados en el punto de control (POC) y los niveles de valor (Value Area). Como vemos en el gráfico, tenemos etiquetas con el precio exacto donde se negoció la mayor cantidad de volumen. La etiqueta superior del gráfico nos muestra el precio más alto, y la última etiqueta que observamos es la de la parte baja, que nos indica el precio más bajo. Estas etiquetas mostradas están dentro del rango de retroceso definido o Lookback Length, que podemos configurar en nuestro indicador. Como observamos, el algoritmo nos muestra una fuerte confluencia entre los canales de soporte Macro y las etiquetas del perfil de volumen, lo que nos confirma las áreas más fuertes del rango. Por último, después de hacer los cálculos de soportes y resistencias desde tres perspectivas distintas, el algoritmo nos proporciona una visión macro del precio en forma de líneas de tendencia. Es decir, nos muestra soportes y resistencias en forma de canales diagonales donde tendremos representadas las tendencias en el mercado y áreas en las que el precio históricamente ha encontrado dificultades para avanzar o retroceder, lo que podemos corroborar con los soportes y resistencias de los que hablamos al principio. Como observamos en el gráfico, el algoritmo también nos muestra las etiquetas con el precio exacto donde se encuentran los soportes angulares del precio y las resistencias angulares. Estos cálculos son importantísimos, ya que nos ofrecen una perspectiva de tendencia y podemos tener una visión de hacia dónde se dirige el precio, combinando estos con los otros cálculos de soportes y resistencias. Recuerden que todos los cálculos anteriores tienen su propia alerta para cuando los soportes o resistencias se quiebren o en su caso, se creen nuevos canales, también cuando haya una ruptura de un "box" o una confirmación de ruptura. El segundo tipo de alerta del indicador está configurada para que nuestros indicadores trabajen para nosotros sin necesidad de estar presentes en el gráfico, esto mediante una programación especial dentro del código del indicador que realizará compras y ventas automáticas en nuestro Exchange de preferencia mediante una alerta configurada para el bot 3Commas. Solo bastará con que pongamos nuestro número de Bot o Bot ID que da el proveedor de 3Commas y lo insertemos en la alerta. Todos los indicadores premium tienen en su configuración una explicación detallada sobre dónde poner tus Bot ID.
Pine Script®指標
由PINE_LABS提供
3
Normalized, Variety, Fast Fourier Transform Explorer [Loxx]Normalized, Variety, Fast Fourier Transform Explorer demonstrates Real, Cosine, and Sine Fast Fourier Transform algorithms. This indicator can be used as a rule of thumb but shouldn't be used in trading. What is the Discrete Fourier Transform? In mathematics, the discrete Fourier transform (DFT) converts a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency. The interval at which the DTFT is sampled is the reciprocal of the duration of the input sequence. An inverse DFT is a Fourier series, using the DTFT samples as coefficients of complex sinusoids at the corresponding DTFT frequencies. It has the same sample-values as the original input sequence. The DFT is therefore said to be a frequency domain representation of the original input sequence. If the original sequence spans all the non-zero values of a function, its DTFT is continuous (and periodic), and the DFT provides discrete samples of one cycle. If the original sequence is one cycle of a periodic function, the DFT provides all the non-zero values of one DTFT cycle. What is the Complex Fast Fourier Transform? The complex Fast Fourier Transform algorithm transforms N real or complex numbers into another N complex numbers. The complex FFT transforms a real or complex signal x in the time domain into a complex two-sided spectrum X in the frequency domain. You must remember that zero frequency corresponds to n = 0, positive frequencies 0 < f < f_c correspond to values 1 ≤ n ≤ N/2 −1, while negative frequencies −fc < f < 0 correspond to N/2 +1 ≤ n ≤ N −1. The value n = N/2 corresponds to both f = f_c and f = −f_c. f_c is the critical or Nyquist frequency with f_c = 1/(2*T) or half the sampling frequency. The first harmonic X corresponds to the frequency 1/(N*T). The complex FFT requires the list of values (resolution, or N) to be a power 2. If the input size if not a power of 2, then the input data will be padded with zeros to fit the size of the closest power of 2 upward. What is Real-Fast Fourier Transform? Has conditions similar to the complex Fast Fourier Transform value, except that the input data must be purely real. If the time series data has the basic type complex64, only the real parts of the complex numbers are used for the calculation. The imaginary parts are silently discarded. What is the Real-Fast Fourier Transform? In many applications, the input data for the DFT are purely real, in which case the outputs satisfy the symmetry X(N-k)=X(k) and efficient FFT algorithms have been designed for this situation (see e.g. Sorensen, 1987). One approach consists of taking an ordinary algorithm (e.g. Cooley–Tukey) and removing the redundant parts of the computation, saving roughly a factor of two in time and memory. Alternatively, it is possible to express an even-length real-input DFT as a complex DFT of half the length (whose real and imaginary parts are the even/odd elements of the original real data), followed by O(N) post-processing operations. It was once believed that real-input DFTs could be more efficiently computed by means of the discrete Hartley transform (DHT), but it was subsequently argued that a specialized real-input DFT algorithm (FFT) can typically be found that requires fewer operations than the corresponding DHT algorithm (FHT) for the same number of inputs. Bruun's algorithm (above) is another method that was initially proposed to take advantage of real inputs, but it has not proved popular. There are further FFT specializations for the cases of real data that have even/odd symmetry, in which case one can gain another factor of roughly two in time and memory and the DFT becomes the discrete cosine/sine transform(s) (DCT/DST). Instead of directly modifying an FFT algorithm for these cases, DCTs/DSTs can also be computed via FFTs of real data combined with O(N) pre- and post-processing. What is the Discrete Cosine Transform? A discrete cosine transform ( DCT ) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT , first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images (such as JPEG and HEIF, where small high-frequency components can be discarded), digital video (such as MPEG and H.26x), digital audio (such as Dolby Digital, MP3 and AAC ), digital television (such as SDTV, HDTV and VOD ), digital radio (such as AAC+ and DAB+), and speech coding (such as AAC-LD, Siren and Opus). DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, telecommunication devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations. The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. The DCTs are generally related to Fourier Series coefficients of a periodically and symmetrically extended sequence whereas DFTs are related to Fourier Series coefficients of only periodically extended sequences. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), whereas in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common. The most common variant of discrete cosine transform is the type-II DCT , which is often called simply "the DCT". This was the original DCT as first proposed by Ahmed. Its inverse, the type-III DCT , is correspondingly often called simply "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform ( DST ), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data. Multidimensional DCTs ( MD DCTs) are developed to extend the concept of DCT to MD signals. There are several algorithms to compute MD DCT . A variety of fast algorithms have been developed to reduce the computational complexity of implementing DCT . One of these is the integer DCT (IntDCT), an integer approximation of the standard DCT ,: ix, xiii, 1, 141–304  used in several ISO /IEC and ITU-T international standards. What is the Discrete Sine Transform? In mathematics, the discrete sine transform (DST) is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using a purely real matrix. It is equivalent to the imaginary parts of a DFT of roughly twice the length, operating on real data with odd symmetry (since the Fourier transform of a real and odd function is imaginary and odd), where in some variants the input and/or output data are shifted by half a sample. A family of transforms composed of sine and sine hyperbolic functions exists. These transforms are made based on the natural vibration of thin square plates with different boundary conditions. The DST is related to the discrete cosine transform (DCT), which is equivalent to a DFT of real and even functions. See the DCT article for a general discussion of how the boundary conditions relate the various DCT and DST types. Generally, the DST is derived from the DCT by replacing the Neumann condition at x=0 with a Dirichlet condition. Both the DCT and the DST were described by Nasir Ahmed T. Natarajan and K.R. Rao in 1974. The type-I DST (DST-I) was later described by Anil K. Jain in 1976, and the type-II DST (DST-II) was then described by H.B. Kekra and J.K. Solanka in 1978. Notable settings windowper = period for calculation, restricted to powers of 2: "16", "32", "64", "128", "256", "512", "1024", "2048", this reason for this is FFT is an algorithm that computes DFT (Discrete Fourier Transform) in a fast way, generally in 𝑂(𝑁⋅log2(𝑁)) instead of 𝑂(𝑁2). To achieve this the input matrix has to be a power of 2 but many FFT algorithm can handle any size of input since the matrix can be zero-padded. For our purposes here, we stick to powers of 2 to keep this fast and neat. read more about this here: Cooley–Tukey FFT algorithm SS = smoothing count, this smoothing happens after the first FCT regular pass. this zeros out frequencies from the previously calculated values above SS count. the lower this number, the smoother the output, it works opposite from other smoothing periods Fmin1 = zeroes out frequencies not passing this test for min value Fmax1 = zeroes out frequencies not passing this test for max value barsback = moves the window backward Inverse = whether or not you wish to invert the FFT after first pass calculation Related indicators Real-Fast Fourier Transform of Price Oscillator STD-Stepped Fast Cosine Transform Moving Average Real-Fast Fourier Transform of Price w/ Linear Regression Variety RSI of Fast Discrete Cosine Transform Additional reading A Fast Computational Algorithm for the Discrete Cosine Transform by Chen et al. Practical Fast 1-D DCT Algorithms With 11 Multiplications by Loeffler et al. Cooley–Tukey FFT algorithm Ahmed, Nasir (January 1991). "How I Came Up With the Discrete Cosine Transform". Digital Signal Processing. 1 (1): 4–5. doi:10.1016/1051-2004(91)90086-Z. DCT-History - How I Came Up With The Discrete Cosine Transform Comparative Analysis for Discrete Sine Transform as a suitable method for noise estimation
Pine Script®指標
由loxx提供
3
Helme-Nikias Weighted Burg AR-SE Extra. of Price [Loxx]Helme-Nikias Weighted Burg AR-SE Extra. of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm, but unlike the usual WB algo, this one uses Helme-Nikias weighting. This method is commonly used in speech modeling and speech prediction engines. This is a linear method of forecasting data. You'll notice that this method uses a different weighting calculation vs Weighted Burg method. This new weighting is the following: w = math.pow(array.get(x, i - 1), 2), the squared lag of the source parameter and w += math.pow(array.get(x, i), 2), the sum of the squared source parameter This take place of the rectangular, hamming and parabolic weighting used in the Weighted Burg method Also, this method includes Levinson–Durbin algorithm. as was already discussed previously in the following indicator: Levinson-Durbin Autocorrelation Extrapolation of Price What is Helme-Nikias Weighted Burg Autoregressive Spectral Estimate Extrapolation of price? In this paper a new stable modification of the weighted Burg technique for autoregressive (AR) spectral estimation is introduced based on data-adaptive weights that are proportional to the common power of the forward and backward AR process realizations. It is shown that AR spectra of short length sinusoidal signals generated by the new approach do not exhibit phase dependence or line-splitting. Further, it is demonstrated that improvements in resolution may be so obtained relative to other weighted Burg algorithms. The method suggested here is shown to resolve two closely-spaced peaks of dynamic range 24 dB whereas the modified Burg schemes employing rectangular, Hamming or "optimum" parabolic windows fail. Data inputs Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar LastBar - bar where to start the prediction PastBars - how many bars back to model LPOrder - order of linear prediction model; 0 to 1 FutBars - how many bars you want to forward predict Things to know Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages. This indicator repaints Further reading A high-resolution modified Burg algorithm for spectral estimation Related Indicators Levinson-Durbin Autocorrelation Extrapolation of Price Weighted Burg AR Spectral Estimate Extrapolation of Price
Pine Script®指標
由loxx提供
MIDAS VWAP Jayy his is just a bash together of two MIDAS VWAP scripts particularly AkifTokuz and drshoe. I added the ability to show more MIDAS curves from the same script. The algorithm primarily uses the "n" number but the date can be used for the 8th VWAP I have not converted the script to version 3. To find bar number go into "Chart Properties" select " "background" then select Indicator Titles and "Indicator values". When you place your cursor over a bar the first number you see adjacent to the script title is the bar number. Put that in the dialogue box midline is MIDAS VWAP . The resistance is a MIDAS VWAP using bar highs. The resistance is MIDAS VWAP using bar lows. In most case using N will suffice. However, if you are flipping around charts inputting a specific date can be handy. In this way, you can compare the same point in time across multiple instruments eg first trading day of the year or an election date. Adding dates into the dialogue box is a bit cumbersome so in this version, it is enabled for only one curve. I have called it VWAP and it follows the typical VWAP algorithm. (Does that make a difference? Read below re my opinion on the Difference between MIDAS VWAP and VWAP ). I have added the ability to start from the bottom or top of the initiating bar. In theory in a probable uptrend pick a low of a bar for a low pivot and start the MIDAS VWAP there using the support. For a downtrend use the high pivot bar and select resistance. The way to see is to play with these values. Difference between MIDAS VWAP and the regular VWAP MIDAS itself as described by Levine uses a time anchored On-Balance Volume (OBV) plotted on a graph where the horizontal (abscissa) arm of the graph is cumulative volume not time. He called his VWAP curves Support/Resistance VWAP or S/R curves. These S/R curves are often referred to as "MIDAS curves". These are the main components of the MIDAS chart. A third algorithm called the Top-Bottom Finder was also described. (Separate script). Additional tools have been described in "MIDAS_Technical_Analysis" Midas Technical Analysis: A VWAP Approach to Trading and Investing in Today’s Markets by Andrew Coles, David G. Hawkins Copyright © 2011 by Andrew Coles and David G. Hawkins. Denoting the different way in which Levine approached the calculation. The difference between "MIDAS" VWAP and VWAP is, in my opinion, much ado about nothing. The algorithms generate identical curves albeit the MIDAS algorithm launches the curve one bar later than the VWAP algorithm which can be a pain in the neck. All of the algorithms that I looked at on Tradingview step back one bar in time to initiate the MIDAS curve. As such the plotted curves are identical to traditional VWAP assuming the initiation is from the candle/bar midpoint. How did Levine intend the curves to be drawn? On a reversal, he suggested the initiation of the Support and Resistance VVWAP (S/R curve) to be started after a reversal. It is clear in his examples this happens occasionally but in many cases he initiates the so-called MIDAS S/R VWAP right at the reversal point. In any case, the algorithm is problematic if you wish to start a curve on the first bar of an IPO . You will get nothing. That is a pain. Also in Levine's writings, he describes simply clicking on the point where a S/R VWAP is to be drawn from. As such, the generally accepted method of initiating the curve at N-1 is a practical and sensible method. The only issue is that you cannot draw the curve from the first bar on any security, as mentioned without resorting to the typical VWAP algorithm. There is another difference. VWAP is launched from the middle of the bar (as per AlphaTrends), You can also launch from the top of the bar or the bottom (or anywhere for that matter). The calculation proceeds using the top or bottom for each new bar. The potential applications are discussed in the MIDAS Technical Analysis book.
Pine Script®指標
由jayy提供
21
Scalper - Pattern Recognition & Price Action Scalper - Pattern Recognition & Price Action Educational Indicator **Originality and Educational Innovation** **Why This Comprehensive Integration Merits a New Publication** This indicator addresses a specific educational gap in technical analysis learning: **the lack of integrated pattern recognition systems that systematically combine traditional candlestick analysis with modern price action concepts in a unified confluence framework**. While individual components like moving averages, RSI, CCI, and basic candlestick patterns are well-established tools, this indicator's originality lies in its **comprehensive educational methodology** and **systematic multi-signal confluence engine**. **Original Educational Framework:** 1. **Multi-Layer Confluence System**: Original algorithm that systematically combines 6+ different signal categories with customizable threshold requirements for educational analysis 2. **Modern Price Action Integration**: Educational implementation of Fair Value Gaps (FVG) and Order Block detection integrated with traditional pattern recognition 3. **Dynamic Support/Resistance Education**: Original strength-validated S/R system using statistical touch-count methodology rather than simple pivot points 4. **Comprehensive Pattern Library**: Educational collection combining basic and advanced candlestick patterns with mathematical validation criteria 5. **Customizable Multi-Timeframe Framework**: Educational tool allowing cross-timeframe analysis for understanding trend context **How the Educational Components Work Together Systematically** **Educational Layer 1 - Trend Context Understanding**: Multi-timeframe moving averages (customizable SMA/EMA/WMA/VWMA/HMA) establish directional bias for learning trend analysis **Educational Layer 2 - Pattern Recognition Learning**: 15+ mathematically-defined candlestick patterns from basic engulfing to complex three-soldier formations demonstrate systematic pattern identification **Educational Layer 3 - Modern Price Action Education**: Fair Value Gaps and Order Blocks teach institutional footprint recognition and market structure analysis **Educational Layer 4 - Dynamic S/R Framework**: Strength-validated support/resistance levels demonstrate statistical validation methodology **Educational Layer 5 - Momentum Analysis Education**: RSI and CCI extreme reversal detection teaches momentum exhaustion identification **Educational Layer 6 - Confluence Analysis Methodology**: Original multi-signal combination system demonstrates how to systematically analyze multiple factors This integrated educational approach provides a comprehensive framework for learning how different technical analysis concepts work together in real market conditions, addressing the common problem of studying indicators in isolation. **Detailed Technical Implementation and Educational Methodology** **Original Multi-Signal Confluence Algorithm** **Educational Confluence Scoring System:** The indicator implements an original systematic approach to signal combination: ``` Bullish Signal Categories (Educational Analysis): - Candlestick Patterns: Strong Engulfing, Morning Star, Hammer, Three White Soldiers - Momentum Indicators: RSI oversold exit (75→70), CCI extreme reversal (-200→-180) - Price Action: Volume-confirmed breakouts above resistance levels Bearish Signal Categories (Educational Analysis): - Candlestick Patterns: Bearish Engulfing, Evening Star, Hanging Man, Three Black Crows - Momentum Indicators: RSI overbought exit (25→30), CCI extreme reversal (200→180) - Price Action: Volume-confirmed breakdowns below support levels Original Confluence Calculation: User-configurable minimum threshold (2-6 signals required) Real-time signal counting with dynamic visual feedback Educational labels showing current signal strength ``` **Why This Systematic Approach is Original:** Most indicators show patterns individually without systematic combination methodology. This indicator provides an educational framework for understanding how to weight and combine different types of analysis systematically. **Advanced Pattern Recognition with Educational Validation** **Original Pattern Validation Methodology:** Each pattern includes multiple educational validation criteria: ``` Strong Engulfing Educational Criteria: - Body size > ATR (volatility filter for market significance) - Current body > previous body (strength confirmation) - Complete price engulfment (mathematical validation) - Volume confirmation (market participation validation) Morning/Evening Star Educational Framework: - First candle: Directional (bull/bear confirmation) - Second candle: Indecision (body < 30% of current body) - Third candle: Reversal confirmation with penetration validation Three Soldiers/Crows Educational Requirements: - Three consecutive candles meeting directional criteria - Each candle body > ATR * 0.5 (significance filter) - Progressive price advancement (momentum validation) ``` **Modern Price Action Educational Implementation** **Original Fair Value Gap Detection Algorithm:** ``` Educational FVG Identification: Bullish FVG: current_low > high AND close > open AND close < open Bearish FVG: current_high < low AND close < open AND close > open Educational Purpose: Understanding institutional inefficiencies Visual Education: Semi-transparent boxes showing gap zones Practical Learning: Identifying potential reversal or continuation areas ``` **Original Order Block Educational Detection:** ``` Educational Order Block Criteria: Bullish: Previous candle bullish AND current close > previous high AND volume > 20-period average Bearish: Previous candle bearish AND current close < previous low AND volume > 20-period average Educational Purpose: Recognizing institutional accumulation/distribution Visual Education: Highlighted zones showing institutional interest Learning Application: Understanding market structure concepts ``` **Dynamic Support/Resistance Educational System** **Original Strength-Validation Algorithm:** ``` Educational S/R Methodology: 1. Identify recent swing highs/lows over user-defined period 2. Calculate ATR-based tolerance levels (volatility adjustment) 3. Count historical touches within tolerance (statistical validation) 4. Create levels only when touches ≥ minimum strength requirement 5. Project levels forward for future price interaction analysis Educational Advantage over Simple Pivots: - Statistical validation through touch counting - Volatility-adjusted tolerance (adapts to market conditions) - Strength-based filtering (reduces noise) - Forward-looking projection (practical application) ``` **Comprehensive Educational Features and Customization** **Multi-Timeframe Educational Framework** - **MA 1**: SMA 34 with customizable timeframe (short-term trend education) - **MA 2**: SMA 63 with customizable timeframe (medium-term trend education) - **MA 3**: User-selectable type (SMA/EMA/WMA/VWMA/HMA) with customizable timeframe - **MA 4**: User-selectable type for long-term trend context - **Educational Purpose**: Understanding cross-timeframe trend analysis **Educational Pattern Detection Parameters** - **ATR Length**: Volatility measurement for pattern significance (default: 14) - **Volume Validation**: Historical volume comparison for pattern confirmation - **Mathematical Ratios**: Precise wick/body ratios for pattern classification - **Strength Filters**: Size and volume thresholds ensuring pattern validity **Educational Price Action Configuration** - **FVG Detection**: Three-candle gap analysis with directional confirmation - **Order Block Settings**: Volume threshold and visual projection length - **S/R Parameters**: Detection period, minimum touch count, tolerance calculation - **Educational Visualization**: Clear boxes and labels for learning identification **Customizable Educational Dashboard** - **Position Control**: 6 different dashboard positions for optimal viewing - **Color Customization**: Full color control for text, backgrounds, and signals - **Real-Time Education**: Current RSI, CCI, ATR values for learning - **Signal Analysis**: Live bullish/bearish signal counts for confluence education - **Educational Branding**: Clear identification as learning tool Educational Applications and Learning Outcomes **Progressive Learning Structure for Technical Analysis** **Beginner Level Education:** - Moving average trend identification across timeframes - Basic candlestick pattern recognition with mathematical criteria - Introduction to support/resistance concepts with visual validation **Intermediate Level Education:** - Multi-pattern analysis and pattern strength assessment - RSI and CCI momentum analysis with extreme level identification - Volume analysis integration with pattern confirmation **Advanced Level Education:** - Fair Value Gap theory and practical institutional analysis - Order Block detection and market structure understanding - Multi-signal confluence methodology and systematic signal combination **Practical Educational Workflow** 1. **Setup Phase**: Configure moving averages for chosen timeframes and enable desired patterns 2. **Context Analysis**: Study trend direction using multi-timeframe MA alignment 3. **Pattern Study**: Identify candlestick formations meeting mathematical validation criteria 4. **Price Action Learning**: Analyze FVG and Order Block formations for institutional insight 5. **Confluence Education**: Count and analyze multiple signal types for probability assessment 6. **Real-Time Practice**: Use dashboard for ongoing market analysis and signal tracking **Educational Risk Management Concepts** - **Pattern Reliability Understanding**: Learning which patterns have higher success rates - **Signal Strength Analysis**: Understanding how confluence affects probability - **Market Context Education**: Learning when patterns are most/least reliable - **Systematic Analysis**: Developing consistent methodology for market evaluation **Technical Requirements and Optimization** **Performance Optimization for Education** - **Visual Element Limits**: 500 maximum boxes, lines, and labels for stable performance - **Efficient Calculations**: S/R updates every 10 bars for smooth operation - **Memory Management**: Proper array management for dynamic level storage - **Clean Interface**: Organized input groups for easy educational navigation **Educational Visualization Standards** - **Color-Coded Learning**: Consistent color scheme for pattern identification - **Clear Labeling**: Educational text labels for all major patterns and signals - **Professional Layout**: Organized visual hierarchy for systematic learning - **Customizable Display**: User control over visual elements and positioning **Educational Disclaimers and Learning Focus** **Educational Purpose Statement** This indicator is designed as a comprehensive educational tool for learning technical analysis concepts. It demonstrates how traditional candlestick analysis, modern price action concepts, and systematic confluence methodology can be integrated for educational purposes. **Learning Tool Disclaimer** The indicator provides an educational framework for studying: - Traditional and modern technical analysis integration - Systematic pattern recognition methodology - Multi-signal confluence analysis techniques - Price action and market structure principles - Statistical validation approaches for support/resistance **Risk Education and Understanding** Technical analysis education requires understanding that: - Patterns and indicators show historical relationships, not future guarantees - Confluence analysis increases probability understanding but not certainty - Educational study should focus on methodology rather than signal generation - Proper risk management principles must be learned alongside technical analysis - Real market conditions may differ from educational examples **Systematic Learning Approach** This educational tool emphasizes: - **Methodology over Signals**: Focus on learning systematic analysis approaches - **Understanding over Automation**: Developing analytical skills rather than relying on automated signals - **Education over Trading**: Comprehensive learning framework for technical analysis concepts - **Progressive Development**: Building skills systematically from basic to advanced concepts **Technical Documentation and Implementation** **Original Algorithm Documentation** All custom algorithms are documented for educational transparency: - Pattern detection mathematics with specific criteria - Confluence scoring methodology with threshold requirements - Support/resistance validation with statistical touch counting - Price action detection with institutional footprint identification **Educational Code Structure** - **Comprehensive Comments**: Every section includes educational purpose explanation - **Version Tracking**: Clear version documentation for educational development - **Performance Notes**: Optimization techniques explained for learning - **Customization Guidance**: Clear parameter explanation for educational experimentation --- **Educational Innovation Summary:** This implementation represents an original approach to technical analysis education, systematically combining traditional pattern recognition with modern price action concepts in an integrated confluence framework. The educational methodology addresses common learning gaps by providing systematic approaches to multi-signal analysis, statistical validation, and institutional footprint recognition. **Learning Value:** The comprehensive educational framework eliminates the need to study multiple separate indicators by providing an integrated learning platform that demonstrates how different technical analysis concepts work together systematically in real market conditions. **Educational Commitment:** This indicator prioritizes education and systematic learning over simple signal generation, providing traders with the analytical framework needed to develop comprehensive technical analysis skills through hands-on practice and systematic methodology development.
Pine Script®指標
由DrFXAi提供
Sniper Divergence M.AtaogluSNIPER DIVERGENCE PRO - ADVANCED MULTI-TIMEFRAME DIVERGENCE DETECTOR DESCRIPTION: Sniper Divergence Pro is a sophisticated technical analysis indicator that combines RSI-based calculations with fractal analysis to detect both regular and hidden divergences across multiple timeframes. This advanced tool provides traders with precise entry and exit signals through its innovative Sniper algorithm and comprehensive visual feedback system. KEY FEATURES: 1. SNIPER ALGORITHM: - Custom RSI-based oscillator with fractal peak/valley detection - Uses Relative Moving Average (RMA) for smooth signal generation - Calculates momentum changes with mathematical precision - Provides real-time divergence analysis with minimal lag 2. DIVERGENCE DETECTION: - Regular Bullish Divergence: Price makes lower lows while indicator makes higher lows - Regular Bearish Divergence: Price makes higher highs while indicator makes lower highs - Hidden Bullish Divergence: Price makes higher lows while indicator makes lower lows - Hidden Bearish Divergence: Price makes lower highs while indicator makes higher highs - Configurable sensitivity levels for both bullish and bearish signals 3. MULTI-TIMEFRAME ANALYSIS: - Simultaneous analysis across 6 timeframes: 15m, 45m, 4h, 1D, 1W, 1M - Real-time signal tracking with "bars ago" information - Comprehensive signal table showing current status across all timeframes - Sniper value display for each timeframe for trend confirmation 4. VISUAL ENHANCEMENTS: - Neon color scheme optimized for dark themes - Dynamic color-coded Sniper line based on market conditions - Background fill areas for overbought/oversold zones - Peak and valley point markers for fractal analysis - Horizontal reference lines with clear level indicators 5. ALERT SYSTEM: - Four distinct alert conditions for different signal types - Real-time notification system for immediate signal detection - Professional-grade alert messages for trading automation TECHNICAL SPECIFICATIONS: CALCULATION METHOD: The indicator uses a modified RSI calculation with fractal analysis: - Source: Close price (configurable) - Period: 21 (default, adjustable 1-1000) - Algorithm: RMA-based momentum calculation with fractal peak/valley detection - Divergence Logic: Price vs. indicator comparison using fractal points SIGNAL LEVELS: - Super Buy Zone: 0-12 (Strong bullish momentum) - Strong Buy Zone: 12-20 (Moderate bullish momentum) - Neutral Lower: 20-30 (Weak bullish to neutral) - Neutral Upper: 30-40 (Weak bearish to neutral) - Strong Sell Zone: 40-50 (Moderate bearish momentum) - Super Sell Zone: 50+ (Strong bearish momentum) DIVERGENCE SETTINGS: - Bullish Divergence Level: 12 (Minimum level for detection) - Bearish Divergence Level: 35 (Maximum level for detection) - Hidden Divergence: Enabled by default for professional signals USAGE INSTRUCTIONS: 1. BASIC SETUP: - Apply to any chart timeframe - Default settings work well for most markets - Adjust RSI period for different market conditions 2. SIGNAL INTERPRETATION: - Green triangles: Bullish divergence signals (buy opportunities) - Red triangles: Bearish divergence signals (sell opportunities) - X-cross symbols: Hidden divergence signals (stronger signals) - Circle markers: Fractal peak/valley points 3. MULTI-TIMEFRAME CONFIRMATION: - Enable signal table for comprehensive analysis - Look for signal alignment across multiple timeframes - Use "NOW" indicators for current signal detection - Monitor Sniper values for trend confirmation 4. RISK MANAGEMENT: - Use divergences as confirmation, not standalone signals - Combine with other technical analysis tools - Set appropriate stop-loss levels - Consider market context and volatility ADVANTAGES: 1. ACCURACY: Fractal-based detection reduces false signals 2. VERSATILITY: Works across all market types and timeframes 3. VISIBILITY: Clear visual feedback with neon color scheme 4. COMPREHENSIVE: Multi-timeframe analysis in single indicator 5. PROFESSIONAL: Advanced algorithms suitable for serious traders 6. CUSTOMIZABLE: Extensive parameter adjustment options LIMITATIONS: 1. LAG: Higher RSI periods may introduce signal delay 2. FALSE SIGNALS: Market noise can generate occasional false positives 3. CONTEXT DEPENDENT: Requires market condition consideration 4. LEARNING CURVE: Advanced features require understanding RECOMMENDED MARKETS: - Forex pairs (all timeframes) - Cryptocurrencies (4h and daily preferred) - Stock indices (daily and weekly) - Commodities (4h and daily) RISK DISCLAIMER: This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always conduct your own analysis and use proper risk management. Trading involves substantial risk of loss and is not suitable for all investors. TECHNICAL REQUIREMENTS: - TradingView Pro or higher recommended - Pine Script v6 compatible - Stable internet connection for real-time data - Sufficient chart history for accurate calculations This indicator represents a significant advancement in divergence detection technology, combining traditional RSI concepts with modern fractal analysis to provide traders with a comprehensive tool for identifying high-probability trading opportunities across multiple timeframes.
Pine Script®指標
由MUCAHIT_ATA_ATAOGLU提供
1
[Pandora] Error Function Treasure Trove - ERF/ERFI/Sigmoids+PRAISE: At this time, I have to graciously thank the wonderful minds behind the new "Pine Profiler Mode" (PPM). Directly prior to this release, it allowed me to ascertain script performance even more. While I usually write mostly in highly optimized Pine code, PPM visually identified a few bottlenecks that would otherwise be hard to identify. Anyone who contributed to PPMs creation and testing before release... BRAVO!!! I commend all of those who assisted in it's state-of-the-art engineering and inception, well done! BACKSTORY: This script is specifically being released in defense of another member, an exceptionally unique PhD. It was brought to my attention that a script-mod-event occurred, regarding the publishing of a measly antiquated error function (ERF) calculation within his script. This sadly resulted in the now former member jumping ship after receiving unmannerly responses amidst his curious inquiries as to why his erf() was modded. To forbid rusty and rudimentary formulations because a mod-on-duty is temporally offended by a non-nefarious release of code, is in MY opinion an injustice to principles of perpetuating open-source code intended to benefit thousands to millions of community members. While Pine is the heart and soul of TV, the mathematical concepts contributed from the minds of members is the inspirational fuel of curiosity that powers it's pertinent reason to exist and evolve. It is an indisputable fact that most members are not greatly skilled Pine Poets. Many members may be incapable of innovating robust function code in Pine, even if they have one or more PhDs. We ALL come from various disciplines of mathematical comprehension and education. Some mathematicians are not greatly skilled at coding, while some coders are not exceptional at math. So... what am I to do to attempt to resolve this circumstantial challenge??? Those who know me best are aware that I will always side with "the right side of history" in order to accomplish my primary self-defined missions I choose to accept. Serving as an algorithmic advocate, I felt compelled to intercede by compiling numerous error functions into elegant code of very high caliber that any and every TV member may choose to employ, so this ERROR never happens again. After weeks of contemplation into algorithms I knew little about, I prioritized myself to resolve an unanticipated matter by creating advanced formulas of exquisitely crafted error functions refined to the best of my current abilities. My aversion for unresolved problems motivated me to eviscerate error function insufficiencies with many more rigid formulations beyond what is thought to exist. ERF needed a proper algorithmic exorcism anyways. In my furiosity, I contemplated an array of madMAXimum diplomatic demolition methods, choosing the chain saw massacre technique to slaughter dysfunctionalities I encountered on a battered ERF roadway. This resulted in prolific solutions that should assuredly endure the test of time. Poetically, as you will come to see, I am ripping the lid off of Pandora's box of error functions in this case to correct wrongs into a splendid bundle of rights for members. INTENTION: Error function (ERF) enthusiasts... PREPARE FOR GLORY!! The specific purpose of this script is to deprecate classic error functions with the creation of a fierce and formidable army of superior formulations, each having varying attributes of computational complexity with differing absolute error ranges in their results for multiple compute scenarios. This is NOT an indicator... It is intended to allow members to embark on endeavors to advance the profound knowledge base of this growing worldwide community of 60+ million inquisitive minds. For those of you who believe computational mathematics and statistics is near completion at its finest; I am here to inform you, this is ridiculous to ponder. We are no where near statistical excellence that can and will exist eventually. At this time, metaphorically speaking, we are merely scratching microns off of the surface of the skin of a statistical apple Isaac Newton once pondered. THIS RELEASE: Following weeks of pondering methodical experiments beyond the ordinary, I am liberating these wild notions of my error function explorations to the entire globe as copyleft code, not just Pine. This Pandora's basket of ERFs is being openly disclosed for the sake of the sanctity of mathematics, empirical science (not the garbage we are told by CONTROLocrats to blindly trust), revolutionary cutting edge engineering, cosmology, physics, information technology, artificial intelligence, and EVERY other mathematical branch of human knowledge being discovered over centuries. I do believe James Glaisher would favor my aims concerning ERF aspirations embracing the "Power of Pine". The included functions are intended for TV members to use in any way they see fit. This is a gift to ALL members to foster future innovative excellence on this platform. Any attempt to moderate this code without notification of "self-evident clear and just cause" will be considered an irrevocable egregious action. The original foundational PURPOSE of establishing script moderation (I clearly remember) was primarily to maintain active vigilance over a growing community against intentional nefarious actions and/or behaviors in blatant disrespect to other author's works AND also thwart rampant copypasting bandit operations, all while accommodating balanced principles of fairness for an educational community cause via open source publishing that should support future algorithmic inventions well beyond my lifespan. APPLICATIONS: The related error functions are used in probability theory, statistics, and numerous and engineering scientific disciplines. Its key characteristics and applications are innumerable in computational realms. Its versatility and significance make it a fundamental tool in arenas of quantitative analysis and scientific research... Probability Theory - Is widely used in probability theory to calculate probabilities and quantiles of the normal distribution. Statistics - It's related to the Gaussian integral and plays a crucial role in statistics, especially in hypothesis testing and confidence interval calculations. Physics - In physics, it arises in the study of diffusion equations, quantum mechanics, and heat conduction problems. Engineering - Applications exist in engineering disciplines such as signal processing, control theory, and telecommunications. Error Analysis - It's employed in error analysis and uncertainty quantification. Numeric Approximations - Due to its lack of a closed-form expression, numerical methods are often employed to approximate erf/erfi(). AI, LLMs, & MACHINE LEARNING: The error function (ERF) is indispensable to various AI applications, particularly due to its relation to Gaussian distributions and error analysis. It is used in Gaussian processes for regression and classification, probabilistic inference for Bayesian networks, soft margin computation in SVMs, neural networks involving Gaussian activation functions or noise, and clustering algorithms like Gaussian Mixture Models. Improved ERF approximations can enhance precision in these applications, reduce computational complexity, handle outliers and noise better, and improve optimization and convergence, possibly leading to more accurate, efficient, and robust AI systems. BONUS ALGORITHMS: While ERFs are versatile, its opposite also exists in the form of inverse error functions (ERFIs). I have also included a modified form of the inverse fisher transform along side MY sigmoid (sigmyod). I am uncertain what sigmyod() may be used for, but it's a culmination of my examinations deep into "sigmoid domains", something I am fascinated by. Whatever implications it may possess, I am unveiling it along with it's cousin functions. For curious minds, this quality of composition seen here is ideally what underlies what I would term "Pandora functionality" that empowers my Pandora indication. I go through hordes of formulations, testing, and inspection to find what appears to be the most beneficial logical/mathematical equation to apply... SCRIPT OPERATION: To showcase the characteristics and performance of my ERF/ERFI formulations, I devised a multi-modal script. By using bar_index , I generated a broad sequence of numeric values to input into the first ERF/ERFI parameter. These sequences allow you to inspect the contours of the error function's outputs for both ERF and ERFI. When combined with compute-intensive precision functions (CIPFs), the polynomial function output values can be subtracted from my CIPFs to obtain results of absolute error, displaying the accuracy of the many polynomial estimation functions I tuned in testing for Pine's float environment. A host of numeric input settings are wildly adjustable to inspect values/curvatures across the range of numeric input sequences. Very large numbers, such as Divisor:100,000,100/Offset:200,000,000 for ERF modes or... Divisor:100,000,100/Offset:100,000,000 for ERFI modes, will display miniscule output values calculated from input values in close proximity to 0.0 for the various estimates, similar to a microscope. ERFI approximations very near in proximity to +/-1.0 will always yield large deviations of absolute error. Dragging/zooming your chart or using the Offset input will aid with visually clipping off those ERFI extremes where float precision functions cannot suffice. NOTICE: perf() and perfi() are intended for precision computation (as good as it basically gets) in a float environment. However, they are CPU intensive (especially perfi). I wouldn't recommend these being used in ANY Pine script unless it's an "absolute necessity" to do so to accomplish your goal. I only built them to obtain "absolute error curvatures" of the error functions for the polynomial approximations. These are visible in the accuracy modes in the indicator Settings.
Pine Script®指標
由ImmortalFreedom提供
7
Ocs Ai TraderThis script perform predictive analytics from a virtual trader perspective! It acts as an AI Trade Assistant that helps you decide the optimal times to buy or sell securities, providing you with precise target prices and stop-loss level to optimise your gains and manage risk effectively. System Components The trading system is built on 4 fundamental layers : Time series Processing layer Signal Processing layer Machine Learning Virtual Trade Emulator Time series Processing layer This is first component responsible for handling and processing real-time and historical time series data. In this layer Signals are extracted from averages such as : volume price mean, adaptive moving average Estimates such as : relative strength stochastics estimates on supertrend Signal Processing layer This second layer processes signals from previous layer using sensitivity filter comprising of an Probability Distribution Confidence Filter The main purpose here is to predict the trend of the underlying, by converging price, volume signals and deltas over a dominant cycle as dimensions and generate signals of action. Key terms Dominant cycle is a time cycle that has a greater influence on the overall behaviour of a system than other cycles. The system uses Ehlers method to calculate Dominant Cycle/ Period. Dominant cycle is used to determine the influencing period for the underlying. Once the dominant cycle/ period is identified, it is treated as a dynamic length for considering further calculations Predictive Adaptive Filter to generate Signals and define Targets and Stops An adaptive filter is a system with a linear filter that has a transfer function controlled by variable parameters and a means to adjust those parameters according to an optimisation algorithm. Because of the complexity of the optimisation algorithms, almost all adaptive filters are digital filters. Thus Helping us classify our intent either long side or short side The indicator use Adaptive Least mean square algorithm, for convergence of the filtered signals into a category of intents, (either buy or sell) Machine Learning The third layer of the System performs classifications using KNN K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique. K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories. K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suite category by using K- NN algorithm. K-NN algorithm can be used for Regression as well as for Classification but mostly it is used for the Classification problems. Virtual Trade Emulator In this last and fourth layer a trade assistant is coded using trade emulation techniques and the Lines and Labels for Buy / Sell Signals, Targets and Stop are forecasted! How to use The system generates Buy and Sell alerts and plots it on charts Buy signal Buy signal constitutes of three targets {namely T1, T2, T3} and one stop level Sell signal Sell signal constitutes of three targets {namely T1, T2, T3} and one stop level What Securities will it work upon ? Volume Informations must be present for the applied security The indicator works on every liquid security : stocks, future, forex, crypto, options, commodities What TimeFrames To Use ? You can use any Timeframe, The indicator is Adaptive in Nature, I personally use timeframes such as : 1m, 5m 10m, 15m, ..... 1D, 1W This Script Uses Tradingview Premium features for working on lower timeframes In case if you are not a Tradingview premium subscriber you should tell the script that after applying on chart, this can be done by going to settings and unchecking "Is your Tradingview Subscription Premium or Above " Option How To Get Access ? You will need to privately message me for access mentioning you want access to "Ocs Ai Trader" Use comment box only for constructive comments. Thanks !
Pine Script®指標
由Ankit_1618提供
117
Bist Manipulation [Projeadam] OVERVIEW | GENEL BAKIŞ ENG: Indicator that detects manipulation candles according to changing market conditions. TR: Değişen piyasa koşullarına göre manipülasyon mumlarını tespit eden gösterge. ENG: IMPORTANT NOTE: This indicator works in BIST Market and only in Future Parities. Example ->> PETKM1! --SASA1! TR: ÖNEMLİ NOT: Bu indikatör BİST Piyasasında ve sadece Future Paritelerde Çalışır. Örnek- >> PETKM1! -- SASA1! ENG: Market makers manipulate the market because most people who trade on the stock exchange act with their emotions and are forced to close the transaction at a loss. TR: Piyasada market yapıcı oluşumlar manipülasyon yaparlar çünkü borsada işlem alan insanların birçoğu duygularıyla hareket eder ve zararla işlem kapatılmaya zorlanır. ENG: If we detect manipulation candles in the market, we can control our fragile psychology and close our transactions in profit by trading with market-making formations in these areas. TR: Marketde manipülasyon mumlarını tespit edersek kırılgan psikolojimizi kontrol edebilir ve bu alanlardan market yapıcı oluşumlarla beraber işlem alarak işlemlerimizi karda kapatabiliriz. ALGORITHM | ALGORİTMA ENG: With the help of this indicator, you can detect manipulation candles in the BIST exchange with the help of the algorithm I created by using volumetric data and wicks created by the price. When there is excessive volatility in price movement, the algorithm in this indicator notices this price volatility and calculates a manipulation value by dividing it by the volatility value in past price movements. TR: Bu indikatör yardımıyla hacimsel veriler ve fiyatın oluşturduğu fitillerden yararlanarak oluşturduğum algoritma yardımıyla siz de BİST borsasında manipülasyon mumlarını tespit edebilirsiniz. Fiyat hareketinde aşırı derece oynaklık olduğunda bu indikatördeki algoritma bu fiyat oynaklığını fark eder ve geçmiş fiyat hareketlerindeki oylanklık degerine bölerek bize bir manipülasyon degeri hesaplar. How does the indicator work? | Gösterge nasıl çalışır? ENG: The manipulation candle does not give us information about the direction of price movement, it is only used as an auxiliary indicator. TR: Manipülasyon mumu bize fiyat hareketinin yönü hakkında bilgi vermez sadece yardımcı bir gösterge olarak kullanılır. ENG: We show our manipulation values as columns. We draw a channel over the values we show and we understand that there is manipulation in the candle of our values above this channel. TR: Manipülasyon degerlerimiz kolonlar şeklinde gösteriyoruz. Gösterdiğimiz değerlerimizin üzerine bir kanal çizdiriyoruz ve bu kanalın üzerinde kalan değerlerimizdeki mumda manipülasyon yapıldığını anlıyoruz. ENG: The indicator shows the manipulation value in the form of columns. Our manipulation value that goes outside the channel we have determined is colored red, within the channel it is colored yellow, and below the channel it is colored green. Red columns indicate candles that are manipulations. TR: İndikatör manipülasyon degerini kolonlar şeklinde gösteriyor. Bizim belirlediğimiz kanal dışına çıkan manipülasyon degerimiz kırmızı, kanal içerisinde sarı, kanal altında yeşil olarak renklendiriliyor. Kırmızı kolonlar manipülasyon olan mumları göstermektedir. Example | Örnek ENG: In our example above, we see a manipulation candle that clears the price gaps, while the market maker clears the orders in the price gaps at the bottom to move the price up. TR: Yukarıdaki örneğimizde oluşan fiyat boşluklarını temizleyen bir manipülasyon mumu görmekteyiz, alt kısımdaki fiyat boşluklarındaki emirleri temizleyen market maker fiyatı yukarı taşımak için buradaki emirleri temizliyor. SETTINGS PANEL | AYARLAR PANELİ ENG: We have only one setting in this indicator. TR: Bu indikatörde tek ayarımız vardır. ENG: Our multiplier value determines the width of the band value formed above our manipulation value. In the chart above, our multiplier value is 3.3. If we reduce our multiplier value, our manipulation sensitivity will decrease as there will be much more candles on the band. TR: Çarpan değerimiz manipülasyon değerimizin üstünde oluşşan band değerinin genişliğini belirlemektedir.Yukarıdaki grafikte çarpan değerimiz 3.3, Eğer çarpan değerimizi azaltırsak band üstünde çok daha fazla mum olacağı için manipülasyon hassasiyetimiz azalacaktır. ENG: When we set our multiplier value to 2.3, we have a more sensitive manipulation skin and it gives signals in more candles. TR: Çarpan değerimizi 2.3 yapınca daha hassas manipülasyon derimiz oluyor ve daha fazla mumda sinyal veriyor. If you have any ideas what to add to my work to add more sources or make calculations cooler, suggest in DM .
Pine Script®指標
由projeadam提供
14
Fusion: Machine Learning SuiteThe Fusion: Machine Learning Suite combines multiple technical analysis dimensions and harnesses the predictive power of machine learning, seamlessly integrating a diverse array of classic and novel indicators to deliver precision, adaptability, and innovation. Features and Capabilities Multidimensional Analysis: Fusion: MLS integrates various technical analysis dimensions to offer a more comprehensive perspective. Machine Learning Integration: Utilizing ML algorithms, Fusion: MLS offers adaptability to market changes. Custom Indicators: Including dimensions like "Moon Lander", "Cap Line" and "Z-Pack" the indicator expands the scope of traditional technical analysis methods. Tailored Customization: With customization options, Fusion: MLS allows traders to configure the tool to suit their specific strategies and market focus. In the following sections, we'll explore the features and settings of Fusion: MLS in detail, providing insights into how it can be utilized. Major Features and Settings The indicator consists of several core components and settings, each designed to provide specific functionalities and insights. Here's an in-depth look: Machine Learning Component Distance Classifier: A Strategic Approach to Market Analysis In the world of trading and investment, the ability to classify and predict price movements is paramount. Machine learning offers powerful tools for this purpose. The Fusion: MLS indicator among others incorporates an Approximate Nearest Neighbors (ANN)* algorithm, a machine learning classification technique, and allows the selection of various distance functions . This flexibility sets Fusion: MLS apart from existing solutions. The available distance functions include: Euclidean: Standard distance metric, commonly used as a default. Chebyshev: Also known as maximum value distance. Manhattan: Sum of absolute differences. Minkowski: Generalized metric that includes Euclidean and Manhattan as special cases. Mahalanobis: Measures distance between points in a correlated space. Lorentzian: Known for its robustness to outliers and noise. *For a deeper understanding of the Approximate Nearest Neighbors (ANN) algorithm, traders are encouraged to refer to the relevant articles that can be found in the public domain. Alternative scoring system Fusion: MLS also includes a custom scoring alternative based on directional price action. "Combined: Directional" and "Alpha: Directional" scoring types represent our own directional change algorithm, simple yet effective in displaying trend direction changes early on. They are visualized by color changes when scoring becomes below or above zero. Changes in scoring quickly reflect shifts in buyer and seller sentiment. Traders may choose signals by Color Change in the indicator settings to get alerts when scoring color shifts, not waiting until the histogram crosses the zero level. Application in Trading Machine learning classification has become an integral part of modern trading, offering innovative ways to analyze and interpret financial data. Many algorithmic trading systems leverage ML classification to automate trading decisions. By continuously learning from real-time data, these systems can adapt to changing market conditions and execute trades with increased efficiency and accuracy. ML classification allows for the development of tailored trading strategies as traders can select specific algorithms, dimensions, and filters that align with their trading style, goals, and the particular market they are operating. We have integrated ML classification with traditional trading tools, such as moving averages and technical indicators. This fusion creates a more robust analysis framework, combining the strengths of classical techniques with the adaptability of machine learning. Whether used independently or in conjunction with other tools, ML classification represents a significant advancement in trading technology, opening new avenues for exploration, innovation, and success in the financial world. ML: Weighting System The Fusion: MLS indicator introduces a unique weighting system that allows traders to customize the influence of various technical indicators in the machine learning process. This feature is not only innovative but also provides a level of control and adaptability that sets it apart from other indicators. Customizable Weights The weighting system allows users to assign specific weights to different indicators such as Moon Lander, RSI, MACD, Money Flow, Bollinger Bands, Cap Line, Z-Pack, Squeeze Momentum*, and MA Crossover. These weights can be adjusted manually, providing the ability to emphasize or de-emphasize specific indicators based on the trader's strategy or market conditions. *Note, we determined via testing that the popular "Squeeze" indicator can actually be well replicated by simply using inputs of 15 & 199 in the bedrock indicator - MACD ; while we employed the standard "Squeeze" formula (developed by J. Carter ) in Fusion: MLS, traders are hereby made aware of our research findings regarding such. The weighting system's importance lies in its ability to provide a more nuanced and personalized analysis. By adjusting the weights of different indicators a trader focusing on momentum strategies might assign higher weights to the Squeeze Momentum and MA Crossover indicators, while a trader looking for volatility might emphasize RSI and Bollinger Bands. The ability to customize weights adds a layer of complexity and adaptability that is rare in standard machine-learning indicators. Custom Indicators: Moon Lander The "Moon Lander" is not just a catchy name; it's a robust feature inspired by principles from aerospace engineering and offers a unique perspective on trading analysis. Here's a conceptual overview: Fast EMA and Kalman Matrix "Moon Lander" incorporates both a Fast Exponential Moving Average (EMA) and a Kalman Matrix in its design. These two elements are combined to create a histogram, providing a specific approach to data analysis. The Kalman Matrix, or Kalman Filter, is a mathematical concept used for estimating variables that can be measured indirectly and contain noise or uncertainty. It's a standard tool in machine learning and control systems, known for its ability to provide optimal estimates based on observed data. Kalman Filter: A Navigational Tool The Kalman filter, an essential part of "Moon Lander," is a mathematical concept known for its applications in navigation and control systems used by NASA in the apollo program : Guidance in Uncertainty: Just as the Kalman filter helped guide complex aerospace missions through uncertain paths, it assists traders in navigating the often unpredictable financial markets. Filtering Noise: In trading, the Kalman filter serves to filter out market noise, allowing traders to focus on the underlying trends. Predictive Capabilities: Its ability to predict future states makes it a valuable tool for forecasting market movements and trend directions. Custom Indicators: Cap Line and Z-Pack Fusion: MLS integrates our additional proprietary custom indicators that have been published on TradingView earlier: Cap Line: Delve into the specific functionalities and applications of our proprietary "Cap Line" indicator in the published description on TradingView. Z-Pack: Explore the analytical perspectives, focused on the z-score methodology, and custom "Z-Pack" indicator by reviewing the published description on TradingView. Buy/Sell Signal Generation Algorithms Fusion: MLS offers various options for generating buy/sell signals, tailored to different trading strategies and perspectives: Fusion: Allows traders to select any number of dimensions to receive buy/sell signals from, offering customized signal generation. ML: Utilizes the machine learning ANN distance for signal generation. Color Change: Generates signals by selected scoring type color change. Displayed Dimension, Alpha Dimension: Generate signals based on specific selected dimensions. These algorithms provide flexibility in determining buy/sell signals, catering to different trading styles and market conditions. Filters Filters are used to refine and selectively include or exclude signals based on specific criteria. Rather than generating signals, these filters act as gatekeepers, ensuring that only the signals meeting certain conditions are considered. Here's an overview of the filters used: Dynamic State Predictor (DSP) The DSP employs the Kalman Matrix to evaluate existing signals by comparing the fast and slow-moving averages, both processed through the Kalman Matrix. Based on the relationship between these averages, the DSP may exclude specific signals, depending on whether they align with upward or downward trends. Average Directional Index (ADX) The ADX filter evaluates the strength of existing trends and filters out signals that do not meet the specified ADX threshold and length, focusing on significant market movements. Feature Engineering: RSI Applies a filter to the existing signals, clearing out those that do not meet the criteria for RSI overbought or oversold threshold condition. Feature Engineering: MACD Assesses existing signals to identify changes in the strength, direction, momentum, and duration of a trend, filtering out those that do not align with MACD trend direction. The Visual Component The machine learning component is an internal component. However, the indicator also offers an equally important and useful visual component. It is a graphical representation of the multiple technical analysis dimensions, that can be combined in various ways (where the name "Fusion" comes from), allowing traders to visualize the underlying data and its analysis. Displayed Dimension: Visualization and Normalization The Fusion: MLS indicator offers a "Displayed Dimension" feature that visualizes various dimensions as a histogram. These dimensions may include RSI, MAs, BBs, MACD, etc. RSI Dimension on the image + ML signals Normalization: Each dimension is normalized. If any dimension has extreme values, a Fisher transformation is applied to bring them within a reasonable range. Combined Dimension: When selecting the "Combined" option , the normalized values of the selected dimensions are combined using techniques such as standardization, normalization, or winsorization. This flexibility enables tailored visualization and analysis. Alpha Dimension: Enhancing Analysis The "Alpha Dimension" feature allows traders to select an additional dimension alongside the Displayed Dimension. This facilitates a combined analysis, enhancing the depth of insights. Theme Selection Fusion: MLS offers various themes such as "Sailfish", "Iceberg", "Moon", "Perl", "Candy" and "Monochrome" Traders can select a theme that resonates with their preference, enhancing visual appeal. There is also a "Custom" theme available that allows the user to choose the colors of the theme. Customizing Fusion: MLS for Various Markets and Strategies Fusion: MLS is designed with customization in mind. Traders can tailor the indicator to suit various markets and trading strategies. Selecting specific dimensions allows it to align with individual trading goals. Selecting Dimensions: Choose the dimensions that resonate with your trading approach, whether focusing on trend-following, momentum, or other strategies. Adjusting Parameters: Fine-tune the parameters of each dimension, including custom ones like "Moon Lander," to suit specific market conditions. Theme Customization: Select a theme that aligns with your visual preferences, enhancing your chart's readability and appeal. Utilizing Research: Leverage the underlying algorithms and research, such as machine learning classification by ANN and the Kalman filter, to deepen your understanding and application of Fusion: MLS. Alerts The indicator includes an alerting system that notifies traders when new buy or sell signals are detected. Disclaimer The information provided herein is intended for informational purposes only and should not be construed as investment advice, endorsement, nor a recommendation to buy or sell any financial instruments. Fusion: MLS is a technical analysis tool, and like all tools, it should be used with caution and in conjunction with other forms of analysis. Traders and investors are encouraged to consult with a licensed financial professional and conduct their own research before making any trading or investment decisions. Past performance of the Fusion: MLS indicator or any trading strategy does not guarantee future results, and all trading involves risk. Users of Fusion: MLS should understand the underlying algorithms and assumptions and consider their individual risk tolerance and investment goals when using this tool.
Pine Script®指標
由BitcoinSpike提供
5
AI Moving Average (Expo)█ Overview The AI Moving Average indicator is a trading tool that uses an AI-based K-nearest neighbors (KNN) algorithm to analyze and interpret patterns in price data. It combines the logic of a traditional moving average with artificial intelligence, creating an adaptive and robust indicator that can identify strong trends and key market levels. █ How It Works The algorithm collects data points and applies a KNN-weighted approach to classify price movement as either bullish or bearish. For each data point, the algorithm checks if the price is above or below the calculated moving average. If the price is above the moving average, it's labeled as bullish (1), and if it's below, it's labeled as bearish (0). The K-Nearest Neighbors (KNN) is an instance-based learning algorithm used in classification and regression tasks. It works on a principle of voting, where a new data point is classified based on the majority label of its 'k' nearest neighbors. The algorithm's use of a KNN-weighted approach adds a layer of intelligence to the traditional moving average analysis. By considering not just the price relative to a moving average but also taking into account the relationships and similarities between different data points, it offers a nuanced and robust classification of price movements. This combination of data collection, labeling, and KNN-weighted classification turns the AI Moving Average (Expo) Indicator into a dynamic tool that can adapt to changing market conditions, making it suitable for various trading strategies and market environments. █ How to Use Dynamic Trend Recognition The color-coded moving average line helps traders quickly identify market trends. Green represents bullish, red for bearish, and blue for neutrality. Trend Strength By adjusting certain settings within the AI Moving Average (Expo) Indicator, such as using a higher 'k' value and increasing the number of data points, traders can gain real-time insights into strong trends. A higher 'k' value makes the prediction model more resilient to noise, emphasizing pronounced trends, while more data points provide a comprehensive view of the market direction. Together, these adjustments enable the indicator to display only robust trends on the chart, allowing traders to focus exclusively on significant market movements and strong trends. Key SR Levels Traders can utilize the indicator to identify key support and resistance levels that are derived from the prevailing trend movement. The derived support and resistance levels are not just based on historical data but are dynamically adjusted with the current trend, making them highly responsive to market changes. █ Settings k (Neighbors): Number of neighbors in the KNN algorithm. Increasing 'k' makes predictions more resilient to noise but may decrease sensitivity to local variations. n (DataPoints): Number of data points considered in AI analysis. This affects how the AI interprets patterns in the price data. maType (Select MA): Type of moving average applied. Options allow for different smoothing techniques to emphasize or dampen aspects of price movement. length: Length of the moving average. A greater length creates a smoother curve but might lag recent price changes. dataToClassify: Source data for classifying price as bullish or bearish. It can be adjusted to consider different aspects of price information dataForMovingAverage: Source data for calculating the moving average. Different selections may emphasize different aspects of price movement. ----------------- Disclaimer The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs. My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Pine Script®指標
由Zeiierman提供
15
Adaptivity: Measures of Dominant Cycles and Price Trend [Loxx]Adaptivity: Measures of Dominant Cycles and Price Trend is an indicator that outputs adaptive lengths using various methods for dominant cycle and price trend timeframe adaptivity. While the information output from this indicator might be useful for the average trader in one off circumstances, this indicator is really meant for those need a quick comparison of dynamic length outputs who wish to fine turn algorithms and/or create adaptive indicators. This indicator compares adaptive output lengths of all publicly known adaptive measures. Additional adaptive measures will be added as they are discovered and made public. The first released of this indicator includes 6 measures. An additional three measures will be added with updates. Please check back regularly for new measures. Ehers: Autocorrelation Periodogram Band-pass Instantaneous Cycle Hilbert Transformer Dual Differentiator Phase Accumulation (future release) Homodyne (future release) Jurik: Composite Fractal Behavior (CFB) Adam White: Veritical Horizontal Filter (VHF) (future release) What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm? From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135: "Adaptive filters can have several different meanings. For example, Perry Kaufman's adaptive moving average (KAMA) and Tushar Chande's variable index dynamic average (VIDYA) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index (RSI), commodity channel index (CCI), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics. The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal." What is this Hilbert Transformer? An analytic signal allows for time-variable parameters and is a generalization of the phasor concept, which is restricted to time-invariant amplitude, phase, and frequency. The analytic representation of a real-valued function or signal facilitates many mathematical manipulations of the signal. For example, computing the phase of a signal or the power in the wave is much simpler using analytic signals. The Hilbert transformer is the technique to create an analytic signal from a real one. The conventional Hilbert transformer is theoretically an infinite-length FIR filter. Even when the filter length is truncated to a useful but finite length, the induced lag is far too large to make the transformer useful for trading. From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, pages 186-187: "I want to emphasize that the only reason for including this section is for completeness. Unless you are interested in research, I suggest you skip this section entirely. To further emphasize my point, do not use the code for trading. A vastly superior approach to compute the dominant cycle in the price data is the autocorrelation periodogram. The code is included because the reader may be able to capitalize on the algorithms in a way that I do not see. All the algorithms encapsulated in the code operate reasonably well on theoretical waveforms that have no noise component. My conjecture at this time is that the sample-to-sample noise simply swamps the computation of the rate change of phase, and therefore the resulting calculations to find the dominant cycle are basically worthless.The imaginary component of the Hilbert transformer cannot be smoothed as was done in the Hilbert transformer indicator because the smoothing destroys the orthogonality of the imaginary component." What is the Dual Differentiator, a subset of Hilbert Transformer? From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 187: "The first algorithm to compute the dominant cycle is called the dual differentiator. In this case, the phase angle is computed from the analytic signal as the arctangent of the ratio of the imaginary component to the real component. Further, the angular frequency is defined as the rate change of phase. We can use these facts to derive the cycle period." What is the Phase Accumulation, a subset of Hilbert Transformer? From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 189: "The next algorithm to compute the dominant cycle is the phase accumulation method. The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample. The phase accumulation method of cycle measurement always uses one full cycle's worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio." What is the Homodyne, a subset of Hilbert Transformer? From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 192: "The third algorithm for computing the dominant cycle is the homodyne approach. Homodyne means the signal is multiplied by itself. More precisely, we want to multiply the signal of the current bar with the complex value of the signal one bar ago. The complex conjugate is, by definition, a complex number whose sign of the imaginary component has been reversed." What is the Instantaneous Cycle? The Instantaneous Cycle Period Measurement was authored by John Ehlers; it is built upon his Hilbert Transform Indicator. From his Ehlers' book Cybernetic Analysis for Stocks and Futures: Cutting-Edge DSP Technology to Improve Your Trading by John F. Ehlers, 2004, page 107: "It is obvious that cycles exist in the market. They can be found on any chart by the most casual observer. What is not so clear is how to identify those cycles in real time and how to take advantage of their existence. When Welles Wilder first introduced the relative strength index (rsi), I was curious as to why he selected 14 bars as the basis of his calculations. I reasoned that if i knew the correct market conditions, then i could make indicators such as the rsi adaptive to those conditions. Cycles were the answer. I knew cycles could be measured. Once i had the cyclic measurement, a host of automatically adaptive indicators could follow. Measurement of market cycles is not easy. The signal-to-noise ratio is often very low, making measurement difficult even using a good measurement technique. Additionally, the measurements theoretically involve simultaneously solving a triple infinity of parameter values. The parameters required for the general solutions were frequency, amplitude, and phase. Some standard engineering tools, like fast fourier transforms (ffs), are simply not appropriate for measuring market cycles because ffts cannot simultaneously meet the stationarity constraints and produce results with reasonable resolution. Therefore i introduced maximum entropy spectral analysis (mesa) for the measurement of market cycles. This approach, originally developed to interpret seismographic information for oil exploration, produces high-resolution outputs with an exceptionally short amount of information. A short data length improves the probability of having nearly stationary data. Stationary data means that frequency and amplitude are constant over the length of the data. I noticed over the years that the cycles were ephemeral. Their periods would be continuously increasing and decreasing. Their amplitudes also were changing, giving variable signal-to-noise ratio conditions. Although all this is going on with the cyclic components, the enduring characteristic is that generally only one tradable cycle at a time is present for the data set being used. I prefer the term dominant cycle to denote that one component. The assumption that there is only one cycle in the data collapses the difficulty of the measurement process dramatically." What is the Band-pass Cycle? From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 47: "Perhaps the least appreciated and most underutilized filter in technical analysis is the band-pass filter. The band-pass filter simultaneously diminishes the amplitude at low frequencies, qualifying it as a detrender, and diminishes the amplitude at high frequencies, qualifying it as a data smoother. It passes only those frequency components from input to output in which the trader is interested. The filtering produced by a band-pass filter is superior because the rejection in the stop bands is related to its bandwidth. The degree of rejection of undesired frequency components is called selectivity. The band-stop filter is the dual of the band-pass filter. It rejects a band of frequency components as a notch at the output and passes all other frequency components virtually unattenuated. Since the bandwidth of the deep rejection in the notch is relatively narrow and since the spectrum of market cycles is relatively broad due to systemic noise, the band-stop filter has little application in trading." From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 59: "The band-pass filter can be used as a relatively simple measurement of the dominant cycle. A cycle is complete when the waveform crosses zero two times from the last zero crossing. Therefore, each successive zero crossing of the indicator marks a half cycle period. We can establish the dominant cycle period as twice the spacing between successive zero crossings." What is Composite Fractal Behavior (CFB)? All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets. Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration. Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action. The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit! This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic. CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis. What is VHF Adaptive Cycle? Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Pine Script®指標
由loxx提供
8
Monte Carlo Range Forecast [DW]This is an experimental study designed to forecast the range of price movement from a specified starting point using a Monte Carlo simulation. Monte Carlo experiments are a broad class of computational algorithms that utilize random sampling to derive real world numerical results. These types of algorithms have a number of applications in numerous fields of study including physics, engineering, behavioral sciences, climate forecasting, computer graphics, gaming AI, mathematics, and finance. Although the applications vary, there is a typical process behind the majority of Monte Carlo methods: -> First, a distribution of possible inputs is defined. -> Next, values are generated randomly from the distribution. -> The values are then fed through some form of deterministic algorithm. -> And lastly, the results are aggregated over some number of iterations. In this study, the Monte Carlo process used generates a distribution of aggregate pseudorandom linear price returns summed over a user defined period, then plots standard deviations of the outcomes from the mean outcome generate forecast regions. The pseudorandom process used in this script relies on a modified Wichmann-Hill pseudorandom number generator (PRNG) algorithm. Wichmann-Hill is a hybrid generator that uses three linear congruential generators (LCGs) with different prime moduli. Each LCG within the generator produces an independent, uniformly distributed number between 0 and 1. The three generated values are then summed and modulo 1 is taken to deliver the final uniformly distributed output. Because of its long cycle length, Wichmann-Hill is a fantastic generator to use on TV since it's extremely unlikely that you'll ever see a cycle repeat. The resulting pseudorandom output from this generator has a minimum repetition cycle length of 6,953,607,871,644. Fun fact: Wichmann-Hill is a widely used PRNG in various software applications. For example, Excel 2003 and later uses this algorithm in its RAND function, and it was the default generator in Python up to v2.2. The generation algorithm in this script takes the Wichmann-Hill algorithm, and uses a multi-stage transformation process to generate the results. First, a parent seed is selected. This can either be a fixed value, or a dynamic value. The dynamic parent value is produced by taking advantage of Pine's timenow variable behavior. It produces a variable parent seed by using a frozen ratio of timenow/time. Because timenow always reflects the current real time when frozen and the time variable reflects the chart's beginning time when frozen, the ratio of these values produces a new number every time the cache updates. After a parent seed is selected, its value is then fed through a uniformly distributed seed array generator, which generates multiple arrays of pseudorandom "children" seeds. The seeds produced in this step are then fed through the main generators to produce arrays of pseudorandom simulated outcomes, and a pseudorandom series to compare with the real series. The main generators within this script are designed to (at least somewhat) model the stochastic nature of financial time series data. The first step in this process is to transform the uniform outputs of the Wichmann-Hill into outputs that are normally distributed. In this script, the transformation is done using an estimate of the normal distribution quantile function. Quantile functions, otherwise known as percent-point or inverse cumulative distribution functions, specify the value of a random variable such that the probability of the variable being within the value's boundary equals the input probability. The quantile equation for a normal probability distribution is μ + σ(√2)erf^-1(2(p - 0.5)) where μ is the mean of the distribution, σ is the standard deviation, erf^-1 is the inverse Gauss error function, and p is the probability. Because erf^-1() does not have a simple, closed form interpretation, it must be approximated. To keep things lightweight in this approximation, I used a truncated Maclaurin Series expansion for this function with precomputed coefficients and rolled out operations to avoid nested looping. This method provides a decent approximation of the error function without completely breaking floating point limits or sucking up runtime memory. Note that there are plenty of more robust techniques to approximate this function, but their memory needs very. I chose this method specifically because of runtime favorability. To generate a pseudorandom approximately normally distributed variable, the uniformly distributed variable from the Wichmann-Hill algorithm is used as the input probability for the quantile estimator. Now from here, we get a pretty decent output that could be used itself in the simulation process. Many Monte Carlo simulations and random price generators utilize a normal variable. However, if you compare the outputs of this normal variable with the actual returns of the real time series, you'll find that the variability in shocks (random changes) doesn't quite behave like it does in real data. This is because most real financial time series data is more complex. Its distribution may be approximately normal at times, but the variability of its distribution changes over time due to various underlying factors. In light of this, I believe that returns behave more like a convoluted product distribution rather than just a raw normal. So the next step to get our procedurally generated returns to more closely emulate the behavior of real returns is to introduce more complexity into our model. Through experimentation, I've found that a return series more closely emulating real returns can be generated in a three step process: -> First, generate multiple independent, normally distributed variables simultaneously. -> Next, apply pseudorandom weighting to each variable ranging from -1 to 1, or some limits within those bounds. This modulates each series to provide more variability in the shocks by producing product distributions. -> Lastly, add the results together to generate the final pseudorandom output with a convoluted distribution. This adds variable amounts of constructive and destructive interference to produce a more "natural" looking output. In this script, I use three independent normally distributed variables multiplied by uniform product distributed variables. The first variable is generated by multiplying a normal variable by one uniformly distributed variable. This produces a bit more tailedness (kurtosis) than a normal distribution, but nothing too extreme. The second variable is generated by multiplying a normal variable by two uniformly distributed variables. This produces moderately greater tails in the distribution. The third variable is generated by multiplying a normal variable by three uniformly distributed variables. This produces a distribution with heavier tails. For additional control of the output distributions, the uniform product distributions are given optional limits. These limits control the boundaries for the absolute value of the uniform product variables, which affects the tails. In other words, they limit the weighting applied to the normally distributed variables in this transformation. All three sets are then multiplied by user defined amplitude factors to adjust presence, then added together to produce our final pseudorandom return series with a convoluted product distribution. Once we have the final, more "natural" looking pseudorandom series, the values are recursively summed over the forecast period to generate a simulated result. This process of generation, weighting, addition, and summation is repeated over the user defined number of simulations with different seeds generated from the parent to produce our array of initial simulated outcomes. After the initial simulation array is generated, the max, min, mean and standard deviation of this array are calculated, and the values are stored in holding arrays on each iteration to be called upon later. Reference difference series and price values are also stored in holding arrays to be used in our comparison plots. In this script, I use a linear model with simple returns rather than compounding log returns to generate the output. The reason for this is that in generating outputs this way, we're able to run our simulations recursively from the beginning of the chart, then apply scaling and anchoring post-process. This allows a greater conservation of runtime memory than the alternative, making it more suitable for doing longer forecasts with heavier amounts of simulations in TV's runtime environment. From our starting time, the previous bar's price, volatility, and optional drift (expected return) are factored into our holding arrays to generate the final forecast parameters. After these parameters are computed, the range forecast is produced. The basis value for the ranges is the mean outcome of the simulations that were run. Then, quarter standard deviations of the simulated outcomes are added to and subtracted from the basis up to 3σ to generate the forecast ranges. All of these values are plotted and colorized based on their theoretical probability density. The most likely areas are the warmest colors, and least likely areas are the coolest colors. An information panel is also displayed at the starting time which shows the starting time and price, forecast type, parent seed value, simulations run, forecast bars, total drift, mean, standard deviation, max outcome, min outcome, and bars remaining. The interesting thing about simulated outcomes is that although the probability distribution of each simulation is not normal, the distribution of different outcomes converges to a normal one with enough steps. In light of this, the probability density of outcomes is highest near the initial value + total drift, and decreases the further away from this point you go. This makes logical sense since the central path is the easiest one to travel. Given the ever changing state of markets, I find this tool to be best suited for shorter term forecasts. However, if the movements of price are expected to remain relatively stable, longer term forecasts may be equally as valid. There are many possible ways for users to apply this tool to their analysis setups. For example, the forecast ranges may be used as a guide to help users set risk targets. Or, the generated levels could be used in conjunction with other indicators for meaningful confluence signals. More advanced users could even extrapolate the functions used within this script for various purposes, such as generating pseudorandom data to test systems on, perform integration and approximations, etc. These are just a few examples of potential uses of this script. How you choose to use it to benefit your trading, analysis, and coding is entirely up to you. If nothing else, I think this is a pretty neat script simply for the novelty of it. ---------- How To Use: When you first add the script to your chart, you will be prompted to confirm the starting date and time, number of bars to forecast, number of simulations to run, and whether to include drift assumption. You will also be prompted to confirm the forecast type. There are two types to choose from: -> End Result - This uses the values from the end of the simulation throughout the forecast interval. -> Developing - This uses the values that develop from bar to bar, providing a real-time outlook. You can always update these settings after confirmation as well. Once these inputs are confirmed, the script will boot up and automatically generate the forecast in a separate pane. Note that if there is no bar of data at the time you wish to start the forecast, the script will automatically detect use the next available bar after the specified start time. From here, you can now control the rest of the settings. The "Seeding Settings" section controls the initial seed value used to generate the children that produce the simulations. In this section, you can control whether the seed is a fixed value, or a dynamic one. Since selecting the dynamic parent option will change the seed value every time you change the settings or refresh your chart, there is a "Regenerate" input built into the script. This input is a dummy input that isn't connected to any of the calculations. The purpose of this input is to force an update of the dynamic parent without affecting the generator or forecast settings. Note that because we're running a limited number of simulations, different parent seeds will typically yield slightly different forecast ranges. When using a small number of simulations, you will likely see a higher amount of variance between differently seeded results because smaller numbers of sampled simulations yield a heavier bias. The more simulations you run, the smaller this variance will become since the outcomes become more convergent toward the same distribution, so the differences between differently seeded forecasts will become more marginal. When using a dynamic parent, pay attention to the dispersion of ranges. When you find a set of ranges that is dispersed how you like with your configuration, set your fixed parent value to the parent seed that shows in the info panel. This will allow you to replicate that dispersion behavior again in the future. An important thing to note when settings alerts on the plotted levels, or using them as components for signals in other scripts, is to decide on a fixed value for your parent seed to avoid minor repainting due to seed changes. When the parent seed is fixed, no repainting occurs. The "Amplitude Settings" section controls the amplitude coefficients for the three differently tailed generators. These amplitude factors will change the difference series output for each simulation by controlling how aggressively each series moves. When "Adjust Amplitude Coefficients" is disabled, all three coefficients are set to 1. Note that if you expect volatility to significantly diverge from its historical values over the forecast interval, try experimenting with these factors to match your anticipation. The "Weighting Settings" section controls the weighting boundaries for the three generators. These weighting limits affect how tailed the distributions in each generator are, which in turn affects the final series outputs. The maximum absolute value range for the weights is . When "Limit Generator Weights" is disabled, this is the range that is automatically used. The last set of inputs is the "Display Settings", where you can control the visual outputs. From here, you can select to display either "Forecast" or "Difference Comparison" via the "Output Display Type" dropdown tab. "Forecast" is the type displayed by default. This plots the end result or developing forecast ranges. There is an option with this display type to show the developing extremes of the simulations. This option is enabled by default. There's also an option with this display type to show one of the simulated price series from the set alongside actual prices. This allows you to visually compare simulated prices alongside the real prices. "Difference Comparison" allows you to visually compare a synthetic difference series from the set alongside the actual difference series. This display method is primarily useful for visually tuning the amplitude and weighting settings of the generators. There are also info panel settings on the bottom, which allow you to control size, colors, and date format for the panel. It's all pretty simple to use once you get the hang of it. So play around with the settings and see what kinds of forecasts you can generate! ---------- ADDITIONAL NOTES & DISCLAIMERS Although I've done a number of things within this script to keep runtime demands as low as possible, the fact remains that this script is fairly computationally heavy. Because of this, you may get random timeouts when using this script. This could be due to either random drops in available runtime on the server, using too many simulations, or running the simulations over too many bars. If it's just a random drop in runtime on the server, hide and unhide the script, re-add it to the chart, or simply refresh the page. If the timeout persists after trying this, then you'll need to adjust your settings to a less demanding configuration. Please note that no specific claims are being made in regards to this script's predictive accuracy. It must be understood that this model is based on randomized price generation with assumed constant drift and dispersion from historical data before the starting point. Models like these not consider the real world factors that may influence price movement (economic changes, seasonality, macro-trends, instrument hype, etc.), nor the changes in sample distribution that may occur. In light of this, it's perfectly possible for price data to exceed even the most extreme simulated outcomes. The future is uncertain, and becomes increasingly uncertain with each passing point in time. Predictive models of any type can vary significantly in performance at any point in time, and nobody can guarantee any specific type of future performance. When using forecasts in making decisions, DO NOT treat them as any form of guarantee that values will fall within the predicted range. When basing your trading decisions on any trading methodology or utility, predictive or not, you do so at your own risk. No guarantee is being issued regarding the accuracy of this forecast model. Forecasting is very far from an exact science, and the results from any forecast are designed to be interpreted as potential outcomes rather than anything concrete. With that being said, when applied prudently and treated as "general case scenarios", forecast models like these may very well be potentially beneficial tools to have in the arsenal.
Pine Script®指標
由DonovanWall提供
79
Machine Learning: LVQ-based StrategyLVQ-based Strategy (FX and Crypto) Description: Learning Vector Quantization (LVQ) can be understood as a special case of an artificial neural network, more precisely, it applies a winner-take-all learning-based approach. It is based on prototype supervised learning classification task and trains its weights through a competitive learning algorithm. Algorithm: Initialize weights Train for 1 to N number of epochs - Select a training example - Compute the winning vector - Update the winning vector Classify test sample The LVQ algorithm offers a framework to test various indicators easily to see if they have got any *predictive value*. One can easily add cog, wpr and others. Note: TradingViews's playback feature helps to see this strategy in action. The algo is tested with BTCUSD/1Hour. Warning: This is a preliminary version! Signals ARE repainting. ***Warning***: Signals LARGELY depend on hyperparams (lrate and epochs). Style tags: Trend Following, Trend Analysis Asset class: Equities, Futures, ETFs, Currencies and Commodities Dataset: FX Minutes/Hours+++/Days
Pine Script®指標
由capissimo提供
6