Lorentzian Classification - Advanced Trading DashboardLorentzian Classification - Relativistic Market Analysis
A Journey from Theory to Trading Reality
What began as fascination with Einstein's relativity and Lorentzian geometry has evolved into a practical trading tool that bridges theoretical physics and market dynamics. This indicator represents months of wrestling with complex mathematical concepts, debugging intricate algorithms, and transforming abstract theory into actionable trading signals.
The Theoretical Foundation
Lorentzian Distance in Market Space
Traditional Euclidean distance treats all feature differences equally, but markets don't behave uniformly. Lorentzian distance, borrowed from spacetime geometry, provides a more nuanced similarity measure:
d(x,y) = Σ ln(1 + |xi - yi|)
This logarithmic formulation naturally handles:
Scale invariance: Large price moves don't overwhelm small but significant patterns
Outlier robustness: Extreme values are dampened rather than dominating
Non-linear relationships: Captures market behavior better than linear metrics
K-Nearest Neighbors with Relativistic Weighting
The algorithm searches historical market states for patterns similar to current conditions. Each neighbor receives weight inversely proportional to its Lorentzian distance:
w = 1 / (1 + distance)
This creates a "gravitational" effect where closer patterns have stronger influence on predictions.
The Implementation Challenge
Creating meaningful market features required extensive experimentation:
Price Features: Multi-timeframe momentum (1, 2, 3, 5, 8 bar lookbacks) Volume Features: Relative volume analysis against 20-period average
Volatility Features: ATR and Bollinger Band width normalization Momentum Features: RSI deviation from neutral and MACD/price ratio
Each feature undergoes min-max normalization to ensure equal weighting in distance calculations.
The Prediction Mechanism
For each current market state:
Feature Vector Construction: 12-dimensional representation of market conditions
Historical Search: Scan lookback period for similar patterns using Lorentzian distance
Neighbor Selection: Identify K nearest historical matches
Outcome Analysis: Examine what happened N bars after each match
Weighted Prediction: Combine outcomes using distance-based weights
Confidence Calculation: Measure agreement between neighbors
Technical Hurdles Overcome
Array Management: Complex indexing to prevent look-ahead bias
Distance Calculations: Optimizing nested loops for performance
Memory Constraints: Balancing lookback depth with computational limits
Signal Filtering: Preventing clustering of identical signals
Advanced Dashboard System
Main Control Panel
The primary dashboard provides real-time market intelligence:
Signal Status: Current prediction with confidence percentage
Neighbor Analysis: How many historical patterns match current conditions
Market Regime: Trend strength, volatility, and volume analysis
Temporal Context: Real-time updates with timestamp
Performance Analytics
Comprehensive tracking system monitors:
Win Rate: Percentage of successful predictions
Signal Count: Total predictions generated
Streak Analysis: Current winning/losing sequence
Drawdown Monitoring: Maximum equity decline
Sharpe Approximation: Risk-adjusted performance estimate
Risk Assessment Panel
Multi-dimensional risk analysis:
RSI Positioning: Overbought/oversold conditions
ATR Percentage: Current volatility relative to price
Bollinger Position: Price location within volatility bands
MACD Alignment: Momentum confirmation
Confidence Heatmap
Visual representation of prediction reliability:
Historical Confidence: Last 10 periods of prediction certainty
Strength Analysis: Magnitude of prediction values over time
Pattern Recognition: Color-coded confidence levels for quick assessment
Input Parameters Deep Dive
Core Algorithm Settings
K Nearest Neighbors (1-20): More neighbors create smoother but less responsive signals. Optimal range 5-8 for most markets.
Historical Lookback (50-500): Deeper history improves pattern recognition but reduces adaptability. 100-200 bars optimal for most timeframes.
Feature Window (5-30): Longer windows capture more context but reduce sensitivity. Match to your trading timeframe.
Feature Selection
Price Changes: Essential for momentum and reversal detection Volume Profile: Critical for institutional activity recognition Volatility Measures: Key for regime change detection Momentum Indicators: Vital for trend confirmation
Signal Generation
Prediction Horizon (1-20): How far ahead to predict. Shorter horizons for scalping, longer for swing trading.
Signal Threshold (0.5-0.9): Confidence required for signal generation. Higher values reduce false signals but may miss opportunities.
Smoothing (1-10): EMA applied to raw predictions. More smoothing reduces noise but increases lag.
Visual Design Philosophy
Color Themes
Professional: Corporate blue/red for institutional environments Neon: Cyberpunk cyan/magenta for modern aesthetics
Matrix: Green/red hacker-inspired palette Classic: Traditional trading colors
Information Hierarchy
The dashboard system prioritizes information by importance:
Primary Signals: Largest, most prominent display
Confidence Metrics: Secondary but clearly visible
Supporting Data: Detailed but unobtrusive
Historical Context: Available but not distracting
Trading Applications
Signal Interpretation
Long Signals: Prediction > threshold with high confidence
Look for volume confirmation
- Check trend alignment
- Verify support levels
Short Signals: Prediction < -threshold with high confidence
Confirm with resistance levels
- Check for distribution patterns
- Verify momentum divergence
- Market Regime Adaptation
Trending Markets: Higher confidence in directional signals
Ranging Markets: Focus on reversal signals at extremes
Volatile Markets: Require higher confidence thresholds
Low Volume: Reduce position sizes, increase caution
Risk Management Integration
Confidence-Based Sizing: Larger positions for higher confidence signals
Regime-Aware Stops: Wider stops in volatile regimes
Multi-Timeframe Confirmation: Align signals across timeframes
Volume Confirmation: Require volume support for major signals
Originality and Innovation
This indicator represents genuine innovation in several areas:
Mathematical Approach
First application of Lorentzian geometry to market pattern recognition. Unlike Euclidean-based systems, this naturally handles market non-linearities.
Feature Engineering
Sophisticated multi-dimensional feature space combining price, volume, volatility, and momentum in normalized form.
Visualization System
Professional-grade dashboard system providing comprehensive market intelligence in intuitive format.
Performance Tracking
Real-time performance analytics typically found only in institutional trading systems.
Development Journey
Creating this indicator involved overcoming numerous technical challenges:
Mathematical Complexity: Translating theoretical concepts into practical code
Performance Optimization: Balancing accuracy with computational efficiency
User Interface Design: Making complex data accessible and actionable
Signal Quality: Filtering noise while maintaining responsiveness
The result is a tool that brings institutional-grade analytics to individual traders while maintaining the theoretical rigor of its mathematical foundation.
Best Practices
- Parameter Optimization
- Start with default settings and adjust based on:
Market Characteristics: Volatile vs. stable
Trading Timeframe: Scalping vs. swing trading
Risk Tolerance: Conservative vs. aggressive
Signal Confirmation
Never trade on Lorentzian signals alone:
Price Action: Confirm with support/resistance
Volume: Verify with volume analysis
Multiple Timeframes: Check higher timeframe alignment
Market Context: Consider overall market conditions
Risk Management
Position Sizing: Scale with confidence levels
Stop Losses: Adapt to market volatility
Profit Targets: Based on historical performance
Maximum Risk: Never exceed 2-3% per trade
Disclaimer
This indicator is for educational and research purposes only. It does not constitute financial advice or guarantee profitable trading results. The Lorentzian classification system reveals market patterns but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose.
Market dynamics are inherently uncertain, and past performance does not guarantee future results. This tool should be used as part of a comprehensive trading strategy, not as a standalone solution.
Bringing the elegance of relativistic geometry to market analysis through sophisticated pattern recognition and intuitive visualization.
Thank you for sharing the idea. You're more than a follower, you're a leader!
@vasanthgautham1221
Trade with precision. Trade with insight.
— Dskyz , for DAFE Trading Systems
Komut dosyalarını "algo" için ara
PhenLabs - Market Fluid Dynamics📊 Market Fluid Dynamics -
Version: PineScript™ v6
📌 Description
The Market Fluid Dynamics - Phen indicator is a new thinking regarding market analysis by modeling price action, volume, and volatility using a fluid system. It attempts to offer traders control over more profound market forces, such as momentum (speed), resistance (thickness), and buying/selling pressure. By visualizing such dynamics, the script allows the traders to decide on the prevailing market flow, its power, likely continuations, and zones of calmness and chaos, and thereby allows improved decision-making.
This measure avoids the usual difficulty of reconciling multiple, often contradictory, market indications by including them within a single overarching model. It moves beyond traditional binary indicators by providing a multi-dimensional view of market behavior, employing fluid dynamic analogs to describe complex interactions in an accessible manner.
🚀 Points of Innovation
Integrated Fluid Dynamics Model: Combines velocity, viscosity, pressure, and turbulence into a single indicator.
Normalized Metrics: Uses ATR and other normalization techniques for consistent readings across different assets and timeframes.
Dynamic Flow Visualization: Main flow line changes color and intensity based on direction and strength.
Turbulence Background: Visually represents market stability with a gradient background, from calm to turbulent.
Comprehensive Dashboard: Provides an at-a-glance summary of key fluid dynamic metrics.
Multi-Layer Smoothing: Employs several layers of EMA smoothing for a clearer, more responsive main flow line.
🔧 Core Components
Velocity Component: Measures price momentum (first derivative of price), normalized by ATR. It indicates the speed and direction of price changes.
Viscosity Component: Represents market resistance to price changes, derived from ATR relative to its historical average. Higher viscosity suggests it’s harder for prices to move.
Pressure Component: Quantifies the force created by volume and price range (close - open), normalized by ATR. It reflects buying or selling pressure.
Turbulence Detection: Calculates a Reynolds number equivalent to identify market stability, ranging from laminar (stable) to turbulent (chaotic).
Main Flow Indicator: Combines the above components, applying sensitivity and smoothing, to generate a primary signal of market direction and strength.
🔥 Key Features
Advanced Smoothing Algorithm: Utilizes multiple EMA layers on the raw flow calculation for a fluid and responsive main flow line, reducing noise while maintaining sensitivity.
Gradient Flow Coloring: The main flow line dynamically changes color from light to deep blue for bullish flow and light to deep red for bearish flow, with intensity reflecting flow strength. This provides an immediate visual cue of market sentiment and momentum.
Turbulence Level Background: The chart background changes color based on calculated turbulence (from calm gray to vibrant orange), offering an intuitive understanding of market stability and potential for erratic price action.
Informative Dashboard: A customizable on-screen table displays critical metrics like Flow State, Flow Strength, Market Viscosity, Turbulence, Pressure Force, Flow Acceleration, and Flow Continuity, allowing traders to quickly assess current market conditions.
Configurable Lookback and Sensitivity: Users can adjust the base lookback period for calculations and the sensitivity of the flow to viscosity, tailoring the indicator to different trading styles and market conditions.
Alert Conditions: Pre-defined alerts for flow direction changes (positive/negative crossover of zero line) and detection of high turbulence states.
🎨 Visualization
Main Flow Line: A smoothed line plotted below the main chart, colored blue for bullish flow and red for bearish flow. The intensity of the color (light to dark) indicates the strength of the flow. This line crossing the zero line can signal a change in market direction.
Zero Line: A dotted horizontal line at the zero level, serving as a baseline to gauge whether the market flow is positive (bullish) or negative (bearish).
Turbulence Background: The indicator pane’s background color changes based on the calculated turbulence level. A calm, almost transparent gray indicates low turbulence (laminar flow), while a more vibrant, semi-transparent orange signifies high turbulence. This helps traders visually assess market stability.
Dashboard Table: An optional table displayed on the chart, showing key metrics like ‘Flow State’, ‘Flow Strength’, ‘Market Viscosity’, ‘Turbulence’, ‘Pressure Force’, ‘Flow Acceleration’, and ‘Flow Continuity’ with their current values and qualitative descriptions (e.g., ‘Bullish Flow’, ‘Laminar (Stable)’).
📖 Usage Guidelines
Setting Categories
Show Dashboard - Default: true; Range: true/false; Description: Toggles the visibility of the Market Fluid Dynamics dashboard on the chart. Enable to see key metrics at a glance.
Base Lookback Period - Default: 14; Range: 5 - (no upper limit, practical limits apply); Description: Sets the primary lookback period for core calculations like velocity, ATR, and volume SMA. Shorter periods make the indicator more sensitive to recent price action, while longer periods provide a smoother, slower signal.
Flow Sensitivity - Default: 0.5; Range: 0.1 - 1.0 (step 0.1); Description: Adjusts how much the market viscosity dampens the raw flow. A lower value means viscosity has less impact (flow is more sensitive to raw velocity/pressure), while a higher value means viscosity has a greater dampening effect.
Flow Smoothing - Default: 5; Range: 1 - 20; Description: Controls the length of the EMA smoothing applied to the main flow line. Higher values result in a smoother flow line but with more lag; lower values make it more responsive but potentially noisier.
Dashboard Position - Default: ‘Top Right’; Range: ‘Top Right’, ‘Top Left’, ‘Bottom Right’, ‘Bottom Left’, ‘Middle Right’, ‘Middle Left’; Description: Determines the placement of the dashboard on the chart.
Header Size - Default: ‘Normal’; Range: ‘Tiny’, ‘Small’, ‘Normal’, ‘Large’, ‘Huge’; Description: Sets the text size for the dashboard header.
Values Size - Default: ‘Small’; Range: ‘Tiny’, ‘Small’, ‘Normal’, ‘Large’; Description: Sets the text size for the metric values in the dashboard.
✅ Best Use Cases
Trend Identification: Identifying the dominant market flow (bullish or bearish) and its strength to trade in the direction of the prevailing trend.
Momentum Confirmation: Using the flow strength and acceleration to confirm the conviction behind price movements.
Volatility Assessment: Utilizing the turbulence metric to gauge market stability, helping to adjust position sizing or avoid choppy conditions.
Reversal Spotting: Watching for divergences between price and flow, or crossovers of the main flow line above/below the zero line, as potential reversal signals, especially when combined with changes in pressure or viscosity.
Swing Trading: Leveraging the smoothed flow line to capture medium-term market swings, entering when flow aligns with the desired trade direction and exiting when flow weakens or reverses.
Intraday Scalping: Using shorter lookback periods and higher sensitivity to identify quick shifts in flow and turbulence for short-term trading opportunities, particularly in liquid markets.
⚠️ Limitations
Lagging Nature: Like many indicators based on moving averages and lookback periods, the main flow line can lag behind rapid price changes, potentially leading to delayed signals.
Whipsaws in Ranging Markets: During periods of low volatility or sideways price action (high viscosity, low flow strength), the indicator might produce frequent buy/sell signals (whipsaws) as the flow oscillates around the zero line.
Not a Standalone System: While comprehensive, it should be used in conjunction with other forms of analysis (e.g., price action, support/resistance levels, other indicators) and not as a sole basis for trading decisions.
Subjectivity in Interpretation: While the dashboard provides quantitative values, the interpretation of “strong” flow, “high” turbulence, or “significant” acceleration can still have a subjective element depending on the trader’s strategy and risk tolerance.
💡 What Makes This Unique
Fluid Dynamics Analogy: Its core strength lies in translating complex market interactions into an intuitive fluid dynamics framework, making concepts like momentum, resistance, and pressure easier to visualize and understand.
Market View: Instead of focusing on a single aspect (like just momentum or just volatility), it integrates multiple factors (velocity, viscosity, pressure, turbulence) to provide a more comprehensive picture of market conditions.
Adaptive Visualization: The dynamic coloring of the flow line and the turbulence background provide immediate, adaptive visual feedback that changes with market conditions.
🔬 How It Works
Price Velocity Calculation: The indicator first calculates price velocity by measuring the rate of change of the closing price over a given ‘lookback’ period. The raw velocity is then normalized by the Average True Range (ATR) of the same lookback period. Normalization enables comparison of momentum between assets or timeframes by scaling for volatility. This is the direction and speed of initial price movement.
Viscosity Calculation: Market ‘viscosity’ or resistance to price movement is determined by looking at the current ATR relative to its longer-term average (SMA of ATR over lookback * 2). The further the current ATR is above its average, the lower the viscosity (less resistance to price movement), and vice-versa. The script inverts this relationship and bounds it so that rising viscosity means more resistance.
Pressure Force Measurement: A ‘pressure’ variable is calculated as a function of the ratio of current volume to its simple moving average, multiplied by the price range (close - open) and normalized by ATR. This is designed to measure the force behind price movement created by volume and intraday price thrusts. This pressure is smoothed by an EMA.
Turbulence State Evaluation: A equivalent ‘Reynolds number’ is calculated by dividing the absolute normalized velocity by the viscosity. This is the proclivity of the market to move in a chaotic or orderly fashion. This ‘reynoldsValue’ is smoothed with an EMA to get the ‘turbulenceState’, which indicates if the market is laminar (stable), transitional, or turbulent.
Main Flow Derivation: The ‘rawFlow’ is calculated by taking the normalized velocity, dampening its impact based on the ‘viscosity’ and user-input ‘sensitivity’, and orienting it by the sign of the smoothed ‘pressureSmooth’. The ‘rawFlow’ is then put through multiple layers of exponential moving average (EMA) smoothing (with ‘smoothingLength’ and derived values) to reach the final ‘mainFlow’ line. The extensive smoothing is designed to give a smooth and clear visualization of the overall market direction and magnitude.
Dashboard Metrics Compilation: Additional metrics like flow acceleration (derivative of mainFlow), and flow continuity (correlation between close and volume) are calculated. All primary components (Flow State, Strength, Viscosity, Turbulence, Pressure, Acceleration, Continuity) are then presented in a user-configurable dashboard for ease of monitoring.
💡 Note:
The “Market Fluid Dynamics - Phen” indicator is designed to offer a unique perspective on market behavior by applying principles from fluid dynamics. It’s most effective when used to understand the underlying forces driving price rather than as a direct buy/sell signal generator in isolation. Experiment with the settings, particularly the ‘Base Lookback Period’, ‘Flow Sensitivity’, and ‘Flow Smoothing’, to find what best suits your trading style and the specific asset you are analyzing. Always combine its insights with robust risk management practices.
[blackcat] L1 Net Volume DifferenceOVERVIEW
The L1 Net Volume Difference indicator serves as an advanced analytical tool designed to provide traders with deep insights into market sentiment by examining the differential between buying and selling volumes over precise timeframes. By leveraging these volume dynamics, it helps identify trends and potential reversal points more accurately, thereby supporting well-informed decision-making processes. The key focus lies in dissecting intraday changes that reflect short-term market behavior, offering critical input for both swing and day traders alike. 📊
Key benefits encompass:
• Precise calculation of net volume differences grounded in real-time data.
• Interactive visualization elements enhancing interpretability effortlessly.
• Real-time generation of buy/sell signals driven by dynamic volume shifts.
TECHNICAL ANALYSIS COMPONENTS
📉 Volume Accumulation Mechanisms:
Monitors cumulative buy/sell volumes derived from comparative closing prices.
Periodically resets accumulation counters aligning with predefined intervals (e.g., 5-minute bars).
Facilitates identification of directional biases reflecting underlying market forces accurately.
🕵️♂️ Sentiment Detection Algorithms:
Employs proprietary logic distinguishing between bullish/bearish sentiments dynamically.
Ensures consistent adherence to predefined statistical protocols maintaining accuracy.
Supports adaptive thresholds adjusting sensitivities based on changing market conditions flexibly.
🎯 Dynamic Signal Generation:
Detects transitions indicating dominance shifts between buyers/sellers promptly.
Triggers timely alerts enabling swift reactions to evolving market dynamics effectively.
Integrates conditional logic reinforcing signal validity minimizing erroneous activations.
INDICATOR FUNCTIONALITY
🔢 Core Algorithms:
Utilizes moving averages along with standardized deviation formulas generating precise net volume measurements.
Implements Arithmetic Mean Line Algorithm (AMLA) smoothing techniques improving interpretability.
Ensures consistent alignment with established statistical principles preserving fidelity.
🖱️ User Interface Elements:
Dedicated plots displaying real-time net volume markers facilitating swift decision-making.
Context-sensitive color coding distinguishing positive/negative deviations intuitively.
Background shading highlighting proximity to key threshold activations enhancing visibility.
STRATEGY IMPLEMENTATION
✅ Entry Conditions:
Confirm bullish/bearish setups validated through multiple confirmatory signals.
Validate entry decisions considering concurrent market sentiment factors.
Assess alignment between net volume readings and broader trend directions ensuring coherence.
🚫 Exit Mechanisms:
Trigger exits upon hitting predetermined thresholds derived from historical analyses.
Monitor continuous breaches signifying potential trend reversals promptly executing closures.
Execute partial/total closes contingent upon cumulative loss limits preserving capital efficiently.
PARAMETER CONFIGURATIONS
🎯 Optimization Guidelines:
Reset Interval: Governs responsiveness versus stability balancing sensitivity/stability.
Price Source: Dictates primary data series driving volume calculations selecting relevant inputs accurately.
💬 Customization Recommendations:
Commence with baseline defaults; iteratively refine parameters isolating individual impacts.
Evaluate adjustments independently prior to combined modifications minimizing disruptions.
Prioritize minimizing erroneous trigger occurrences first optimizing signal fidelity.
Sustain balanced risk-reward profiles irrespective of chosen settings upholding disciplined approaches.
ADVANCED RISK MANAGEMENT
🛡️ Proactive Risk Mitigation Techniques:
Enforce strict compliance with pre-defined maximum leverage constraints adhering strictly to guidelines.
Mandatorily apply trailing stop-loss orders conforming to script outputs reinforcing discipline.
Allocate positions proportionately relative to available capital reserves managing exposures prudently.
Conduct periodic reviews gauging strategy effectiveness rigorously identifying areas needing refinement.
⚠️ Potential Pitfalls & Solutions:
Address frequent violations arising during heightened volatility phases necessitating manual interventions judiciously.
Manage false alerts warranting immediate attention avoiding adverse consequences systematically.
Prepare contingency plans mitigating margin call possibilities preparing proactive responses effectively.
Continuously assess automated system reliability amidst fluctuating conditions ensuring seamless functionality.
PERFORMANCE AUDITS & REFINEMENTS
🔍 Critical Evaluation Metrics:
Assess win percentages consistently across diverse trading instruments gauging reliability.
Calculate average profit ratios per successful execution measuring profitability efficiency accurately.
Measure peak drawdown durations alongside associated magnitudes evaluating downside risks comprehensively.
Analyze signal generation frequencies revealing hidden patterns potentially skewing outcomes uncovering systematic biases.
📈 Historical Data Analysis Tools:
Maintain comprehensive records capturing every triggered event meticulously documenting results.
Compare realized profits/losses against backtested simulations benchmarking actual vs expected performances accurately.
Identify recurrent systematic errors demanding corrective actions implementing iterative refinements steadily.
Document evolving performance metrics tracking progress dynamically addressing identified shortcomings proactively.
PROBLEM SOLVING ADVICE
🔧 Frequent Encountered Challenges:
Unpredictable behaviors emerging within thinly traded markets requiring filtration processes.
Latency issues manifesting during abrupt price fluctuations causing missed opportunities.
Overfitted models yielding suboptimal results post-extensive tuning demanding recalibrations.
Inaccuracies stemming from incomplete/inaccurate data feeds necessitating verification procedures.
💡 Effective Resolution Pathways:
Exclude low-liquidity assets prone to erratic movements enhancing signal integrity.
Introduce buffer intervals safeguarding major news/event impacts mitigating distortions effectively.
Limit ongoing optimization attempts preventing model degradation maintaining optimal performance levels consistently.
Verify reliable connections ensuring uninterrupted data flows guaranteeing accurate interpretations reliably.
USER ENGAGEMENT SEGMENT
🤝 Community Contributions Welcome
Highly encourage active participation sharing experiences & recommendations!
THANKS
Heartfelt acknowledgment extends to all developers contributing invaluable insights about volume-based trading methodologies! ✨
BollingerBands MTF | AlchimistOfCrypto🌌 Bollinger Bands – Unveiling Market Volatility Fields 🌌
"The Bollinger Bands, reimagined through quantum mechanics principles, visualizes the probabilistic distribution of price movements within a multi-dimensional volatility field. This indicator employs principles from wave function mathematics where standard deviation creates probabilistic boundaries, similar to electron cloud models in quantum physics. Our implementation features algorithmically enhanced visualization derived from extensive mathematical modeling, creating a dynamic representation of volatility compression and expansion cycles with adaptive glow effects that highlight the critical moments where volatility phase transitions occur."
📊 Professional Trading Application
The Bollinger Bands Quantum transcends traditional volatility measurement with a sophisticated gradient illumination system that reveals the underlying structure of market volatility fields. Scientifically calibrated for multiple timeframes and featuring eight distinct visual themes, it enables traders to perceive volatility contractions and expansions with unprecedented clarity.
⚙️ Indicator Configuration
- Volatility Field Parameters 📏
Python-optimized settings for specific market conditions:
- Period: 20 (default) - The quantum time window for volatility calculation
- StdDev Multiplier: 2.0 - The probabilistic boundary coefficient
- MA Type: SMA/EMA/VWMA/WMA/RMA - The quantum field smoothing algorithm
- Visual Theming 🎨
Eight scientifically designed visual palettes optimized for volatility pattern recognition:
- Neon (default): High-contrast green/red scheme enhancing volatility transition visibility
- Cyan-Magenta: Vibrant palette for maximum volatility boundary distinction
- Yellow-Purple: Complementary colors for enhanced compression/expansion detection
- Specialized themes (Green-Red, Forest Green, Blue Ocean, Orange-Red, Grayscale): Each calibrated for different market environments
- Opacity Control 🔍
- Variable transparency system (0-100) allowing seamless integration with price action
- Adaptive glow effect that intensifies during volatility phase transitions
- Quantum field visualization that reveals the probabilistic nature of price movements
🚀 How to Use
1. Select Visualization Parameters ⏰: Adjust period and standard deviation to match market conditions
2. Choose MA Type 🎚️: Select the appropriate smoothing algorithm for your trading strategy
3. Select Visual Theme 🌈: Choose a color scheme that enhances your personal pattern recognition
4. Adjust Opacity 🔎: Fine-tune visualization intensity to complement your chart analysis
5. Identify Volatility Phases ✅: Monitor band width to detect compression (pre-breakout) and expansion (trend)
6. Trade with Precision 🛡️: Enter during band contraction for breakouts, or trade mean reversion using band boundaries
7. Manage Risk Dynamically 🔐: Use band width as volatility-based position sizing parameter
Chan Theory - Chanlun UltraChan Theory -Chanlun Ultra
Overview
This script is based on the core technical framework of Chan Theory, transforming complex market fluctuations into a multi-layered, quantifiable structural analysis system. Through real-time dynamic computation, it automatically parses key components in price movements such as fractals, pens, segments, and pivot zones. Integrated with momentum analysis and trading signal alerts, it provides traders with comprehensive market insights from micro to macro perspectives. The core distinction of Chan Theory from traditional technical indicators lies in its rigorous recursive logic and human-centric market philosophy. This script faithfully restores Chan Theory's essence of "using Zen to resolve market complexity," decomposing spiral price movements into an orderly trading decision system.
Technical Principles
This indicator implements the complete recognition process from candlesticks to fractals, pens, segments, and pivot zones using pure Pine Script under Chan Theory's framework. Core technical implementations include:
1. Candlestick Containment Processing
Employs specific algorithms to handle candlestick containment relationships, eliminating random noise:
In uptrends: Select the higher high and higher low values
In downtrends: Select the lower high and lower low values
Ensure complete elimination of containment through recursive processing
2. Fractal Identification System
Performs strict fractal judgment on processed candlesticks:
Top Fractal: The middle candlestick's high is higher than both adjacent candlesticks
Bottom Fractal: The middle candlestick's low is lower than both adjacent candlesticks
Validate fractal effectiveness via the filterOperateType function
3. Pen Construction Mechanism & Type Selection
Connects valid top/bottom fractals to form pen structures, offering four pen types:
Classic Pen: Traditional Chan Theory definition, strictly following classic rules
Optimized Pen: Enhanced algorithm for short-term volatility recognition
4K Pen: Builds pens based on fractals formed by at least 4 candlesticks (improves stability)
Strict Pen: Employs the most stringent validation conditions for reliability
4. Segment Partitioning Algorithm
Applies segment rules to pen sequences with three modes:
- Dynamic Real-time Progressive Correction: Adjusts forming segments continuously with new data
- Strict Mode: Fully complies with Chan Theory definitions
- Extension Mode: Flexible handling of trend developments
5. Pivot Zone Recognition Technology
Identifies pen-level and segment-level pivot zones
Calculates pivot zone price ranges and time durations
Analyzes pivot zone evolution characteristics
Supports display of pivot zones across different levels
Trading Signal System & Filters
Trading Signal Filtering System
This indicator provides comprehensive filtering functions:
Fractal Validity Filter: Verifies fractal patterns and post-fractal developments
Basic Fractal Filter: Eliminates non-compliant fractals through basic feature checks
Type I MACD Divergence Filter: Enhances Type I signal reliability via MACD divergence analysis
Type II Signal Filter: Custom conditions for Type II signals
-False Signal Trap Avoidance: Detects and bypasses deceptive price patterns
Chan Theory Trading Signal Principles
Type I Signals (Trend Reversals)
Principle: Forms when price makes new highs/lows with weakening internal momentum (divergence)
Identification: Compares structural features of adjacent same-direction pens
Application: Early trend reversal signals for swing trading
Type II Signals (Pullback Entries)
Principle: Occurs during retracements as sub-level reversal signals
Identification: Determined by pivot zone support/resistance and fractal combinations
Application: Optimal positions for pullback trades with controlled risk
Type III Signals (Breakout Confirmations)
Principle: Confirms pivot zone breakouts
Identification: Price breaks prior pivot zone boundaries with valid fractals
Application: Trend continuation signals for trend-following strategies
Indicator Features
Multi-Level Structural Analysis
Distinguishes structures across levels via level parameters
Higher-level trends guide lower-level operations
Implements cross-level collaborative logic
Displays sub-level pivot zones
Structural Visualization
Pens: Displayed per selected pen type
Segments: Rendered according to chosen segment mode
Pivot Zones: Color gradients indicate consolidation strength
Technical Implementation
Data Structure Design
Pen Object: Stores direction, timestamps, and price attributes
Segment Object: Manages segments and constituent pens
Pivot Object: Defines pivot zone ranges and characteristics
Grade Object: Organizes analysis results across levels
User Guide
Parameter Settings
Pen Type: Classic/Optimized/4K/Strict (adapt to analysis needs)
Segment Mode: Dynamic/Strict/Extension (match trading strategies)
Signal Filters: Enable/disable specific filters
Pivot Display: Toggle sub-level pivot zones
Divergence Settings: Configure types (regular/hidden) and display styles
Strategy Settings: Set trading rules linked to signals
Strategy Configuration
Follow Segments: Trade in alignment with segment direction
Signal Participation: Enable/disable Type I/II/III signals
Signal Conditions: Require signals to appear post-pivot zone formation
Prevent Early Entries:
Type I signals require ≥1 pivot zone or 5 pens
Type II Safety Control: Participate only if Type III signals are absent
Practical Recommendations
Select pen types/segment modes per market conditions
Adjust filters for different instruments and timeframes
Enhance accuracy through multi-level analysis
Confirm Type I signals with divergence indicators
Choose strategy parameters aligned with risk tolerance
Value Proposition
Systematizes Chan Theory into computable structures
Multiple pen/segment methods adapt to diverse markets
Advanced filtering significantly improves signal quality (historically validated)
Multi-level analysis provides holistic market insights
This tool is for technical analysis only. It does not constitute investment advice. Users must exercise independent judgment based on personal risk tolerance and objectives.
概述
本脚本基于缠论核心技术框架，将复杂的市场波动转化为多层次、可量化的结构分析系统。通过实时动态演算，自动解析价格走势中的分型、笔、线段、中枢等核心组件，并融合动量分析与交易信号预警功能，为交易者提供从微观到宏观的全方位市场透视。缠论区别于传统技术指标的核心在于其严格的递归逻辑与人性化市场哲学，本脚本忠实还原缠论"以禅破缠"的思想精髓，将螺旋缠绕的价格运动分解为有序的交易决策体系。
技术原理
本指标基于缠论技术分析框架，通过纯Pine Script实现了从K线到分型、笔、线段和中枢的完整识别流程。核心技术实现包括：
1. K线包含处理
采用特定算法处理K线包含关系，消除随机波动干扰：
- 上涨趋势中取高点高值、低点高值
- 下跌趋势中取高点低值、低点低值
- 通过递归处理确保包含关系完全消除
2. 分型识别系统
在处理后的K线基础上实现严格的分型判断：
- 顶分型：中间K线高点高于两侧K线
- 底分型：中间K线低点低于两侧K线
- 通过`filterOperateType`函数实现分型有效性验证
3. 笔的构建机制与类型选择
连接有效顶底分型形成笔结构，提供四种笔类型选择：
- **老笔**：传统缠论笔定义，严格遵循经典规则
- **新笔**：优化算法，增强对短期波动的识别能力
- **4K**：基于至少4根K线形成的分型构建笔，提高稳定性
- **严笔**：采用最严格的条件验证，确保形成的笔结构可靠
4. 线段划分算法
基于笔序列应用线段划分规则，支持三种线段模式：
- **当下延伸后修正**：实时计算当前形成中的线段，并随新数据更新修正
- **严格模式**：要求线段完全符合缠论定义，减少假信号
- **延伸模式**：更灵活地处理线段延伸情况，适合趋势分析
5. 中枢识别技术
- 实现笔中枢和线段中枢识别
- 计算中枢价格区间与时间范围
- 分析中枢演变特征
- 支持显示不同级别中枢功能
买卖点系统与过滤机制
买卖点过滤系统
本指标提供全面的买卖点过滤功能：
- **买卖点分型过滤**：检验分型形态有效性，验证分型后续发展
- **买卖点分型基础过滤**：针对分型基本特征进行验证，排除不合格分型
- **1买卖macd背驰过滤**：通过MACD判断背驰情况，提高一类买卖点可靠性
- **2买卖点过滤**：专门针对二类买卖点的过滤条件
- **防狼术**：避免陷阱式买卖点，提高交易安全性
缠论买卖点原理
1. **一类买卖点**
- 原理：基于趋势背驰原理，当价格创新高/低但内部结构力度减弱时形成
- 识别方法：通过比较相邻同向笔的结构特征判断力度变化
- 应用：提供趋势可能反转的早期信号，适合波段操作
2. **二类买卖点**
- 原理：发生在回调过程中，属于次级别转折信号
- 识别方法：通过中枢支撑位与分型组合判断
- 应用：回调买入或做空的较佳位置，风险相对可控
3. **三类买卖点**
- 原理：中枢突破确认信号
- 识别方法：价格突破前中枢边界并形成有效分型
- 应用：趋势延续的确认信号，适合追踪趋势
指标特点
多级别结构分析
本指标支持多级别联动分析：
- 通过级别参数区分不同级别结构
- 高级别趋势指导低级别操作
- 实现级别间的协同判断逻辑
- 支持显示次级别中枢功能
结构可视化
- 笔结构：根据选择的笔类型显示
- 线段结构：按照选定的线段模式呈现
- 中枢区域：颜色渐变标识不同强度
技术实现说明
数据结构设计
指标设计了完整的面向对象结构：
- Pen结构：存储笔的方向、时间、价格等属性
- Segment结构：管理线段及其组成笔
- Pivot结构：表示中枢范围和特性
- Grade结构：区分不同级别的分析结果
使用指南
参数设置
- 笔的类型：选择老笔、新笔、4K或严笔以适应不同分析需求
- 线段模式：根据交易策略选择合适的线段计算方式
- 买卖点过滤：根据需要启用不同的过滤机制
- 中枢显示：选择是否显示次级别中枢
- 背离设置：选择背离类型、显示方式和样式
- 策略设置：配置与买卖点相关的交易策略选项
策略应用配置
- 跟随线段：根据线段方向进行交易
- 买卖点参与设置：可选择性参与一类、二类和三类买卖点
- 买卖点条件限制：可设置买卖点需要在中枢形成后出现
- 防止过早进场：可要求一类买卖点至少出现一个中枢后或至少5笔后才参与
- 二类买卖点安全性控制：可选择仅在未出现三类买卖点的情况下参与
实际应用建议
- 结合市场环境选择合适的笔类型和线段模式
- 针对不同品种和时间周期调整过滤设置
- 通过多级别分析提高判断准确性
- 使用背离指标确认一类买卖点的有效性
- 根据策略风格选择适合的策略配置参数
技术特点与价值
本指标通过系统化实现缠论结构分析，提供了一种客观的技术分析工具。它的核心价值在于：
1. 将复杂的缠论理论系统化为可计算的结构
2. 提供多种笔、线段判断方法以适应不同市场环境
3. 完善的买卖点过滤系统大幅提高信号质量
4. 多级别联动分析提供全面市场视角
*本指标仅提供技术分析参考，不构成投资建议。用户应根据自身风险承受能力和投资目标进行判断。*
Machine Learning RSI ║ BullVisionOverview:
Introducing the Machine Learning RSI with KNN Adaptation – a cutting-edge momentum indicator that blends the classic Relative Strength Index (RSI) with machine learning principles. By leveraging K-Nearest Neighbors (KNN), this indicator aims at identifying historical patterns that resemble current market behavior and uses this context to refine RSI readings with enhanced sensitivity and responsiveness.
Unlike traditional RSI models, which treat every market environment the same, this version adapts in real-time based on how similar past conditions evolved, offering an analytical edge without relying on predictive assumptions.
Key Features:
🔁 KNN-Based RSI Refinement
This indicator uses a machine learning algorithm (K-Nearest Neighbors) to compare current RSI and price action characteristics to similar historical conditions. The resulting RSI is weighted accordingly, producing a dynamically adjusted value that reflects historical context.
📈 Multi-Feature Similarity Analysis
Pattern similarity is calculated using up to five customizable features:
RSI level
RSI momentum
Volatility
Linear regression slope
Price momentum
Users can adjust how many features are used to tailor the behavior of the KNN logic.
🧠 Machine Learning Weight Control
The influence of the machine learning model on the final RSI output can be fine-tuned using a simple slider. This lets you blend traditional RSI and machine learning-enhanced RSI to suit your preferred level of adaptation.
🎛️ Adaptive Filtering
Additional smoothing options (Kalman Filter, ALMA, Double EMA) can be applied to the RSI, offering better visual clarity and helping to reduce noise in high-frequency environments.
🎨 Visual & Accessibility Settings
Custom color palettes, including support for color vision deficiencies, ensure that trend coloring remains readable for all users. A built-in neon mode adds high-contrast visuals to improve RSI visibility across dark or light themes.
How It Works:
Similarity Matching with KNN:
At each candle, the current RSI and optional market characteristics are compared to historical bars using a KNN search. The algorithm selects the closest matches and averages their RSI values, weighted by similarity. The more similar the pattern, the greater its influence.
Feature-Based Weighting:
Similarity is determined using normalized values of the selected features, which gives a more refined result than RSI alone. You can choose to use only 1 (RSI) or up to all 5 features for deeper analysis.
Filtering & Blending:
After the machine learning-enhanced RSI is calculated, it can be optionally smoothed using advanced filters to suppress short-term noise or sharp spikes. This makes it easier to evaluate RSI signals in different volatility regimes.
Parameters Explained:
📊 RSI Settings:
Set the base RSI length and select your preferred smoothing method from 10+ moving average types (e.g., EMA, ALMA, TEMA).
🧠 Machine Learning Controls:
Enable or disable the KNN engine
Select how many nearest neighbors to compare (K)
Choose the number of features used in similarity detection
Control how much the machine learning engine affects the RSI calculation
🔍 Filtering Options:
Enable one of several advanced smoothing techniques (Kalman Filter, ALMA, Double EMA) to adjust the indicator’s reactivity and stability.
📏 Threshold Levels:
Define static overbought/oversold boundaries or reference dynamically adjusted thresholds based on historical context identified by the KNN algorithm.
🎨 Visual Enhancements:
Select between trend-following or impulse coloring styles. Customize color palettes to accommodate different types of color blindness. Enable neon-style effects for visual clarity.
Use Cases:
Swing & Trend Traders
Can use the indicator to explore how current RSI readings compare to similar market phases, helping to assess trend strength or potential turning points.
Intraday Traders
Benefit from adjustable filters and fast-reacting smoothing to reduce noise in shorter timeframes while retaining contextual relevance.
Discretionary Analysts
Use the adaptive OB/OS thresholds and visual cues to supplement broader confluence zones or market structure analysis.
Customization Tips:
Higher Volatility Periods: Use more neighbors and enable filtering to reduce noise.
Lower Volatility Markets: Use fewer features and disable filtering for quicker RSI adaptation.
Deeper Contextual Analysis: Increase KNN lookback and raise the feature count to refine pattern recognition.
Accessibility Needs: Switch to Deuteranopia or Monochrome mode for clearer visuals in specific color vision conditions.
Final Thoughts:
The Machine Learning RSI combines familiar momentum logic with statistical context derived from historical similarity analysis. It does not attempt to predict price action but rather contextualizes RSI behavior with added nuance. This makes it a valuable tool for those looking to elevate traditional RSI workflows with adaptive, research-driven enhancements.
Stochastic and MACD HistogramStochastic-MACD Fusion Histogram (concept)
How It Works:
This indicator combines Stochastic Oscillator and MACD Histogram to create a unique momentum-tracking histogram. It blends stochastic-based overbought/oversold levels with MACD-based trend strength, helping traders identify potential reversals and trend momentum more effectively.
Stochastic Component: Measures where the price is relative to its recent range, highlighting overbought/oversold conditions.
MACD Component: Captures momentum shifts by calculating the difference between two EMAs and a signal line.
Fusion Algorithm: The MACD histogram is normalized and combined with the Stochastic %K using a weighted formula (60% Stoch, 40% MACD) to smooth fluctuations and improve signal clarity.
Usage:
Histogram Colors:
Blue / SkyBlue: Positive momentum increasing.
Red / LightRed: Negative momentum increasing.
Levels:
Overbought (>30): Potential selling pressure.
Oversold (<-30): Potential buying pressure.
Zero Line: Momentum shift zone.
Notes:
Best to combine it with others indicators for trend confirmation, like Moving Average, MACD, etc.
This indicator is good for quick entry/exit in futures market, from few seconds up to minutes.
It works well on 5 minutes candle. Regular Hours works better.
To sell wait for histogram to go OVER overbought level, once the first candle reach BELOW the overbought level hit sell. Same strategy for buy when it hits oversold level. Make sure you won't use the indicator alone.
Invictus📝 Invictus – Probabilistic Trading Indicator
🔍 1. General Introduction
Invictus is a technical trading indicator designed to support traders by identifying potential buy and sell signals through a probabilistic and adaptive analytical approach. It aims to enhance the analytical process rather than provide explicit trading recommendations. The indicator integrates multiple analytical components—price pattern detection, momentum analysis (RSI), dynamic trend lines (Kalman Line), and volatility bands (ATR)—to offer traders a structured and contextual framework for making informed decisions.
Invictus does not guarantee profitable outcomes but seeks to enhance analytical clarity and support cautious decision-making through multiple validation layers.
⚙️ 2. Main Components
🌊 2.1. Price Pattern Detection
Invictus identifies potential market shifts by analyzing specific candlestick sequences:
Bearish Patterns (Sell): Detected when consecutive candles close below their openings, indicating increased selling pressure.
Bullish Patterns (Buy): Detected when consecutive candles close above their openings, suggesting increased buying interest.
These patterns provide historical insights rather than absolute predictions for market movements.
⚡ 2.2. Momentum Confirmation (RSI)
To improve signal clarity, Invictus employs the Relative Strength Index (RSI):
Buy Signal: RSI below a predefined threshold (e.g., 30), signaling potential oversold conditions.
Sell Signal: RSI above a threshold (e.g., 70), signaling potential overbought conditions.
RSI acts exclusively as an additional validation filter to reduce, though not eliminate, false signals derived solely from price patterns.
🌀 2.3. Kalman Dynamic Line
The Kalman Dynamic Line smooths price action and dynamically tracks trends using a Kalman filter algorithm:
Noise Reduction: Minimizes minor price fluctuations.
Trend Direction Indicator: Line slope visually represents bullish or bearish market bias.
Adaptive Support/Resistance: Adjusts continuously to market conditions.
Volatility Sensitivity: Adjustments use ATR to scale proportionally with market volatility.
This adaptive dynamic line provides clear context, aiding traders by filtering short-term volatility.
📊 2.4. Volatility Bands (ATR-based)
ATR-based volatility bands define potential breakout zones and market extremes dynamically:
Upper/Lower Bands: Positioned relative to the Kalman Line based on ATR (volatility multiplier).
Volatility Zones: Highlight potential areas of trend continuation or reversal due to significant price movements.
These bands assist traders in visually assessing significant market movements and reducing the focus on minor fluctuations.
🧠 3. Component Interaction and Validation Logic
Invictus is designed to enhance analytical clarity by integrating multiple technical components, requiring independent confirmations before signals may be considered as potentially actionable
🔗 Step 1: Pattern + RSI Validation
Initial identification of price patterns.
Signal validation through RSI conditions (oversold/overbought).
🔗 Step 2: Trend Alignment (Kalman Line)
Validated signals undergo further assessment with respect to the Kalman Dynamic Line.
Buy signals require price action above the Kalman Line; sell signals require price action below.
🔗 Step 3: Volatility Confirmation (ATR Bands)
Price action must penetrate and close beyond the corresponding volatility band.
Ensures signals align with adequate market volatility and momentum.
🔄 4. Comprehensive Decision-Making Flow
Identify price patterns (initial indication).
Confirm momentum via RSI.
Verify trend alignment using the Kalman Line.
Confirm adequate volatility via ATR bands.
💡 5. Practical Example (Buy Scenario)
Invictus signals a potential buy scenario.
Trader waits for the price to cross above the Kalman Line.
Entry consideration occurs only after a confirmed close above the upper ATR volatility band.
⚠️ 6. Important Limitations
Do not rely solely on Invictus signals; always perform broader market analysis.
Invictus performs optimally in trending markets; exercise caution in sideways or range-bound markets.
Always evaluate broader market context and the dominant trend before making decisions.
📝 7. Risk Management & Responsible Trading
Invictus serves as an analytical support tool, not a guarantee of market outcomes:
Set prudent stop-loss levels.
Apply conservative leverage, especially in volatile conditions.
Conduct thorough backtesting and practice on a demo account before live trading.
⚠️ Disclaimer: Trading involves significant risks. Invictus generates signals based on historical and technical analysis. Past performance is not indicative of future results. Responsible trading practices are strongly advised.
💡 8. Final Considerations
Invictus provides an analytical framework integrating various supportive technical methodologies designed to enhance decision-making and comprehensive analysis. Its multi-layered validation process encourages disciplined analysis and informed decision-making without implying any guarantees of profitability.
Traders should incorporate Invictus within broader strategic frameworks, consistently applying disciplined risk management and thorough market analysis.
Wall Street Ai**Wall Street Ai – Advanced Technical Indicator for Market Analysis**
**Overview**
Wall Street Ai is an advanced, AI-powered technical indicator meticulously engineered to provide traders with in-depth market analysis and insight. By leveraging state-of-the-art artificial intelligence algorithms and comprehensive historical price data, Wall Street Ai is designed to identify significant market turning points and key price levels. Its sophisticated analytical framework enables traders to uncover potential shifts in market momentum, assisting in the formulation of strategic trading decisions while maintaining the highest standards of objectivity and reliability.
**Key Features**
- **Intelligent Pattern Recognition:**
Wall Street Ai employs advanced machine learning techniques to analyze historical price movements and detect recurring patterns. This capability allows it to differentiate between typical market noise and meaningful signals indicative of potential trend reversals.
- **Robust Noise Reduction:**
The indicator incorporates a refined volatility filtering system that minimizes the impact of minor price fluctuations. By isolating significant price movements, it ensures that the analytical output focuses on substantial market shifts rather than ephemeral variations.
- **Customizable Analytical Parameters:**
With a wide range of adjustable settings, Wall Street Ai can be fine-tuned to align with diverse trading strategies and risk appetites. Traders can modify sensitivity, threshold levels, and other critical parameters to optimize the indicator’s performance under various market conditions.
- **Comprehensive Data Analysis:**
By harnessing the power of artificial intelligence, Wall Street Ai performs a deep analysis of historical data, identifying statistically significant highs and lows. This analysis not only reflects past market behavior but also provides valuable insights into potential future turning points, thereby enhancing the predictive aspect of your trading strategy.
- **Adaptive Market Insights:**
The indicator’s dynamic algorithm continuously adjusts to current market conditions, adapting its analysis based on real-time data inputs. This adaptive quality ensures that the indicator remains relevant and effective across different market environments, whether the market is trending strongly, consolidating, or experiencing volatility.
- **Objective and Reliable Analysis:**
Wall Street Ai is built on a foundation of robust statistical methods and rigorous data validation. Its outputs are designed to be objective and free from any exaggerated claims, ensuring that traders receive a clear, unbiased view of market conditions.
**How It Works**
Wall Street Ai integrates advanced AI and deep learning methodologies to analyze a vast array of historical price data. Its core algorithm identifies and evaluates critical market levels by detecting patterns that have historically preceded significant market movements. By filtering out non-essential fluctuations, the indicator emphasizes key price extremes and trend changes that are likely to impact market behavior. The system’s adaptive nature allows it to recalibrate its analytical parameters in response to evolving market dynamics, providing a consistently reliable framework for market analysis.
**Usage Recommendations**
- **Optimal Timeframes:**
For the most effective application, it is recommended to utilize Wall Street Ai on higher timeframe charts, such as hourly (H1) or higher. This approach enhances the clarity of the detected patterns and provides a more comprehensive view of long-term market trends.
- **Market Versatility:**
Wall Street Ai is versatile and can be applied across a broad range of financial markets, including Forex, indices, commodities, cryptocurrencies, and equities. Its adaptable design ensures consistent performance regardless of the asset class being analyzed.
- **Complementary Analytical Tools:**
While Wall Street Ai provides profound insights into market behavior, it is best utilized in combination with other analytical tools and techniques. Integrating its analysis with additional indicators—such as trend lines, support/resistance levels, or momentum oscillators—can further refine your trading strategy and enhance decision-making.
- **Strategy Testing and Optimization:**
Traders are encouraged to test Wall Street Ai extensively in a simulated trading environment before deploying it in live markets. This allows for thorough calibration of its settings according to individual trading styles and risk management strategies, ensuring optimal performance across diverse market conditions.
**Risk Management and Best Practices**
Wall Street Ai is intended to serve as an analytical tool that supports informed trading decisions. However, as with any technical indicator, its outputs should be interpreted as part of a comprehensive trading strategy that includes robust risk management practices. Traders should continuously validate the indicator’s findings with additional analysis and maintain a disciplined approach to position sizing and risk control. Regular review and adjustment of trading strategies in response to market changes are essential to mitigate potential losses.
**Conclusion**
Wall Street Ai offers a cutting-edge, AI-driven approach to technical analysis, empowering traders with detailed market insights and the ability to identify potential turning points with precision. Its intelligent pattern recognition, adaptive analytical capabilities, and extensive noise reduction make it a valuable asset for both experienced traders and those new to market analysis. By integrating Wall Street Ai into your trading toolkit, you can enhance your understanding of market dynamics and develop a more robust, data-driven trading strategy—all while adhering to the highest standards of analytical integrity and performance.
Lowess Channel + (RSI) [ChartPrime]The Lowess Channel + (RSI) indicator applies the LOWESS (Locally Weighted Scatterplot Smoothing) algorithm to filter price fluctuations and construct a dynamic channel. LOWESS is a non-parametric regression method that smooths noisy data by fitting weighted linear regressions at localized segments. This technique is widely used in statistical analysis to reveal trends while preserving data structure.
In this indicator, the LOWESS algorithm is used to create a central trend line and deviation-based bands. The midline changes color based on trend direction, and diamonds are plotted when a trend shift occurs. Additionally, an RSI gauge is positioned at the end of the channel to display the current RSI level in relation to the price bands.
lowess_smooth(src, length, bandwidth) =>
sum_weights = 0.0
sum_weighted_y = 0.0
sum_weighted_xy = 0.0
sum_weighted_x2 = 0.0
sum_weighted_x = 0.0
for i = 0 to length - 1
x = float(i)
weight = math.exp(-0.5 * (x / bandwidth) * (x / bandwidth))
y = nz(src , 0)
sum_weights := sum_weights + weight
sum_weighted_x := sum_weighted_x + weight * x
sum_weighted_y := sum_weighted_y + weight * y
sum_weighted_xy := sum_weighted_xy + weight * x * y
sum_weighted_x2 := sum_weighted_x2 + weight * x * x
mean_x = sum_weighted_x / sum_weights
mean_y = sum_weighted_y / sum_weights
beta = (sum_weighted_xy - mean_x * mean_y * sum_weights) / (sum_weighted_x2 - mean_x * mean_x * sum_weights)
alpha = mean_y - beta * mean_x
alpha + beta * float(length / 2) // Centered smoothing
⯁ KEY FEATURES
LOWESS Price Filtering – Smooths price fluctuations to reveal the underlying trend with minimal lag.
Dynamic Trend Coloring – The midline changes color based on trend direction (e.g., bullish or bearish).
Trend Shift Diamonds – Marks points where the midline color changes, indicating a possible trend shift.
Deviation-Based Bands – Expands above and below the midline using ATR-based multipliers for volatility tracking.
RSI Gauge Display – A vertical gauge at the right side of the chart shows the current RSI level relative to the price channel.
Fully Customizable – Users can adjust LOWESS length, band width, colors, and enable or disable the RSI gauge and adjust RSIlength.
⯁ HOW TO USE
Use the LOWESS midline as a trend filter —bullish when green, bearish when purple.
Watch for trend shift diamonds as potential entry or exit signals.
Utilize the price bands to gauge overbought and oversold zones based on volatility.
Monitor the RSI gauge to confirm trend strength—high RSI near upper bands suggests overbought conditions, while low RSI near lower bands indicates oversold conditions.
⯁ CONCLUSION
The Lowess Channel + (RSI) indicator offers a powerful way to analyze market trends by applying a statistically robust smoothing algorithm. Unlike traditional moving averages, LOWESS filtering provides a flexible, responsive trendline that adapts to price movements. The integrated RSI gauge enhances decision-making by displaying momentum conditions alongside trend dynamics. Whether used for trend-following or mean reversion strategies, this indicator provides traders with a well-rounded perspective on market behavior.
*Auto Backtest & Optimize EngineFull-featured Engine for Automatic Backtesting and parameter optimization. Allows you to test millions of different combinations of stop-loss and take profit parameters, including on any connected indicators.
⭕️ Key Futures
Quickly identify the optimal parameters for your strategy.
Automatically generate and test thousands of parameter combinations.
A simple Genetic Algorithm for result selection.
Saves time on manual testing of multiple parameters.
Detailed analysis, sorting, filtering and statistics of results.
Detailed control panel with many tooltips.
Display of key metrics: Profit, Win Rate, etc..
Comprehensive Strategy Score calculation.
In-depth analysis of the performance of different types of stop-losses.
Possibility to use to calculate the best Stop-Take parameters for your position.
Ability to test your own functions and signals.
Customizable visualization of results.
Flexible Stop-Loss Settings:
• Auto ━ Allows you to test all types of Stop Losses at once(listed below).
• S.VOLATY ━ Static stop based on volatility (Fixed, ATR, STDEV).
• Trailing ━ Classic trailing stop following the price.
• Fast Trail ━ Accelerated trailing stop that reacts faster to price movements.
• Volatility ━ Dynamic stop based on volatility indicators.
• Chandelier ━ Stop based on price extremes.
• Activator ━ Dynamic stop based on SAR.
• MA ━ Stop based on moving averages (9 different types).
• SAR ━ Parabolic SAR (Stop and Reverse).
Advanced Take-Profit Options:
• R:R: Risk/Reward ━ sets TP based on SL size.
• T.VOLATY ━ Calculation based on volatility indicators (Fixed, ATR, STDEV).
Testing Modes:
• Stops ━ Cyclical stop-loss testing
• Pivot Point Example ━ Example of using pivot points
• External Example ━ Built-in example how test functions with different parameters
• External Signal ━ Using external signals
⭕️ Usage
━ First Steps:
When opening, select any point on the chart. It will not affect anything until you turn on Manual Start mode (more on this below).
The chart will immediately show the best results of the default Auto mode. You can switch Part's to try to find even better results in the table.
Now you can display any result from the table on the chart by entering its ID in the settings.
Repeat steps 3-4 until you determine which type of Stop Loss you like best. Then set it in the settings instead of Auto mode.
* Example: I flipped through 14 parts before I liked the first result and entered its ID so I could visually evaluate it on the chart.
Then select the stop loss type, choose it in place of Auto mode and repeat steps 3-4 or immediately follow the recommendations of the algorithm.
Now the Genetic Algorithm at the bottom right will prompt you to enter the Parameters you need to search for and select even better results.
Parameters must be entered All at once before they are updated. Enter recommendations strictly in fields with the same names.
Repeat steps 5-6 until there are approximately 10 Part's left or as you like. And after that, easily pour through the remaining Parts and select the best parameters.
━ Example of the finished result.
━ Example of use with Takes
You can also test at the same time along with Take Profit. In this example, I simply enabled Risk/Reward mode and immediately specified in the TP field Maximum RR, Minimum RR and Step. So in this example I can test (3-1) / 0.1 = 20 Takes of different sizes. There are additional tips in the settings.
━
* Soon you will start to understand how the system works and things will become much easier.
* If something doesn't work, just reset the engine settings and start over again.
* Use the tips I have left in the settings and on the Panel.
━ Details:
Sort ━ Sorting results by Score, Profit, Trades, etc..
Filter ━ Filtring results by Score, Profit, Trades, etc..
Trade Type ━ Ability to disable Long\Short but only from statistics.
BackWin ━ Backtest Window Number of Candle the script can test.
Manual Start ━ Enabling it will allow you to call a Stop from a selected point. which you selected when you started the engine.
* If you have a real open position then this mode can help to save good Stop\Take for it.
1 - 9 Сheckboxs ━ Allow you to disable any stop from Auto mode.
Ex Source - Allow you to test Stops/Takes from connected indicators.
Connection guide:
//@version=6
indicator("My script")
rsi = ta.rsi(close, 14)
buy = not na(rsi) and ta.crossover (rsi, 40) // OS = 40
sell = not na(rsi) and ta.crossunder(rsi, 60) // OB = 60
Signal = buy ? +1 : sell ? -1 : 0
plot(Signal, "🔌Connector🔌", display = display.none)
* Format the signal for your indicator in a similar style and then select it in Ex Source.
⭕️ How it Works
Hypothesis of Uniform Distribution of Rare Elements After Mixing.
'This hypothesis states that if an array of N elements contains K valid elements, then after mixing, these valid elements will be approximately uniformly distributed.'
'This means that in a random sample of k elements, the proportion of valid elements should closely match their proportion in the original array, with some random variation.'
'According to the central limit theorem, repeated sampling will result in an average count of valid elements following a normal distribution.'
'This supports the assumption that the valid elements are evenly spread across the array.'
'To test this hypothesis, we can conduct an experiment:'
'Create an array of 1,000,000 elements.'
'Select 1,000 random elements (1%) for validation.'
'Shuffle the array and divide it into groups of 1,000 elements.'
'If the hypothesis holds, each group should contain, on average, 1~ valid element, with minor variations.'
* I'd like to attach more details to My hypothesis but it won't be very relevant here. Since this is a whole separate topic, I will leave the minimum part for understanding the engine.
Practical Application
To apply this hypothesis, I needed a way to generate and thoroughly mix numerous possible combinations. Within Pine, generating over 100,000 combinations presents significant challenges, and storing millions of combinations requires excessive resources.
I developed an efficient mechanism that generates combinations in random order to address these limitations. While conventional methods often produce duplicates or require generating a complete list first, my approach guarantees that the first 10% of possible combinations are both unique and well-distributed. Based on my hypothesis, this sampling is sufficient to determine optimal testing parameters.
Most generators and randomizers fail to accommodate both my hypothesis and Pine's constraints. My solution utilizes a simple Linear Congruential Generator (LCG) for pseudo-randomization, enhanced with prime numbers to increase entropy during generation. I pre-generate the entire parameter range and then apply systematic mixing. This approach, combined with a hybrid combinatorial array-filling technique with linear distribution, delivers excellent generation quality.
My engine can efficiently generate and verify 300 unique combinations per batch. Based on the above, to determine optimal values, only 10-20 Parts need to be manually scrolled through to find the appropriate value or range, eliminating the need for exhaustive testing of millions of parameter combinations.
For the Score statistic I applied all the same, generated a range of Weights, distributed them randomly for each type of statistic to avoid manual distribution.
Score ━ based on Trade, Profit, WinRate, Profit Factor, Drawdown, Sharpe & Sortino & Omega & Calmar Ratio.
⭕️ Notes
For attentive users, a little tricks :)
To save time, switch parts every 3 seconds without waiting for it to load. After 10-20 parts, stop and wait for loading. If the pause is correct, you can switch between the rest of the parts without loading, as they will be cached. This used to work without having to wait for a pause, but now it does slower. This will save a lot of time if you are going to do a deeper backtest.
Sometimes you'll get the error “The scripts take too long to execute.”
For a quick fix you just need to switch the TF or Ticker back and forth and most likely everything will load.
The error appears because of problems on the side of the site because the engine is very heavy. It can also appear if you set too long a period for testing in BackWin or use a heavy indicator for testing.
Manual Start - Allow you to Start you Result from any point. Which in turn can help you choose a good stop-stick for your real position.
* It took me half a year from idea to current realization. This seems to be one of the few ways to build something automatic in backtest format and in this particular Pine environment. There are already better projects in other languages, and they are created much easier and faster because there are no limitations except for personal PC. If you see solutions to improve this system I would be glad if you share the code. At the moment I am tired and will continue him not soon.
Also You can use my previosly big Backtest project with more manual settings(updated soon)
Clustering & Divergences (RSI-Stoch-CCI) [Sam SDF-Solutions]The Clustering & Divergences (RSI-Stoch-CCI) indicator is a comprehensive technical analysis tool that consolidates three popular oscillators—Relative Strength Index (RSI), Stochastic, and Commodity Channel Index (CCI)—into one unified metric called the Score. This Score offers traders an aggregated view of market conditions, allowing them to quickly identify whether the market is oversold, balanced, or overbought.
Functionality:
Oscillator Clustering: The indicator calculates the values of RSI, Stochastic, and CCI using user-defined periods. These oscillator values are then normalized using one of three available methods: MinMax, Z-Score, or Z-Bins.
Score Calculation: Each normalized oscillator value is multiplied by its respective weight (which the user can adjust), and the weighted values are summed to generate an overall Score. This Score serves as a single, interpretable metric representing the combined oscillator behavior.
Market Clustering: The indicator performs clustering on the Score over a configurable window. By dividing the Score range into a set number of clusters (also configurable), the tool visually represents the market’s state. Each cluster is assigned a unique color so that traders can quickly see if the market is trending toward oversold, balanced, or overbought conditions.
Divergence Detection: The script automatically identifies both Regular and Hidden divergences between the price action and the Score. By using pivot detection on both price and Score data, the indicator marks potential reversal signals on the chart with labels and connecting lines. This helps in pinpointing moments when the price and the underlying oscillator dynamics diverge.
Customization Options: Users have full control over the indicator’s behavior. They can adjust:
The periods for each oscillator (RSI, Stochastic, CCI).
The weights applied to each oscillator in the Score calculation.
The normalization method and its manual boundaries.
The number of clusters and whether to invert the cluster order.
Parameters for divergence detection (such as pivot sensitivity and the minimum/maximum bar distance between pivots).
Visual Enhancements:
Depending on the user’s preference, either the Score or the Cluster Index (derived from the clustering process) is plotted on the chart. Additionally, the script changes the color of the price bars based on the identified cluster, providing an at-a-glance visual cue of the current market regime.
Logic & Methodology:
Input Parameters: The script starts by accepting user inputs for clustering settings, oscillator periods, weights, divergence detection, and manual boundary definitions for normalization.
Oscillator Calculation & Normalization: It computes RSI, Stochastic, and CCI values from the price data. These values are then normalized using either the MinMax method (scaling between a lower and upper band) or the Z-Score method (standardizing based on mean and standard deviation), or using Z-Bins for an alternative scaling approach.
Score Computation: Each normalized oscillator is multiplied by its corresponding weight. The sum of these products results in the overall Score that represents the combined oscillator behavior.
Clustering Algorithm: The Score is evaluated over a moving window to determine its minimum and maximum values. Using these values, the script calculates a cluster index that divides the Score into a predefined number of clusters. An option to invert the cluster calculation is provided to adjust the interpretation of the clustering.
Divergence Analysis: The indicator employs pivot detection (using left and right bar parameters) on both the price and the Score. It then compares recent pivot values to detect regular and hidden divergences. When a divergence is found, the script plots labels and optional connecting lines to highlight these key moments on the chart.
Plotting: Finally, based on the user’s selection, the indicator plots either the Score or the Cluster Index. It also overlays manual boundary lines (for the chosen normalization method) and adjusts the bar colors according to the cluster to provide clear visual feedback on market conditions.
_________
By integrating multiple oscillator signals into one cohesive tool, the Clustering & Divergences (RSI-Stoch-CCI) indicator helps traders minimize subjective analysis. Its dynamic clustering and automated divergence detection provide a streamlined method for assessing market conditions and potentially enhancing the accuracy of trading decisions.
For further details on using this indicator, please refer to the guide available at:
Percentage Based ZigZag█ OVERVIEW
The Percentage-Based ZigZag indicator is a custom technical analysis tool designed to highlight significant price reversals while filtering out market noise. Unlike many standard zigzag tools that rely solely on fixed price moves or generic trend-following methods, this indicator uses a configurable percentage threshold to dynamically determine meaningful pivot points. This approach not only adapts to different market conditions but also helps traders distinguish between minor fluctuations and truly significant trend shifts—whether scalping on shorter timeframes or analyzing longer-term trends.
█ KEY FEATURES & ORIGINALITY
Dynamic Pivot Detection
The indicator identifies pivot points by measuring the percentage change from the previous extreme (high or low). Only when this change exceeds a user-defined threshold is a new pivot recognized. This method ensures that only substantial moves are considered, making the indicator robust in volatile or noisy markets.
Enhanced ZigZag Visualization
By connecting significant highs and lows with a continuous line, the indicator creates a clear visual map of price swings. Each pivot point is labelled with the corresponding price and the percentage change from the previous pivot, providing immediate quantitative insight into the magnitude of the move.
Trend Reversal Projections
In addition to marking completed reversals, the script computes and displays potential future reversal points based on the current trend’s momentum. This forecasting element gives traders an advanced look at possible turning points, which can be particularly useful for short-term scalping strategies.
Customizable Visual Settings
Users can tailor the appearance by:
• Setting the percentage threshold to control sensitivity.
• Customizing colors for bullish (e.g., green) and bearish (e.g., red) reversals.
• Enabling optional background color changes that visually indicate the prevailing trend.
█ UNDERLYING METHODOLOGY & CALCULATIONS
Percentage-Based Filtering
The script continuously monitors price action and calculates the relative percentage change from the last identified pivot. A new pivot is confirmed only when the price moves a preset percentage away from this pivot, ensuring that minor fluctuations do not trigger false signals.
Pivot Point Logic
The indicator tracks the highest high and the lowest low since the last pivot. When the price reverses by the required percentage from these extremes, the algorithm:
1 — Labels the point as a significant high or low.
2 — Draws a connecting line from the previous pivot to the current one.
3 — Resets the extreme-tracking for detecting the next move.
Real-Time Reversal Estimation
Building on traditional zigzag methods, the script incorporates a projection calculation. By analyzing the current trend’s strength and recent percentage moves, it estimates where a future reversal might occur, offering traders actionable foresight.
█ HOW TO USE THE INDICATOR
1 — Apply the Indicator
• Add the Percentage-Based ZigZag indicator to your trading chart.
2 — Adjust Settings for Your Market
• Percentage Move – Set a threshold that matches your trading style:
- Lower values for sensitive, high-frequency analysis (ideal for scalping).
- Higher values for filtering out noise on longer timeframes.
• Visual Customization – Choose your preferred colors for bullish and bearish signals and enable background color changes for visual trend cues.
• Reversal Projection – Enable or disable the projection feature to display potential upcoming reversal points.
3 — Interpret the Signals
• ZigZag Lines – White lines trace significant high-to-low or low-to-high movements, visually connecting key swing points.
• Pivot Labels – Each pivot is annotated with the exact price level and percentage change, providing quantitative insight into market momentum.
• Trend Projections – When enabled, projected reversal levels offer insight into where the current trend might change.
4 — Integrate with Your Trading Strategy
• Use the indicator to identify support and resistance zones derived from significant pivots.
• Combine the quantitative data (percentage changes) with your risk management strategy to set optimal stop-loss and take-profit levels.
• Experiment with different threshold settings to adapt the indicator for various instruments or market conditions.
█ CONCLUSION
The Percentage-Based ZigZag indicator goes beyond traditional trend-following tools by filtering out market noise and providing clear, quantifiable insights into price action. With its percentage threshold for pivot detection and real-time reversal projections, this original methodology and customizable feature set offer traders a versatile edge for making informed trading decisions.
Cluster Reversal Zones📌 Cluster Reversal Zones – Smart Market Turning Point Detector
📌 Category : Public (Restricted/Closed-Source) Indicator
📌 Designed for : Traders looking for high-accuracy reversal zones based on price clustering & liquidity shifts.
🔍 Overview
The Cluster Reversal Zones Indicator is an advanced market reversal detection tool that helps traders identify key turning points using a combination of price clustering, order flow analysis, and liquidity tracking. Instead of relying on static support and resistance levels, this tool dynamically adjusts to live market conditions, ensuring traders get the most accurate reversal signals possible.
📊 Core Features:
✅ Real-Time Reversal Zone Mapping – Detects high-probability market turning points using price clustering & order flow imbalance.
✅ Liquidity-Based Support/Resistance Detection – Identifies strong rejection zones based on real-time liquidity shifts.
✅ Order Flow Sensitivity for Smart Filtering – Filters out weak reversals by detecting real market participation behind price movements.
✅ Momentum Divergence for Confirmation – Aligns reversal zones with momentum divergences to increase accuracy.
✅ Adaptive Risk Management System – Adjusts risk parameters dynamically based on volatility and trend state.
🔒 Justification for Mashup
The Cluster Reversal Zones Indicator contains custom-built methodologies that extend beyond traditional support/resistance indicators:
✔ Smart Price Clustering Algorithm: Instead of plotting fixed support/resistance lines, this system analyzes historical price clustering to detect active reversal areas.
✔ Order Flow Delta & Liquidity Shift Sensitivity: The tool tracks real-time order flow data, identifying price zones with the highest accumulation or distribution levels.
✔ Momentum-Based Reversal Validation: Unlike traditional indicators, this tool requires a momentum shift confirmation before validating a potential reversal.
✔ Adaptive Reversal Filtering Mechanism: Uses a combination of historical confluence detection + live market validation to improve accuracy.
🛠️ How to Use:
• Works well for reversal traders, scalpers, and swing traders seeking precise turning points.
• Best combined with VWAP, Market Profile, and Delta Volume indicators for confirmation.
• Suitable for Forex, Indices, Commodities, Crypto, and Stock markets.
🚨 Important Note:
For educational & analytical purposes only.
Ehlers Maclaurin Ultimate Smoother [CT]Ehlers Maclaurin Ultimate Smoother
Introduction
The Ehlers Maclaurin Ultimate Smoother is an innovative enhancement of the classic Ehlers SuperSmoother. By leveraging advanced Maclaurin series approximations, this indicator offers superior market analysis and signal generation.
The indicator combines Ehlers' Ultimate Smoother with Maclaurin series approximations to create a more efficient and accurate smoothing mechanism:
Input price data passes through the initial smoothing phase
Maclaurin series approximates trigonometric functions
Enhanced high-pass filter removes market noise
Final smoothing phase produces the output signal
Why the Maclaurin Approach?
The Maclaurin series is a special form of the Taylor series, centered around 0. It provides an efficient way to approximate complex functions using polynomial terms. In this indicator, we use the Maclaurin approach to improve the sine and cosine functions, resulting in:
Faster Calculations: By using polynomial approximations, we significantly reduce computational complexity.
Improved Stability: The approximation provides a more stable numerical basis for calculations.
Preservation of Precision: Despite the approximation, we maintain the precision needed for price smoothing.
Calculations
The indicator employs several key mathematical components:
Maclaurin Series Approximation:
sin(x) ≈ x - x³/3! + x⁵/5! - x⁷/7! + x⁹/9!
cos(x) ≈ 1 - x²/2! + x⁴/4! - x⁶/6! + x⁸/8!
Smoothing Algorithm:
Uses exponential smoothing with optimized coefficients
Implements high-pass filtering for noise reduction
Applies dynamic weighting based on market conditions
Mathematical Foundation
Utilizes Maclaurin series for trigonometric approximation
Implements Ehlers' smoothing principles
Incorporates advanced filtering techniques
Technical Advantages
Signal Processing:
Lag Reduction: Faster signal detection with less delay.
Noise Filtration: Effective elimination of high-frequency noise.
Precision Enhancement: Preservation of critical price movements.
Adaptive Processing: Dynamic response to market volatility.
Visual Enhancements:
Smart color intensity mapping.
Real-time visualization of trend strength.
Adaptive opacity based on movement significance.
Implementation
Core Configuration:
Plot Type: Choose between the original and the Maclaurin enhanced version.
Length: Default set to 30, optimal for daily timeframes.
hpLength: Default set to 10 for enhanced noise reduction.
Advanced Parameters:
The indicator offers advanced control with:
Dual processing modes (Original/Maclaurin).
Dynamic color intensity system.
Customizable smoothing parameters.
Professional Analysis Tools:
Accurate trend reversal identification.
Advanced support/resistance detection.
Superior performance in volatile markets.
Technical Specifications
Maclaurin Series Implementation:
The indicator employs a 5-term Maclaurin series approximation for both sine and cosine, ensuring efficient and accurate computation.
Performance Metrics
Improved processing efficiency.
Reduced memory utilization.
Increased signal accuracy.
Licensing & Attribution
© 2024 Mupsje aka CasaTropical
Professional Credits
Original Ultimate and SuperSmoother concept: John F. Ehlers
Maclaurin enhancement: Casa Tropical (CT)
www.mathsisfun.com
True Amplitude Envelopes (TAE)The True Envelopes indicator is an adaptation of the True Amplitude Envelope (TAE) method, based on the research paper " Improved Estimation of the Amplitude Envelope of Time Domain Signals Using True Envelope Cepstral Smoothing " by Caetano and Rodet. This indicator aims to create an asymmetric price envelope with strong predictive power, closely following the methodology outlined in the paper.
Due to the inherent limitations of Pine Script, the indicator utilizes a Kernel Density Estimator (KDE) in place of the original Cepstral Smoothing technique described in the paper. While this approach was chosen out of necessity rather than superiority, the resulting method is designed to be as effective as possible within the constraints of the Pine environment.
This indicator is ideal for traders seeking an advanced tool to analyze price dynamics, offering insights into potential price movements while working within the practical constraints of Pine Script. Whether used in dynamic mode or with a static setting, the True Envelopes indicator helps in identifying key support and resistance levels, making it a valuable asset in any trading strategy.
Key Features:
Dynamic Mode: The indicator dynamically estimates the fundamental frequency of the price, optimizing the envelope generation process in real-time to capture critical price movements.
High-Pass Filtering: Uses a high-pass filtered signal to identify and smoothly interpolate price peaks, ensuring that the envelope accurately reflects significant price changes.
Kernel Density Estimation: Although implemented as a workaround, the KDE technique allows for flexible and adaptive smoothing of the envelope, aimed at achieving results comparable to the more sophisticated methods described in the original research.
Symmetric and Asymmetric Envelopes: Provides options to select between symmetric and asymmetric envelopes, accommodating various trading strategies and market conditions.
Smoothness Control: Features adjustable smoothness settings, enabling users to balance between responsiveness and the overall smoothness of the envelopes.
The True Envelopes indicator comes with a variety of input settings that allow traders to customize the behavior of the envelopes to match their specific trading needs and market conditions. Understanding each of these settings is crucial for optimizing the indicator's performance.
Main Settings
Source: This is the data series on which the indicator is applied, typically the closing price (close). You can select other price data like open, high, low, or a custom series to base the envelope calculations.
History: This setting determines how much historical data the indicator should consider when calculating the envelopes. A value of 0 will make the indicator process all available data, while a higher value restricts it to the most recent n bars. This can be useful for reducing the computational load or focusing the analysis on recent market behavior.
Iterations: This parameter controls the number of iterations used in the envelope generation algorithm. More iterations will typically result in a smoother envelope, but can also increase computation time. The optimal number of iterations depends on the desired balance between smoothness and responsiveness.
Kernel Style: The smoothing kernel used in the Kernel Density Estimator (KDE). Available options include Sinc, Gaussian, Epanechnikov, Logistic, and Triangular. Each kernel has different properties, affecting how the smoothing is applied. For example, Gaussian provides a smooth, bell-shaped curve, while Epanechnikov is more efficient computationally with a parabolic shape.
Envelope Style: This setting determines whether the envelope should be Static or Dynamic. The Static mode applies a fixed period for the envelope, while the Dynamic mode automatically adjusts the period based on the fundamental frequency of the price data. Dynamic mode is typically more responsive to changing market conditions.
High Q: This option controls the quality factor (Q) of the high-pass filter. Enabling this will increase the Q factor, leading to a sharper cutoff and more precise isolation of high-frequency components, which can help in better identifying significant price peaks.
Symmetric: This setting allows you to choose between symmetric and asymmetric envelopes. Symmetric envelopes maintain an equal distance from the central price line on both sides, while asymmetric envelopes can adjust differently above and below the price line, which might better capture market conditions where upside and downside volatility are not equal.
Smooth Envelopes: When enabled, this setting applies additional smoothing to the envelopes. While this can reduce noise and make the envelopes more visually appealing, it may also decrease their responsiveness to sudden market changes.
Dynamic Settings
Extra Detrend: This setting toggles an additional high-pass filter that can be applied when using a long filter period. The purpose is to further detrend the data, ensuring that the envelope focuses solely on the most recent price oscillations.
Filter Period Multiplier: This multiplier adjusts the period of the high-pass filter dynamically based on the detected fundamental frequency. Increasing this multiplier will lengthen the period, making the filter less sensitive to short-term price fluctuations.
Filter Period (Min) and Filter Period (Max): These settings define the minimum and maximum bounds for the high-pass filter period. They ensure that the filter period stays within a reasonable range, preventing it from becoming too short (and overly sensitive) or too long (and too sluggish).
Envelope Period Multiplier: Similar to the filter period multiplier, this adjusts the period for the envelope generation. It scales the period dynamically to match the detected price cycles, allowing for more precise envelope adjustments.
Envelope Period (Min) and Envelope Period (Max): These settings establish the minimum and maximum bounds for the envelope period, ensuring the envelopes remain adaptive without becoming too reactive or too slow.
Static Settings
Filter Period: In static mode, this setting determines the fixed period for the high-pass filter. A shorter period will make the filter more responsive to price changes, while a longer period will smooth out more of the price data.
Envelope Period: This setting specifies the fixed period used for generating the envelopes in static mode. It directly influences how tightly or loosely the envelopes follow the price action.
TAE Smoothing: This controls the degree of smoothing applied during the TAE process in static mode. Higher smoothing values result in more gradual envelope curves, which can be useful in reducing noise but may also delay the envelope’s response to rapid price movements.
Visual Settings
Top Band Color: This setting allows you to choose the color for the upper band of the envelope. This band represents the resistance level in the price action.
Bottom Band Color: Similar to the top band color, this setting controls the color of the lower band, which represents the support level.
Center Line Color: This is the color of the central price line, often referred to as the carrier. It represents the detrended price around which the envelopes are constructed.
Line Width: This determines the thickness of the plotted lines for the top band, bottom band, and center line. Thicker lines can make the envelopes more visible, especially when overlaid on price data.
Fill Alpha: This controls the transparency level of the shaded area between the top and bottom bands. A lower alpha value will make the fill more transparent, while a higher value will make it more opaque, helping to highlight the envelope more clearly.
The envelopes generated by the True Envelopes indicator are designed to provide a more precise and responsive representation of price action compared to traditional methods like Bollinger Bands or Keltner Channels. The core idea behind this indicator is to create a price envelope that smoothly interpolates the significant peaks in price action, offering a more accurate depiction of support and resistance levels.
One of the critical aspects of this approach is the use of a high-pass filtered signal to identify these peaks. The high-pass filter serves as an effective method of detrending the price data, isolating the rapid fluctuations in price that are often lost in standard trend-following indicators. By filtering out the lower frequency components (i.e., the trend), the high-pass filter reveals the underlying oscillations in the price, which correspond to significant peaks and troughs. These oscillations are crucial for accurately constructing the envelope, as they represent the most responsive elements of the price movement.
The algorithm works by first applying the high-pass filter to the source price data, effectively detrending the series and isolating the high-frequency price changes. This filtered signal is then used to estimate the fundamental frequency of the price movement, which is essential for dynamically adjusting the envelope to current market conditions. By focusing on the peaks identified in the high-pass filtered signal, the algorithm generates an envelope that is both smooth and adaptive, closely following the most significant price changes without overfitting to transient noise.
Compared to traditional envelopes and bands, such as Bollinger Bands and Keltner Channels, the True Envelopes indicator offers several advantages. Bollinger Bands, which are based on standard deviations, and Keltner Channels, which use the average true range (ATR), both tend to react to price volatility but do not necessarily follow the peaks and troughs of the price with precision. As a result, these traditional methods can sometimes lag behind or fail to capture sudden shifts in price momentum, leading to either false signals or missed opportunities.
In contrast, the True Envelopes indicator, by using a high-pass filtered signal and a dynamic period estimation, adapts more quickly to changes in price behavior. The envelopes generated by this method are less prone to the lag that often affects standard deviation or ATR-based bands, and they provide a more accurate representation of the price's immediate oscillations. This can result in better predictive power and more reliable identification of support and resistance levels, making the True Envelopes indicator a valuable tool for traders looking for a more responsive and precise approach to market analysis.
In conclusion, the True Envelopes indicator is a powerful tool that blends advanced theoretical concepts with practical implementation, offering traders a precise and responsive way to analyze price dynamics. By adapting the True Amplitude Envelope (TAE) method through the use of a Kernel Density Estimator (KDE) and high-pass filtering, this indicator effectively captures the most significant price movements, providing a more accurate depiction of support and resistance levels compared to traditional methods like Bollinger Bands and Keltner Channels. The flexible settings allow for extensive customization, ensuring the indicator can be tailored to suit various trading strategies and market conditions.
Hybrid Adaptive Double Exponential Smoothing🙏🏻 This is HADES (Hybrid Adaptive Double Exponential Smoothing) : fully data-driven & adaptive exponential smoothing method, that gains all the necessary info directly from data in the most natural way and needs no subjective parameters & no optimizations. It gets applied to data itself -> to fit residuals & one-point forecast errors, all at O(1) algo complexity. I designed it for streaming high-frequency univariate time series data, such as medical sensor readings, orderbook data, tick charts, requests generated by a backend, etc.
The HADES method is:
fit & forecast = a + b * (1 / alpha + T - 1)
T = 0 provides in-sample fit for the current datum, and T + n provides forecast for n datapoints.
y = input time series
a = y, if no previous data exists
b = 0, if no previous data exists
otherwise:
a = alpha * y + (1 - alpha) * a
b = alpha * (a - a ) + (1 - alpha) * b
alpha = 1 / sqrt(len * 4)
len = min(ceil(exp(1 / sig)), available data)
sig = sqrt(Absolute net change in y / Sum of absolute changes in y)
For the start datapoint when both numerator and denominator are zeros, we define 0 / 0 = 1
...
The same set of operations gets applied to the data first, then to resulting fit absolute residuals to build prediction interval, and finally to absolute forecasting errors (from one-point ahead forecast) to build forecasting interval:
prediction interval = data fit +- resoduals fit * k
forecasting interval = data opf +- errors fit * k
where k = multiplier regulating intervals width, and opf = one-point forecasts calculated at each time t
...
How-to:
0) Apply to your data where it makes sense, eg. tick data;
1) Use power transform to compensate for multiplicative behavior in case it's there;
2) If you have complete data or only the data you need, like the full history of adjusted close prices: go to the next step; otherwise, guided by your goal & analysis, adjust the 'start index' setting so the calculations will start from this point;
3) Use prediction interval to detect significant deviations from the process core & make decisions according to your strategy;
4) Use one-point forecast for nowcasting;
5) Use forecasting intervals to ~ understand where the next datapoints will emerge, given the data-generating process will stay the same & lack structural breaks.
I advise k = 1 or 1.5 or 4 depending on your goal, but 1 is the most natural one.
...
Why exponential smoothing at all? Why the double one? Why adaptive? Why not Holt's method?
1) It's O(1) algo complexity & recursive nature allows it to be applied in an online fashion to high-frequency streaming data; otherwise, it makes more sense to use other methods;
2) Double exponential smoothing ensures we are taking trends into account; also, in order to model more complex time series patterns such as seasonality, we need detrended data, and this method can be used to do it;
3) The goal of adaptivity is to eliminate the window size question, in cases where it doesn't make sense to use cumulative moving typical value;
4) Holt's method creates a certain interaction between level and trend components, so its results lack symmetry and similarity with other non-recursive methods such as quantile regression or linear regression. Instead, I decided to base my work on the original double exponential smoothing method published by Rob Brown in 1956, here's the original source , it's really hard to find it online. This cool dude is considered the one who've dropped exponential smoothing to open access for the first time🤘🏻
R&D; log & explanations
If you wanna read this, you gotta know, you're taking a great responsability for this long journey, and it gonna be one hell of a trip hehe
Machine learning, apprentissage automatique, машинное обучение, digital signal processing, statistical learning, data mining, deep learning, etc., etc., etc.: all these are just artificial categories created by the local population of this wonderful world, but what really separates entities globally in the Universe is solution complexity / algorithmic complexity.
In order to get the game a lil better, it's gonna be useful to read the HTES script description first. Secondly, let me guide you through the whole R&D; process.
To discover (not to invent) the fundamental universal principle of what exponential smoothing really IS, it required the review of the whole concept, understanding that many things don't add up and don't make much sense in currently available mainstream info, and building it all from the beginning while avoiding these very basic logical & implementation flaws.
Given a complete time t, and yet, always growing time series population that can't be logically separated into subpopulations, the very first question is, 'What amount of data do we need to utilize at time t?'. Two answers: 1 and all. You can't really gain much info from 1 datum, so go for the second answer: we need the whole dataset.
So, given the sequential & incremental nature of time series, the very first and basic thing we can do on the whole dataset is to calculate a cumulative , such as cumulative moving mean or cumulative moving median.
Now we need to extend this logic to exponential smoothing, which doesn't use dataset length info directly, but all cool it can be done via a formula that quantifies the relationship between alpha (smoothing parameter) and length. The popular formulas used in mainstream are:
alpha = 1 / length
alpha = 2 / (length + 1)
The funny part starts when you realize that Cumulative Exponential Moving Averages with these 2 alpha formulas Exactly match Cumulative Moving Average and Cumulative (Linearly) Weighted Moving Average, and the same logic goes on:
alpha = 3 / (length + 1.5) , matches Cumulative Weighted Moving Average with quadratic weights, and
alpha = 4 / (length + 2) , matches Cumulative Weighted Moving Average with cubic weghts, and so on...
It all just cries in your shoulder that we need to discover another, native length->alpha formula that leverages the recursive nature of exponential smoothing, because otherwise, it doesn't make sense to use it at all, since the usual CMA and CMWA can be computed incrementally at O(1) algo complexity just as exponential smoothing.
From now on I will not mention 'cumulative' or 'linearly weighted / weighted' anymore, it's gonna be implied all the time unless stated otherwise.
What we can do is to approach the thing logically and model the response with a little help from synthetic data, a sine wave would suffice. Then we can think of relationships: Based on algo complexity from lower to higher, we have this sequence: exponential smoothing @ O(1) -> parametric statistics (mean) @ O(n) -> non-parametric statistics (50th percentile / median) @ O(n log n). Based on Initial response from slow to fast: mean -> median Based on convergence with the real expected value from slow to fast: mean (infinitely approaches it) -> median (gets it quite fast).
Based on these inputs, we need to discover such a length->alpha formula so the resulting fit will have the slowest initial response out of all 3, and have the slowest convergence with expected value out of all 3. In order to do it, we need to have some non-linear transformer in our formula (like a square root) and a couple of factors to modify the response the way we need. I ended up with this formula to meet all our requirements:
alpha = sqrt(1 / length * 2) / 2
which simplifies to:
alpha = 1 / sqrt(len * 8)
^^ as you can see on the screenshot; where the red line is median, the blue line is the mean, and the purple line is exponential smoothing with the formulas you've just seen, we've met all the requirements.
Now we just have to do the same procedure to discover the length->alpha formula but for double exponential smoothing, which models trends as well, not just level as in single exponential smoothing. For this comparison, we need to use linear regression and quantile regression instead of the mean and median.
Quantile regression requires a non-closed form solution to be solved that you can't really implement in Pine Script, but that's ok, so I made the tests using Python & sklearn:
paste.pics
^^ on this screenshot, you can see the same relationship as on the previous screenshot, but now between the responses of quantile regression & linear regression.
I followed the same logic as before for designing alpha for double exponential smoothing (also considered the initial overshoots, but that's a little detail), and ended up with this formula:
alpha = sqrt(1 / length) / 2
which simplifies to:
alpha = 1 / sqrt(len * 4)
Btw, given the pattern you see in the resulting formulas for single and double exponential smoothing, if you ever want to do triple (not Holt & Winters) exponential smoothing, you'll need len * 2 , and just len * 1 for quadruple exponential smoothing. I hope that based on this sequence, you see the hint that Maybe 4 rounds is enough.
Now since we've dealt with the length->alpha formula, we can deal with the adaptivity part.
Logically, it doesn't make sense to use a slower-than-O(1) method to generate input for an O(1) method, so it must be something universal and minimalistic: something that will help us measure consistency in our data, yet something far away from statistics and close enough to topology.
There's one perfect entity that can help us, this is fractal efficiency. The way I define fractal efficiency can be checked at the very beginning of the post, what matters is that I add a square root to the formula that is not typically added.
As explained in the description of my metric QSFS , one of the reasons for SQRT-transformed values of fractal efficiency applied in moving window mode is because they start to closely resemble normal distribution, yet with support of (0, 1). Data with this interesting property (normally distributed yet with finite support) can be modeled with the beta distribution.
Another reason is, in infinitely expanding window mode, fractal efficiency of every time series that exhibits randomness tends to infinitely approach zero, sqrt-transform kind of partially neutralizes this effect.
Yet another reason is, the square root might better reflect the dimensional inefficiency or degree of fractal complexity, since it could balance the influence of extreme deviations from the net paths.
And finally, fractals exhibit power-law scaling -> measures like length, area, or volume scale in a non-linear way. Adding a square root acknowledges this intrinsic property, while connecting our metric with the nature of fractals.
---
I suspect that, given analogies and connections with other topics in geometry, topology, fractals and most importantly positive test results of the metric, it might be that the sqrt transform is the fundamental part of fractal efficiency that should be applied by default.
Now the last part of the ballet is to convert our fractal efficiency to length value. The part about inverse proportionality is obvious: high fractal efficiency aka high consistency -> lower window size, to utilize only the last data that contain brand new information that seems to be highly reliable since we have consistency in the first place.
The non-obvious part is now we need to neutralize the side effect created by previous sqrt transform: our length values are too low, and exponentiation is the perfect candidate to fix it since translating fractal efficiency into window sizes requires something non-linear to reflect the fractal dynamics. More importantly, using exp() was the last piece that let the metric shine, any other transformations & formulas alike I've tried always had some weird results on certain data.
That exp() in the len formula was the last piece that made it all work both on synthetic and on real data.
^^ a standalone script calculating optimal dynamic window size
Omg, THAT took time to write. Comment and/or text me if you need
...
"Versace Pip-Boy, I'm a young gun coming up with no bankroll" 👻
∞
PrimeMomentum 1.1The PrimeMomentum indicator is not just an adaptation of classic tools like MA, BB, RSI, or WaveTrend. It is an innovative tool that combines several key elements and offers a unique methodology for market analysis. Its primary goal is to help traders avoid false entries and provide signals for making trading decisions.
What Makes PrimeMomentum Unique?
Integration of Multi-Timeframe Data with a Unique Signal Filtering Approach
PrimeMomentum processes data from four timeframes simultaneously, not merely to display trends but to assess the synchronization of momentum across each timeframe. This allows traders to receive signals only when all intervals confirm the direction. This approach minimizes the risk of false signals often encountered with standard tools.
PrimeMomentum analyzes the market across four timeframes:
TF1 (long-term): Displays the overall market direction.
TF2 (medium-term): Refines the current dynamics.
TF3 (short-term): Provides detailed analysis.
TF4 (very short-term): Confirms entry or exit points.
The combination of data from these timeframes allows traders to avoid frequent switching between intervals, simplifying analysis.
Innovative Reversal Logic
PrimeMomentum features a specialized algorithm for identifying trend reversals. Its uniqueness lies in the interaction between dynamic smoothing (EMA) and multi-level momentum assessment, enabling accurate identification of potential trend reversal points.
Dynamic Adaptation to Market Conditions
The indicator automatically adjusts smoothing parameters and threshold values based on market volatility. This enables it to adapt effectively to both calm and volatile markets.
Signals for entering Long or Short positions are generated only when the following conditions are met:
- Momentum shifts from negative to positive (for Long) or from positive to negative (for Short).
- Dynamic smoothing confirms the trend.
- Defined thresholds are reached.
Trend Strength Assessment
Unlike traditional indicators, PrimeMomentum evaluates not only the direction but also the strength of a trend by analyzing the relationship between momentum across each timeframe. This helps traders understand how stable the current market movement is.
The indicator analyzes price changes over a specific period, determining how much current prices deviate from previous ones. This data allows for assessing the strength of market movements.
Combination of Classic Elements with Proprietary Logic
While PrimeMomentum may utilize some widely known components like EMA, its algorithm is built on proprietary logic for evaluating market conditions. This sets it apart from standard solutions that merely display basic indicators without deeper analysis.
Added Value of PrimeMomentum
Trend Visualization with Concept Explanations
PrimeMomentum provides traders with clear visual signals, simplifying market analysis. Each element (color, line direction) is based on momentum and trend-smoothing concepts, enabling traders to make decisions quickly.
Results are displayed as color-coded lines:
- Dark violet: Long-term trend.
- Blue: Medium-term trend.
- Turquoise and light blue: Short-term trends.
If all momentum lines reach a peak and begin turning downward, it may indicate an approaching bearish trend.
If all lines reach a bottom and start turning upward, it may signal the beginning of a bullish trend.
Reversals can also serve as signals for exiting positions.
MoneyFlow
The PrimeMomentum indicator includes a visualization of MoneyFlow, allowing traders to assess capital flows within the selected timeframe. This functionality helps to analyze market trends more accurately and make well-informed decisions.
MoneyFlow Features:
Dynamic MoneyFlow Visualization:
MoneyFlow is displayed as an area that changes color based on its value:
- Green (with transparency) when MoneyFlow is above zero (positive flow).
- Red (with transparency) when MoneyFlow is below zero (negative flow).
Automatic Scaling:
MoneyFlow values automatically adjust to the chart’s scale to ensure visibility alongside the Momentum lines.
Double Smoothing:
To ensure a smoother and more representation, MoneyFlow uses double smoothing based on EMA.
Customizable Colors and Transparency:
Traders can customize the colors for positive and negative MoneyFlow and adjust the transparency level to fit their preferences.
How MoneyFlow Works:
- MoneyFlow calculations are based on the MFI (Money Flow Index), which considers both price and volume.
- MoneyFlow values are integrated into the overall PrimeMomentum chart and combined with other signals for deeper analysis.
Advantages of the New Functionality:
- Helps quickly identify capital flows into or out of the market.
- Complements Momentum analysis to provide a more comprehensive view of market conditions.
- Enhances decision-making efficiency through flexible visualization settings.
Note: MoneyFlow adapts to the selected timeframe and displays data corresponding to the current interval on the price chart.
Simplicity for Beginners and Depth for Professionals
The indicator is designed to be user-friendly for traders of all experience levels. Beginners benefit from intuitive signals, while experienced traders can leverage in-depth analysis for more complex strategies.
PrimeMomentum Usage Modes
PrimeMomentum adapts to various strategies and supports three modes:
Short-term: Recommended to use a 2H timeframe. Optimal for intraday trading with small TakeProfit levels.
Medium-term: Recommended to use a 1D timeframe for trades lasting several days.
Long-term: Use the 1W timeframe for analyzing global trends.
Support for Different Strategies
Thanks to its flexible settings and support for multiple timeframes, PrimeMomentum is suitable for both day trading and long-term analysis.
Why Is PrimeMomentum Worth Your Attention?
Unlike standard indicators, which often rely solely on basic mathematical models or publicly available components, PrimeMomentum offers a comprehensive approach to market analysis. It combines unique momentum assessment algorithms, multi-timeframe analysis, and volatility adaptation. This not only provides traders with signals but also helps them understand the underlying market processes, making it a truly innovative solution.
Disclaimer
The PrimeMomentum indicator is designed to assist traders in market analysis but does not guarantee future profitability. Its use should be combined with traders' own research and informed decision-making.
Fourier Extrapolation of PriceThis advanced algorithm leverages Fourier analysis to predict price trends by decomposing historical price data into its frequency components. Unlike traditional algorithms that often operate in lower-dimensional spaces, this method harnesses a multidimensional approach to capture intricate market behaviors. By utilizing additional dimensions, the algorithm identifies and extrapolates subtle patterns and oscillations that are typically overlooked, providing a more robust and nuanced forecast.
Ideal for traders seeking a deeper understanding of market dynamics, this tool offers an enhanced predictive capability by aligning its calculations with the complexity of real-world financial systems.
ViPlay Signal Indicator ProViPlay Signal Indicator Pro is an innovative tool designed for traders looking to enhance the accuracy and effectiveness of their trading decisions. It provides a comprehensive approach to market analysis, generating informative trend change signals based on in-depth market analysis and advanced algorithms.
By adjusting the RISK parameter, traders can customize the signal frequency to match their preferences and trading strategies. This versatile tool is suitable for various trading styles and assets, including Forex, stocks, cryptocurrencies, and commodities, helping traders make informed decisions across any market.
Key Features of the Indicator
1. The RISK parameter controls the frequency of trend change signals. The lower the value, the more frequent the signals will appear, and vice versa. This gives users flexibility in adjusting the indicator according to their strategy.
2. Signal Generation:
Modified Range Oscillator (MRO):
This is the core element of the indicator's functionality. It works in two stages:
– MRO1: This stage focuses on short-term price movements, identifying volatility peaks and potential reversal points that may indicate an upcoming trend change. It is particularly useful for traders looking for quick opportunities.
– MRO2: This stage analyzes long-term trends, filtering out minor market fluctuations. It helps traders focus on more stable movements, reducing the impact of noise.
Williams %R:
This indicator works in conjunction with MRO, confirming reversal points by analyzing market overbought or oversold conditions. This reduces the likelihood of false signals, providing additional confidence in forecasts.
The combination of MRO and Williams %R ensures that traders receive reliable and timely signals, reflecting both immediate market conditions (via MRO1) and long-term trends (via MRO2), making the tool suitable for different trading horizons.
How the components work together:
MRO performs the primary task of identifying potential trend reversal points, dividing the analysis into short-term and long-term perspectives. In the first stage (MRO1), it evaluates market volatility and predicts potential reversals. In the second stage (MRO2), it filters out random fluctuations, providing more stable signals. Williams %R acts as an additional layer of confirmation: if MRO indicates a trend reversal and Williams %R confirms it by showing overbought or oversold conditions, the signal is considered more reliable.
In an uptrend, MRO1 indicates a reversal when the price reaches a local high, while MRO2 confirms the trend's stability. Williams %R further validates this signal, reducing the likelihood of a false entry. In a downtrend, the indicator works similarly, helping traders lock in profits or open short positions.
Williams %R:
Complements MRO by assessing market conditions for overbought or oversold levels. If MRO1 indicates a reversal and Williams %R confirms it, the likelihood of a false signal is significantly reduced.
RISK parameter:
Controls the sensitivity of MRO1 to changes in volatility. At higher values, minor fluctuations are filtered out, which is useful for long-term strategies. At lower values, the signals become more frequent, making it suitable for scalping.
3. Visual Signals:
– Green Up Arrow: Marks potential upward trends.
– Red Down Arrow: Marks potential downward trends, helping traders identify possible entry points
4. How levels are calculated:
Support and resistance levels are calculated based on historical price data. Specifically:
Support 1: This is the minimum price (low) over the last 200 bars.
Support 2: This is the minimum price over the last 500 bars.
Support 3: This is the minimum price over the last 1000 bars.
Resistance 1: This is the maximum price (high) over the last 200 bars.
Resistance 2: This is the maximum price over the last 500 bars.
Resistance 3: This is the maximum price over the last 1000 bars.
The levels are not static; they update with every bar, allowing traders to see current price zones. Users can enable or disable the display of different levels through parameters.
Support and resistance levels help traders identify key points for potential price reversals. The indicator automatically calculates these levels and displays them on the chart, allowing the user to use them for making trading decisions.
How to Use ViPlay Signal Indicator Pro
1. Add the Indicator to the Chart
2. Choose a Timeframe suitable for your trading strategy. The indicator supports all timeframes.
3. Customize Parameters:
Adjust the RISK parameter to control signal frequency (1–49, default 49).
Set the Take-Profit percentage (default 7%).
Configure moving average periods.
Adjust support and resistance levels.
4. Analyze:
– Use informative buy and sell signals based on market analysis.
– Use a customizable Take-Profit level based on the entry price to determine optimal exit points.
– Utilize key support and resistance levels on the selected timeframe to identify optimal entry and exit points.
– The information in the table indicates the strength of the current trend. When the value reaches 0 or 100, the trend changes.
* Note that the indicator serves as an analytical tool and does not replace sound trading strategies.
Uniqueness and Originality
1. Innovative Algorithms
The combination of Modified Range Oscillator (MRO) and Williams %R is not a standard pairing in trading tools. The two-phase approach of MRO provides users with a comprehensive understanding of the market, offering information on both short-term fluctuations and long-term trends, while Williams %R serves as an additional filter to eliminate false signals.
2. The indicator uses mathematical functions such as True Range (TR) to analyze volatility and identify potential entry and exit points.
3. Versatility
The indicator supports all financial market assets, including Forex, stocks, cryptocurrencies, and commodities. It adapts to any trading style or strategy. Additionally, it is compatible with all timeframes, benefiting both short-term and long-term traders.
4. Ease of Use
5. All elements of the indicator can be customized or hidden according to the user’s needs, making it a convenient tool for market analysis. The indicator is compatible with all financial market assets, including Forex, stocks, cryptocurrencies, and commodities.
Important Notes
This indicator is an analytical tool and does not guarantee profits. Signals should be used alongside personal analysis and risk management strategies. Traders should note that no indicator can provide 100% accurate predictions, and there is always a possibility of false signals.
The Forexation: Super Trend SignalsOverview:
The Forexation: Super Trend Signals (STS) indicator was crafted to enhance visualization of market trends by integrating multiple technical analysis tools and adding logic to them so they color bullish, bearish, counter trends, and cautious trends. By combining standard and higher-timeframe Supertrends with dynamic EMAs and VWAP, STS offers a multi-dimensional view of market dynamics. This synergy allows traders to:
Assess Trend Strength and Alignment
Identify Momentum Shifts and Reversals
Gauge Market Sentiment through Volume-Weighted Pricing
Filter Out Market Noise for Clearer Signals
Key Features and Synergy:
1. Dual Supertrend Analysis:
Standard Supertrend:
Utilizes the Average True Range (ATR) and a multiplier factor to detect immediate market trends.
Customizable ATR Length and Factor to adjust sensitivity to market volatility.
Used as a guide to help follow the trend and identify where if price breaks through we can be reversing trend or entering a counter/cautious trend.
Higher Time Frame (HTF) Supertrend:
Integrates Supertrend data from a higher timeframe for a broader market perspective.
Smoothing applied via an EMA to reduce lag and false signals.
**Synergistic Effect:
Trend Alignment: By analyzing both standard and HTF Supertrends, STS identifies when short-term trends align with long-term trends, increasing the reliability of trend signals.
Dynamic Adjustments: Traders can adjust parameters to fine-tune the balance between responsiveness and stability.
2. Customized EMAs with Contextual Color-Coding:
Fast and Slow EMAs:
Customizable periods to match different trading strategies and timeframes.
EMAs are used to identify momentum shifts and potential reversals through crossovers.
Dynamic Color-Coding:
EMA lines change color based on their relationship with each other, the Supertrends, and VWAP.
Visual Interpretation:
Bullish Alignment: Fast EMA above Slow EMA, both above Supertrend and VWAP, signals strong upward momentum.
Bearish Alignment: Fast EMA below Slow EMA, both below Supertrend and VWAP, signals strong downward momentum.
Caution Zones: Misalignment or crossovers indicate potential reversals or consolidation.
**Synergistic Effect:
Momentum Confirmation: EMA crossovers are validated against Supertrend directions, reducing false signals.
Support and Resistance Zones: The area between EMAs acts as dynamic support/resistance, visualized through an optional fill.
3. VWAP Integration for Volume-Weighted Insights:
VWAP Analysis:
Calculates the average price weighted by volume, providing insights into institutional trading levels and market sentiment.
**Synergistic Effect:
Trend Validation: Confirms trend strength by analyzing whether price and EMAs are above or below VWAP.
Counter-Trend Detection: Identifies potential pullbacks or reversals when price interacts with VWAP against the prevailing trend of the standard and higher time frame SuperTrend.
4. Composite Signal Generation:
Color-Coded Market Conditions:
Bullish Signals (Green): Strong upward trends with alignment across standard + HTF Supertrend, EMAs, and price above VWAP.
Bearish Signals (Red): Strong downward trends with inverse alignment.
Caution State (Orange): Potential market reversals or uncertainty when indicators are misaligned. (Example: price above VWAP but under HTF SuperTrend)
Counter-Trend Conditions (Yellow): Signals possible pullbacks or consolidations when price or EMAs cross VWAP. (Example: Price is above VWAP & HTF SuperTrend but the EMAs and Standard SuperTrend are in a down trend)
**Synergistic Effect:
Enhanced Signal Accuracy: By requiring multiple confirmations across different indicators and timeframes, STS filters out noise and increases the probability of trends in the market.
Timely Alerts: Alerts are generated when critical conditions are met, keeping traders informed of significant market movements.
Underlying Concepts and Calculations:
Supertrend Algorithm:
Calculation:
Supertrend is calculated using ATR to set a dynamic trailing stop that follows price movements.
The indicator switches between bullish and bearish modes when price crosses the Supertrend line.
Customization:
ATR Length and Factor can be adjusted to make the Supertrend more or less sensitive to price changes.
In STS: Both standard and HTF Supertrends are used, with the HTF providing longer-term trend context.
Exponential Moving Averages (EMAs):
Calculation:
EMAs apply more weight to recent prices, making them more responsive than Simple Moving Averages (SMAs).
Crossovers between Fast and Slow EMAs signal potential momentum shifts.
Customization:
Periods for Fast and Slow EMAs are user-defined to suit different trading styles.
In STS: EMA behavior is analyzed in conjunction with Supertrend and VWAP to validate signals.
Volume Weighted Average Price (VWAP):
Calculation:
VWAP accumulates total dollars traded (price times volume) divided by total volume over a specific period.
Reflects the average price at which the instrument has traded throughout the day based on both price and volume.
**In STS:
VWAP serves as a dynamic support/resistance level.
Interaction with VWAP can indicate shifts in market sentiment, especially when combined with other indicators.
Justifying the Value of STS:
Holistic Market Analysis:
STS doesn't just merge indicators; it creates a cohesive system where each component validates and enhances the others.
This integrated approach offers a more reliable analysis than using individual indicators in isolation.
Customizable and Adaptive:
Traders have control over key parameters, allowing STS to be tailored to different markets and trading styles.
The ability to adjust sensitivity helps in adapting to varying market conditions.
Enhanced Decision-Making:
By providing clear visual cues and alerts, STS aids in quick interpretation of complex market data.
The indicator helps in identifying high-probability trend opportunities and managing risk effectively with trailing SuperTrend guidance.
Unique Signal Filtering:
The combination of multiple confirmations reduces the likelihood of false trend signals.
The use of higher timeframe data and volume-weighted analysis adds depth to trend assessment.
How to Use STS Effectively:
1. Configuring Settings:
Supertrend Settings:
Adjust ATR Length and Factor to set the desired sensitivity.
Select the Higher Time Frame for the HTF Supertrend to align with your trading horizon.
Set the Smoothing Period for the EMA applied to the HTF Supertrend.
EMA Settings:
Define periods for Fast and Slow EMAs based on your strategy.
Ensure the Fast EMA period is shorter than the Slow EMA for effective crossovers.
Color and Display Settings:
Customize colors for different market conditions to enhance visual clarity.
Choose whether to display the HTF Supertrend, EMA lines, EMA fill, and VWAP.
2. Interpreting Signals:
Bullish Scenario:
Supertrends indicate an uptrend.
Fast EMA crosses above Slow EMA, both trending upwards.
Price and EMAs are above VWAP.
Action: Consider long positions, using the standard Supertrend as a trailing stop.
Bearish Scenario:
Supertrends indicate a downtrend.
Fast EMA crosses below Slow EMA, both trending downwards.
Price and EMAs are below VWAP.
Action: Consider short positions. using the standard Supertrend as a trailing stop
Caution and Counter-Trend Signals:
Misalignment between indicators or color changes to orange/yellow.
Action: Exercise caution, tighten stops, or wait for clearer signals.
4. Setting Up Alerts:
Access the Alerts menu.
Configure alerts for:
Supertrend Direction Changes
EMA Crossovers
Price Crossing VWAP
Set alert actions and ensure they trigger on confirmed data by selecting "Once Per Bar Close."
Example Trading Strategies:
Trend Following:
Use STS to identify strong trends where all indicators are aligned.
Enter positions in the direction of the trend.
Use Supertrend lines as dynamic stop-loss levels.
Pullback Entries:
Wait for price to pull back to the EMA fill area or VWAP in a prevailing trend.
Look for bounce signals off these levels when supported by Supertrend direction.
Counter-Trend Opportunities:
Identify potential reversals when caution or counter-trend signals appear.
Confirm with additional analysis or indicators before taking positions against the main trend.
Disclaimer:
This indicator is intended to aid in technical analysis and should be used as part of a comprehensive trading strategy. It does not guarantee profits and carries the risk of loss. Trading financial instruments involves significant risk; please consult with a qualified financial advisor before making any investment decisions. Past performance is not indicative of future results.
Final Notes:
The Forexation: Super Trend Signals (STS) indicator represents a thoughtfully engineered tool that brings together multiple technical elements to provide a more nuanced understanding of market behavior. By leveraging the strengths of Supertrend, EMAs, and VWAP in unison, STS aims to enhance trading precision and confidence in the trends the market creates but also guide risk management levels for managing a trade and stop loss areas.
We are committed to continuous improvement and value user feedback. Please share your experiences and suggestions to help us refine the indicator further.
Happy Trading!
Volume Based Price Prediction [EdgeTerminal]This indicator combines price action, volume analysis, and trend prediction to forecast potential future price movements. The indicator creates a dynamic prediction zone with confidence bands, helping you visualize possible price trajectories based on current market conditions.
Key Features
Dynamic price prediction based on volume-weighted trend analysis
Confidence bands showing potential price ranges
Volume-based candle coloring for enhanced market insight
VWAP and Moving Average overlay
Customizable prediction parameters
Real-time updates with each new bar
Technical Components:
Volume-Price Correlation: The indicator analyzes the relationship between price movements and volume, Identifies stronger trends through volume confirmation and uses Volume-Weighted Average Price (VWAP) for price equilibrium
Trend Strength Analysis: Calculates trend direction using exponential moving averages, weights trend strength by relative volume and incorporates momentum for improved accuracy
Prediction Algorithm: combines current price, trend, and volume metrics, projects future price levels using weighted factors and generates confidence bands based on price volatility
Customizable Parameters:
Moving Average Length: Controls the smoothing period for calculations
Volume Weight Factor: Adjusts how much volume influences predictions
Prediction Periods: Number of bars to project into the future
Confidence Band Width: Controls the width of prediction bands
How to use it:
Look for strong volume confirmation with green candles, watch for prediction line slope changes, use confidence bands to gauge potential volatility and compare predictions with key support/resistance levels
Some useful tips:
Start with default settings and adjust gradually
Use wider confidence bands in volatile markets
Consider prediction lines as zones rather than exact levels
Best applications of this indicator:
Trend continuation probability assessment
Potential reversal point identification
Risk management through confidence bands
Volume-based trend confirmation
MACD Cloud with Moving Average and ATR BandsThe algorithm implements a technical analysis indicator that combines the MACD Cloud, Moving Averages (MA), and volatility bands (ATR) to provide signals on market trends and potential reversal points. It is divided into several sections:
🎨 Color Bars:
Activated based on user input.
Controls bar color display according to price relative to ATR levels and moving average (MA).
Logic:
⚫ Black: Potential bearish reversal (price above the upper ATR band).
🔵 Blue: Potential bullish reversal (price below the lower ATR band).
o
🟢 Green: Bullish trend (price between the MA and upper ATR band).
o
🔴 Red: Bearish trend (price between the lower ATR band and MA).
o
📊 MACD Bars:
Description:
The MACD Bars section is activated by default and can be modified based on user input.
🔴 Red: Indicates a bearish trend, shown when the MACD line is below the Signal line (Signal line is a moving average of MACD).
🔵 Blue: Indicates a bullish trend, shown when the MACD line is above the Signal line.
Matching colors between MACD Bars and MACD Cloud visually confirms trend direction.
MACD Cloud Logic: The MACD Cloud is based on Moving Average Convergence Divergence (MACD), a momentum indicator showing the relationship between two moving averages of price.
MACD and Signal Lines: The cloud visualizes the MACD line relative to the Signal line. If the MACD line is above the Signal line, it indicates a potential bullish trend, while below it suggests a potential bearish trend.
☁️ MA Cloud:
The MA Cloud uses three moving averages to analyze price direction:
Moving Average Relationship: Three MAs of different periods are plotted. The cloud turns green when the shorter MA is above the longer MA, indicating an uptrend, and red when below, suggesting a downtrend.
Trend Visualization: This graphical representation shows the trend direction.
📉 ATR Bands:
The ATR bands calculate overbought and oversold limits using a weighted moving average (WMA) and ATR.
Center (matr): Shows general trend; prices above suggest an uptrend, while below indicate a downtrend.
Up ATR 1: Marks the first overbought level, suggesting a potential bearish reversal if the price moves above this band.
Down ATR 1: Marks the first oversold level, suggesting a possible bullish reversal if the price moves below this band.
Up ATR 2: Extends the overbought range to an extreme, reinforcing the possibility of a bearish reversal at this level.
Down ATR 2: Extends the oversold range to an extreme, indicating a stronger bullish reversal possibility if price reaches here.
Español:
El algoritmo implementa un indicador de análisis técnico que combina la nube MACD, promedios móviles (MA) y bandas de volatilidad (ATR) para proporcionar señales sobre tendencias del mercado y posibles puntos de reversión. Se divide en varias secciones:
🎨 Barras de Color:
- Activado según la entrada del usuario.
- Controla la visualización del color de las barras según el precio en relación con los niveles de ATR y el promedio móvil (MA).
- **Lógica:**
- ⚫ **Negro**: Reversión bajista potencial (precio por encima de la banda superior ATR).
- 🔵 **Azul**: Reversión alcista potencial (precio por debajo de la banda inferior ATR).
- 🟢 **Verde**: Tendencia alcista (precio entre el MA y la banda superior ATR).
- 🔴 **Rojo**: Tendencia bajista (precio entre la banda inferior ATR y el MA).
### 📊 Barras MACD:
- **Descripción**:
- La sección de barras MACD se activa por defecto y puede modificarse según la entrada del usuario.
- 🔴 **Rojo**: Indica una tendencia bajista, cuando la línea MACD está por debajo de la línea de señal (la línea de señal es una media móvil de la MACD).
- 🔵 **Azul**: Indica una tendencia alcista, cuando la línea MACD está por encima de la línea de señal.
- La coincidencia de colores entre las barras MACD y la nube MACD confirma visualmente la dirección de la tendencia.
### 🌥️ Nube MACD:
- **Lógica de la Nube MACD**: Basada en el indicador de convergencia-divergencia de medias móviles (MACD), que muestra la relación entre dos medias móviles del precio.
- **Líneas MACD y de Señal**: La nube visualiza la relación entre la línea MACD y la línea de señal. Si la línea MACD está por encima de la de señal, indica una tendencia alcista potencial; si está por debajo, sugiere una tendencia bajista.
### ☁️ Nube MA:
- **Relación entre Medias Móviles**: Se trazan tres medias móviles de diferentes períodos. La nube se vuelve verde cuando la media más corta está por encima de la más larga, indicando una tendencia alcista, y roja cuando está por debajo, sugiriendo una tendencia bajista.
- **Visualización de Tendencias**: Proporciona una representación gráfica de la dirección de la tendencia.
### 📉 Bandas ATR:
- Las bandas ATR calculan límites de sobrecompra y sobreventa usando una media ponderada y el ATR.
- **Centro (matr)**: Muestra la tendencia general; precios por encima indican tendencia alcista y debajo, bajista.
- **Up ATR 1**: Marca el primer nivel de sobrecompra, sugiriendo una reversión bajista potencial si el precio sube por encima de esta banda.
- **Down ATR 1**: Marca el primer nivel de sobreventa, sugiriendo una reversión alcista potencial si el precio baja por debajo de esta banda.
- **Up ATR 2**: Amplía el rango de sobrecompra a un nivel extremo, reforzando la posibilidad de reversión bajista.
- **Down ATR 2**: Extiende el rango de sobreventa a un nivel extremo, sugiriendo una reversión alcista más fuerte si el precio alcanza esta banda.
