Sortino Ratio -> PROFABIGHI_CAPITAL🌟 Overview
This Sortino Ratio → PROFABIGHI_CAPITAL implements advanced risk-adjusted performance measurement focusing specifically on downside volatility for superior portfolio evaluation.
It provides Enhanced Sortino Ratio calculation with downside deviation analysis , Customizable risk-free rate benchmarking for different market environments , EMA smoothing for trend clarity and noise reduction , and Dynamic threshold-based visualization with performance classification for comprehensive risk-adjusted return analysis.
🔧 Advanced Risk Measurement Architecture
- Professional Sortino Ratio implementation focusing exclusively on downside risk measurement for accurate performance evaluation
- Source Selection Framework with customizable price input allowing close, high, low, or other price sources for flexible analysis adaptation
- Calculation Period Management with adjustable lookback period for statistical significance balancing responsiveness versus stability
- Annual Risk-Free Rate Configuration enabling benchmark comparison against government bonds, treasury rates, or other risk-free instruments
- EMA Smoothing System reducing noise and providing clearer trend identification through exponential moving average filtering
- Dynamic Threshold Framework with strong and weak performance classification levels for objective performance assessment
- Cryptocurrency Annualization using 365-day factor for proper crypto market risk-adjusted return calculation
📊 Sortino Ratio Calculation Engine
- Periodic Returns Computation calculating bar-to-bar percentage changes for accurate return measurement across different timeframes
- Risk-Free Rate Conversion transforming annual risk-free rates into period-appropriate benchmarks for proper comparison
- Mean Return Analysis using Simple Moving Average over calculation period for statistical trend identification
- Downside Deviation Framework measuring only negative deviations below risk-free rate for true downside risk assessment
- Mathematical Precision implementing squared deviation calculations for proper statistical variance measurement
- Zero-Division Protection preventing calculation errors through proper mathematical validation and edge case handling
- Annualization Factor Application scaling periodic calculations to annual equivalents for standardized performance comparison
🔬 Advanced Statistical Implementation
- Downside-Only Risk Measurement focusing exclusively on negative returns below risk-free threshold for accurate risk assessment
- Squared Deviation Accumulation using proper statistical methodology for variance calculation with mathematical precision
- Mean Downside Squared Calculation averaging squared negative deviations over calculation period for statistical accuracy
- Square Root Standard Deviation converting variance to standard deviation for proper risk measurement units
- Excess Return Calculation measuring portfolio performance above risk-free rate for true alpha generation assessment
- Mathematical Validation Framework ensuring proper handling of edge cases and preventing division by zero errors
- Statistical Significance using sufficient calculation periods for reliable Sortino Ratio measurement and trend identification
📈 EMA Smoothing and Trend Analysis
- Exponential Moving Average Application reducing short-term noise while preserving trend direction for clearer signal interpretation
- Smoothing Period Configuration balancing responsiveness versus stability through adjustable EMA length parameters
- Trend Persistence Analysis identifying sustained performance improvements or deteriorations through smoothed ratio tracking
- Signal Quality Enhancement filtering market noise while maintaining sensitivity to genuine performance changes
- Null Value Protection using nz() function to handle missing values and ensure continuous ratio calculation
- Real-Time Updates providing current smoothed Sortino values for immediate performance assessment and decision making
🎨 Dynamic Visualization Framework
- Performance-Based Color Coding using green for strong performance above upper threshold and red for weak performance below lower threshold
- Neutral Zone Visualization displaying gray coloring for performance between thresholds indicating moderate risk-adjusted returns
- Threshold Reference Lines showing strong and weak performance boundaries through horizontal dashed lines for clear performance classification
- Dynamic Line Width using prominent line display for clear trend identification and professional chart presentation
- Real-Time Color Updates adjusting visualization based on current performance relative to threshold settings
- Professional Styling implementing institutional-grade visual elements for serious portfolio analysis and performance tracking
⚖️ Risk-Adjusted Performance Assessment
- Downside Risk Focus measuring only negative volatility for more accurate risk assessment compared to traditional Sharpe ratio
- Asymmetric Risk Recognition acknowledging that upside volatility is desirable while downside volatility represents true risk
- Benchmark Relative Performance comparing returns against risk-free alternatives for absolute performance measurement
- Statistical Robustness using proper mathematical formulation for reliable risk-adjusted return calculation
- Performance Classification providing objective strong/weak performance thresholds for systematic evaluation
- Trend Analysis Capability identifying improving or deteriorating risk-adjusted performance through smoothed trending
🔍 Advanced Configuration Options
- Flexible Source Selection accommodating different price sources for various analysis requirements and asset characteristics
- Adaptive Calculation Periods allowing adjustment for different market conditions and analysis timeframes
- Risk-Free Rate Customization enabling comparison against various benchmarks including government bonds and treasury rates
- Smoothing Parameter Control balancing signal clarity versus responsiveness through adjustable EMA periods
- Performance Threshold Management setting custom strong and weak performance boundaries for specific strategy requirements
- Precision Control using three decimal places for accurate ratio measurement and detailed performance tracking
📊 Professional Portfolio Analysis Applications
- Strategy Performance Evaluation measuring risk-adjusted returns for trading strategy assessment and optimization
- Portfolio Comparison comparing multiple strategies or assets using standardized Sortino measurements
- Risk Management Integration identifying periods of poor risk-adjusted performance for strategy adjustment
- Benchmark Outperformance tracking excess returns above risk-free alternatives for alpha generation measurement
- Performance Monitoring continuous assessment of strategy effectiveness through smoothed ratio trending
- Institutional-Grade Analysis providing professional portfolio management metrics for serious investment analysis
🔧 Technical Implementation Features
- Mathematical Accuracy implementing proper Sortino formula with correct statistical methodology and precision handling
- Computational Efficiency using optimized loops and calculations for real-time performance measurement
- Error Prevention incorporating comprehensive validation and edge case handling for reliable operation
- Memory Management efficient variable usage and calculation methods for optimal indicator performance
- Real-Time Processing providing immediate updates with each new bar for current performance assessment
- Professional Standards following institutional portfolio analysis methodology for serious risk management applications
✅ Key Takeaways
- Advanced Sortino Ratio implementation focusing exclusively on downside risk for superior portfolio performance measurement
- Customizable risk-free rate benchmarking enabling comparison against various market alternatives and investment environments
- EMA smoothing system reducing noise while preserving trend identification for clearer performance signal interpretation
- Dynamic threshold-based visualization providing objective performance classification through color-coded strong/weak boundaries
- Professional statistical implementation using proper mathematical methodology for institutional-grade risk-adjusted return analysis
- Flexible configuration options accommodating different analysis requirements, timeframes, and market conditions
- Comprehensive risk management integration enabling continuous strategy performance monitoring and optimization for superior portfolio management
Analisis Trend
YBL – PAC PREMIUM COMPACT MEDIUM (6 filas, 1 col. derecha)
📑 Document Structure:
Cover Page → YBL logo + Indicator title.
General Description → What the panel is and its purpose.
Row-by-Row Explanation (6 modules):
Volume with Delta
Power vs USD
NY Session
Climax
Trend / Momentum
Correlation
Visual Example → How to interpret values when green, red, or neutral.
Practical Tips → Quick trading rules (e.g., “if strong Δ + Climax rejection = watch for reversal”).
⚡ Now the same question for you:
Do you prefer the PDF in a technical style (with formulas and detailed calculations), or in a practical style (quick guide for traders, with examples and short phrases)?
RED: MomentumRED: Momentum Panel
This indicator is designed to track the balance of buying and selling pressure in the market and highlight key momentum phases.
It simplifies complex conditions into clear momentum states, helping traders quickly understand whether the market is in a strong zone or transitioning.
- Top zones → when selling pressure reaches extreme levels.
- Bottom zones → when buying pressure reaches extreme levels.
- Momentum Bearish → when momentum shifts down after a strong top.
- Momentum Bullish → when momentum shifts up after a strong bottom.
The panel uses a scoring system in the background to filter noise and show only the dominant side (Buy vs Sell).
Horizontal thresholds make it easy to spot when the market enters or exits extreme conditions.
This tool is not meant to give signals by itself but to provide an intuitive view of where momentum stands right now, top, bottom, bullish, or bearish, at a glance.
Ichimoku Fractal Flow### Ichimoku Fractal Flow (IFF)
By Gurjit Singh
Ichimoku Fractal Flow (IFF) distills the Ichimoku system into a single oscillator by merging fractal echoes of price and cloud dynamics into one flow signal. Instead of static Ichimoku lines, it measures the "flow" between Conversion/Base, Span A/B, price echoes, and cloud echoes. The result is a multidimensional oscillator that reveals hidden rhythm, momentum shifts, and trend bias.
#### 📌 Key Features
1. Fourfold Fusion – The oscillator blends:
* Phase: Tenkan vs. Kijun spread (short vs. medium trend).
* Kumo Phase: Span A vs. Span B spread (cloud thickness).
* Echo: Price vs lagged reflection.
* Cloud Echo: Price vs. projected cloud center.
2. Oscillator Output – A unified flow line oscillating around zero.
3. Dual Calculation Modes – Oscillator can be built using:
* High-Low Midpoint (classic Ichimoku-style averaging).
* Wilder’s RMA (smoother, less noisy averaging averaging).
4. Optional Smoothing – EMA or Wilder’s RMA creates a trend line, enabling MACD-style crossovers.
5. Dynamic Coloring – Bullish/Bearish color shifts for quick bias recognition.
6. Fill Styling – Highlighted regions between oscillator & smoothing line.
7. Zero Line Reference – Acts as a structural pivot (bull vs. bear).
#### 🔑 How to Use
1. Add to Chart: Works across all assets and timeframes.
2. Flow Bias (Zero Line):
* Above 0 → Bullish flow 🐂
* Below 0 → Bearish flow 🐻
3. With Signal Line:
* Oscillator above smoothing line → Possible upward trend shift.
* Oscillator below smoothing line → Possible downward trend shift.
4. Strength:
* Wide separation from smoothing = strong trend.
* Flat, tight clustering = indecision/range.
5. Contextual Edge: Combine signals with Ichimoku Cloud analysis for stronger confluence.
#### ⚙️ Inputs & Options
* Conversion Line (Tenkan, default 9)
* Base Line (Kijun, default 26)
* Leading Span B (default 52)
* Lag/Lead Shift (default 26)
* Oscillator Mode: High-Low Midpoint vs Wilder’s RMA
* Use Smoothing (toggle on/off)
* Signal Smoothing: Wilder/EMA option
* Smoothing Length (default 9)
* Bullish/Bearish Colors + Transparency
#### 💡 Tips
* Wilder’s RMA (both oscillator & smoothing) is gentler, reducing whipsaws in sideways markets.
* High-Low Mid captures pure Ichimoku-style ranges, good for structure-based traders.
* EMA reacts faster than RMA; use if you want early momentum signals.
* Zero-line flips act like momentum pivots—watch them near cloud boundaries.
* Signal line crossovers behave like MACD-style triggers.
* Strongest signals appear when oscillator, signal line, and Ichimoku Cloud all align.
👉 In short: Ichimoku Fractal Flow compresses multi-layered Ichimoku system into a single fractal oscillator that detects flow, pivotal shifts, and momentum with clarity—bridging price, cloud, and echoes into one signal. Where the cloud shows structure, IFF reveals the underlying flow. Together, they offer a fractal lens into market rhythm.
Strong tendence detector - Detector de Fuerte TendenciaThis chart shows when an asset is in a strong uptrend or downtrend. The legend on the left indicates if the RSI is above 62 or below 38 on the monthly, weekly, and daily timeframes. A strong uptrend is confirmed when all three timeframes are above 62, while a strong downtrend is confirmed when they are all below 38. Periods of a strong uptrend are highlighted with a green background, and periods of a strong downtrend are highlighted in red.
Lanxang V6 – Trend FollowingLanxang V6 – Trend Following
The Lanxang V6 is a clean and simple trend-following tool that helps traders stay aligned with the market’s direction and catch key momentum shifts.
🔑 Features
- Trend Direction – The system colors moving averages and chart areas to make bullish and bearish trends easy to spot at a glance.
- Clear Buy/Sell Tags – When the market shifts direction, the indicator plots Buy or Sell tags directly on the chart for quick confirmation.
- Pullback Highlights – Bars are marked to signal potential continuation setups during trending conditions.
- Custom Visuals – Traders can adjust tag size, padding, and colors to match their chart style.
- Alerts – Real-time alerts for Buy/Sell signals keep you notified of trend changes without watching the screen all the time.
📈 How to Use
- Follow the Trend: Use the trend color as your main directional bias (green for bullish, red for bearish).
- Entry Signals: Take Buy/Sell tags as confirmation points when the trend shifts.
- Pullback Opportunities: Highlighted bars may indicate continuation trades within the existing trend.
- Risk Management: Always confirm with your own analysis and manage risk properly.
⚠️ Disclaimer: This tool is for educational purposes only and does not guarantee results. Always test on demo before applying to live trading.
Lao Version below:
Lanxang V6 ແມ່ນເຄື່ອງມື ຕິດຕາມແນວໂນ້ມ ທີ່ອອກແບບມາໃຫ້ຊ່ວຍນັກລົງທຶນມອງເຫັນທິດທາງຂອງຕະຫຼາດ ແລະ ຈັບໂອກາດໃນການເຄື່ອນໄຫວສໍາຄັນໄດ້ຊັດເຈນຂຶ້ນ.
🔑 ຄຸນນະສົມບັດ
- ການກໍານົດແນວໂນ້ມ – ລະບົບຈະສະແດງສີເສັ້ນ Moving Average ແລະ ພື້ນຫຼັງໃນການຊັດເຈນທັນທີ (ຂຽວ = ແນວໂນ້ມຂຶ້ນ, ແດງ = ແນວໂນ້ມລົງ).
- ສັນຍານ Buy/Sell ຊັດເຈນ – ເມື່ອຕະຫຼາດປ່ຽນທິດທາງ ໂຕຊີ້ Buy ຫຼື Sell ຈະປາກົດໃນກາຟ.
- ການເນັ້ນແທ່ງ Pullback – ກ່ອນຈະໄປຕໍ່ແນວໂນ້ມ ບາງແທ່ງຈະຖືກເນັ້ນເພື່ອໃຫ້ເຫັນໂອກາດໃນການເຂົ້າ.
- ການປັບແຕ່ງຮູບແບບ – ປັບຂະໜາດ ແລະ ສີຂອງສັນຍານໄດ້ຕາມຄວາມຕ້ອງການ.
- Alert ແບບ Real-time – ຮັບແຈ້ງເຕືອນທັນທີເມື່ອມີສັນຍານ Buy/Sell.
📈 ວິທີໃຊ້
- ຕິດຕາມແນວໂນ້ມ: ໃຊ້ສີຂອງເສັ້ນເພື່ອກໍານົດທິດທາງ (ຂຽວ = ຂຶ້ນ, ແດງ = ລົງ).
- ສັນຍານເຂົ້າ: ຕິດຕາມສັນຍານ Buy/Sell ທີ່ປາກົດໃນກາຟ.
- ໂອກາດ Pullback: ແທ່ງທີ່ເນັ້ນອາດຈະບອກໂອກາດໃນການເຂົ້າຕໍ່ຕາມແນວໂນ້ມ.
- ຈັດການຄວາມສ່ຽງ: ຢ່າລືມກວດສອບກັບການວິເຄາະຂອງຕົນເອງ ແລະ ຈັດການຄວາມສ່ຽງໃຫ້ດີ.
⚠️ ຄໍາເຕືອນ: ເຄື່ອງມືນີ້ເປັນໄວ້ໃຊ້ເພື່ອການສຶກສາ ແລະ ບໍ່ຮັບປະກັນຜົນກໍາໄລ. ກ່ອນນໍາໃຊ້ໃນບັນຊີຈິງ ຄວນທົດສອບໃນ Demo ກ່ອນ.
Chanlun clmacd MACDThe commonly used MACD version in China has default parameters of 12, 26, 9. It is slightly different from the built-in MACD on the official TradingView website but generally similar. This MACD version is tailored to the usage habits of domestic users and is mainly designed to be used in conjunction with my Chanlun Theory indicators.
国内常用的macd版本，默认参数12,26,9，跟tradingview官网自带的有些不同，总体差不多，适合国内用户习惯的版本的macd,主要是配套我这边缠论指标使用
High Probability Order Blocks [AlgoAlpha]🟠 OVERVIEW
This script detects and visualizes high-probability order blocks by combining a volatility-based z-score trigger with a statistical survival model inspired by Kaplan-Meier estimation. It builds and manages bullish and bearish order blocks dynamically on the chart, displays live survival probabilities per block, and plots optional rejection signals. What makes this tool unique is its use of historical mitigation behavior to estimate and plot how likely each zone is to persist, offering traders a probabilistic perspective on order block strength—something rarely seen in retail indicators.
🟠 CONCEPTS
Order blocks are regions of strong institutional interest, often marked by large imbalances between buying and selling. This script identifies those areas using z-score thresholds on directional distance (up or down candles), detecting statistically significant moves that signal potential smart money footprints. A bullish block is drawn when a strong up-move (zUp > 4) follows a down candle, and vice versa for bearish blocks. Over time, each block is evaluated: if price “mitigates” it (i.e., closes cleanly past the opposite side and confirmed with a 1 bar delay), it’s considered resolved and logged. These resolved blocks then inform a Kaplan-Meier-like survival curve, estimating the likelihood that future blocks of a given age will remain unbroken. The indicator then draws a probability curve for each side (bull/bear), updating it in real time.
🟠 FEATURES
Live label inside each block showing survival probability or “N.E.D.” if insufficient data.
Kaplan-Meier survival curves drawn directly on the chart to show estimated strength decay.
Rejection markers (▲ ▼) if price bounces cleanly off an active order block.
Alerts for zone creation and rejection signals, supporting rule-based trading workflows.
🟠 USAGE
Read the label inside each block for Age | Survival% (or N.E.D. if there aren’t enough samples yet); higher survival % suggests blocks of that age have historically lasted longer.
Use the right-side survival curves to gauge how probability decays with age for bull vs bear blocks, and align entries with the side showing stronger survival at current age.
Treat ▲ (bullish rejection) and ▼ (bearish rejection) as optional confluence when price tests a boundary and fails to break.
Turn on alerts for “Bullish Zone Created,” “Bearish Zone Created,” and rejection signals so you don’t need to watch constantly.
If your chart gets crowded, enable Prevent Overlap ; tune Max Box Age to your timeframe; and adjust KM Training Window / Minimum Samples to trade off responsiveness vs stability.
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level:
Tzotchev Trend Measure: A Statistical Approach to Trend Following
The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies.
Mathematical Foundation and Statistical Theory
The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility.
This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework.
The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions.
Comparative Analysis with Traditional Trend Measurement Methods
The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically.
Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels.
The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment.
Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies.
The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification.
Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research.
Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach.
The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems.
Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting.
Advanced Noise Filtering and Market Regime Detection
A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies.
The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action.
The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions.
Professional Implementation and Trading Applications
The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements.
Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts.
The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes.
Performance Optimization and Computational Efficiency
The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached.
Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data.
Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold.
Visual Design and User Interface
The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications.
Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms.
The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously.
Theoretical Implications and Research Context
The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions.
The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions.
The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential.
Practical Applications and Trading Strategy Integration
The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions.
The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems.
Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions.
Implementation Guide and Parameter Configuration
The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization.
Core Calculation Parameters
The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification.
The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly.
The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument.
Trading Profile Configuration
The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels.
Advanced Noise Filtering System
The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications.
The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations.
The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions.
Visual Customization and Display Options
The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments.
The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations.
The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals.
Performance Optimization Settings
The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources.
The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications.
Alert System Configuration
The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations.
The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification.
Table Display and Information Management
The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements.
The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection.
Risk Management Integration
Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies.
The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly.
Timeframe Considerations and Multi-Timeframe Analysis
The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates.
For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment.
Conclusion
The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms.
The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management.
References
Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards.
Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182.
Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58.
Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.
Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons.
Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250.
Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
Kyoshiro - FVG + Order Blocks📌 Kyoshiro – FVG + Order Blocks
This indicator combines Order Block (OB) detection with an intelligent auto-management system and a clean visual display on the chart.
It is designed to help traders better identify institutional zones where price frequently reacts.
⚙️ Key Features:
✅ Real-time detection of bullish and bearish Order Blocks.
✅ Automatic cleanup: invalidated OBs are removed to keep the chart clean.
✅ Customizable display:
Maximum number of visible OBs (bullish / bearish).
Zone colors, outlines, and midlines.
Line styles (solid, dashed, dotted) and adjustable width.
✅ Choice of mitigation method:
Wick
Close
✅ Built-in alerts:
Formation of bullish or bearish OB.
Mitigation of an existing OB.
🔔 Available Alerts:
Bullish OB Formed → A bullish order block is detected.
Bearish OB Formed → A bearish order block is detected.
Bullish OB Mitigated → A bullish OB has been invalidated.
Bearish OB Mitigated → A bearish OB has been invalidated.
🎯 Use Cases:
Quickly identify key liquidity zones.
Track institutional activity in the market.
Improve entry and exit precision.
Multiple Divergence Scanner (move to candles and merge scales)This indicator detects and visualizes multiple types of RSI-based divergences, including Regular, Hidden, and Dual-source (Multi) Bullish/Bearish signals. Not limited with RSI only. You can add move functions and it will automaticly combine your options.
It offers customizable score filtering, label positioning, and visual styling.
Ideal for traders who seek both technical precision and symbolic clarity in their charts.
You have to drag it to your candles after adding to your chart. Then right click on price->Merge all scales to right/left.
ETFs Sector PerformanceDisplays a table of the Top 8 performing ETFs over a selected period (1M / 2M / 3M / 6M) to quickly identify industry strength.
Pre-Set Universe (39 ETFs)
ITA — iShares U.S. Aerospace & Defense ETF
DBA — Invesco DB Agriculture Fund
BOTZ — Global X Robotics & Artificial Intelligence ETF
JETS — U.S. Global Jets ETF
XLB — Materials Select Sector SPDR Fund
XBI — SPDR S&P Biotech ETF
PKB — Invesco Dynamic Building & Construction ETF
ICLN — iShares Global Clean Energy ETF
SKYY — First Trust Cloud Computing ETF
DBC — Invesco DB Commodity Index Tracking Fund
XLY — Consumer Discretionary Select Sector SPDR Fund
XLP — Consumer Staples Select Sector SPDR Fund
BLOK — Amplify Transformational Data Sharing ETF
KARS — KraneShares Electric Vehicles & Future Mobility ETF
XLE — Energy Select Sector SPDR Fund
ESPO — VanEck Video Gaming and eSports ETF
XLF — Financial Select Sector SPDR Fund
PBJ — Invesco Dynamic Food & Beverage ETF
ITB — iShares U.S. Home Construction ETF
XLI — Industrial Select Sector SPDR Fund
PAVE — Global X U.S. Infrastructure Development ETF
PEJ — Invesco Dynamic Leisure & Entertainment ETF
LIT — Global X Lithium & Battery Tech ETF
IHI — iShares U.S. Medical Devices ETF
XME — SPDR S&P Metals & Mining ETF
FCG — First Trust Natural Gas ETF
URA — Global X Uranium ETF
PPH — VanEck Pharmaceutical ETF
QTUM — Defiance Quantum Computing & Machine Learning ETF
IYR — iShares U.S. Real Estate ETF
XRT — SPDR S&P Retail ETF
SOXX — iShares Semiconductor ETF
BOAT — SonicShares Global Shipping ETF
IGV — iShares Expanded Tech-Software Sector ETF
TAN — Invesco Solar ETF
SLX — VanEck Steel ETF
IYZ — iShares U.S. Telecommunications ETF
IYT — iShares U.S. Transportation ETF
XLU — Utilities Select Sector SPDR Fund
Bollinger Breakout A3 updateBollinger Breakout A3 update from LuxAlgo signal
You can try it with some another signal.
All-In-One MA Stack ScalperWhat is this Indicator?
This tool is an advanced, multi-layered breakout and trend-following indicator designed for lower timeframes. It identifies high-conviction buy and sell signals by combining moving average stacking with a suite of professional-grade filters.
How Does It Work?
A signal is generated only when ALL of the following conditions are met:
Moving Average Stack (5M Chart):
Buy: The close price is above all five moving averages (MAs: 100, 48, 36, 24, 12).
Sell: The close price is below all five MAs.
Volatility Filter (ATR):
Signals only print when the current ATR (14) is at least 80% of its 100-period average, ensuring you only trade in actively moving markets.
Candle Structure Filter:
The current candle must have a real body that is at least 35% of the candle’s total range, filtering out dojis and indecision bars.
Big Candle Filter:
The candle’s total range must be at least 40% of the current ATR, avoiding signals on minor, insignificant moves.
Volume Filter:
The current volume must be at least 80% of its 50-period average, filtering out signals during illiquid or quiet market conditions.
Minimum Distance from All MAs:
Price must be a minimum distance (20% ATR) away from each MA, confirming a clean breakout and avoiding signals in tight MA clusters or ranging markets.
RSI Momentum Filter:
Buy: RSI(14) must be greater than 55.
Sell: RSI(14) must be less than 45.
This ensures trades are only taken in the direction of momentum.
ADX Trend Filter:
ADX(14,14) must be above 20, ensuring signals only print in trending conditions (not in chop/range).
Minimum Bars Between Signals:
Only one signal per direction is allowed every 10 bars to avoid overtrading and signal clustering.
What Does This Achieve?
Reduces noise and false signals common in basic MA cross or stack systems.
Captures only strong, high-momentum, and high-conviction moves.
Helps you avoid chop, range, and news whipsaws by combining multiple market filters.
Perfect for advanced scalpers, intraday trend followers, or as a trade filter for algos/EAs.
How to Use It:
Apply to your 5-minute chart.
Green BUY signals: Only when all bullish conditions align.
Red SELL signals: Only when all bearish conditions align.
Use as a stand-alone system or as a filter for your own entries.
Recommended For:
Scalpers & intraday traders who want only the best opportunities.
EA and bot builders seeking reliable signal logic.
Manual traders seeking confirmation of high-probability breakouts.
Tip:
Adjust any of the filters (e.g., RSI/ADX thresholds, minBars, minDist) to make it more/less selective for your style or market.
Ripping and Dipping (Reversal + Trend Signals)Waits for a series of EMAs to be stacking from fastest to slowest for a user input X bars, then signals trend or reversal trades based on a simple close above/below the high/low of the last bar. Designed to catch quick trend trades once strength is confirmed, and quick reversal trades once trend has overextended.
SMC Structure Suite — BOS/CHOCH, Order Blocks, TrendsSMC Structure Suite — Market Structure, BOS/CHOCH, Order Blocks
Advanced Smart Money Concepts Analysis Tool
This comprehensive market structure indicator provides institutional-grade analysis for professional traders seeking precise market timing and trend identification. Built on rigorous Smart Money Concepts methodology, the indicator delivers reliable structural analysis with mathematical validation.
Core Functionality:
Market Structure Analysis: Automated detection and classification of HH, HL, LH, and LL using a proprietary pullback validation algorithm. Eliminates false signals through systematic confirmation requirements.
Break of Structure & Change of Character: Real-time identification of structural breaks and trend reversals. Provides clear visual confirmation of institutional order flow shifts and market sentiment changes.
Order Block Detection: Algorithmic identification of institutional supply and demand zones with automatic invalidation logic. Pinpoints areas where smart money has previously executed significant positions.
Trend Classification System: Dynamic trend state analysis with immediate updates upon structural confirmation. Provides clear directional bias for optimal entry and exit timing.
Technical Specifications:
Zero repainting architecture ensures signal reliability
Multi-timeframe compatibility across all market sessions
Configurable analysis periods and visual parameters
Professional labeling system with institutional terminology
Comprehensive backtesting and validation capabilities
Designed for traders following Smart Money Concepts strategy and methodology.
Forex Currency Strength What this indicator does
It compares the relative strength of the 8 major currencies (USD, EUR, GBP, JPY, AUD, CAD, NZD, CHF) by looking at all 28 currency pairs. Each currency is smoothed (averaged) with a moving average to reduce noise.
From this it shows:
• Currency strength lines → how each major currency is performing over time (optional view).
• Pair divergence histogram → the difference in strength between the two currencies of the chart pair (e.g. EUR vs USD on an EURUSD chart). Green means the base currency is stronger, red means the quote currency is stronger.
• Ranking table → shows the strongest to weakest currency at the current moment. The strongest is highlighted green, the weakest red.
• Session highlighting → shows your chosen trading session on the chart (background shading, optional vertical line at the session start).
• Alerts → you can set TradingView alerts when:
• the pair divergence crosses above or below zero
• the divergence strength gets big enough (above your threshold)
• the difference between the strongest and weakest currency becomes large
⸻
👉 In plain words:
This indicator helps you quickly see which currencies are strong, which are weak, and whether the pair you are trading has a clear directional bias. It also highlights trading sessions and can notify you when strong moves or imbalances appear.
// ─────────────────────────────────────────────────────────────
// Forex Currency Strength (8 Majors, %R) + Divergence + Ranking
// ─────────────────────────────────────────────────────────────
//
// === Inputs ===
//
// exchPrefix → Broker/feed prefix (e.g. "OANDA:", "FX:", or "" for ICMarkets)
// tf → Data timeframe (empty = chart timeframe)
// smoothLen → Smoothing length (MA) for currency strength (default = 14)
// smoothMethod → MA method (SMA, EMA, WMA, DEMA)
// viewMode → Display mode: "Strength Lines", "Pair Divergence", "Both"
// (Tip: set to "Pair Divergence" to hide lines by default)
// barsLimit → Number of bars to display
//
// sessionStr → Trading session time (e.g. "0800-1700"); session is highlighted on chart
//
// alertDivAbs → Threshold for alerts on |divergence|
// alertGapTF → Threshold for alerts on Top–Flop ranking gap
//
// scaleK → Scaling factor (here ×1000)
//
// rankPos → Position of the ranking table (top/bottom left/right)
// rankTextSize → Font size for the ranking table (tiny, small, normal, large, huge)
//
// === Outputs ===
//
// • 8 currency strength lines (optionally visible)
// • Divergence (current pair) as histogram
// • Ranking table (top & flop highlighted)
// • Session highlighting (background color + optional vertical line)
// • Alerts on divergence crosses, |divergence| thresholds & top–flop gaps
//
// === Alert Conditions ===
//
// longDivCross → Divergence (current pair) crosses above 0
// shortDivCross → Divergence (current pair) crosses below 0
// divAbsUp → |Divergence| exceeds alertDivAbs threshold
// gapUp → Top–Flop ranking gap exceeds alertGapTF threshold
//
// ─────────────────────────────────────────────────────────────
MS - Crypto RSI-Based Trading StrategyThis is a comprehensive trend-following and momentum-based strategy designed for the cryptocurrency market. It combines multiple leading indicators to filter out market noise and generate high-quality buy and sell signals.
Key Indicators:
Moving Average (MA): To determine the main trend direction.
Relative Strength Index (RSI): To measure momentum and identify overbought/oversold conditions.
Directional Movement Index (DMI): To confirm the strength of the trend.
Volume & ATR: To validate market interest and filter out excessive volatility.
Buy Conditions (All Must Be True):
Price and Trend Alignment: The current price is above the MA50 (with a 5% buffer).
Momentum Confirmation: The RSI is between 50 and 70.
Trend Strength: The +DI is greater than the -DI.
Market Interest: Volume is 1.5 times its moving average.
Low Volatility: The ATR is below its average.
Sell Conditions (Any One Is True):
Trend Reversal: The price drops below the MA50 (with a 5% buffer).
Momentum Loss: The RSI drops below 45.
Trend Weakness: The -DI crosses above the +DI.
Market Fatigue: Volume drops below 50% of its moving average.
High Volatility: The ATR is above its average.
Disclaimer: This is a backtesting tool and not financial advice. Past performance is not an indicator of future results. Always use proper risk management and conduct your own research before trading.
[MAB] Fibbonacci-Retracement-Tool🔹 Overview
Fibonacci retracement helps map potential support/resistance during pullbacks. This tool lets you manually select two swing points A & B ; it then plots retracement levels ( 38.2% , 61.8% , 78.6% ) and displays whether the retracement remains valid or becomes invalid (devalidation) based on price interaction. It provides a structured, visual framework to study price behavior—not a prediction engine.
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⚙ Key Features
Custom Swing Selection — Choose swing points A & B to generate the structure.
Focused Levels — Plots only 38.2%, 61.8%, 78.6.
Validation / Devalidation — Clearly shows when the retracement setup holds or fails.
Bullish & Bearish Modes — Works in both trend directions.
Clean Visuals — Minimal clutter, clear chart structure.
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📖 How to Use
Select Point A (a swing low or high).
Select Point B (the opposite swing). Important: Point B must always come after Point A on the chart. If B is placed before A, the indicator will show an error.
The indicator plots retracements (38.2%, 61.8%, 78.6).
Observe validation or devalidation as price interacts with these levels.
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🖼️ Visual Representation / Chart Explanation
Examples to illustrate how the tool validates or devalidates a Fibonacci structure.
Bearish Validation Criteria — Price action validating a bearish Fibonacci setup:
Bullish Validation Criteria — Price action validating a bullish Fibonacci setup:
Devalidation Criteria — Candle closes beyond a Fibonacci level, invalidating the setup:
Devalidation Example — Setup de-validated because the candle closed above the devalidation line:
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📊 Recommended Charting
Markets: Stocks, Indices, Forex, Crypto.
Timeframes: Best on 15m → Daily.
Confluence: Improves with trendlines, MAs, or candlestick context.
____________________________________________________________
🛡 Risk Management
Treat Fibonacci as a guide , not a prediction.
Always apply your own trading discipline and position sizing.
____________________________________________________________
⚠️Important Notes
This indicator is for educational and visual analysis only.
Not financial advice or a performance guarantee.
____________________________________________________________
✅ Conclusion and Access
This Fibonacci Tool offers a disciplined, visual approach to studying retracements. By focusing on the key levels (38.2, 61.8, 78.6) and plotting validation/devalidation, it enhances chart analysis while remaining simple, flexible, and educational.
👉 For how to request access, please see the Author’s Instructions section below.
____________________________________________________________
⚠️ Disclaimer
📘 For educational purposes only.
🙅 Not SEBI registered.
❌ Not a buy/sell recommendation.
🧠 Purely a learning resource.
📊 Not Financial Advice.
STOCK SCHOOL | FVGThe Stock School FVG Indicator is designed to help traders identify and trade Fair Value Gaps (FVGs) and Inverse FVGs (IFVGs) with precision.
Built for both intraday and swing traders, this tool highlights high-probability trading zones where institutions leave imbalances in the market.
✨ Key Features:
Auto-detects FVGs & IFVGs in real-time
Works on all timeframes and instruments (Nifty, BankNifty, Stocks, Forex, Crypto)
Non-repainting logic for reliable signals
Clean and easy-to-use interface with Stock School styling
Perfect for Smart Money Concept (SMC) traders
🚀 With this indicator, you can:
Spot institutional footprints quickly
Combine with BOS, CHoCH, Order Blocks for high accuracy
Trade liquidity sweeps + FVG collisions with confidence
💡 Disclaimer:
This indicator is for educational purposes only. Trading involves risk. Always use proper risk management.
TRAPPER TRENDLINES — RSIBuilds dynamic RSI trendlines by connecting the two most recent confirmed RSI swing points (highs→highs for resistance, lows→lows for support). Includes optional channel shading for the 30–70 zone, an RSI moving average, clean break alerts, and simple bullish/bearish divergence alerts versus price.
How it works
RSI pivots: A point on RSI is a swing high/low only if it is the most extreme value compared with a set number of bars on the left and the right (the Pivot Lookback).
RSI trendlines:
Resistance connects the last two confirmed RSI swing highs.
Support connects the last two confirmed RSI swing lows.
Lines can be Full Extend (update into the future) or Pivot Only.
Channel block: Optional fill of the 30–70 range for fast visual context.
Alerts:
Breaks of RSI support/resistance trendlines.
Basic bullish/bearish RSI divergences versus price pivots.
Inputs
RSI
RSI Length: Default 14 (standard).
Pivot Lookback: Bars to the left/right required to confirm an RSI swing.
Overbought / Oversold: 70 / 30 by default.
Line Extension: Full Extend or Pivot Only.
Visuals
Show RSI Moving Average / Signal Length: Optional smoothing line on RSI.
RSI/Signal colors: Customize plot colors.
Show 30–70 Channel Block: Toggle the middle-zone fill.
Tint pane background when RSI in channel: Optional subtle background when RSI is between OB/OS.
Divergences & Alerts
Enable RSI TL Break Alerts: Alert conditions for RSI line breaks.
Enable Divergence Alerts: Bullish/Bearish divergence alerts versus price.
Pairing with price for confluence/divergence
For accurate confluence and clearer divergences, align this RSI tool with your price trendline tool (for example, TRAPPER TRENDLINES — PRICE):
Set RSI Pivot Lookback equal to the Pivot Left/Right size used on price.
Example: Price uses Pivot Left = 50 and Pivot Right = 50 → set RSI Pivot Lookback = 50.
Keep RSI Length = 14 and OB/OS = 70/30 unless you have a specific edge.
Interpretation:
Confluence: Price reacts at its trendline while RSI reacts at its own line in the same direction.
Divergence: Price makes a higher high while RSI makes a lower high (bearish), or price makes a lower low while RSI makes a higher low (bullish), using matched pivot windows.
Suggested settings
Higher timeframes (4H / 1D / 1W): Pivot Lookback = 50; optional RSI MA length 14; channel block ON.
Intraday (15m / 30m / 1H): Pivot Lookback = 30; optional RSI MA length 14.
Always mirror your price pivot size to this RSI Pivot Lookback for consistent swings.
Reading the signals
RSI trendline touch/hold: Momentum reacting at structure; look for confluence with price levels.
RSI Trendline Break Up / Down: Momentum shift; consider price structure and retests.
Bullish/Bearish Divergence: Confirm only when pivots are matched and the new swing is confirmed.
Notes & limitations
Pivots require future bars to confirm by design; trendlines update as new swings confirm.
Divergence logic compares RSI pivots to price pivots with the same lookback; mismatched windows can produce false positives.
No strategy entries/exits or performance claims are provided. This is an analytical tool.
Alerts (titles/messages)
RSI: Trendline Break Up — “RSI broke falling resistance line.”
RSI: Trendline Break Down — “RSI broke rising support line.”
RSI: Bullish Divergence — “Bullish RSI divergence confirmed.”
RSI: Bearish Divergence — “Bearish RSI divergence confirmed.”
Quick start
Add the indicator to a separate pane.
Set Pivot Lookback to match your price tool’s pivot size (e.g., 50).
Optionally toggle the RSI MA and Channel Block for clarity.
Enable alerts if you want notifications on RSI line breaks and divergences.
Use with TRAPPER TRENDLINES — PRICE or any price-based trendline tool for confluence/divergence analysis.
Compliance
This script is for educational purposes only and does not constitute financial advice. Trading involves risk. Past performance does not guarantee future results. No performance claims are made.
