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Deviation Trend Profile [BigBeluga]🔵 OVERVIEW A statistical trend analysis tool that combines moving average dynamics with standard deviation zones and trend-specific price distribution. This is an experimental indicator designed for educational and learning purposes only. 🔵 CONCEPTS Trend Detection via SMA Slope: Detects trend shifts when the slope of the SMA exceeds a ±0.1 threshold. Standard Deviation Zones: Calculates ±1, ±2, and ±3 levels from the SMA using ATR, forming dynamic envelopes around the mean. Trend Distribution Profile: Builds a histogram that shows how often price closed within each deviation zone during the active trend phase. 🔵 FEATURES Trend Signals: Immediate shift markers using colored circles at trend reversals. SMA Gradient Coloring: The SMA line dynamically changes color based on its directional slope. Trend Duration Label: A label above the histogram shows how many bars the current trend has lasted. Trend Distribution Histogram: Visual bin-based profile showing frequency of price closes within deviation bands during trend lookback period. Adjustable Bin Count: Set the granularity of the distribution using the “Bins Amount” input. Deviation Labels and Zones: Clearly marked ±1, ±2, ±3 lines with consistent color scheme. Trend Strength Insight:   • Wide profile skewed to ±2/3 = strong directional trend.   • Profile clustered near SMA = potential trend exhaustion or range. 🔵 HOW TO USE Use trend shift dots as entry signals:   • 🔵 = Bullish start   • 🔴 = Bearish start Trade with the trend when price clusters in outer zones (±2 or ±3). Be cautious or fade the trend when price distribution contracts toward the SMA. View across multiple timeframes for trend confluence or divergence. 🔵 CONCLUSION Deviation Trend Profile visualizes how price distributes during trends relative to statistical deviation zones. It’s a powerful confluence tool for identifying strength, exhaustion, and the rhythm of price behavior—ideal for swing traders and volatility analysts alike.
Wskaźnik Pine Script®
od BigBeluga
16
Rolling Z-Score Trend [QuantAlgo]🟢 Overview The Rolling Z-Score Trend measures how far the current price deviates from its rolling mean in terms of standard deviations. It transforms price data into standardized scores to identify overbought and oversold conditions while tracking momentum shifts. The indicator displays a Z-Score line showing price deviation from statistical norms, with background momentum columns showing the rate of change in these deviations. This helps traders and investors identify mean reversion opportunities and momentum shifts across different asset classes and timeframes. 🟢 How It Works The indicator uses the Z-Score formula: Z = (X - μ) / σ, where X is the current closing price, μ is the rolling mean, and σ is the rolling standard deviation over a user-defined lookback period. This creates a dynamic baseline that adapts to changing market conditions and standardizes price movements for interpretation across different assets and volatility conditions. The raw Z-Score undergoes 3-period EMA smoothing to reduce noise while maintaining responsiveness to market signals. Beyond the basic Z-Score calculation, the indicator measures the rate of change in Z-Score values between successive bars, displayed as background momentum columns. This momentum component shows acceleration and deceleration of statistical deviations. All calculations are processed through confirmation filters, displaying signals only on confirmed bars to reduce premature signals based on incomplete price action. 🟢 How to Use 1. Z-Score Interpretation and Threshold Zones Positive Values (Above Zero) : Price trading above statistical mean, suggesting bullish momentum or potential overbought conditions Negative Values (Below Zero) : Price trading below statistical mean, suggesting bearish momentum or potential oversold conditions Zero Line Crosses : Signal transitions between statistical regimes and potential trend changes Upper Threshold Zone : Area above entry threshold (default 1.5) indicating potential overbought conditions Lower Threshold Zone : Area below negative entry threshold (default -1.5) indicating potential oversold conditions Extreme Values (±2.0 or higher) : Statistically significant deviations that may indicate reversal opportunities 2. Momentum Background Analysis and Info Table Green Columns : Accelerating positive momentum in Z-Score values Red Columns : Accelerating negative momentum in Z-Score values Column Height : Magnitude of momentum change between bars Momentum Divergence : When columns contradict primary Z-Score direction, often signals impending reversals Info Table : Displays real-time numerical values for both Z-Score and momentum, including trend direction indicators and bar-to-bar change calculations for position management 3. Preconfigured Settings Default : Balanced performance across multiple timeframes and asset classes for general trading and medium-term position management. Scalping : Responsive setup for ultra-short-term trading on 1-15 minute charts with frequent signals and increased sensitivity to quick price movements. Swing Trading : Optimized for multi-day positions with noise filtering, focusing on larger price swings. Most effective on 1-4 hour and daily timeframes. Trend Following : Maximum smoothing that prioritizes established trends over short-term volatility. Generates fewer signals for daily and weekly charts.
Wskaźnik Pine Script®
od QuantAlgo
3
Yelober - Sector Rotation Detector# Yelober - Sector Rotation Detector: User Guide ## Overview The Yelober - Sector Rotation Detector is a TradingView indicator designed to track sector performance and identify market rotations in real-time. It monitors key sector ETFs, calculates performance metrics, and provides actionable stock recommendations based on sector strength and weakness. ## Purpose This indicator helps traders identify when capital is moving from one sector to another (sector rotation), which can provide valuable trading opportunities. It also detects risk-off conditions in the market and highlights sectors with abnormal trading volume. ## Table Columns Explained ### 1. Sector Displays the sector name being monitored. The indicator tracks six primary sectors plus the S&P 500: - Energy (XLE) - Financial (XLF) - Technology (XLK) - Consumer Staples (XLP) - Utilities (XLU) - Consumer Discretionary (XLY) - S&P 500 (SPY) ### 2. Perf % Shows the daily percentage performance of each sector ETF. Values are color-coded: - Green: Positive performance - Red: Negative performance Positive values display with a "+" sign (e.g., +1.25%) ### 3. RSI Displays the Relative Strength Index value for each sector, which helps identify overbought or oversold conditions: - Values above 70 (highlighted in red): Potentially overbought - Values below 30 (highlighted in green): Potentially oversold - Values between 30-70 (highlighted in blue): Neutral territory ### 4. Vol Ratio Shows the volume ratio, which compares today's volume to the average volume over the lookback period: - Values above 1.5x (highlighted in yellow): Indicates abnormally high trading volume - Values below 1.5x (highlighted in blue): Normal trading volume This helps identify sectors with unusual activity that may signal important price movements. ### 5. Trend Displays the current price trend direction with symbols: - ▲ (green): Uptrend (today's close > yesterday's close) - ▼ (red): Downtrend (today's close < yesterday's close) - ◆ (gray): Neutral (today's close = yesterday's close) ## Summary & Recommendations Section The summary section provides: 1. **Sector Rotation Detection**: Identifies when there's a significant performance gap (>2%) between the strongest and weakest sectors. 2. **Risk-Off Mode Detection**: Alerts when defensive sectors (Consumer Staples and Utilities) are positive while Technology is negative, which often signals investors are moving to safer assets. 3. **Strong Volume Detection**: Indicates when any sector shows abnormally high trading volume. 4. **Stock Recommendations**: Suggests specific stocks to consider for long positions (from the strongest sectors) and short positions (from the weakest sectors). ## Example Interpretations ### Example 1: Sector Rotation If you see: - Technology: -1.85% - Financial: +2.10% - Summary shows: "SECTOR ROTATION DETECTED: Rotation from Technology to Financial" **Interpretation**: Capital is moving out of tech stocks and into financial stocks. This could be due to rising interest rates, which typically benefit banks while pressuring high-growth tech companies. Consider looking at financial stocks like JPM, BAC, and WFC for potential long positions. ### Example 2: Risk-Off Conditions If you see: - Consumer Staples: +0.80% - Utilities: +1.20% - Technology: -1.50% - Summary shows: "RISK-OFF MODE DETECTED" **Interpretation**: Investors are seeking safety in defensive sectors while selling growth-oriented tech stocks. This often occurs during market uncertainty or ahead of economic concerns. Consider reducing exposure to high-beta stocks and possibly adding defensive names like PG, KO, or NEE. ### Example 3: Volume Spike If you see: - Energy: +3.20% with Volume Ratio 2.5x (highlighted in yellow) - Summary shows: "STRONG VOLUME DETECTED" **Interpretation**: The energy sector is making a strong move with significantly higher-than-average volume, suggesting conviction behind the price movement. This could indicate the beginning of a sustained trend in energy stocks. Consider names like XOM, CVX, and COP. ## How to Use the Indicator 1. Apply the indicator to any chart (works best on daily timeframes). 2. Customize settings if needed: - Timeframe: Choose between intraday (60 or 240 minutes), daily, or weekly - Lookback Period: Adjust the historical comparison period (default: 20) - RSI Period: Modify the RSI calculation period (default: 14) 3. To refresh the data: Click the settings icon, increase the "Click + to refresh data" counter, and click "OK". 4. Identify opportunities based on sector performance, RSI levels, volume ratios, and the summary recommendations. This indicator helps traders align with market rotation trends and identify which sectors (and specific stocks) may outperform or underperform in the near term.
Wskaźnik Pine Script®
od yelober
2
TASC 2025.07 Laguerre Filters█ OVERVIEW This script implements the Laguerre filter and oscillator described by John F. Ehlers in the article "A Tool For Trend Trading, Laguerre Filters" from the July 2025 edition of TASC's Traders' Tips . The new Laguerre filter utilizes the UltimateSmoother filter in place of an exponential moving average (EMA) in its calculation, offering improved responsiveness and reduced lag. █ CONCEPTS As Ehlers explains in his article, the Laguerre filter is a form of transversal filter . A transversal filter calculates an output signal using a tapped delay line . It creates multiple delayed versions of an input signal, applies weight to each delay, and then calculates their sum to generate the filtered result. The Laguerre filter's structure relies on Laguerre polynomials — solutions to a differential equation solved by Edmond Laguerre in the 1800s. When Ehlers analyzed the formula for these polynomials on discrete systems (e.g., financial time series), he found that the first term's expression corresponds to an EMA response, and all subsequent terms correspond to an all-pass response. In contrast to other filter types, an all-pass filter produces phase shift (i.e., delay) in an input signal's components without affecting its amplitude. Ehlers observed that these characteristics of Laguerre polynomials make them suitable for use in a transversal filter structure, and thus the Laguerre filter was born. However, he notes that EMAs are not great filters in general. As such, to improve on the Laguerre filter's design, Ehlers modified it by replacing the EMA term with his UltimateSmoother filter. The resulting Laguerre filter has significantly reduced lag, achieving a tighter response to market fluctuations while maintaining smoothness. Ehlers suggests that traders can analyze crossings between the UltimateSmoother and this Laguerre filter, or those between two Laguerre filters of different order, for helpful buy and sell signals. In addition to the Laguerre filter, Ehlers derived a smooth, low-lag oscillator based on the difference between the first and second terms in the modified filter structure, scaled by the root mean square (RMS). The resulting oscillator provides an alternative filtered representation of market data, which can help traders identify swing and mean-reversion signals. █ USAGE This indicator calculates both the Laguerre filter and the Laguerre oscillator described in Ehlers' article. It displays the Laguerre filter on the main chart pane and the oscillator in a separate pane. Users can control the behavior of the filter and oscillator with the inputs in the "Settings/Inputs" tab: The "Period" input defines the critical period of the UltimateSmoother used in the Laguerre filter and oscillator calculations. Its default value is 30. The "Gamma" input determines the weighting behavior of the Laguerre filter and oscillator. It accepts a positive value between 0 and 1. Use a lower value for quicker responsiveness to market changes, and a higher value for trends. The default value is 0.5. The "RMS length" input determines the length of the RMS calculation for oscillator normalization. The default value is 100 bars.
Wskaźnik Pine Script®
od PineCodersTASC
3
Trend Impulse Channels (Zeiierman)█ Overview Trend Impulse Channels (Zeiierman) is a precision-engineered trend-following system that visualizes discrete trend progression using volatility-scaled step logic. It replaces traditional slope-based tracking with clearly defined “trend steps,” capturing directional momentum only when price action decisively confirms a shift through an ATR-based trigger. This tool is ideal for traders who prefer structured, stair-step progression over fluid curves, and value the clarity of momentum-based bands that reveal breakout conviction, pullback retests, and consolidation zones. The channel width adapts automatically to market volatility, while the step logic filters out noise and false flips. ⚪ The Structural Assumption This indicator is built on a core market structure observation: After each strong trend impulse, the market typically enters a “cooling-off” phase as profit-taking occurs and counter-trend participants enter. This often results in a shallow pullback or stall, creating a slight negative slope in an uptrend (or a positive slope in a downtrend). These “cooling-off” phases don’t reverse the trend — they signal temporary pressure before the next leg continues. By tracking trend steps discretely and filtering for this behavior, Trend Impulse Channels helps traders align with the rhythm of impulse → pause → impulse. █ How It Works ⚪ Step-Based Trend Engine At the heart of this tool is a dynamic step engine that progresses only when price crosses a predefined ATR-scaled trigger level: Trigger Threshold (× ATR) – Defines how far price must break beyond the current trend state to register a new trend step. Step Size (Volatility-Guided) – Each trend continuation moves the trend line in discrete units, scaling with ATR and trend persistence. Trend Direction State – Maintains a +1/-1 internal bias to support directional filters and step tracking. ⚪ Volatility-Adaptive Channel Each step is wrapped inside a dynamic envelope scaled to current volatility: Upper and Lower Bands – Derived from ATR and band multipliers to expand/contract as volatility changes. ⚪ Retest Signal System Optional signal markers show when price re-tests the upper or lower band: Upper Retest → Pullback into resistance during a bearish trend. Lower Retest → Pullback into support during a bullish trend. ⚪ Trend Step Signals Circular markers can be shown to mark each time the trend steps forward, making it easy to identify structurally significant moments of continuation within a larger trend. █ How to Use ⚪ Trend Alignment Use the Trend Line and Step Markers to visually confirm the direction of momentum. If multiple trend steps occur in sequence without reversal, this typically signals strong conviction and trend persistence. ⚪ Retest-Based Entries Wait for pullbacks into the channel and monitor for triangle retest signals. When used in confluence with trend direction, these offer high-quality continuation setups. ⚪ Breakouts Look for breakouts beyond the upper or lower band after a longer period of pause. For higher likelihood of success, look for breakouts in the direction of the trend. █ Settings Trigger Threshold (× ATR) - Defines how far price must move to register a new trend step. Controls sensitivity to trend flips. Max Step Size (× ATR) - Caps how far each trend step can extend. Prevents runaway step expansion in high volatility. Band Multiplier (× ATR) - Expands the upper and lower channels. Controls how much breathing room the bands allow. Trend Hold (bars) - Minimum number of bars the trend must remain active before allowing a flip. Helps reduce noise. Filter by Trend - Restrict retest signals to those aligned with the current trend direction. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Wskaźnik Pine Script®
od Zeiierman
9
Magnificent 7 OscillatorThe Magnificent 7 Oscillator is a sophisticated momentum-based technical indicator designed to analyze the collective performance of the seven largest technology companies in the U.S. stock market (Apple, Microsoft, Alphabet, Amazon, NVIDIA, Tesla, and Meta). This indicator incorporates established momentum factor research and provides three distinct analytical modes: absolute momentum tracking, equal-weighted market comparison, and relative performance analysis. The tool integrates five different oscillator methodologies and includes advanced breadth analysis capabilities. Theoretical Foundation Momentum Factor Research The indicator's foundation rests on seminal momentum research in financial markets. Jegadeesh and Titman (1993) demonstrated that stocks with strong price performance over 3-12 month periods tend to continue outperforming in subsequent periods¹. This momentum effect was later incorporated into formal factor models by Carhart (1997), who extended the Fama-French three-factor model to include a momentum factor (UMD - Up Minus Down)². The momentum calculation methodology follows the academic standard: Momentum(t) = / P(t-n) × 100 Where P(t) is the current price and n is the lookback period. The focus on the "Magnificent 7" stocks reflects the increasing market concentration observed in recent years. Fama and French (2015) noted that a small number of large-cap stocks can drive significant market movements due to their substantial index weights³. The combined market capitalization of these seven companies often exceeds 25% of the total S&P 500, making their collective momentum a critical market indicator. Indicator Architecture Core Components 1. Data Collection and Processing The indicator employs robust data collection with error handling for missing or invalid security data. Each stock's momentum is calculated independently using the specified lookback period (default: 14 periods). 2. Composite Oscillator Calculation Following Fama-French factor construction methodology, the indicator offers two weighting schemes: - Equal Weight: Each active stock receives identical weighting (1/n) - Market Cap Weight: Reserved for future enhancement 3. Oscillator Transformation Functions The indicator provides five distinct oscillator types, each with established technical analysis foundations: a) Momentum Oscillator (Default) - Pure rate-of-change calculation - Centered around zero - Direct implementation of Jegadeesh & Titman methodology b) RSI (Relative Strength Index) - Wilder's (1978) relative strength methodology - Transformed to center around zero for consistency - Scale: -50 to +50 c) Stochastic Oscillator - George Lane's %K methodology - Measures current position within recent range - Transformed to center around zero d) Williams %R - Larry Williams' range-based oscillator - Inverse stochastic calculation - Adjusted for zero-centered display e) CCI (Commodity Channel Index) - Donald Lambert's mean reversion indicator - Measures deviation from moving average - Scaled for optimal visualization Operational Modes Mode 1: Magnificent 7 Analysis Tracks the collective momentum of the seven constituent stocks. This mode is optimal for: - Technology sector analysis - Growth stock momentum assessment - Large-cap performance tracking Mode 2: S&P 500 Equal Weight Comparison Analyzes momentum using an equal-weighted S&P 500 reference (typically RSP ETF). This mode provides: - Broader market momentum context - Size-neutral market analysis - Comparison baseline for relative performance Mode 3: Relative Performance Analysis Calculates the momentum differential between Magnificent 7 and S&P 500 Equal Weight. This mode enables: - Sector rotation analysis - Style factor assessment (Growth vs. Value) - Relative strength identification Formula: Relative Performance = MAG7_Momentum - SP500EW_Momentum Signal Generation and Thresholds Signal Classification The indicator generates three signal states: - Bullish: Oscillator > Upper Threshold (default: +2.0%) - Bearish: Oscillator < Lower Threshold (default: -2.0%) - Neutral: Oscillator between thresholds Relative Performance Signals In relative performance mode, specialized thresholds apply: - Outperformance: Relative momentum > +1.0% - Underperformance: Relative momentum < -1.0% Alert System Comprehensive alert conditions include: - Threshold crossovers (bullish/bearish signals) - Zero-line crosses (momentum direction changes) - Relative performance shifts - Breadth Analysis Component The indicator incorporates market breadth analysis, calculating the percentage of constituent stocks with positive momentum. This feature provides insights into: - Strong Breadth (>60%): Broad-based momentum - Weak Breadth (<40%): Narrow momentum leadership - Mixed Breadth (40-60%): Neutral momentum distribution Visual Design and User Interface Theme-Adaptive Display The indicator automatically adjusts color schemes for dark and light chart themes, ensuring optimal visibility across different user preferences. Professional Data Table A comprehensive data table displays: - Current oscillator value and percentage - Active mode and oscillator type - Signal status and strength - Component breakdowns (in relative performance mode) - Breadth percentage - Active threshold levels Custom Color Options Users can override default colors with custom selections for: - Neutral conditions (default: Material Blue) - Bullish signals (default: Material Green) - Bearish signals (default: Material Red) Practical Applications Portfolio Management - Sector Allocation: Use relative performance mode to time technology sector exposure - Risk Management: Monitor breadth deterioration as early warning signal - Entry/Exit Timing: Utilize threshold crossovers for position sizing decisions Market Analysis - Trend Identification: Zero-line crosses indicate momentum regime changes - Divergence Analysis: Compare MAG7 performance against broader market - Volatility Assessment: Oscillator range and frequency provide volatility insights Strategy Development - Factor Timing: Implement growth factor timing strategies - Momentum Strategies: Develop systematic momentum-based approaches - Risk Parity: Use breadth metrics for risk-adjusted portfolio construction Configuration Guidelines Parameter Selection - Momentum Period (5-100): Shorter periods (5-20) for tactical analysis, longer periods (50-100) for strategic assessment - Smoothing Period (1-50): Higher values reduce noise but increase lag - Thresholds: Adjust based on historical volatility and strategy requirements Timeframe Considerations - Daily Charts: Optimal for swing trading and medium-term analysis - Weekly Charts: Suitable for long-term trend analysis - Intraday Charts: Useful for short-term tactical decisions Limitations and Considerations Market Concentration Risk The indicator's focus on seven stocks creates concentration risk. During periods of significant rotation away from large-cap technology stocks, the indicator may not represent broader market conditions. Momentum Persistence While momentum effects are well-documented, they are not permanent. Jegadeesh and Titman (1993) noted momentum reversal effects over longer time horizons (2-5 years). Correlation Dynamics During market stress, correlations among the constituent stocks may increase, reducing the diversification benefits and potentially amplifying signal intensity. Performance Metrics and Backtesting The indicator includes hidden plots for comprehensive backtesting: - Individual stock momentum values - Composite breadth percentage - S&P 500 Equal Weight momentum - Relative performance calculations These metrics enable quantitative strategy development and historical performance analysis. References ¹Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91. Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82. Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22. Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Wskaźnik Pine Script®
od EdgeTools
5
SmartPhase Analyzer📝 SmartPhase Analyzer – Composite Market Regime Classifier SmartPhase Analyzer is an adaptive regime classification tool that scores market conditions using a customizable set of statistical indicators. It blends multiple normalized metrics into a composite score, which is dynamically evaluated against rolling statistical thresholds to determine the current market regime. ✅ Features: Composite score calculated from 13+ toggleable statistical indicators: Sharpe, Sortino, Omega, Alpha, Beta, CV, R², Entropy, Drawdown, Z-Score, PLF, SRI, and Momentum Rank Uses dynamic thresholds (mean ± std deviation) to classify regime states: 🟢 BULL – Strongly bullish 🟩 ACCUM – Mildly bullish ⚪ NEUTRAL – Sideways 🟧 DISTRIB – Mildly bearish 🔴 BEAR – Strongly bearish Color-coded histogram for composite score clarity Real-time regime label plotted on chart Benchmark-aware metrics (Alpha, Beta, etc.) Modular design using the StatMetrics library by RWCS_LTD 🧠 How to Use: Enable/disable metrics in the settings panel to customize your composite model Use the composite histogram and regime background for discretionary or systematic analysis ⚠️ Disclaimer: This indicator is for educational and informational purposes only. It does not constitute financial advice or a trading recommendation. Always consult your financial advisor before making investment decisions.
Wskaźnik Pine Script®
od RWCS_LTD
5
ATR RopeATR Rope is inspired by DonovanWall's "Range Filter". It implements a similar concept of filtering out smaller market movements and adjusting only for larger moves. In addition, this indicator goes one step deeper by producing actionable zones to determine market state. (Trend vs. Consolidation) > Background When reading up on the Range Filter indicator, it reminded me exactly of a Rope stabilization drawing tool in a program I use frequently. Rope stabilization essentially attaches a fixed length "rope" to your cursor and an anchor point (Brush). As you move your cursor, you are pulling the brush behind it. The cursor (of course) will not pull the brush until the rope is fully extended, this behavior filters out jittery movements and is used to produce smoother drawing curves. If compared visually side-by-side, you will notice that this indicator bears striking resemblance to its inspiration. > Goal Other than simply distinguishing price movements between meaningful and noise, this indicator strives to create a rigid structure to frame market movements and lack-there-of, such as when to anticipate trend, and when to suspect consolidation. Since the indicator works based on an ATR range, the resulting ATR Channel does well to get reactions from price at its extremes. Naturally, when consolidating, price will remain within the channel, neither pushing the channel significantly up or down. Likewise, when trending, price will continue to push the channel in a single direction. With the goal of keeping it quick and simple, this indicator does not do any smoothing of data feeds, and is simply based on the deviation of price from the central rope. Adjusting the rope when price extends past the threshold created by +/- ATR from the rope. > Features & Behaviors - ATR Rope ATR Rope is displayed as a 3 color single line. This can be considered the center line, or the directional line, whichever you'd prefer. The main point of the Rope display is to indicate direction, however it also is factually the center of the current working range. - ATR Rope Color When the rope's value moves up, it changes to green (uptrend), when down, red (downtrend). When the source crosses the rope, it turns blue (flat). With these simple rules, we've formed a structure to view market movements. - Consolidation Zones Consolidation Zones generate from "Flat" areas, and extend into subsequent trend areas. Consolidation is simply areas where price has crossed the Rope and remains inside the range. Over these periods, the upper and lower values are accumulated and averaged together to form the "Consolidation Zone" values. These zones are draw live, so values are averaged as the flat areas progress and don't repaint, so all values seen historically are as they would appear live. - ATR Channel ATR Channel displays the upper and lower bounds of the working range. When the source moves beyond this range, the rope is adjusted based on the distance from the source to the channel. This range can be extremely useful to view, but by default it is hidden. > Application This indicator is not created to provide signals, or serve as a "complete" system. (People who didn't read this far will still comment for signals. :) ) This is created to be used alongside manual interpretation and intuition. This indicator is not meant to constrain any users into a box, and I would actually encourage an open mind and idea generation, as the application of this indicator can take various forms. > Examples As you would probably already know, price movement can be fast impulses, and movement can be slow bleeds. In the screenshot below, we are using movements from and to consolidation zones to classify weak trend and strong trend. As you can see, there are also areas of consolidation which get broken out of and confirmed for the larger moves. Author's Note: In each of these examples, I have outlined the start and end of each session. These examples come from 1 Min Future charts, and have specifically been framed with day trading in mind. "Breakout Retest" or "Support/Resistance Flips" or "Structure Retests" are all generally the same thing, with different traders referring to them by different names, all of which can be seen throughout these examples. In the next example, we have a day which started with an early reversal leading into long, slow, trend. Notice how each area throughout the trend essentially moves slightly higher, then consolidates while holding support of the previous zone. This day had a few sharp movements, however there was a large amount of neutrality throughout this day with continuous higher lows. In contrast to the previous example, next up, we have a very choppy day. Throughout which we see a significant amount of retests before fast directional movements. We also see a few examples of places where previous zones remained relevant into the future. While the zones only display into the resulting trend area, they do not become immediately meaningless once they stop drawing. > Abstract In the screenshot below, I have stacked 2 of these indicators, using the high as the source for one and the low as the source for the other. I've hidden lines of the high and low channels to create a 4 lined channel based on the wicks of price. This is not necessary to use the indicator, but should help provide an idea of creative ways the simple indicator could be used to produce more complicated analysis. If you've made it this far, I would hope it's clear to you how this indicator could provide value to your trading. Thank you to DonovonWall for the inspiration. Enjoy!
Wskaźnik Pine Script®
od SamRecio
7
Langlands-Operadic Möbius Vortex (LOMV)Langlands-Operadic Möbius Vortex (LOMV) Where Pure Mathematics Meets Market Reality A Revolutionary Synthesis of Number Theory, Category Theory, and Market Dynamics 🎓 THEORETICAL FOUNDATION The Langlands-Operadic Möbius Vortex represents a groundbreaking fusion of three profound mathematical frameworks that have never before been combined for market analysis: The Langlands Program: Harmonic Analysis in Markets Developed by Robert Langlands (Fields Medal recipient), the Langlands Program creates bridges between number theory, algebraic geometry, and harmonic analysis. In our indicator: L-Function Implementation: - Utilizes the Möbius function μ(n) for weighted price analysis - Applies Riemann zeta function convergence principles - Calculates quantum harmonic resonance between -2 and +2 - Measures deep mathematical patterns invisible to traditional analysis The L-Function core calculation employs: L_sum = Σ(return_val × μ(n) × n^(-s)) Where s is the critical strip parameter (0.5-2.5), controlling mathematical precision and signal smoothness. Operadic Composition Theory: Multi-Strategy Democracy Category theory and operads provide the mathematical framework for composing multiple trading strategies into a unified signal. This isn't simple averaging - it's mathematical composition using: Strategy Composition Arity (2-5 strategies): - Momentum analysis via RSI transformation - Mean reversion through Bollinger Band mathematics - Order Flow Polarity Index (revolutionary T3-smoothed volume analysis) - Trend detection using Directional Movement - Higher timeframe momentum confirmation Agreement Threshold System: Democratic voting where strategies must reach consensus before signal generation. This prevents false signals during market uncertainty. Möbius Function: Number Theory in Action The Möbius function μ(n) forms the mathematical backbone: - μ(n) = 1 if n is a square-free positive integer with even number of prime factors - μ(n) = -1 if n is a square-free positive integer with odd number of prime factors - μ(n) = 0 if n has a squared prime factor This creates oscillating weights that reveal hidden market periodicities and harmonic structures. 🔧 COMPREHENSIVE INPUT SYSTEM Langlands Program Parameters Modular Level N (5-50, default 30): Primary lookback for quantum harmonic analysis. Optimized by timeframe: - Scalping (1-5min): 15-25 - Day Trading (15min-1H): 25-35 - Swing Trading (4H-1D): 35-50 - Asset-specific: Crypto 15-25, Stocks 30-40, Forex 35-45 L-Function Critical Strip (0.5-2.5, default 1.5): Controls Riemann zeta convergence precision: - Higher values: More stable, smoother signals - Lower values: More reactive, catches quick moves - High frequency: 0.8-1.2, Medium: 1.3-1.7, Low: 1.8-2.3 Frobenius Trace Period (5-50, default 21): Galois representation lookback for price-volume correlation: - Measures harmonic relationships in market flows - Scalping: 8-15, Day Trading: 18-25, Swing: 25-40 HTF Multi-Scale Analysis: Higher timeframe context prevents trading against major trends: - Provides market bias and filters signals - Improves win rates by 15-25% through trend alignment Operadic Composition Parameters Strategy Composition Arity (2-5, default 4): Number of algorithms composed for final signal: - Conservative: 4-5 strategies (higher confidence) - Moderate: 3-4 strategies (balanced approach) - Aggressive: 2-3 strategies (more frequent signals) Category Agreement Threshold (2-5, default 3): Democratic voting minimum for signal generation: - Higher agreement: Fewer but higher quality signals - Lower agreement: More signals, potential false positives Swiss-Cheese Mixing (0.1-0.5, default 0.382): Golden ratio φ⁻¹ based blending of trend factors: - 0.382 is φ⁻¹, optimal for natural market fractals - Higher values: Stronger trend following - Lower values: More contrarian signals OFPI Configuration: - OFPI Length (5-30, default 14): Order Flow calculation period - T3 Smoothing (3-10, default 5): Advanced exponential smoothing - T3 Volume Factor (0.5-1.0, default 0.7): Smoothing aggressiveness control Unified Scoring System Component Weights (sum ≈ 1.0): - L-Function Weight (0.1-0.5, default 0.3): Mathematical harmony emphasis - Galois Rank Weight (0.1-0.5, default 0.2): Market structure complexity - Operadic Weight (0.1-0.5, default 0.3): Multi-strategy consensus - Correspondence Weight (0.1-0.5, default 0.2): Theory-practice alignment Signal Threshold (0.5-10.0, default 5.0): Quality filter producing: - 8.0+: EXCEPTIONAL signals only - 6.0-7.9: STRONG signals - 4.0-5.9: MODERATE signals - 2.0-3.9: WEAK signals 🎨 ADVANCED VISUAL SYSTEM Multi-Dimensional Quantum Aura Bands Five-layer resonance field showing market energy: - Colors: Theme-matched gradients (Quantum purple, Holographic cyan, etc.) - Expansion: Dynamic based on score intensity and volatility - Function: Multi-timeframe support/resistance zones Morphism Flow Portals Category theory visualization showing market topology: - Green/Cyan Portals: Bullish mathematical flow - Red/Orange Portals: Bearish mathematical flow - Size/Intensity: Proportional to signal strength - Recursion Depth (1-8): Nested patterns for flow evolution Fractal Grid System Dynamic support/resistance with projected L-Scores: - Multiple Timeframes: 10, 20, 30, 40, 50-period highs/lows - Smart Spacing: Prevents level overlap using ATR-based minimum distance - Projections: Estimated signal scores when price reaches levels - Usage: Precise entry/exit timing with mathematical confirmation Wick Pressure Analysis Rejection level prediction using candle mathematics: - Upper Wicks: Selling pressure zones (purple/red lines) - Lower Wicks: Buying pressure zones (purple/green lines) - Glow Intensity (1-8): Visual emphasis and line reach - Application: Confluence with fractal grid creates high-probability zones Regime Intensity Heatmap Background coloring showing market energy: - Black/Dark: Low activity, range-bound markets - Purple Glow: Building momentum and trend development - Bright Purple: High activity, strong directional moves - Calculation: Combines trend, momentum, volatility, and score intensity Six Professional Themes - Quantum: Purple/violet for general trading and mathematical focus - Holographic: Cyan/magenta optimized for cryptocurrency markets - Crystalline: Blue/turquoise for conservative, stability-focused trading - Plasma: Gold/magenta for high-energy volatility trading - Cosmic Neon: Bright neon colors for maximum visibility and aggressive trading 📊 INSTITUTIONAL-GRADE DASHBOARD Unified AI Score Section - Total Score (-10 to +10): Primary decision metric - >5: Strong bullish signals - <-5: Strong bearish signals - Quality ratings: EXCEPTIONAL > STRONG > MODERATE > WEAK - Component Analysis: Individual L-Function, Galois, Operadic, and Correspondence contributions Order Flow Analysis Revolutionary OFPI integration: - OFPI Value (-100% to +100%): Real buying vs selling pressure - Visual Gauge: Horizontal bar chart showing flow intensity - Momentum Status: SHIFTING, ACCELERATING, STRONG, MODERATE, or WEAK - Trading Application: Flow shifts often precede major moves Signal Performance Tracking - Win Rate Monitoring: Real-time success percentage with emoji indicators - Signal Count: Total signals generated for frequency analysis - Current Position: LONG, SHORT, or NONE with P&L tracking - Volatility Regime: HIGH, MEDIUM, or LOW classification Market Structure Analysis - Möbius Field Strength: Mathematical field oscillation intensity - CHAOTIC: High complexity, use wider stops - STRONG: Active field, normal position sizing - MODERATE: Balanced conditions - WEAK: Low activity, consider smaller positions - HTF Trend: Higher timeframe bias (BULL/BEAR/NEUTRAL) - Strategy Agreement: Multi-algorithm consensus level Position Management When in trades, displays: - Entry Price: Original signal price - Current P&L: Real-time percentage with risk level assessment - Duration: Bars in trade for timing analysis - Risk Level: HIGH/MEDIUM/LOW based on current exposure 🚀 SIGNAL GENERATION LOGIC Balanced Long/Short Architecture The indicator generates signals through multiple convergent pathways: Long Entry Conditions: - Score threshold breach with algorithmic agreement - Strong bullish order flow (OFPI > 0.15) with positive composite signal - Bullish pattern recognition with mathematical confirmation - HTF trend alignment with momentum shifting - Extreme bullish OFPI (>0.3) with any positive score Short Entry Conditions: - Score threshold breach with bearish agreement - Strong bearish order flow (OFPI < -0.15) with negative composite signal - Bearish pattern recognition with mathematical confirmation - HTF trend alignment with momentum shifting - Extreme bearish OFPI (<-0.3) with any negative score Exit Logic: - Score deterioration below continuation threshold - Signal quality degradation - Opposing order flow acceleration - 10-bar minimum between signals prevents overtrading ⚙️ OPTIMIZATION GUIDELINES Asset-Specific Settings Cryptocurrency Trading: - Modular Level: 15-25 (capture volatility) - L-Function Precision: 0.8-1.3 (reactive to price swings) - OFPI Length: 10-20 (fast correlation shifts) - Cascade Levels: 5-7, Theme: Holographic Stock Index Trading: - Modular Level: 25-35 (balanced trending) - L-Function Precision: 1.5-1.8 (stable patterns) - OFPI Length: 14-20 (standard correlation) - Cascade Levels: 4-5, Theme: Quantum Forex Trading: - Modular Level: 35-45 (smooth trends) - L-Function Precision: 1.6-2.1 (high smoothing) - OFPI Length: 18-25 (disable volume amplification) - Cascade Levels: 3-4, Theme: Crystalline Timeframe Optimization Scalping (1-5 minute charts): - Reduce all lookback parameters by 30-40% - Increase L-Function precision for noise reduction - Enable all visual elements for maximum information - Use Small dashboard to save screen space Day Trading (15 minute - 1 hour): - Use default parameters as starting point - Adjust based on market volatility - Normal dashboard provides optimal information density - Focus on OFPI momentum shifts for entries Swing Trading (4 hour - Daily): - Increase lookback parameters by 30-50% - Higher L-Function precision for stability - Large dashboard for comprehensive analysis - Emphasize HTF trend alignment 🏆 ADVANCED TRADING STRATEGIES The Mathematical Confluence Method 1. Wait for Fractal Grid level approach 2. Confirm with projected L-Score > threshold 3. Verify OFPI alignment with direction 4. Enter on portal signal with quality ≥ STRONG 5. Exit on score deterioration or opposing flow The Regime Trading System 1. Monitor Aether Flow background intensity 2. Trade aggressively during bright purple periods 3. Reduce position size during dark periods 4. Use Möbius Field strength for stop placement 5. Align with HTF trend for maximum probability The OFPI Momentum Strategy 1. Watch for momentum shifting detection 2. Confirm with accelerating flow in direction 3. Enter on immediate portal signal 4. Scale out at Fibonacci levels 5. Exit on flow deceleration or reversal ⚠️ RISK MANAGEMENT INTEGRATION Mathematical Position Sizing - Use Galois Rank for volatility-adjusted sizing - Möbius Field strength determines stop width - Fractal Dimension guides maximum exposure - OFPI momentum affects entry timing Signal Quality Filtering - Trade only STRONG or EXCEPTIONAL quality signals - Increase position size with higher agreement levels - Reduce risk during CHAOTIC Möbius field periods - Respect HTF trend alignment for directional bias 🔬 DEVELOPMENT JOURNEY Creating the LOMV was an extraordinary mathematical undertaking that pushed the boundaries of what's possible in technical analysis. This indicator almost didn't happen. The theoretical complexity nearly proved insurmountable. The Mathematical Challenge Implementing the Langlands Program required deep research into: - Number theory and the Möbius function - Riemann zeta function convergence properties - L-function analytical continuation - Galois representations in finite fields The mathematical literature spans decades of pure mathematics research, requiring translation from abstract theory to practical market application. The Computational Complexity Operadic composition theory demanded: - Category theory implementation in Pine Script - Multi-dimensional array management for strategy composition - Real-time democratic voting algorithms - Performance optimization for complex calculations The Integration Breakthrough Bringing together three disparate mathematical frameworks required: - Novel approaches to signal weighting and combination - Revolutionary Order Flow Polarity Index development - Advanced T3 smoothing implementation - Balanced signal generation preventing directional bias Months of intensive research culminated in breakthrough moments when the mathematics finally aligned with market reality. The result is an indicator that reveals market structure invisible to conventional analysis while maintaining practical trading utility. 🎯 PRACTICAL IMPLEMENTATION Getting Started 1. Apply indicator with default settings 2. Select appropriate theme for your markets 3. Observe dashboard metrics during different market conditions 4. Practice signal identification without trading 5. Gradually adjust parameters based on observations Signal Confirmation Process - Never trade on score alone - verify quality rating - Confirm OFPI alignment with intended direction - Check fractal grid level proximity for timing - Ensure Möbius field strength supports position size - Validate against HTF trend for bias confirmation Performance Monitoring - Track win rate in dashboard for strategy assessment - Monitor component contributions for optimization - Adjust threshold based on desired signal frequency - Document performance across different market regimes 🌟 UNIQUE INNOVATIONS 1. First Integration of Langlands Program mathematics with practical trading 2. Revolutionary OFPI with T3 smoothing and momentum detection 3. Operadic Composition using category theory for signal democracy 4. Dynamic Fractal Grid with projected L-Score calculations 5. Multi-Dimensional Visualization through morphism flow portals 6. Regime-Adaptive Background showing market energy intensity 7. Balanced Signal Generation preventing directional bias 8. Professional Dashboard with institutional-grade metrics 📚 EDUCATIONAL VALUE The LOMV serves as both a practical trading tool and an educational gateway to advanced mathematics. Traders gain exposure to: - Pure mathematics applications in markets - Category theory and operadic composition - Number theory through Möbius function implementation - Harmonic analysis via L-function calculations - Advanced signal processing through T3 smoothing ⚖️ RESPONSIBLE USAGE This indicator represents advanced mathematical research applied to market analysis. While the underlying mathematics are rigorously implemented, markets remain inherently unpredictable. Key Principles: - Use as part of comprehensive trading strategy - Implement proper risk management at all times - Backtest thoroughly before live implementation - Understand that past performance does not guarantee future results - Never risk more than you can afford to lose The mathematics reveal deep market structure, but successful trading requires discipline, patience, and sound risk management beyond any indicator. 🔮 CONCLUSION The Langlands-Operadic Möbius Vortex represents a quantum leap forward in technical analysis, bringing PhD-level pure mathematics to practical trading while maintaining visual elegance and usability. From the harmonic analysis of the Langlands Program to the democratic composition of operadic theory, from the number-theoretic precision of the Möbius function to the revolutionary Order Flow Polarity Index, every component works in mathematical harmony to reveal the hidden order within market chaos. This is more than an indicator - it's a mathematical lens that transforms how you see and understand market structure. Trade with mathematical precision. Trade with the LOMV. *"Mathematics is the language with which God has written the universe." - Galileo Galilei* *In markets, as in nature, profound mathematical beauty underlies apparent chaos. The LOMV reveals this hidden order.* — Dskyz, Trade with insight. Trade with anticipation.
Wskaźnik Pine Script®
od DskyzInvestments
21
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment ## 1. Introduction Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985). The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014). ## 2. Theoretical Foundation ### 2.1 Momentum Theory and Market Anomalies The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015). ### 2.2 Drawdown Analysis and Risk Management Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators. ### 2.3 Adaptive Smoothing and Market Regimes The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets. ## 3. Methodology ### 3.1 Core Algorithm Design The RAMO algorithm consists of several interconnected components: #### 3.1.1 Risk-Adjusted Momentum Calculation The fundamental innovation of RAMO lies in its risk adjustment mechanism: Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor) Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05) This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952). #### 3.1.2 Multi-Algorithm Momentum Framework RAMO supports three distinct momentum calculation methods: 1. Rate of Change: Traditional percentage-based momentum (Pring, 2002) 2. Price Momentum: Absolute price differences 3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997) This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013). ### 3.2 Leading Indicator Components #### 3.2.1 Momentum Acceleration Analysis The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes: Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n) This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969). #### 3.2.2 Linear Regression Prediction RAMO incorporates linear regression-based prediction to project momentum values forward: Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset) This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes. #### 3.2.3 Volume-Based Exhaustion Detection The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns: Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20) This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989). ### 3.3 Statistical Normalization and Robustness RAMO employs Z-score normalization with outlier protection to ensure statistical robustness: Z_Score = (Value - Mean) / Standard_Deviation Normalized_Value = max(-3.5, min(3.5, Z_Score)) This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions. ### 3.4 Adaptive Threshold Calculation Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992): Upper_Threshold = Mean + (Multiplier × Standard_Deviation) Lower_Threshold = Mean - (Multiplier × Standard_Deviation) This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992). ## 4. Implementation Details ### 4.1 Adaptive Smoothing Algorithm The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility: Volatility_Percentile = Percentrank(Volatility, 100) Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100) This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995). ### 4.2 Risk Environment Classification RAMO classifies market conditions into three risk environments: - Low Risk: Current_DD < 30% × Max_DD - Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD - High Risk: Current_DD ≥ 70% × Max_DD This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014). ## 5. Signal Generation and Interpretation ### 5.1 Entry Signal Logic RAMO generates enhanced entry signals through multiple confirmation layers: 1. Primary Signal: Crossover between indicator and signal line 2. Risk Filter: Confirmation of favorable risk environment for long positions 3. Leading Component: Early warning signals via acceleration analysis 4. Exhaustion Filter: Volume-based reversal detection This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992). ### 5.2 Divergence Analysis RAMO incorporates both traditional and leading divergence detection: - Traditional Divergence: Price and indicator divergence over 3-5 periods - Slope Divergence: Momentum slope versus price direction - Acceleration Divergence: Changes in momentum acceleration This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999). ## 6. Empirical Advantages and Applications ### 6.1 Risk-Adjusted Performance The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980). ### 6.2 Regime Awareness RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments. ### 6.3 Institutional Applications The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013). ## 7. Limitations and Future Research ### 7.1 Parameter Sensitivity Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions. ### 7.2 Market Microstructure Considerations RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance. ### 7.3 Transaction Cost Integration Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004). ## References Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons. Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press. Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin. Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985. Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120. Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764. Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press. Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58. Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247. Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84. Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall. Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291. Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill. Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082. Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594. Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765. Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91. Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance. Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library. Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill. Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press. Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790. Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House. Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314. Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239. Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Wskaźnik Pine Script®
od EdgeTools
2
TrendMaster Pro 2.3 with Alerts Hello friends, A member of the community approached me and asked me how to write an indicator that would achieve a particular set of goals involving comprehensive trend analysis, risk management, and session-based trading controls. Here is one example method of how to create such a system: Core Strategy Components Multi-Moving Average System - Uses configurable MA types (EMA, SMA, SMMA) with short-term (9) and long-term (21) periods for primary signal generation through crossovers Higher Timeframe Trend Filter - Optional trend confirmation using a separate MA (default 50-period) to ensure trades align with broader market direction Band Power Indicator - Dynamic high/low bands calculated using different MA types to identify price channels and volatility zones Advanced Signal Filtering Bollinger Bands Volatility Filter - Prevents trading during low-volatility ranging markets by requiring sufficient band width RSI Momentum Filter - Uses customizable thresholds (55 for longs, 45 for shorts) to confirm momentum direction MACD Trend Confirmation - Ensures MACD line position relative to signal line aligns with trade direction Stochastic Oscillator - Adds momentum confirmation with overbought/oversold levels ADX Strength Filter - Only allows trades when trend strength exceeds 25 threshold Session-Based Trading Management Four Trading Sessions - Asia (18:00-00:00), London (00:00-08:00), NY AM (08:00-13:00), NY PM (13:00-18:00) Individual Session Limits - Separate maximum trade counts for each session (default 5 per session) Automatic Session Closure - All positions close at specified market close time Risk Management Features Multiple Stop Loss Options - Percentage-based, MA cross, or band-based SL methods Risk/Reward Ratio - Configurable TP levels based on SL distance (default 1:2) Auto-Risk Calculation - Dynamic position sizing based on dollar risk limits ($150-$250 range) Daily Limits - Stop trading after reaching specified TP or SL counts per day Support & Resistance System Multiple Pivot Types - Traditional, Fibonacci, Woodie, Classic, DM, and Camarilla calculations Flexible Timeframes - Auto-adjusting or manual timeframe selection for S/R levels Historical Levels - Configurable number of past S/R levels to display Visual Customization - Individual color and display settings for each S/R level Additional Features Alert System - Customizable buy/sell alert messages with once-per-bar frequency Visual Trade Management - Color-coded entry, SL, and TP levels with fill areas Session Highlighting - Optional background colors for different trading sessions Comprehensive Filtering - All signals must pass through multiple confirmation layers before execution This approach demonstrates how to build a professional-grade trading system that combines multiple technical analysis methods with robust risk management and session-based controls, suitable for algorithmic trading across different market sessions. Good luck and stay safe!
Strategia Pine Script®
od everget
16
Support and Resistance Logistic Regression | Flux Charts💎 GENERAL OVERVIEW Introducing our new Logistic Regression Support / Resistance indicator! This tool leverages advanced statistical modeling "Logistic Regressions" to identify and project key price levels where the market is likely to find support or resistance. For more information about the process, please check the "HOW DOES IT WORK ?" section. Logistic Regression Support / Resistance Features : Intelligent S/R Identification : The indicator uses a logistic regression model to intelligently identify and plot significant support and resistance levels. Predictive Probability : Each identified level comes with a calculated probability, indicating how likely it is to act as a true support or resistance based on historical data. Retest & Break Labels : The indicator clearly marks on your chart when a detected support or resistance level is retested (price touches and respects the level) or broken (price decisively crosses through the level). Alerts : Real-time alerts for support retests, resistance retests, support breaks, and resistance breaks. Customizable : You can change support & resistance line style, width and colors. 🚩 UNIQUENESS What makes this indicator truly unique is its application of logistic regression to the concept of support and resistance. Instead of merely identifying historical highs and lows, our indicator uses a statistical model to predict the future efficacy of these levels. It analyzes underlying market conditions (like RSI and body size at pivot formation) to assign a probability to each potential S/R zone. This predictive insight, combined with dynamic, real-time labeling of retests and breaks, provides a more robust and adaptive understanding of market structure than traditional, purely historical methods. 📌HOW DOES IT WORK ? The Logistic Regression Support / Resistance indicator operates in several key steps: First, it identifies significant pivot highs and lows on the chart based on a user-defined "Pivot Length." These pivots are potential areas of support or resistance. For each detected pivot, the indicator extracts relevant market data at that specific point, including the RSI (Relative Strength Index) and the Body Size (the absolute difference between the open and close price of the candle). These serve as input features for the model. The core of the indicator lies in its logistic regression model. This model is continuously trained on past pivot data and their subsequent behavior (i.e., whether they were "respected" as support/resistance multiple times). It learns the relationship between the extracted features (RSI, Body Size) and the likelihood of a pivot becoming a significant S/R level. When a new pivot is identified, the model uses its learned insights to calculate a prediction value—a probability (from 0 to 1) that this specific pivot will act as a strong support or resistance. If the calculated probability exceeds a user-defined "Probability Threshold," the pivot is designated a "Regression Pivot" and drawn on the chart as a support or resistance line. The indicator then actively tracks how price interacts with these levels, displaying "R" labels for retests when the price bounces off the level and "B" labels for breaks when the price closes beyond it. ⚙️ SETTINGS 1. General Configuration Pivot Length: This setting defines the number of bars used to determine a significant high or low for pivot detection. Target Respects: This input specifies how many times a level must be "respected" by price action for it to be considered a strong support or resistance level by the underlying model. Probability Threshold: This is the minimum probability output from the logistic regression model for a detected pivot to be considered a valid support or resistance level and be plotted on the chart. 2. Style Show Prediction Labels: Enable or disable labels that display the calculated probability of a newly identified regression S/R level. Show Retests: Toggle the visibility of "R" labels on the chart, which mark instances where price has retested a support or resistance level. Show Breaks: Toggle the visibility of "B" labels on the chart, which mark instances where price has broken through a support or resistance level.
Wskaźnik Pine Script®
od fluxchart
15
Mandelbrot-Fibonacci Cascade Vortex (MFCV)Mandelbrot-Fibonacci Cascade Vortex (MFCV) - Where Chaos Theory Meets Sacred Geometry A Revolutionary Synthesis of Fractal Mathematics and Golden Ratio Dynamics What began as an exploration into Benoit Mandelbrot's fractal market hypothesis and the mysterious appearance of Fibonacci sequences in nature has culminated in a groundbreaking indicator that reveals the hidden mathematical structure underlying market movements. This indicator represents months of research into chaos theory, fractal geometry, and the golden ratio's manifestation in financial markets. The Theoretical Foundation Mandelbrot's Fractal Market Hypothesis Traditional efficient market theory assumes normal distributions and random walks. Mandelbrot proved markets are fractal - self-similar patterns repeating across all timeframes with power-law distributions. The MFCV implements this through: Hurst Exponent Calculation: H = log(R/S) / log(n/2) Where: R = Range of cumulative deviations S = Standard deviation n = Period length This measures market memory: H > 0.5: Trending (persistent) behavior H = 0.5: Random walk H < 0.5: Mean-reverting (anti-persistent) behavior Fractal Dimension: D = 2 - H This quantifies market complexity, where higher dimensions indicate more chaotic behavior. Fibonacci Vortex Theory Markets don't move linearly - they spiral. The MFCV reveals these spirals using Fibonacci sequences: Vortex Calculation: Vortex(n) = Price + sin(bar_index × φ / Fn) × ATR(Fn) × Volume_Factor Where: φ = 0.618 (golden ratio) Fn = Fibonacci number (8, 13, 21, 34, 55) Volume_Factor = 1 + (Volume/SMA(Volume,50) - 1) × 0.5 This creates oscillating spirals that contract and expand with market energy. The Volatility Cascade System Markets exhibit volatility clustering - Mandelbrot's "Noah Effect." The MFCV captures this through cascading volatility bands: Cascade Level Calculation: Level(i) = ATR(20) × φ^i Each level represents a different fractal scale, creating a multi-dimensional view of market structure. The golden ratio spacing ensures harmonic resonance between levels. Implementation Architecture Core Components: Fractal Analysis Engine Calculates Hurst exponent over user-defined periods Derives fractal dimension for complexity measurement Identifies market regime (trending/ranging/chaotic) Fibonacci Vortex Generator Creates 5 independent spiral oscillators Each spiral follows a Fibonacci period Volume amplification creates dynamic response Cascade Band System Up to 8 volatility levels Golden ratio expansion between levels Dynamic coloring based on fractal state Confluence Detection Identifies convergence of vortex and cascade levels Highlights high-probability reversal zones Real-time confluence strength calculation Signal Generation Logic The MFCV generates two primary signal types: Fractal Signals: Generated when: Hurst > 0.65 (strong trend) AND volatility expanding Hurst < 0.35 (mean reversion) AND RSI < 35 Trend strength > 0.4 AND vortex alignment Cascade Signals: Triggered by: RSI > 60 AND price > SMA(50) AND bearish vortex RSI < 40 AND price < SMA(50) AND bullish vortex Volatility expansion AND trend strength > 0.3 Both signals implement a 15-bar cooldown to prevent overtrading. Advanced Input System Mandelbrot Parameters: Cascade Levels (3-8): Controls number of volatility bands Crypto: 5-7 (high volatility) Indices: 4-5 (moderate volatility) Forex: 3-4 (low volatility) Hurst Period (20-200): Lookback for fractal calculation Scalping: 20-50 Day Trading: 50-100 Swing Trading: 100-150 Position Trading: 150-200 Cascade Ratio (1.0-3.0): Band width multiplier 1.618: Golden ratio (default) Higher values for trending markets Lower values for ranging markets Fractal Memory (21-233): Fibonacci retracement lookback Uses Fibonacci numbers for harmonic alignment Fibonacci Vortex Settings: Spiral Periods: Comma-separated Fibonacci sequence Fast: "5,8,13,21,34" (scalping) Standard: "8,13,21,34,55" (balanced) Extended: "13,21,34,55,89" (swing) Rotation Speed (0.1-2.0): Controls spiral oscillation frequency 0.618: Golden ratio (balanced) Higher = more signals, more noise Lower = smoother, fewer signals Volume Amplification: Enables dynamic spiral expansion Essential for stocks and crypto Disable for forex (no central volume) Visual System Architecture Cascade Bands: Multi-level volatility envelopes Gradient coloring from primary to secondary theme Transparency increases with distance from price Fill between bands shows fractal structure Vortex Spirals: 5 Fibonacci-period oscillators Blue above price (bullish pressure) Red below price (bearish pressure) Multiple display styles: Lines, Circles, Dots, Cross Dynamic Fibonacci Levels: Auto-updating retracement levels Smart update logic prevents disruption near levels Distance-based transparency (closer = more visible) Updates every 50 bars or on volatility spikes Confluence Zones: Highlighted boxes where indicators converge Stronger confluence = stronger support/resistance Key areas for reversal trades Professional Dashboard System Main Fractal Dashboard: Displays real-time: Hurst Exponent with market state Fractal Dimension with complexity level Volatility Cascade status Vortex rotation impact Market regime classification Signal strength percentage Active indicator levels Vortex Metrics Panel: Shows: Individual spiral deviations Convergence/divergence metrics Real-time vortex positioning Fibonacci period performance Fractal Metrics Display: Tracks: Dimension D value Market complexity rating Self-similarity strength Trend quality assessment Theory Guide Panel: Educational reference showing: Mandelbrot principles Fibonacci vortex concepts Dynamic trading suggestions Trading Applications Trend Following: High Hurst (>0.65) indicates strong trends Follow cascade band direction Use vortex spirals for entry timing Exit when Hurst drops below 0.5 Mean Reversion: Low Hurst (<0.35) signals reversal potential Trade toward vortex spiral convergence Use Fibonacci levels as targets Tighten stops in chaotic regimes Breakout Trading: Monitor cascade band compression Watch for vortex spiral alignment Volatility expansion confirms breakouts Use confluence zones for targets Risk Management: Position size based on fractal dimension Wider stops in high complexity markets Tighter stops when Hurst is extreme Scale out at Fibonacci levels Market-Specific Optimization Cryptocurrency: Cascade Levels: 5-7 Hurst Period: 50-100 Rotation Speed: 0.786-1.2 Enable volume amplification Stock Indices: Cascade Levels: 4-5 Hurst Period: 80-120 Rotation Speed: 0.5-0.786 Moderate cascade ratio Forex: Cascade Levels: 3-4 Hurst Period: 100-150 Rotation Speed: 0.382-0.618 Disable volume amplification Commodities: Cascade Levels: 4-6 Hurst Period: 60-100 Rotation Speed: 0.5-1.0 Seasonal adjustment consideration Innovation and Originality The MFCV represents several breakthrough innovations: First Integration of Mandelbrot Fractals with Fibonacci Vortex Theory Unique synthesis of chaos theory and sacred geometry Novel application of Hurst exponent to spiral dynamics Dynamic Volatility Cascade System Golden ratio-based band expansion Multi-timeframe fractal analysis Self-adjusting to market conditions Volume-Amplified Vortex Spirals Revolutionary spiral calculation method Dynamic response to market participation Multiple Fibonacci period integration Intelligent Signal Generation Cooldown system prevents overtrading Multi-factor confirmation required Regime-aware signal filtering Professional Analytics Dashboard Institutional-grade metrics display Real-time fractal analysis Educational integration Development Journey Creating the MFCV involved overcoming numerous challenges: Mathematical Complexity: Implementing Hurst exponent calculations efficiently Visual Clarity: Displaying multiple indicators without cluttering Performance Optimization: Managing array operations and calculations Signal Quality: Balancing sensitivity with reliability User Experience: Making complex theory accessible The result is an indicator that brings PhD-level mathematics to practical trading while maintaining visual elegance and usability. Best Practices and Guidelines Start Simple: Use default settings initially Match Timeframe: Adjust parameters to your trading style Confirm Signals: Never trade MFCV signals in isolation Respect Regimes: Adapt strategy to market state Manage Risk: Use fractal dimension for position sizing Color Themes Six professional themes included: Fractal: Balanced blue/purple palette Golden: Warm Fibonacci-inspired colors Plasma: Vibrant modern aesthetics Cosmic: Dark mode optimized Matrix: Classic green terminal Fire: Heat map visualization Disclaimer This indicator is for educational and research purposes only. It does not constitute financial advice. While the MFCV reveals deep market structure through advanced mathematics, markets remain inherently unpredictable. Past performance does not guarantee future results. The integration of Mandelbrot's fractal theory with Fibonacci vortex dynamics provides unique market insights, but should be used as part of a comprehensive trading strategy. Always use proper risk management and never risk more than you can afford to lose. Acknowledgments Special thanks to Benoit Mandelbrot for revolutionizing our understanding of markets through fractal geometry, and to the ancient mathematicians who discovered the golden ratio's universal significance. "The geometry of nature is fractal... Markets are fractal too." - Benoit Mandelbrot Revealing the Hidden Order in Market Chaos Trade with Mathematical Precision. Trade with MFCV. — Created with passion for the TradingView community Trade with insight. Trade with anticipation. — Dskyz , for DAFE Trading Systems
Wskaźnik Pine Script®
od DskyzInvestments
22
EMD Trend [InvestorUnknown]EMD Trend is a dynamic trend-following indicator that utilizes Exponential Moving Deviation (EMD) to build adaptive channels around a selected moving average. Designed for traders who value responsive trend signals with built-in volatility sensitivity, this tool highlights directional bias, market regime shifts, and potential breakout opportunities. How It Works Instead of using standard deviation, EMD Trend employs the exponential moving average of the absolute deviation from a moving average—producing smoother, faster-reacting upper and lower bounds: Bullish (Risk-ON Long): Price crosses above the upper EMD band Bearish (Risk-ON Short): Price crosses below the lower EMD band Neutral: Price stays within the channel, indicating potential mean reversion or low momentum Trend direction is defined by price interaction with these bands, and visual cues (color-coded bars and fills) help quickly identify market conditions. Features 7 Moving Average Types: SMA, EMA, HMA, DEMA, TEMA, RMA, FRAMA Custom Price Source: Choose close, hl2, ohlc4, or others EMD Multiplier: Controls the width of the deviation envelope Bar Coloring: Candles change color based on current trend Intra-bar Signal Option: Enables faster updates (with optional repainting) Speculative Zones: Fills highlight aggressive momentum moves beyond EMD bounds Backtest Mode Switch to Backtest Mode for performance evaluation over historical data: Equity Curve Plot: Compare EMD Trend strategy vs. Buy & Hold Trade Metrics Table: View number of trades, win/loss stats, profits Performance Metrics Table: Includes CAGR, Sharpe, max drawdown, and more Custom Start Date: Select from which date the backtest should begin Trade Sizing: Configure capital and trade percentage per entry Signal Filters: Choose from Long Only, Short Only, or Both Alerts Built-in alerts let you automate entries, exits, and trend transitions: LONG (EMD Trend) - Trend flips to Long SHORT (EMD Trend) - Trend flips to Short RISK-ON LONG - Price crosses above upper EMD band RISK-OFF LONG - Price crosses back below upper EMD band RISK-ON SHORT - Price crosses below lower EMD band RISK-OFF SHORT - Price crosses back above lower EMD band Use Cases Trend Confirmation with volatility-sensitive boundaries Momentum Entry Filtering via breakout zones Mean Reversion Avoidance in sideways markets Backtesting & Strategy Building with real-time metrics Disclaimer This indicator is intended for informational and educational purposes only. It does not constitute investment advice. Historical performance does not guarantee future results. Always backtest and use in simulation before live trading.
Wskaźnik Pine Script®
od InvestorUnknown
5
Bear Market Probability Model# Bear Market Probability Model: A Multi-Factor Risk Assessment Framework The Bear Market Probability Model represents a comprehensive quantitative framework for assessing systemic market risk through the integration of 13 distinct risk factors across four analytical categories: macroeconomic indicators, technical analysis factors, market sentiment measures, and market breadth metrics. This indicator synthesizes established financial research methodologies to provide real-time probabilistic assessments of impending bear market conditions, offering institutional-grade risk management capabilities to retail and professional traders alike. ## Theoretical Foundation ### Historical Context of Bear Market Prediction Bear market prediction has been a central focus of financial research since the seminal work of Dow (1901) and the subsequent development of technical analysis theory. The challenge of predicting market downturns gained renewed academic attention following the market crashes of 1929, 1987, 2000, and 2008, leading to the development of sophisticated multi-factor models. Fama and French (1989) demonstrated that certain financial variables possess predictive power for stock returns, particularly during market stress periods. Their three-factor model laid the groundwork for multi-dimensional risk assessment, which this indicator extends through the incorporation of real-time market microstructure data. ### Methodological Framework The model employs a weighted composite scoring methodology based on the theoretical framework established by Campbell and Shiller (1998) for market valuation assessment, extended through the incorporation of high-frequency sentiment and technical indicators as proposed by Baker and Wurgler (2006) in their seminal work on investor sentiment. The mathematical foundation follows the general form: Bear Market Probability = Σ(Wi × Ci) / ΣWi × 100 Where: - Wi = Category weight (i = 1,2,3,4) - Ci = Normalized category score - Categories: Macroeconomic, Technical, Sentiment, Breadth ## Component Analysis ### 1. Macroeconomic Risk Factors #### Yield Curve Analysis The inclusion of yield curve inversion as a primary predictor follows extensive research by Estrella and Mishkin (1998), who demonstrated that the term spread between 3-month and 10-year Treasury securities has historically preceded all major recessions since 1969. The model incorporates both the 2Y-10Y and 3M-10Y spreads to capture different aspects of monetary policy expectations. Implementation: - 2Y-10Y Spread: Captures market expectations of monetary policy trajectory - 3M-10Y Spread: Traditional recession predictor with 12-18 month lead time Scientific Basis: Harvey (1988) and subsequent research by Ang, Piazzesi, and Wei (2006) established the theoretical foundation linking yield curve inversions to economic contractions through the expectations hypothesis of the term structure. #### Credit Risk Premium Assessment High-yield credit spreads serve as a real-time gauge of systemic risk, following the methodology established by Gilchrist and Zakrajšek (2012) in their excess bond premium research. The model incorporates the ICE BofA High Yield Master II Option-Adjusted Spread as a proxy for credit market stress. Threshold Calibration: - Normal conditions: < 350 basis points - Elevated risk: 350-500 basis points - Severe stress: > 500 basis points #### Currency and Commodity Stress Indicators The US Dollar Index (DXY) momentum serves as a risk-off indicator, while the Gold-to-Oil ratio captures commodity market stress dynamics. This approach follows the methodology of Akram (2009) and Beckmann, Berger, and Czudaj (2015) in analyzing commodity-currency relationships during market stress. ### 2. Technical Analysis Factors #### Multi-Timeframe Moving Average Analysis The technical component incorporates the well-established moving average convergence methodology, drawing from the work of Brock, Lakonishok, and LeBaron (1992), who provided empirical evidence for the profitability of technical trading rules. Implementation: - Price relative to 50-day and 200-day simple moving averages - Moving average convergence/divergence analysis - Multi-timeframe MACD assessment (daily and weekly) #### Momentum and Volatility Analysis The model integrates Relative Strength Index (RSI) analysis following Wilder's (1978) original methodology, combined with maximum drawdown analysis based on the work of Magdon-Ismail and Atiya (2004) on optimal drawdown measurement. ### 3. Market Sentiment Factors #### Volatility Index Analysis The VIX component follows the established research of Whaley (2009) and subsequent work by Bekaert and Hoerova (2014) on VIX as a predictor of market stress. The model incorporates both absolute VIX levels and relative VIX spikes compared to the 20-day moving average. Calibration: - Low volatility: VIX < 20 - Elevated concern: VIX 20-25 - High fear: VIX > 25 - Panic conditions: VIX > 30 #### Put-Call Ratio Analysis Options flow analysis through put-call ratios provides insight into sophisticated investor positioning, following the methodology established by Pan and Poteshman (2006) in their analysis of informed trading in options markets. ### 4. Market Breadth Factors #### Advance-Decline Analysis Market breadth assessment follows the classic work of Fosback (1976) and subsequent research by Brown and Cliff (2004) on market breadth as a predictor of future returns. Components: - Daily advance-decline ratio - Advance-decline line momentum - McClellan Oscillator (Ema19 - Ema39 of A-D difference) #### New Highs-New Lows Analysis The new highs-new lows ratio serves as a market leadership indicator, based on the research of Zweig (1986) and validated in academic literature by Zarowin (1990). ## Dynamic Threshold Methodology The model incorporates adaptive thresholds based on rolling volatility and trend analysis, following the methodology established by Pagan and Sossounov (2003) for business cycle dating. This approach allows the model to adjust sensitivity based on prevailing market conditions. Dynamic Threshold Calculation: - Warning Level: Base threshold ± (Volatility × 1.0) - Danger Level: Base threshold ± (Volatility × 1.5) - Bounds: ±10-20 points from base threshold ## Professional Implementation ### Institutional Usage Patterns Professional risk managers typically employ multi-factor bear market models in several contexts: #### 1. Portfolio Risk Management - Tactical Asset Allocation: Reducing equity exposure when probability exceeds 60-70% - Hedging Strategies: Implementing protective puts or VIX calls when warning thresholds are breached - Sector Rotation: Shifting from growth to defensive sectors during elevated risk periods #### 2. Risk Budgeting - Value-at-Risk Adjustment: Incorporating bear market probability into VaR calculations - Stress Testing: Using probability levels to calibrate stress test scenarios - Capital Requirements: Adjusting regulatory capital based on systemic risk assessment #### 3. Client Communication - Risk Reporting: Quantifying market risk for client presentations - Investment Committee Decisions: Providing objective risk metrics for strategic decisions - Performance Attribution: Explaining defensive positioning during market stress ### Implementation Framework Professional traders typically implement such models through: #### Signal Hierarchy: 1. Probability < 30%: Normal risk positioning 2. Probability 30-50%: Increased hedging, reduced leverage 3. Probability 50-70%: Defensive positioning, cash building 4. Probability > 70%: Maximum defensive posture, short exposure consideration #### Risk Management Integration: - Position Sizing: Inverse relationship between probability and position size - Stop-Loss Adjustment: Tighter stops during elevated risk periods - Correlation Monitoring: Increased attention to cross-asset correlations ## Strengths and Advantages ### 1. Comprehensive Coverage The model's primary strength lies in its multi-dimensional approach, avoiding the single-factor bias that has historically plagued market timing models. By incorporating macroeconomic, technical, sentiment, and breadth factors, the model provides robust risk assessment across different market regimes. ### 2. Dynamic Adaptability The adaptive threshold mechanism allows the model to adjust sensitivity based on prevailing volatility conditions, reducing false signals during low-volatility periods and maintaining sensitivity during high-volatility regimes. ### 3. Real-Time Processing Unlike traditional academic models that rely on monthly or quarterly data, this indicator processes daily market data, providing timely risk assessment for active portfolio management. ### 4. Transparency and Interpretability The component-based structure allows users to understand which factors are driving risk assessment, enabling informed decision-making about model signals. ### 5. Historical Validation Each component has been validated in academic literature, providing theoretical foundation for the model's predictive power. ## Limitations and Weaknesses ### 1. Data Dependencies The model's effectiveness depends heavily on the availability and quality of real-time economic data. Federal Reserve Economic Data (FRED) updates may have lags that could impact model responsiveness during rapidly evolving market conditions. ### 2. Regime Change Sensitivity Like most quantitative models, the indicator may struggle during unprecedented market conditions or structural regime changes where historical relationships break down (Taleb, 2007). ### 3. False Signal Risk Multi-factor models inherently face the challenge of balancing sensitivity with specificity. The model may generate false positive signals during normal market volatility periods. ### 4. Currency and Geographic Bias The model focuses primarily on US market indicators, potentially limiting its effectiveness for global portfolio management or non-USD denominated assets. ### 5. Correlation Breakdown During extreme market stress, correlations between risk factors may increase dramatically, reducing the model's diversification benefits (Forbes and Rigobon, 2002). ## References Akram, Q. F. (2009). Commodity prices, interest rates and the dollar. Energy Economics, 31(6), 838-851. Ang, A., Piazzesi, M., & Wei, M. (2006). What does the yield curve tell us about GDP growth? Journal of Econometrics, 131(1-2), 359-403. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680. Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636. Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292. Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16-24. Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181-192. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764. Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27. Campbell, J. Y., & Shiller, R. J. (1998). Valuation ratios and the long-run stock market outlook. The Journal of Portfolio Management, 24(2), 11-26. Dow, C. H. (1901). Scientific stock speculation. The Magazine of Wall Street. Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61. Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49. Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261. Fosback, N. G. (1976). Stock market logic: A sophisticated approach to profits on Wall Street. The Institute for Econometric Research. Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692-1720. Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305-333. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. Magdon-Ismail, M., & Atiya, A. F. (2004). Maximum drawdown. Risk, 17(10), 99-102. Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220. Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46. Pan, J., & Poteshman, A. M. (2006). The information in option volume for future stock prices. The Review of Financial Studies, 19(3), 871-908. Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House. Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105. Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research. Zarowin, P. (1990). Size, seasonality, and stock market overreaction. Journal of Financial and Quantitative Analysis, 25(1), 113-125. Zweig, M. E. (1986). Winning on Wall Street. Warner Books.
Wskaźnik Pine Script®
od EdgeTools
11
Range Filter [DW]This is an experimental study designed to filter out minor price action for a clearer view of trends. Inspired by the QQE's volatility filter, this filter applies the process directly to price rather than to a smoothed RSI. First, a smooth average price range is calculated for the basis of the filter and multiplied by a specified amount. Next, the filter is calculated by gating price movements that do not exceed the specified range. Lastly the target ranges are plotted to display the prices that will trigger filter movement. Custom bar colors are included. The color scheme is based on the filtered price trend.
Wskaźnik Pine Script®
od DonovanWall
102
Delta Volume Columns Pro [LucF]█  OVERVIEW This indicator displays volume​​ delta information calculated with intrabar inspection on historical bars, and feed updates when running in realtime. It is designed to run in a pane and can display either stacked buy/sell volume​ columns or a signal line which can be calculated and displayed in many different ways. Five different models are offered to reveal different characteristics of the calculated volume​ delta information. Many options are offered to visualize the calculations, giving you much leeway in morphing the indicator's visuals to suit your needs. If you value delta volume​ information, I hope you will find the time required to master Delta Volume​ Columns Pro well worth the investment. I am confident that if you combine a proper understanding of the indicator's information with an intimate knowledge of the volume​ idiosyncrasies on the markets you trade, you can extract useful market intelligence using this tool. █  WARNINGS 1. The indicator only works on markets where volume​ information is available,   Please validate that your symbol's feed carries volume​ information before asking me why the indicator doesn't plot values. 2. When you refresh your chart or re-execute the script on the chart, the indicator will repaint because elapsed realtime bars will then recalculate as historical bars. 3. Because the indicator uses different modes of calculation on historical and realtime bars, it's critical that you understand the differences between them. Details are provided further down. 4. Calculations using intrabar inspection on historical bars can only be done from some chart timeframes. See further down for a list of supported timeframes.   If the chart's timeframe is not supported, no historical volume​ delta will display. █  CONCEPTS Chart bars Three different types of bars are used in charts:  1. Historical bars are bars that have already closed when the script executes on them.  2. The realtime bar is the current, incomplete bar where a script is running on an open market. There is only one active realtime bar on your chart at any given time.    The realtime bar is where alerts trigger.  3. Elapsed realtime bars are bars that were calculated when they were realtime bars but have since closed.    When a script re-executes on a chart because the browser tab is refreshed or some of its inputs are changed, elapsed realtime bars are recalculated as historical bars. Why does this indicator use two modes of calculation? Historical bars on TradingView charts contain OHLCV data only, which is insufficient to calculate volume​ delta on them with any level of precision. To mine more detailed information from those bars we look at intrabars , i.e., bars from a smaller timeframe (we call it the intrabar timeframe ) that are contained in one chart bar. If your chart Is running at 1D on a 24x7 market for example, most 1D chart bars will contain 24 underlying 1H bars in their dilation. On historical bars, this indicator looks at those intrabars to amass volume​ delta information. If the intrabar is up, its volume​ goes in the Buy bin, and inversely for the Sell bin. When price does not move on an intrabar, the polarity of the last known movement is used to determine in which bin its volume​ goes. In realtime, we have access to price and volume​ change for each update of the chart. Because a 1D chart bar can be updated tens of thousands of times during the day, volume​ delta calculations on those updates is much more precise. This precision, however, comes at a price:  — The script must be running on the chart for it to keep calculating in realtime.  — If you refresh your chart you will lose all accumulated realtime calculations on elapsed realtime bars, and the realtime bar.   Elapsed realtime bars will recalculate as historical bars, i.e., using intrabar inspection, and the realtime bar's calculations will reset.   When the script recalculates elapsed realtime bars as historical bars, the values on those bars will change, which means the script repaints in those conditions.  — When the indicator first calculates on a chart containing an incomplete realtime bar, it will count ALL the existing volume​ on the bar as Buy or Sell volume​,   depending on the polarity of the bar at that point. This will skew calculations for that first bar. Scripts have no access to the history of a realtime bar's previous updates,   and intrabar inspection cannot be used on realtime bars, so this is the only to go about this.  — Even if alerts only trigger upon confirmation of their conditions after the realtime bar closes, they are repainting alerts   because they would perhaps not have calculated the same way using intrabar inspection.  — On markets like stocks that often have different EOD​ and intraday feeds and volume​ information,   the volume's scale may not be the same for the realtime bar if your chart is at 1D, for example,   and the indicator is using an intraday timeframe to calculate on historical bars.  — Any chart timeframe can be used in realtime mode, but plots that include moving averages in their calculations may require many elapsed realtime bars before they can calculate.   You might prefer drastically reducing the periods of the moving averages, or using the volume​ columns mode, which displays instant values, instead of the line. Volume Delta Balances This indicator uses a variety of methods to evaluate five volume​ delta balances and derive other values from those balances. The five balances are: 1 — On Bar Balance : This is the only balance using instant values; it is simply the subtraction of the Sell volume​ from the Buy volume​ on the bar. 2 — Average Balance : Calculates a distinct EMA for both the Buy and Sell volumes, and subtracts the Sell EMA from the Buy EMA. 3 — Momentum Balance : Starts by calculating, separately for both Buy and Sell volumes, the difference between the same EMAs used in "Average Balance" and   an SMA of double the period used for the "Average Balance" EMAs. The difference for the Sell side is subtracted from the difference for the Buy side,   and an RSI of that value is calculated and brought over the −50/+50 scale. 4 — Relative Balance : The reference values used in the calculation are the Buy and Sell EMAs used in the "Average Balance".   From those, we calculate two intermediate values using how much the instant Buy and Sell volumes on the bar exceed their respective EMA — but with a twist.   If the bar's Buy volume​ does not exceed the EMA of Buy volume​, a zero value is used. The same goes for the Sell volume​ with the EMA of Sell volume​.   Once we have our two intermediate values for the Buy and Sell volumes exceeding their respective MA, we subtract them. The final "Relative Balance" value is an ALMA of that subtraction.   The rationale behind using zero values when the bar's Buy/Sell volume​ does not exceed its EMA is to only take into account the more significant volume​.   If both instant volume​ values exceed their MA, then the difference between the two is the signal's value.   The signal is called "relative" because the intermediate values are the difference between the instant Buy/Sell volumes and their respective MA.   This balance flatlines when the bar's Buy/Sell volumes do not exceed their EMAs, which makes it useful to spot areas where trader interest dwindles, such as consolidations.   The smaller the period of the final value's ALMA, the more easily you will see the balance flatline. These flat zones should be considered no-trade zones. 5 — Percent Balance : This balance is the ALMA of the ratio of the "On Bar Balance" value, i.e., the volume​ delta balance on the bar (which can be positive or negative),   over the total volume for that bar. From the balances and marker conditions, two more values are calculated: 1 — Marker Bias : It sums the up/down (+1/‒1) occurrences of the markers 1 to 4 over a period you define, so it ranges from −4 to +4, times the period.   Its calculation will depend on the modes used to calculate markers 3 and 4. 2 — Combined Balances : This is the sum of the bull/bear (+1/−1) states of each of the five balances, so it ranges from −5 to +5. █  FEATURES The indicator has two main modes of operation: Columns and Line . Columns • In Columns mode you can display stacked Buy/Sell volume​ columns. • The buy section always appears above the centerline, the sell section below. • The top and bottom sections can be colored independently using eight different methods. • The EMAs of the Buy/Sell values can be displayed (these are the same EMAs used to calculate the "Average Balance"). Line • Displays one of seven signals: the five balances or one of two complementary values, i.e., the "Marker Bias" or the "Combined Balances". • You can color the line and its fill using independent calculation modes to pack more information in the display.  You can thus appraise the state of 3 different values using the line itself, its color and the color of its fill. • A "Divergence Levels" feature will use the line to automatically draw expanding levels on divergence events. Default settings Using the indicator's default settings, this is the information displayed: • The line is calculated on the "Average Balance". • The line's color is determined by the bull/bear state of the "Percent Balance". • The line's fill gradient is determined by the advances/declines of the "Momentum Balance". • The orange divergence dots are calculated using discrepancies between the polarity of the "On Bar Balance" and the chart's bar. • The divergence levels are determined using the line's level when a divergence occurs. • The background's fill gradient is calculated on advances/declines of the "Marker Bias". • The chart bars are colored using advances/declines of the "Relative Balance". Divergences are shown in orange. • The intrabar timeframe is automatically determined from the chart's timeframe so that a minimum of 50 intrabars are used to calculate volume​ delta on historical bars. Alerts The configuration of the marker conditions explained further is what determines the conditions that will trigger alerts created from this script. Note that simply selecting the display of markers does not create alerts. To create an alert on this script, you must use ALT-A from the chart. You can create multiple alerts triggering on different conditions from this same script; simply configure the markers so they define the trigger conditions for each alert before creating the alert. The configuration of the script's inputs is saved with the alert, so from then on you can change them without affecting the alert. Alert messages will mention the marker(s) that triggered the specific alert event. Keep in mind, when creating alerts on small chart timeframes, that discrepancies between alert triggers and markers displayed on your chart are to be expected. This is because the alert and your chart are running two distinct instances of the indicator on different servers and different feeds. Also keep in mind that while alerts only trigger on confirmed conditions, they are calculated using realtime calculation mode, which entails that if you refresh your chart and elapsed realtime bars recalculate as historical bars using intrabar inspection, markers will not appear in the same places they appeared in realtime. So it's important to understand that even though the alert conditions are confirmed when they trigger, these alerts will repaint. Let's go through the sections of the script's inputs. Columns The size of the Buy/Sell columns always represents their respective importance on the bar, but the coloring mode for tops and bottoms is independent. The default setup uses a standard coloring mode where the Buy/Sell columns are always in the bull/bear color with a higher intensity for the winning side. Seven other coloring modes allow you to pack more information in the columns. When choosing to color the top columns using a bull/bear gradient on "Average Balance", for example, you will have bull/bear colored tops. In order for the color of the bottom columns to continue to show the instant bar balance, you can then choose the "On Bar Balance — Dual Solid Colors" coloring mode to make those bars the color of the winning side for that bar. You can display the averages of the Buy and Sell columns. If you do, its coloring is controlled through the "Line" and "Line fill" sections below. Line and Line fill You can select the calculation mode and the thickness of the line, and independent calculations to determine the line's color and fill. Zero Line The zero line can display dots when all five balances are bull/bear. Divergences You first select the detection mode. Divergences occur whenever the up/down direction of the signal does not match the up/down polarity of the bar. Divergences are used in three components of the indicator's visuals: the orange dot, colored chart bars, and to calculate the divergence levels on the line. The divergence levels are dynamic levels that automatically build from the line's values on divergence events. On consecutive divergences, the levels will expand, creating a channel. This implementation of the divergence levels corresponds to my view that divergences indicate anomalies, hesitations, points of uncertainty if you will. It precludes any attempt to identify a directional bias to divergences. Accordingly, the levels merely take note of divergence events and mark those points in time with levels. Traders then have a reference point from which they can evaluate further movement. The bull/bear/neutral colors used to plot the levels are also congruent with this view in that they are determined by the line's position relative to the levels, which is how I think divergences can be put to the most effective use. One of the coloring modes for the line's fill uses advances/declines in the line after divergence events. Background The background can show a bull/bear gradient on six different calculations. As with other gradients, you can adjust its brightness to make its importance proportional to how you use it in your analysis. Chart bars Chart bars can be colored using seven different methods. You have the option of emptying the body of bars where volume​ does not increase, as does my TLD indicator, and you can choose whether you want to show divergences. Intrabar Timeframe This is the intrabar timeframe that will be used to calculate volume​ delta using intrabar inspection on historical bars. You can choose between four modes. The three "Auto-steps" modes calculate, from the chart's timeframe, the intrabar timeframe where the said number of intrabars will make up the dilation of chart bars. Adjustments are made for non-24x7 markets. "Fixed" mode allows you to select the intrabar timeframe you want. Checking the "Show TF" box will display in the lower-right corner the intrabar timeframe used at any given moment. The proper selection of the intrabar timeframe is important. It must achieve maximal granularity to produce precise results while not unduly slowing down calculations, or worse, causing runtime errors. Note that historical depth will vary with the intrabar timeframe. The smaller the timeframe, the shallower historical plots you will be. Markers Markers appear when the required condition has been confirmed on a closed bar. The configuration of the markers when you create an alert is what determines when the alert will trigger. Five markers are available: • Balances Agreement : All five balances are either bullish​ or bearish​. • Double Bumps : A double bump is two consecutive up/down bars with +/‒ volume​​ delta, and rising Buy/Sell volume​ above its average. • Divergence confirmations : A divergence is confirmed up/down when the chosen balance is up/down on the previous bar when that bar was down/up, and this bar is up/down. • Balance Shifts : These are bull/bear transitions of the selected signal. • Marker Bias Shifts : Marker bias shifts occur when it crosses into bull/bear territory. Periods Allows control over the periods of the different moving averages used to calculate the balances. Volume​ Discrepancies Stock exchanges do not report the same volume​ for intraday and daily (or higher) resolutions. Other variations in how volume​ information is reported can also occur in other markets, namely Forex, where volume​ irregularities can even occur between different intraday timeframes. This will cause discrepancies between the total volume​ on the bar at the chart's timeframe, and the total volume​​ calculated by adding the volume​​ of the intrabars in that bar's dilation. This does not necessarily invalidate the volume​​ delta information calculated from intrabars, but it tells us that we are using partial volume​ data. A mechanism to detect chart vs intrabar timeframe volume​​ discrepancies is provided. It allows you to define a threshold percentage above which the background will indicate a difference has been detected. Other Settings You can control here the display of the gray dot reminder on realtime bars, and the display of error messages if you are using a chart timeframe that is not greater than the fixed intrabar timeframe, when you use that mode. Disabling the message can be useful if you only use realtime mode at chart timeframes that do not support intrabar inspection. █  RAMBLINGS On Volume​ Delta Volume​ is arguably the best complement to interpret price action, and I consider volume​ delta to be the most effective way of processing volume​ information. In periods of low-volatility price consolidations, volume​ will typically also be lower than normal, but slight imbalances in the trend of the buy/sell volume​ balance can sometimes help put early odds on the direction of the break from consolidation. Additionally, the progression of the volume​ imbalance can help determine the proximity of the breakout. I also find volume​ delta and the number of divergences very useful to evaluate the strength of trends. In trends, I am looking for "slow and steady", i.e., relatively low volatility​ and pauses where price action doesn't look like world affairs are being reassessed. In my personal mythology, this type of trend is often more resilient than high-volatility breakouts, especially when volume​ balance confirms the general agreement of traders signaled by the low-volatility usually accompanying this type of trend. The volume​ action on pauses will often help me decide between aggressively taking profits, tightening a stop or going for a longer-term movement. As for reversals, they generally occur in high-volatility areas where entering trades is more expensive and riskier. While the identification of counter-trend reversals fascinates many traders to no end, they represent poor opportunities in my view. Volume​ imbalances often precede reversals, but I prefer to use volume​ delta information to identify the areas following reversals where I can confirm them and make relatively low-cost entries with better odds. On "Buy/Sell" Volume Buying or selling volume​ are misnomers, as every unit of volume​ transacted is both bought and sold by two different traders. While this does not keep me from using the terms, there is no such thing as “buy only” or “sell only” volume​. Trader lingo is riddled with peculiarities. Divergences The divergence detection method used here relies on a difference between the direction of a signal and the polarity (up/down) of a chart bar. When using the default "On Bar Balance" to detect divergences, however, only the bar's volume​ delta is used. You may wonder how there can be divergences between buying/selling volume​ information and price movement on one bar. This will sometimes be due to the calculation's shortcomings, but divergences may also occur in instances where because of order book structure, it takes less volume​ to increase the price of an asset than it takes to decrease it. As usual, divergences are points of interest because they reveal imbalances, which may or may not become turning points. To your pattern-hungry brain, the divergences displayed by this indicator will — as they do on other indicators — appear to often indicate turnarounds. My opinion is that reality is generally quite sobering and I have no reliable information that would tend to prove otherwise. Exercise caution when using them. Consequently, I do not share the overwhelming enthusiasm of traders in identifying bullish​/bearish​ divergences. For me, the best course of action when a divergence occurs is to wait and see what happens from there. That is the rationale underlying how my divergence levels work; they take note of a signal's level when a divergence occurs, and it's the signal's behavior from that point on that determines if the post-divergence action is bullish​/bearish​. Superfluity In "The Bed of Procrustes", Nassim Nicholas Taleb writes: To bankrupt a fool, give him information . This indicator can display lots of information. While learning to use a new indicator inevitably requires an adaptation period where we put it through its paces and try out all its options, once you have become used to it and decide to adopt it, rigorously eliminate the components you don't use and configure the remaining ones so their visual prominence reflects their relative importance in your analysis. I tried to provide flexible options for traders to control this indicator's visuals for that exact reason — not for window dressing. █  LIMITATIONS • This script uses a special characteristic of the `security()` function allowing the inspection of intrabars — which is not officially supported by TradingView.  It has the advantage of permitting a more robust calculation of volume​ delta than other methods on historical bars, but also has its limits. • Intrabar inspection only works on some chart timeframes: 3, 5, 10, 15 and 30 minutes, 1, 2, 3, 4, 6, and 12 hours, 1 day, 1 week and 1 month.  The script’s code can be modified to run on other resolutions. • When the difference between the chart’s timeframe and the intrabar timeframe is too great, runtime errors will occur. The Auto-Steps selection mechanisms should avoid this. • All volume​ is not created equally. Its source, components, quality and reliability will vary considerably with sectors and instruments.  The higher the quality, the more reliably volume​ delta information can be used to guide your decisions.  You should make it your responsibility to understand the volume​ information provided in the data feeds you use. It will help you make the most of volume​ delta. █  NOTES For traders • The Data Window shows key values for the indicator. • While this indicator displays some of the same information calculated in my Delta Volume Columns ,  I have elected to make it a separate publication so that traders continue to have a simpler alternative available to them. Both code bases will continue to evolve separately. • All gradients used in this indicator determine their brightness intensities using advances/declines in the signal—not their relative position in a pre-determined scale. • Volume​ delta being relative, by nature, it is particularly well-suited to Forex markets, as it filters out quite elegantly the cyclical volume​ data characterizing the sector. If you are interested in volume​ delta, consider having a look at my other "Delta Volume" indicators: • Delta Volume Realtime Action displays realtime volume​ delta and tick information on the chart. • Delta Volume Candles builds volume​​ delta candles on the chart. • Delta Volume Columns is a simpler version of this indicator. For coders • I use the `f_c_gradientRelativePro()` from the PineCoders Color Gradient Framework to build my gradients.  This function has the advantage of allowing begin/end colors for both the bull and bear colors. It also allows us to define the number of steps allowed for each gradient.  I use this to modulate the gradients so they perform optimally on the combination of the signal used to calculate advances/declines,  but also the nature of the visual component the gradient applies to. I use fewer steps for choppy signals and when the gradient is used on discrete visual components  such as volume​ columns or chart bars. • I use the PineCoders Coding Conventions for Pine to write my scripts. • I used functions modified from the PineCoders MTF Selection Framework for the selection of timeframes. █  THANKS TO: — The devs from TradingView's Pine and other teams, and the PineCoders who collaborate with them. They are doing amazing work,  and much of what this indicator does could not be done without their recent improvements to Pine. — A guy called Kuan who commented on a Backtest Rookies presentation of their Volume Profile indicator using a `for` loop.  This indicator started from the intrabar inspection technique illustrated in Kuan's snippet. — theheirophant , my partner in the exploration of the sometimes weird abysses of `security()`’s behavior at intrabar timeframes. — midtownsk8rguy , my brilliant companion in mining the depths of Pine graphics.
Wskaźnik Pine Script®
od LucF
125
BERLIN CandlesA problem with Heikin Ashi is that while it gives you a great overview of overall direction, it is rarely possible to use it as a replacement for normal japanese candlesticks. The reason for this is that actual price data is lost, since the candles are more akin to a moving average than a different way to see price action. Also, with Heikin Ashi, most of the actual price action is lost, because the candles can be bigger than the high and low of the underlying japanese candlestick. With BERLIN Candles I have tried to fix that problem. By using a smoothed out version of the previous Heikin Ashi candle close as the current BERLIN Candle open, the high and low of the actual japanese candlestick for the high and low of the BERLIN Candle, and the current Heikin Ashi close as the BERLIN Candle close, while setting hard limits for BERLIN Candle open and close values so that they can never exceed the high and low of the underlying japanese candlestick. One problem still persists though. The actual current price data is lost. However, the BERLIN Candles have solved this by adding a fifth part to the candles. The close of the underlying japanese candlesticks are indicated with a plus-sign. This way, actual price data is never lost, while keeping all of the other benefits of this type of candles. A few added bonuses: The addition of the 14 period ATR at the latest candle The baseline from Ichimoku is included as an option The 14 period ATR value of each candle can be seen in the indicator data as the orange value
Wskaźnik Pine Script®
od lejmer
16
Support Resistance ChannelsHello All, For Long time I was planning to make Support/Resistance Channels script, finally I had time and here it is. How this script works? - it finds and keeps Pivot Points - when it found a new Pivot Point it clears older S/R channels then; - for each pivot point it searches all pivot points in its own channel with dynamic width - while creating the S/R channel it calculates its strength - then sorts all S/R channels by strength - it shows the strongest S/R channels, before doing this it checks old location in the list and adjust them for better visibility - if any S/R channel was broken on last move then it gives alert and put shape below/above the candle - The colors of the S/R channels are adjusted automatically You can set/change following settings: - Pivot Period - Source : High/Low or Close/Open can be used - Maximum Channel Width %: this is the maximum channel width rate, this is calculated using Highest/Lowest levels in last 300 bars - Number of S/R to show : this is the number of Strongest S/R to show - Loopback Period: While calculating S/R levels it checks Pivot Points in LoopBack Period - Show S/R on last # Bars: To see S/R levels only on last N bars - Start Date: the script starts calculating Pivot Point from this date, the reason I put this option is for visuality. Explained below - You can set colors/transparency - and You can enable/disable shapes for broken S/R levels Examples: You can change colors as you wish: here " Show S/R on last # Bars " set 100: Sometimes visuality may corrupt because of old S/R levels, to solve it you need to set "Start Date" in the options to start the script in visual part (last 292 bars) here in first screenshot it doesn't look good (shrink), then on second screenshot I set the "Start Date" it looks better, if you change time frame don't forget to set it again :) Enjoy!
Wskaźnik Pine Script®
od LonesomeTheBlue
299
MathSpecialFunctionsConvolve1DLibrary "MathSpecialFunctionsConvolve1D" Convolution is one of the most important mathematical operations used in signal processing. This simple mathematical operation pops up in many scientific and industrial applications, from its use in a billion-layer large CNN to simple image denoising. ___ Reference: www.algorithm-archive.org numpy.org lloydrochester.com www.geeksforgeeks.org f(signal, filter)   Convolve   Parameters:      signal (array) : List with signal data.      filter (array) : List with weights to apply to the signal data.   Returns: Discrete, linear convolution of `signal` and `filter`.
Biblioteka Pine Script®
od RicardoSantos
2
ErrorFunctionsLibrary "ErrorFunctions" A collection of functions used to approximate the area beneath a Gaussian curve. Because an ERF (Error Function) is an integral, there is no closed-form solution to calculating the area beneath the curve. Meaning all ERFs are approximations; precisely wrong, but mostly accurate. How close you need to get to the actual area depends entirely on your use case, with more precision being less efficient. The internal precision of floats in Pine Script is 1e-16 (16 decimals, aka. double precision). This library adapts well known algorithms designed to efficiently reach double precision. Single precision alternates are also included. All of them were made free to use, modify, and distribute by their original authors. HASTINGS Adaptation of a single precision ERF by Cecil Hastings Jr, published through Princeton University in 1955. It was later documented by Abramowitz and Stegun as equation 7.1.26 in their 1972 Handbook of Mathematical Functions. Fast, efficient, and ideal when precision beyond a few decimals is unnecessary. GILES Adaptation of a single precision Inverse ERF by Michael Giles, published through the University of Oxford in 2012. It reverses the ERF, estimating an X coordinate from an area. It too is fast, efficient, and ideal when precision beyond a few decimals is unnecessary. LIBC Adaptation of the double precision ERF & ERFC in the standard C library (aka. libc). It is also the same ERF & ERFC that SciPy uses. While not quite as efficient as the Hastings approximation, it's still very fast and fully maximizes Pines precision. BOOST Adaptation of the double precision Inverse ERF & Inverse ERFC in the Boost Math C++ library. SciPy uses these as well. These reverse the ERF & ERFC, estimating an X coordinate from an area. It too isn't quite as efficient as the Giles approximation, but still fast and fully maximizes Pines precision. While these algorithms are not exported directly, they are available through their exported counterparts. - - - ERROR FUNCTIONS erf(x, precise)   An Error Function estimates the theoretical error of a measurement.   Parameters:      x (float) : (float) Upper limit of the integration.      precise (bool) : Double precision (true) or single precision (false).   Returns: (float) Between -1 and 1. erfc(x, precise)   A Complementary Error Function estimates the difference between a theoretical error and infinity.   Parameters:      x (float) : (float) Lower limit of the integration.      precise (bool) : Double precision (true) or single precision (false).   Returns: (float) Between 0 and 2. erfinv(x, precise)   An Inverse Error Function reverses the erf() by estimating the original measurement from the theoretical error.   Parameters:      x (float) : (float) Theoretical error.      precise (bool) : Double precision (true) or single precision (false).   Returns: (float) Between 0 and ± infinity. erfcinv(x, precise)   An Inverse Complementary Error Function reverses the erfc() by estimating the original measurement from the difference between the theoretical error and infinity.   Parameters:      x (float) : (float) Difference between the theoretical error and infinity.      precise (bool) : Double precision (true) or single precision (false).   Returns: (float) Between 0 and ± infinity. - - - DISTRIBUTION FUNCTIONS pdf(x, m, s)   A Probability Density Function estimates the probability density . For clarity, density is not a probability .   Parameters:      x (float) : (float) X coordinate for which a density will be estimated.      m (float) : (float) Mean      s (float) : (float) Sigma   Returns: (float) Between 0 and ∞. cdf(z, precise)   A Cumulative Distribution Function estimates the area under a Gaussian curve between negative infinity and the Z Score.   Parameters:      z (float) : (float) Z Score.      precise (bool) : Double precision (true) or single precision (false).   Returns: (float) Between 0 and 1. cdfinv(a, precise)   An Inverse Cumulative Distribution Function reverses the cdf() by estimating the Z Score from an area.   Parameters:      a (float) : (float) Area between 0 and 1.      precise (bool) : Double precision (true) or single precision (false).   Returns: (float) Between -∞ and +∞ cdfab(z1, z2, precise)   A Cumulative Distribution Function from A to B estimates the area under a Gaussian curve between two Z Scores (A and B).   Parameters:      z1 (float) : (float) First Z Score.      z2 (float) : (float) Second Z Score.      precise (bool) : Double precision (true) or single precision (false).   Returns: (float) Between 0 and 1. ttt(z, precise)   A Two-Tailed Test estimates the area under a Gaussian curve between symmetrical ± Z scores and ± infinity.   Parameters:      z (float) : (float) One of the symmetrical Z Scores.      precise (bool) : Double precision (true) or single precision (false).   Returns: (float) Between 0 and 1. tttinv(a, precise)   An Inverse Two-Tailed Test reverses the ttt() by estimating the absolute Z Score from an area.   Parameters:      a (float) : (float) Area between 0 and 1.      precise (bool) : Double precision (true) or single precision (false).   Returns: (float) Between 0 and ∞. ott(z, precise)   A One-Tailed Test estimates the area under a Gaussian curve between an absolute Z Score and infinity.   Parameters:      z (float) : (float) Z Score.      precise (bool) : Double precision (true) or single precision (false).   Returns: (float) Between 0 and 1. ottinv(a, precise)   An Inverse One-Tailed Test Reverses the ott() by estimating the Z Score from a an area.   Parameters:      a (float) : (float) Area between 0 and 1.      precise (bool) : Double precision (true) or single precision (false).   Returns: (float) Between 0 and ∞.
Biblioteka Pine Script®
od liquid-trader
6
Trend Volatility Index (TVI)Trend Volatility Index (TVI) A robust nonparametric oscillator for structural trend volatility detection ⸻ What is this? TVI is a volatility oscillator designed to measure the strength and emergence of price trends using nonparametric statistics. It calculates a U-statistic based on the Gini mean difference across multiple simple moving averages. This allows for objective, robust, and unbiased quantification of trend volatility in tick-scale values. ⸻ What can it do? • Quantify trend strength as a continuous value aligned with tick price scale • Detect trend breakouts and volatility expansions • Identify range-bound market states • Detect early signs of new trends with minimal lag ⸻ What can’t it do? • Predict future price levels • Predict trend direction before confirmation ⸻ How it works TVI computes a nonparametric dispersion metric (Gini mean difference) from multiple SMAs of different lengths. As this metric shares the same dimension as price ticks, it can be directly interpreted on the chart as a volatility gauge. The output is plotted using candlestick-style charts to enhance visibility of change rate and trend behavior. ⸻ Disclaimer TVI does not predict price. It is a structural indicator designed to support discretionary judgment. Trading carries inherent risk, and this tool does not guarantee profitability. Use at your own discretion. ⸻ Innovation This indicator introduces a novel approach to trend volatility by applying U-statistics over time series to produce a nonparametric, unbiased, and robust estimate of structural volatility. 日本語要約 Trend Volatility Index (TVI) は、ノンパラメトリックなU統計量（Gini平均差）を使ってトレンドの強度を客観的に測定することを目的に開発されたボラティリティ・オシレーターです。 ティック単位で連続的に変化し、トレンドのブレイク・レンジ・初動の予兆を定量的に検出します。 未来の価格や方向は予測せず、現在の構造的ばらつきだけをロバストに評価します。
Wskaźnik Pine Script®
od chikaharu
7
Cointegration Heatmap & Spread Table [EdgeTerminal]The Cointegration Heatmap is a powerful visual and quantitative tool designed to uncover deep, statistically meaningful relationships between assets. Unlike traditional indicators that react to price movement, this tool analyzes the underlying statistical relationship between two time series and tracks when they diverge from their long-term equilibrium — offering actionable signals for mean-reversion trades . What Is Cointegration? Most traders are familiar with correlation, which measures how two assets move together in the short term. But correlation is shallow — it doesn’t imply a stable or predictable relationship over time. Cointegration, however, is a deeper statistical concept: Two assets are cointegrated if a linear combination of their prices or returns is stationary , even if the individual series themselves are non-stationary. Cointegration is a foundational concept in time series analysis, widely used by hedge funds, proprietary trading firms, and quantitative researchers. This indicator brings that institutional-grade concept into an easy-to-use and fully visual TradingView indicator. This tool helps answer key questions like: “Which stocks tend to move in sync over the long term?” “When are two assets diverging beyond statistical norms?” “Is now the right time to short one and long the other?” Using a combination of regression analysis, residual modeling, and Z-score evaluation, this indicator surfaces opportunities where price relationships are stretched and likely to snap back — making it ideal for building low-risk, high-probability trade setups. In simple terms: Cointegrated assets drift apart temporarily, but always come back together over time. This behavior is the foundation of successful pairs trading. How the Indicator Works Cointegration Heatmap indicator works across any market supported on TradingView — from stocks and ETFs to cryptocurrencies and forex pairs. You enter your list of symbols, choose a timeframe, and the indicator updates every bar with live cointegration scores, spread signals, and trade-ready insights. Indicator Settings: Symbol list: a customizable list of symbols separated by commas Returns timeframe: time frame selection for return sampling (Weekly or Monthly) Max periods: max periods to limit the data to a certain time and to control indicator performance This indicator accomplishes three major goals in one streamlined package: Identifies stable long-term relationships (cointegration) between assets, using a heatmap visualization. Tracks the spread — the difference between actual prices and the predicted linear relationship — between each pair. Generates trade signals based on Z-score deviations from the mean spread, helping traders know when a pair is statistically overextended and likely to mean revert. The math: Returns are calculated using spread tickers to ensure alignment in time and adjust for dividends, splits, and other inconsistencies. For each unique pair of symbols, we perform a linear regression Yt​=α+βXt​+ε Then we compute the residuals (errors from the regression): Spreadt​=Yt​−(α+βXt​) Calculate the standard deviation of the spread over a moving window (default: 100 samples) and finally, define the Cointegration Score: S=1/Standard Deviation of Residuals This means, the lower the deviation, the tighter the relationship, so higher scores indicate stronger cointegration. Always remember that cointegration can break down so monitor the asset over time and over multiple different timeframes before making a decision. How to use the indicator The heatmap table: The indicator displays 2 very important tables, one in the middle and one on the right side. After entering your symbols, the first table to pay attention to is the middle heatmap table. Any assets with a cointegration value of 25% is something to pay attention to and have a strong and stable relationship. Anything below is weak and not tradable. Additionally, the 40% level is another important line to cross. Assets that have a cointegration score of over 40% will most likely have an extremely strong relationship. Think about it this way, the higher the percentage, the tighter and more statistically reliable the relationship is. The spread table: After finding a good asset pair using heatmap, locate the same pair in the spread table (right side). Here’s what you’ll see on the table: Spread: Current difference between the two symbols based on the regression fit Mean: Historical average of that spread Z-score: How far current spread is from the mean in standard deviations Signal: Trade suggestion: Short, Long, or Neutral Since you’re expecting mean reversion, the idea is that the spread will return to the average. You want to take a trade when the z-score is either over +2 or below -2 and exit when z-score returns to near 0. You will usually see the trade suggestion on the spread chart but you can make your own decision based on your risk level. Keep in mind that the Z-score for each pair refers to how off the first asset is from the mean compared to the second one, so for example if you see STOCKA vs STOCKB with a Z-score of -1.55, we are regressing STOCKB (Y) on STOCKA (X). In this case, STOCKB is the quoted asset and STOCKA is the base asset. In this case, this means that STOCKB is much lower than expected relative to STOCKA, so the trade would be a long position on stock B and short position on stock A.
Wskaźnik Pine Script®
od EdgeTerminal
2