立即開始

趨勢分析

Kyoshiro - FVG + Order Blocks📌 Kyoshiro – FVG + Order Blocks This indicator combines Order Block (OB) detection with an intelligent auto-management system and a clean visual display on the chart. It is designed to help traders better identify institutional zones where price frequently reacts. ⚙️ Key Features: ✅ Real-time detection of bullish and bearish Order Blocks. ✅ Automatic cleanup: invalidated OBs are removed to keep the chart clean. ✅ Customizable display: Maximum number of visible OBs (bullish / bearish). Zone colors, outlines, and midlines. Line styles (solid, dashed, dotted) and adjustable width. ✅ Choice of mitigation method: Wick Close ✅ Built-in alerts: Formation of bullish or bearish OB. Mitigation of an existing OB. 🔔 Available Alerts: Bullish OB Formed → A bullish order block is detected. Bearish OB Formed → A bearish order block is detected. Bullish OB Mitigated → A bullish OB has been invalidated. Bearish OB Mitigated → A bearish OB has been invalidated. 🎯 Use Cases: Quickly identify key liquidity zones. Track institutional activity in the market. Improve entry and exit precision.
Pine Script®指標
由KyoshiroG提供
NQ Bias — Fixed Session (6PM & Midnight Opens)📰 Script Summary — NQ Bias (Fixed Session) 📌 Purpose: This Pine Script helps traders track bias shifts on Nasdaq futures (NQ) using ICT-style reference points like the 6PM open, Midnight open, and 4PM close. ⚙️ How It Works: Plots session opens (6PM, Midnight) as bias reference lines. Highlights when price is trading above or below the 4PM New York Close → tells you if the market is leaning bullish or bearish. Uses background coloring (green/red) to visually show bias. Lets you filter by specific trading dates for focused backtesting. Designed with ICT principles → focus on daily opens, session shifts, and premium/discount logic. 📈 Trader Benefit: Quickly see if NQ is in bullish or bearish context relative to key opens. Simplifies daily bias framework into an easy chart overlay. Helps align trades with the higher-probability side of the market.
Pine Script®指標
由ictsniperkills提供
Pivot Point TrendOverview A trend-following trailing line built from confirmed pivot highs/lows and ATR bands. The line turns green in uptrends and red in downtrends. A flip happens only when price closes on the other side of the opposite trail, helping filter noise. How it works: Finds confirmed swing points (pivots) and builds a smoothed center from them. From that center, creates ATR-based bands. The active trail “locks” in the trend: in uptrends it never moves down; in downtrends it never moves up. Close above the prior upper trail → bullish; close below the prior lower trail → bearish. Inputs Pivot Point Period (prd) – strictness of pivot confirmation (delay = prd bars). ATR Period (pd) and ATR Factor (factor) – band width; higher values = fewer flips. Calculation timeframe (calcTF) – leave empty to use chart TF, or set a hard TF like 1D, 4H. Show Center Line – optional central guide. Line Width – trail thickness. Alerts Bullish Flip – trend turns bullish. Bearish Flip – trend turns bearish. Trend Changed – any flip event. Usage tips Typical crypto intraday starters: prd 2–5, pd 10–14, factor 2.5–3.5. For smoother signals, compute on a higher TF (e.g., calcTF = 1D) and time entries on your lower TF. Prefer actions on bar close of the calculation TF to avoid intrabar whipsaw. Notes on repainting The script uses request.security(..., lookahead_off). Pivots confirm after prd bars by design; once confirmed, the center and trails do not use future data. Evaluate flips on bar close for consistency, especially when calcTF > chart TF. Disclaimer Educational use only. Not financial advice. Trading involves risk.
Pine Script®指標
由trading_mura提供
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level: Tzotchev Trend Measure: A Statistical Approach to Trend Following The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies. Mathematical Foundation and Statistical Theory The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility. This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework. The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions. Comparative Analysis with Traditional Trend Measurement Methods The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically. Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels. The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment. Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies. The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification. Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research. Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach. The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems. Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting. Advanced Noise Filtering and Market Regime Detection A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies. The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action. The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions. Professional Implementation and Trading Applications The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements. Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts. The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes. Performance Optimization and Computational Efficiency The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached. Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data. Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold. Visual Design and User Interface The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications. Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms. The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously. Theoretical Implications and Research Context The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions. The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions. The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential. Practical Applications and Trading Strategy Integration The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions. The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems. Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions. Implementation Guide and Parameter Configuration The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization. Core Calculation Parameters The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification. The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly. The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument. Trading Profile Configuration The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels. Advanced Noise Filtering System The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications. The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations. The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions. Visual Customization and Display Options The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments. The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations. The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals. Performance Optimization Settings The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources. The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications. Alert System Configuration The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations. The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification. Table Display and Information Management The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements. The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection. Risk Management Integration Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies. The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly. Timeframe Considerations and Multi-Timeframe Analysis The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates. For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment. Conclusion The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms. The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management. References Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards. Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182. Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985. Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill. Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273. Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58. Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720. Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons. Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250. Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
Pine Script®指標
由EdgeTools提供
Confluence Engine Confluence Engine is a practical, non-repainting decision aid that scores market conditions from −100…+100 by combining six proven modules: Trend, Momentum, Volatility, Volume, Structure, and an HTF confirmation. It’s designed for crypto, forex, indices, and stocks, and it fires entries only on confirmed bar closes. What’s inside Trend: EMA 20/50/200 alignment plus a Supertrend/KAMA toggle (you choose the baseline). Momentum: RSI + MACD with confirmed-pivot divergence detection. Volatility: ATR% and Bollinger Band width vs its average to favor expansion over chop. Volume: OBV-style cumulative flow slope + volume surge vs SMA×multiplier. Market Structure: Confirmed pivots, BOS (break of structure) and CHOCH (change of character). HTF Filter: Closed higher-timeframe context via request.security(..., barmerge.gaps_on, barmerge.lookahead_off). Why it does not repaint Signals are computed and plotted on closed bars only. Pivots/divergences use confirmed pivot points (no forward look). HTF series are fetched with lookahead_off and use the last closed HTF bar in realtime. No future bar references are used for entries or alerts. How to use (3 steps) Pick a timeframe pair: use a 4–6× HTF multiplier (5m→30m, 15m→1h, 1h→4h, 4h→1D, 1D→1W). Trade with the HTF: take longs only when the HTF filter is bullish; shorts only when bearish. Prefer expansion: act when BB width > its average and ATR% is elevated; skip most signals in compression. Suggested presets (start here) Crypto (BTC/ETH): 15m→1h, 1h→4h. stLen=10, stMult=3.0, bbLen=20, surgeMul=1.8–2.2, thresholds +40 / −40 (intraday can try +35 / −35). Forex majors: 15m→1h, 1h→4h. stLen=10–14, stMult=2.5–3.0, surgeMul=1.5–1.8, thresholds +35 / −35 (swing: +45 / −45). US equities (liquid): 5m→30m/1h, 15m→1h/2h. stMult=3.0–3.5, surgeMul=1.6–2.0, thresholds +45 / −45 to reduce chop. Indices (ES/NQ): 5m→30m, 15m→1h. Defaults are fine; start at +40 / −40. Gold/Oil: 15m→1h, 1h→4h. Thresholds +35 / −35, surgeMul=1.6–1.9. Inputs (plain English) Use Supertrend (off = KAMA): choose the trend baseline. EMA Fast/Mid/Slow: 20/50/200 by default for classic stack. RSI/MACD + divergence pivots: momentum and exhaustion context. ATR Length & BB Length: volatility regime detection. Volume SMA & Surge Multiplier: defines “meaningful” volume spikes. Pivot left/right & “Confirm BOS/CHOCH on Close”: structure strictness. Enable HTF & Higher Timeframe: confirms the lower timeframe direction. Thresholds (+long / −short): when the score crosses these, you get signals. Signals & alerts (IDs preserved) Entry shapes plot at bar close when the score crosses thresholds. Alerts you can enable: CONFLUENCE LONG — long entry signal CONFLUENCE SHORT — short entry signal BULLISH BIAS — score turned positive BEARISH BIAS — score turned negative Best practices Focus on signals with HTF agreement and volatility expansion; require volume participation (surge or rising OBV slope) for higher quality. Raise thresholds (+45/−45 or +50/−50) to reduce whipsaws in choppy sessions. Lower thresholds (+35/−35) only if you also require volatility/volume filters. Performance & scope Works across crypto/FX/equities/indices; no broker data or special feeds required. No repainting by design; signals/alerts are computed on closed bars. As with any tool, results vary by regime; always combine with risk management. Disclosure This script is for educational purposes only and is not financial advice. Trading involves risk. Test on historical data and paper trade before using live.
Pine Script®指標
由Finntech1提供
1
ARVELOV ORB + HighlightARVELOV ORB + Highlight This indicator is designed to identify and visualize the Opening Range Breakout (ORB) for a selected trading session. The ORB is defined as the high and low price range during the first few minutes of the market open. Traders often use this range to spot potential breakout opportunities or gauge early market sentiment. Key Features: Customizable ORB Session: Default is 09:30–09:45 (15 minutes), but users can adjust the start and end times. ORB High and Low Lines: Plots the highest and lowest prices reached during the ORB session. Highlighted ORB Zone: The area between the ORB high and low is shaded to provide a clear visual of the opening range. Intraday Timeframe Friendly: Works best on intraday charts with a timeframe equal to or smaller than the ORB duration. Visual Aid for Breakouts: Helps traders quickly spot when the price breaks above or below the ORB, a common trigger for intraday trading strategies. How It Works: When the session starts, the indicator captures the first bar’s high and low as the initial ORB. As the session progresses, it updates the ORB high and low if new highs or lows occur within the session. The highlighted rectangle between the ORB high and low makes it easy to see the early trading range at a glance. Use Case: Traders can use this indicator to monitor potential breakout levels. The ORB zone can also act as support/resistance for early intraday trades.
Pine Script®指標
由arvelov提供
RS Rating vs SPX🇷🇺 Описание на русском языке 📈 RS Rating vs SPX — Относительная сила акции против S&P 500 Этот индикатор рассчитывает рейтинг относительной силы (RS Rating) акции или инструмента по методике, аналогичной Investor’s Business Daily (IBD). Он показывает, насколько хорошо актив показывает себя по сравнению с рынком в целом, используя S&P 500 (SPX) как бенчмарк. 🔍 Как это работает? Сравнение с рынком: Индикатор вычисляет темпы роста (ROC) актива за 3, 6, 9 и 12 месяцев — и вычитает из них темпы роста S&P 500 за те же периоды. Это даёт относительную производительность. Взвешенный рейтинг: Результаты комбинируются по формуле: 40% (3 мес) + 20% (6 мес) + 20% (9 мес) + 20% (12 мес) — чтобы учесть как краткосрочную динамику, так и долгосрочный тренд. Нормализация 1–99: Полученное значение нормализуется по истории за последние N периодов (по умолчанию — 250), чтобы присвоить активу рейтинг от 1 до 99, где: 90–99 — исключительная сила (лучшие 10%) 70–89 — сильные лидеры 30–69 — средние показатели 1–29 — слабые, отстающие от рынка Визуализация: Рейтинг отображается в виде цветного квадратного окна, которое можно разместить в любом углу графика (настраивается в параметрах). Цвет подсказывает силу: 🟢 Зелёный — сильный (≥70) 🟠 Оранжевый — средний (30–69) 🔴 Красный — слабый (<30) 🛠️ Для кого это? Для инвесторов по методике IBD, ищущих сильные акции. Для свинг-трейдеров, которые хотят входить в активы, обгоняющие рынок. Для анализа относительной силы — не абсолютной доходности, а именно превосходства над индексом. ⚙️ Настройки Lookback Period — период нормализации (по умолчанию 250 баров). Approx. Visible Bars Back — оценка видимой ширины графика (для позиционирования слева). Position — выбор расположения индикатора на графике (9 вариантов). 🇬🇧 Description in English 📈 RS Rating vs SPX — Relative Strength of Stock vs S&P 500 This indicator calculates the Relative Strength (RS) Rating of a stock or instrument using a methodology similar to Investor’s Business Daily (IBD). It measures how well the asset is performing relative to the overall market, using the S&P 500 (SPX) as the benchmark. 🔍 How it works: Market Comparison: The indicator calculates the Rate-of-Change (ROC) of the asset over 3, 6, 9, and 12 months — then subtracts the ROC of the S&P 500 over the same periods. This gives the relative performance vs the market. Weighted Score: Results are combined using the formula: 40% (3mo) + 20% (6mo) + 20% (9mo) + 20% (12mo) — balancing short-term momentum and long-term trend. Normalized 1–99 Scale: The score is normalized against its own history over the last N bars (default: 250) to assign a rating from 1 to 99, where: 90–99 — exceptional strength (top 10%) 70–89 — strong leaders 30–69 — average performers 1–29 — weak, underperforming the market Visual Display: The rating is shown in a color-coded box, which can be positioned in any corner of the chart (configurable in settings). Color indicates strength: 🟢 Green — strong (≥70) 🟠 Orange — moderate (30–69) 🔴 Red — weak (<30) 🛠️ Who is it for? IBD-style investors looking for market-leading stocks. Swing traders who want to enter assets outperforming the broader market. Anyone analyzing relative strength — not absolute returns, but performance relative to SPX. ⚙️ Settings Lookback Period — normalization window (default: 250 bars). Approx. Visible Bars Back — estimated chart width for left-edge positioning. Position — choose where to display the box (9 options: Top Left, Bottom Right, etc.).
Pine Script®指標
由optatus提供
Bitcoin Power Law with Cycle BandsBitcoin Power Law with Cycle Bands DescriptionUnlock the power of Bitcoin’s long-term trends with the Bitcoin Power Law with Cycle Bands script, exclusively available through Bitcoin Wealth Edge! This custom TradingView indicator, built for Pine Script v6, models Bitcoin’s price behavior using a 96% R² power law trendline, derived from days since its genesis (January 3, 2009). Designed to predict cycle tops and bottoms, it features:Power Law Trendline: A cyan line representing fair value (e.g., ~$111,000 as of September 2025), based on a logarithmic regression with adjustable coefficients (a = -17.02, b = 5.83). Cycle Bands: Adjustable red (upper) and green (lower) bands, defaulting to 3.5x and -3.5x multipliers, aligning with historical peaks (e.g., $69K in 2021) and troughs (e.g., $16K in 2022). Dynamic Labels: Real-time labels displaying fair value, upper limit ($180K), and lower limit ($40K), updated on the last bar for quick insights. Follow @HodlerRanch for updates!
Pine Script®指標
由PlebDave提供
All-In-One MA Stack ScalperWhat is this Indicator? This tool is an advanced, multi-layered breakout and trend-following indicator designed for lower timeframes. It identifies high-conviction buy and sell signals by combining moving average stacking with a suite of professional-grade filters. How Does It Work? A signal is generated only when ALL of the following conditions are met: Moving Average Stack (5M Chart): Buy: The close price is above all five moving averages (MAs: 100, 48, 36, 24, 12). Sell: The close price is below all five MAs. Volatility Filter (ATR): Signals only print when the current ATR (14) is at least 80% of its 100-period average, ensuring you only trade in actively moving markets. Candle Structure Filter: The current candle must have a real body that is at least 35% of the candle’s total range, filtering out dojis and indecision bars. Big Candle Filter: The candle’s total range must be at least 40% of the current ATR, avoiding signals on minor, insignificant moves. Volume Filter: The current volume must be at least 80% of its 50-period average, filtering out signals during illiquid or quiet market conditions. Minimum Distance from All MAs: Price must be a minimum distance (20% ATR) away from each MA, confirming a clean breakout and avoiding signals in tight MA clusters or ranging markets. RSI Momentum Filter: Buy: RSI(14) must be greater than 55. Sell: RSI(14) must be less than 45. This ensures trades are only taken in the direction of momentum. ADX Trend Filter: ADX(14,14) must be above 20, ensuring signals only print in trending conditions (not in chop/range). Minimum Bars Between Signals: Only one signal per direction is allowed every 10 bars to avoid overtrading and signal clustering. What Does This Achieve? Reduces noise and false signals common in basic MA cross or stack systems. Captures only strong, high-momentum, and high-conviction moves. Helps you avoid chop, range, and news whipsaws by combining multiple market filters. Perfect for advanced scalpers, intraday trend followers, or as a trade filter for algos/EAs. How to Use It: Apply to your 5-minute chart. Green BUY signals: Only when all bullish conditions align. Red SELL signals: Only when all bearish conditions align. Use as a stand-alone system or as a filter for your own entries. Recommended For: Scalpers & intraday traders who want only the best opportunities. EA and bot builders seeking reliable signal logic. Manual traders seeking confirmation of high-probability breakouts. Tip: Adjust any of the filters (e.g., RSI/ADX thresholds, minBars, minDist) to make it more/less selective for your style or market.
Pine Script®指標
由jonesdaniel2112提供
Strong tendence detector - Detector de Fuerte TendenciaThis chart shows when an asset is in a strong uptrend or downtrend. The legend on the left indicates if the RSI is above 62 or below 38 on the monthly, weekly, and daily timeframes. A strong uptrend is confirmed when all three timeframes are above 62, while a strong downtrend is confirmed when they are all below 38. Periods of a strong uptrend are highlighted with a green background, and periods of a strong downtrend are highlighted in red.
Pine Script®指標
由guilleli提供
Ichimoku Fractal Flow### Ichimoku Fractal Flow (IFF) By Gurjit Singh Ichimoku Fractal Flow (IFF) distills the Ichimoku system into a single oscillator by merging fractal echoes of price and cloud dynamics into one flow signal. Instead of static Ichimoku lines, it measures the "flow" between Conversion/Base, Span A/B, price echoes, and cloud echoes. The result is a multidimensional oscillator that reveals hidden rhythm, momentum shifts, and trend bias. #### 📌 Key Features 1. Fourfold Fusion – The oscillator blends: * Phase: Tenkan vs. Kijun spread (short vs. medium trend). * Kumo Phase: Span A vs. Span B spread (cloud thickness). * Echo: Price vs lagged reflection. * Cloud Echo: Price vs. projected cloud center. 2. Oscillator Output – A unified flow line oscillating around zero. 3. Dual Calculation Modes – Oscillator can be built using: * High-Low Midpoint (classic Ichimoku-style averaging). * Wilder’s RMA (smoother, less noisy averaging averaging). 4. Optional Smoothing – EMA or Wilder’s RMA creates a trend line, enabling MACD-style crossovers. 5. Dynamic Coloring – Bullish/Bearish color shifts for quick bias recognition. 6. Fill Styling – Highlighted regions between oscillator & smoothing line. 7. Zero Line Reference – Acts as a structural pivot (bull vs. bear). #### 🔑 How to Use 1. Add to Chart: Works across all assets and timeframes. 2. Flow Bias (Zero Line): * Above 0 → Bullish flow 🐂 * Below 0 → Bearish flow 🐻 3. With Signal Line: * Oscillator above smoothing line → Possible upward trend shift. * Oscillator below smoothing line → Possible downward trend shift. 4. Strength: * Wide separation from smoothing = strong trend. * Flat, tight clustering = indecision/range. 5. Contextual Edge: Combine signals with Ichimoku Cloud analysis for stronger confluence. #### ⚙️ Inputs & Options * Conversion Line (Tenkan, default 9) * Base Line (Kijun, default 26) * Leading Span B (default 52) * Lag/Lead Shift (default 26) * Oscillator Mode: High-Low Midpoint vs Wilder’s RMA * Use Smoothing (toggle on/off) * Signal Smoothing: Wilder/EMA option * Smoothing Length (default 9) * Bullish/Bearish Colors + Transparency #### 💡 Tips * Wilder’s RMA (both oscillator & smoothing) is gentler, reducing whipsaws in sideways markets. * High-Low Mid captures pure Ichimoku-style ranges, good for structure-based traders. * EMA reacts faster than RMA; use if you want early momentum signals. * Zero-line flips act like momentum pivots—watch them near cloud boundaries. * Signal line crossovers behave like MACD-style triggers. * Strongest signals appear when oscillator, signal line, and Ichimoku Cloud all align. 👉 In short: Ichimoku Fractal Flow compresses multi-layered Ichimoku system into a single fractal oscillator that detects flow, pivotal shifts, and momentum with clarity—bridging price, cloud, and echoes into one signal. Where the cloud shows structure, IFF reveals the underlying flow. Together, they offer a fractal lens into market rhythm.
Pine Script®指標
由singhxgurjit提供
Multiple Divergence Scanner (move to candles and merge scales)This indicator detects and visualizes multiple types of RSI-based divergences, including Regular, Hidden, and Dual-source (Multi) Bullish/Bearish signals. Not limited with RSI only. You can add move functions and it will automaticly combine your options. It offers customizable score filtering, label positioning, and visual styling. Ideal for traders who seek both technical precision and symbolic clarity in their charts. You have to drag it to your candles after adding to your chart. Then right click on price->Merge all scales to right/left.
Pine Script®指標
由Mirach35提供
Cumulative Volume Delta Candles Aggregated (Lite)Cumulative Volume Delta Candles Aggregated (Lite) Multi-Exchange CVD Visualization in Candle Format This indicator provides an aggregated Cumulative Volume Delta (CVD) view across multiple major perpetual futures exchanges in one chart. It is designed for traders who want a broader perspective on buying and selling pressure without switching between multiple charts or relying on a single exchange feed. What It Does Combines volume delta data from Binance, Bybit, OKX, Bitget, and Coinbase into a unified representation. Displays CVD as candlesticks, where each candle reflects the open, high, low, and close of cumulative delta for the selected period. Offers a clear visual of how buying and selling pressure evolves across markets in a structured, familiar candle format. Key Features ✔ Exchange Aggregation: Incorporates data from several top futures markets available on TradingView. ✔ Anchor Reset: Allows you to set an anchor timeframe (e.g., daily) to reset cumulative values for better readability and analysis. ✔ Unit Normalization: Since exchanges report volumes in different units, the indicator normalizes all data and outputs values denominated in the base currency (coins) for consistency. ✔ Hidden OHLC Outputs: Provides open, high, low, and close values as hidden plots for use with other indicators or strategies. ✔ Lightweight Design: Optimized for efficiency while handling multiple real-time data requests. Why It’s Different Unlike standard single-exchange CVD tools, this indicator aggregates the majority of perpetual volume data available on TradingView, offering a more representative view of market sentiment. The candle-based representation of CVD introduces an additional layer of structure, helping traders identify shifts in momentum and pressure with more context than a simple cumulative line. Use Cases - Spotting aggregated buy/sell pressure trends across multiple exchanges. - Confirming breakout or reversal signals with broader volume delta context. - Serving as a custom data source for other indicators or algorithmic strategies.
Pine Script®指標
由mxdvt07提供