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Alpha Trend IndicatorThe Alpha Trend Indicator is a custom-built trend-following tool designed to help traders identify market direction, trend strength, and potential reversal points with high accuracy. It combines multiple technical concepts—ATR (Average True Range), CCI (Commodity Channel Index), and either MFI (Money Flow Index) or RSI (Relative Strength Index)—to create a dynamic trend detection system. 🔑 Key Features: Adaptive Trend Line – Uses ATR and CCI logic (similar to Trend Magic) to plot an Alpha Trend line that adjusts to volatility. MFI / RSI Flexibility – Option to switch between MFI (volume-based momentum) or RSI (price-based momentum) for more tailored analysis. Alpha Trend Offset – Plots a 2-bar shifted reference line to detect changes in price momentum. Buy & Sell Signals – Automatically detects bullish and bearish crossovers between Alpha Trend and its offset line, marking potential entries and exits. Visual Trend Zones – Fills the space between Alpha Trend and its offset with green (bullish) or red (bearish) highlights, making trend direction easy to see at a glance. Alerts – Built-in alert conditions for Buy and Sell signals so traders never miss an opportunity. 📊 How It Works: Trend Detection – When CCI > 0, the Alpha Trend line is set below recent lows minus volatility (ATR). When CCI < 0, it shifts above recent highs plus volatility. Momentum Confirmation – MFI/RSI adds an extra confirmation layer to avoid false signals. Trading Signals – Buy Signal: When Alpha Trend crosses above its offset line. Sell Signal: When Alpha Trend crosses below its offset line. ⚡ Best Use: Works on all timeframes (scalping, swing trading, long-term analysis). Suitable for Forex, Crypto, Stocks, and Commodities. Can be combined with support/resistance or volume analysis for even stronger setups.
Индикатор Pine Script®
от Jaxon007
AlphaTrend Pro — Trend & Momentum Indicator📌 AlphaTrend Pro — Trend & Momentum Indicator The AlphaTrend Pro is a professional trend-following & momentum confirmation tool designed to identify high-probability buy and sell signals in any market (Forex, Crypto, Indices, Commodities). 🔎 How It Works AlphaTrend Bands (EMA ± ATR): Dynamic support/resistance zones based on volatility. Trend Detection: ✅ Price above Alpha Upper Band → Bullish Trend (BUY Zone). ❌ Price below Alpha Lower Band → Bearish Trend (SELL Zone). ⚪ Price between bands → Neutral (No Trade). RSI Filter (Optional): Prevents false entries in ranging markets. Confirms momentum before signals. 📊 Features Clear BUY/SELL signals with arrows on chart. Trend-colored candles for quick visual bias. Built-in alerts for automation (works with bots, Telegram, webhooks). On-screen dashboard showing live trend status. Works on all timeframes & markets (Scalping, Swing, Position Trading). ⚡ Best Use Use on higher timeframes (1H, 4H, Daily) for trend confirmation. Enter on lower timeframes for precise entries. Combine with price action or support/resistance for maximum accuracy.
Индикатор Pine Script®
от Jaxon007
CQ_Historical Candle Color Changer🎯 Purpose This indicator visually distinguishes candles based on how old they are—specifically within a user-defined range (e.g., 1 to 7 days old). It helps traders quickly isolate recent price action from older data, making it easier to interpret overlays like moving averages, volume profiles, or momentum indicators. ⚙️ Key Features - User-Defined Age Range: Set minimum and maximum age in days (e.g., highlight candles that are 1–7 days old). - Custom Colors: Choose highlight colors for candles within the range. - Timeframe Awareness: Works across any chart timeframe (1m, 1h, 1D, etc.), calculating candle age based on actual time elapsed. - Non-Intrusive Display: Candles outside the range retain their default appearance, preserving overall chart readability. 📐 How It Works - The script calculates the age of each candle by comparing its timestamp to the current time. - If the candle falls within the user-defined age range, it’s recolored using the selected style. - Candles older or newer than the range are left untouched. 🧠 Use Cases - Trend Isolation: Focus on recent price action without losing sight of broader context.
Индикатор Pine Script®
от CQuevedo345
2
Zarattini Intra-day Threshold Bands (ZITB)This indicator implements the intraday threshold band methodology described in the research paper by Carlo Zarattini et al. papers.ssrn.com Overview: Plots intraday threshold bands based on daily open/close levels. Supports visualization of BaseUp/BaseDown levels and Threshold Upper/Lower bands. Optional shading between threshold bands for easier interpretation. Usage Notes / Limitations: Originally studied on SPY (US equities), this implementation is adapted for NSE intraday market timing, specifically the NIFTY50 index. Internally, 2-minute candles are used if the chart timeframe is less than 2 minutes. Values may be inaccurate if the chart timeframe is more than 1 day. Lookback days are auto-capped to avoid exceeding TradingView’s 5000-bar limit. The indicator automatically aligns intraday bars across multiple days to compute average deltas. For better returns, it is recommended to use this indicator in conjunction with VWAP and a volatility-based position sizing mechanism. Can be used as a reference for Open Range Breakout (ORB) strategies. Customizations: Toggle plotting of base levels and thresholds. Toggle shading between thresholds. Line colors and styles can be adjusted in the Style tab. Author: Gokul Ramachandran – software architect, engineer, programmer. Interested in trading and investment. Currently trading and researching strategies that can be employed in NSE (Indian market). Contact: (mailto:gokul4trading@gmail.com) LinkedIn: www.linkedin.com Intended for educational and research purposes only.
Индикатор Pine Script®
от gokul4trading
1
by A.Alasfoor RSI + Stochastic Alert with Doji Confirmationa buy and sell signals upon : 1- Sell : red hammer break (lower body) of 5min , ensure the wick is equal to or longer than the red body range. 2- Buy: Green hammer (upper body) break of 5min , ensure the wick is equal to or longer than the red body range. Trigger buy/sell upon the break clean at 1min of the body closing level, risking (SL) the earlier 1 min tops/lows. Credits to Ahmed Alasfoor & Zakariya Hamad AlJulandani by Sumou Oman
Индикатор Pine Script®
от amdalasfoor
عكفة الماكد المتقدمة - أبو فارس ©// 🔒 Advanced MACD Curve © 2025 // 💡 Idea & Creativity: Engineer Abu Elias // 🛠️ Development & Implementation: Abu Fares // 📜 All intellectual rights reserved - Copying, modifying, or redistributing is not permitted // 🚫 Any attempt to tamper with this code or violate intellectual property rights is legally prohibited // 📧 For inquiries and licensing: Please contact the developer, Abu Fares
Индикатор Pine Script®
от ABU_FARIS
عكفة الماكد المتقدمة - أبو فارس ©// 🔒 عكفة الماكد المتقدمة © 2025 // 💡 فكرة وإبداع: المهندس أبو الياس // 🛠️ تطوير وتنفيذ: أبو فارس // 📜 جميع الحقوق الفكرية محفوظة - لا يُسمح بالنسخ أو التعديل أو إعادة التوزيع // 🚫 أي محاولة للعبث بهذا الكود أو انتهاك الحقوق الفكرية مرفوضة قانونياً // 📧 للاستفسارات والتراخيص: يرجى التواصل مع المطور أبو فارس // 🔒 Advanced MACD Curve © 2025 // 💡 Idea & Creativity: Engineer Abu Elias // 🛠️ Development & Implementation: Abu Fares // 📜 All intellectual rights reserved - Copying, modifying, or redistributing is not permitted // 🚫 Any attempt to tamper with this code or violate intellectual property rights is legally prohibited // 📧 For inquiries and licensing: Please contact the developer, Abu Fares
Индикатор Pine Script®
от ABU_FARIS
Highlight Specific Time CandleThis is a simple Pine Script tool that marks candles occurring at a chosen time of the day. You can set the hour and minute (in 24-hour format) from the inputs, and whenever a candle’s timestamp matches that time, the indicator highlights it with a symbol above the bar and an optional background colour. This is useful for: Identifying key intraday times (e.g., market open, midday, closing). Spotting how price reacts at scheduled events (economic data releases, news times).
Индикатор Pine Script®
от amanchaure
IU Trade ManagementDESCRIPTION IU Trade Management is a powerful utility tool designed to help traders manage their trades with precision and clarity. It provides automated Stop Loss, Take Profit, and Break Even calculations using multiple customizable methods. Along with clear SL/TP plotting on the chart, it also displays a detailed trade status table that tracks every important detail including entry price, SL/TP levels, break-even, PNL, and trade duration. This tool is perfect for traders who want to manage risk and rewards visually and systematically. USER INPUTS : -Entry Candle Time: Default 20 Jul 2021 00:00 +0300 (select the candle from which the trade begins) - Entry Price: Default 2333 (define the price at which the trade is executed) - Trade Direction: Default Long (choose between Long or Short) - SL/TP Method: Default ATR (options: ATR, Points/Pips, Percentage %, Standard Deviation, Highest/Lowest, Previous High/Low) - Risk to Reward: Default 3 (set custom risk-to-reward ratio) - Use Break Even: Default false (option to enable break-even) - Plot Break Even Line: Default false (option to display BE line) - RTR of Break Even Point: Default 2 (factor used for BE calculation) SL/TP Method Specific Inputs: - ATR Length: Default 14 - ATR Factor: Default 2 - Points/Pips: Default 100 - Percentage: Default 1% - Standard Deviation Length: Default 20 - Standard Deviation Factor: Default 2 - Highest/Lowest Length: Default 10 Trade Status Table Settings: - Show Trade Status: Default true - Table Size: Default small (options: normal, tiny, small, large) - Table Position: Default top right - Frame Width: Default 2 - Table Color: Default black - Frame Color: Default gray - Border Width: Default 2 - Border Color: Default gray - Text Color: Default purple (RGB 212, 0, 255) HOW TO USE THE INDICATOR: 1. Set the entry candle time and entry price manually. 2. Select whether the trade is Long or Short. 3. Choose the preferred SL/TP calculation method (ATR, Percentage, Points, STD, High/Low, Previous High/Low). 4. Define your risk-to-reward ratio and enable break-even if required. 5. The indicator will automatically plot your Entry, Stop Loss, Take Profit, and Break Even levels on the chart. 6. A detailed trade management table will appear, showing trade direction, SL, TP, PNL (points and %), SL/TP method, and total trade time. WHY IT IS UNIQUE: - Offers multiple methods to calculate SL and TP (ATR, Percentage, Points, Standard Deviation, High/Low, Previous High/Low) - Built-in Break Even functionality for risk-free trade management - Real-time PNL tracking in both points and percentage - Trade status table for complete transparency on all trade details - Visual plotting of SL, TP, and Entry with color-coded zones for clarity HOW USER CAN BENEFIT FROM IT : - Helps traders manage risk and reward with discipline - Eliminates guesswork by automating SL and TP levels - Provides clear visual guidance on trade exits and risk management - Enhances decision-making with live trade tracking and performance statistics - Suitable for manual traders as a trade manager and for strategy developers as a risk management reference
Индикатор Pine Script®
от Shivam_Mandrai
SuperTrendSAP1212This indicator combines Supertrend, VWAP with bands, and an optional RSI filter to generate Buy/Sell signals. How it works Supertrend Flip (ATR-based): Detects when trend direction changes (from bearish to bullish, or bullish to bearish). VWAP Band Filter: Signals only trigger if the candle close is beyond the VWAP bands: Buy = Supertrend flips up AND close > VWAP Upper Band Sell = Supertrend flips down AND close < VWAP Lower Band Optional RSI Filter: Buy requires RSI < 20 Sell requires RSI > 80 Can be enabled/disabled in settings. Features Choice of VWAP band calculation mode: Standard Deviation or ATR. Adjustable ATR/StDev length and multiplier for VWAP bands. Toggle Supertrend, VWAP lines, and Buy/Sell labels. Alerts included: add alerts on BUY or SELL conditions (use Once Per Bar Close to avoid intrabar signals). Use Works best on intraday or higher timeframes where VWAP is relevant. Use the RSI filter for more selective signals. Can be combined with your own stop-loss and risk management rules. ⚠️ Disclaimer: This script is for educational and research purposes only. It is not financial advice. Always test thoroughly and trade at your own risk.
Индикатор Pine Script®
от InvestingIsBoring
AlphaTrend Strategy – Advanced Trend & Momentum Trading SystemThe AlphaTrend Strategy is a powerful trading system designed to capture trend-following opportunities while filtering out low-quality setups. It combines multiple layers of confirmation, including: ✅ AlphaTrend entry & exit signals based on dynamic ATR and MFI calculations ✅ Trend filter with customizable moving averages (SMA, EMA, WMA, VWMA, HMA) ✅ Momentum filter using ADX with optional DI+ / DI– checks ✅ Session-based trading to restrict entries to specific market hours This script supports both long & short trades, provides session highlights, and plots risk-reward levels for better trade management. Traders can fine-tune the multipliers, lookback periods, and filters to adapt the strategy across different assets and timeframes. ⚡ Ideal for forex, crypto, and indices where trend-following strategies thrive.
Стратегия Pine Script®
от Jaxon007
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.
Индикатор Pine Script®
от EdgeTools