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Trade Holding Time Background HighlighterTrade Holding Time Background Highlighter This script visually highlights the chart background based on how old each bar is relative to the current time. It’s designed for crypto futures traders (and other active traders) who want to quickly see whether price action falls inside a day trading window, a swing trading window, or is considered older history. ⸻ 🔑 Features • Dynamic Background Highlighting • Day Trader Zone (default = last 24 hours, light green). • Swing Trader Zone (default = last 2 weeks, light yellow). • Older Zone (beyond 2 weeks, light gray). • Customizable Colors • Choose your own background colors for each zone. • Adjust opacity to make shading subtle or bold. • Adjustable Timeframes • Change day trading hours (default: 24 hours). • Change swing trading window (default: 14 days). • Simple, Intuitive Design • Instantly see whether the current market structure is suitable for scalps/day trades, swing trades, or simply part of older price action. ⸻ 🎯 Why Use This? As a futures/perpetual trader, knowing the context of price action is crucial: • Scalpers/Day Traders focus on the most recent 24h. • Swing Traders look back 1–2 weeks. • Anything older often has less weight for short-term setups. This script highlights those zones automatically, saving you time and giving clarity on whether you’re trading inside a fresh opportunity window or old, less relevant price action.
مؤشر Pine Script®
من ‎marktan93‎
RED: Buy, Sell & TargetsRED: Buy, Sell & Targets What it is - Buy & Sell Alerts — a multi-factor scoring engine that highlights potential reversal/exhaustion areas for both longs and shorts. - Buy & Price Target — a bottom-finder that proposes the nearest meaningful upside target and tracks hits. Both modules can be toggled independently in the settings; they’re enabled by default. How to read the chart 1) Buy/Sell panel (scored signals) - Each bar receives a Buy score and a Sell score from 0 to 10. - When the score passes the adaptive threshold, you’ll see: A transparent label (hover to see a checklist tooltip). If the score is very high, a colored badge with the number appears near the bar. - Color intensity ≈ conviction (deeper green/red = stronger confluence). - Small dots mark qualified signals with scores below the “very high” tier. Score interpretation (rule of thumb) - 7+: Valid setup (moderate confluence). - 8+: Stronger confluence. - 9–10: High-conviction / rare events. The minimum score to confirm a signal adapts to the timeframe; higher timeframes naturally demand comparable or slightly lower scores. Note: On symbols without usable volume, you’ll see a top-right warning and the maximum possible score becomes 9. 2) Buy & Target panel (entries + exits) - When a qualified bottom is detected and the nearest overhead structure offers enough room, a BUY label shows: 💰 Entry (close of the signal bar) 🎯 Target (nearest pivot-based objective above price) - When price tags the target later, the script prints a 🎯 exit marker above that bar. - A live stats table (top-right) summarizes Buys, Wins, Open trades, Win rate, Net P&L % for these target plays. Alerts This indicator ships with multiple alert conditions, including: - Buy/Sell score tiers (e.g., “BUY ≥ 9”, “SELL ≥ 9”) - Target module (“🎯 BUY (target ≥3%)”, “🎯 Target reached”) Important: The checkboxes in settings only authorize alerts to fire; you still need to create alerts in TradingView and choose the desired condition. Practical tips - Prefer bar close for decisions and alerts to reduce noise. - Cross-check signals across multiple timeframes (e.g., daily with intraday). - Use the score as a confidence meter, not as an all-in trigger; combine with your risk management.
مؤشر Pine Script®
من ‎REDexplorateur‎
CF Cycle Low Projection V2Overview This indicator helps traders analyze repeating market cycles by detecting significant pivot lows and projecting when the next cycle low may occur. It provides timing context to support decision-making but does not generate direct buy/sell signals. How it works Pivot detection : Confirms swing lows using left/right bars. Filters (minimum % move and optional ATR separation) ensure only meaningful lows are counted. Cycle averaging : Calculates the average interval (and standard deviation) between recent pivot lows. Projection : Adds the average interval to the last pivot low to forecast the next potential cycle low. If that point lies in the past, the script rolls forward until the projection is in the future. Timing window : A shaded area around the ETA is drawn, based on either standard deviation or a percentage of the average, showing when a low is statistically more likely to occur. Visualization: • Vertical line = projected cycle low • Shaded box = timing window • Label = countdown in weeks/days/hours • HUD = status, ETA, intervals used How to use Select your preferred timeframe (works on intraday and higher). Allow pivots to accumulate; once the HUD shows Status: OK, projections will appear. Use the ETA line and timing window together with structure, liquidity levels, and support/resistance zones. Combine with your own strategy and risk management rules. Notes Works on any market supported by TradingView (crypto, stocks, forex, indices). Filters can be adjusted to reduce noise (e.g., increase % move or ATR multiplier). This tool is designed for cycle timing analysis only. It does not predict exact prices or guarantee outcomes. Some traders refer to this approach as “camel cycle trading,” but here it is implemented as a pivot-based cycle projection tool.
مؤشر Pine Script®
من ‎Hugors00‎
5
YBL – EMA Pro (Rellena + Borde) + Alertas🔹 Full Description (long) The YBL – EMA Pro indicator is a professional moving average tool designed for traders who want clarity, precision, and flexibility in trend analysis. It combines classic EMA logic with modern visual enhancements and alerts for more effective trading decisions. ✅ Features: Dual EMA Display (fast & slow, user-selectable periods). Filled Zone between EMAs (colored area highlights trend bias). Dynamic Border Lines (outline effect for extra clarity). Customizable Colors & Transparency for both fill and borders. Multi-Timeframe Support (use higher-timeframe EMA on your current chart). Smart Alerts: EMA crossovers (bullish/bearish). Price crossing above/below EMA. Custom alert conditions for flexible strategy building. Scalping & Swing Trading Friendly — adapts to different timeframes. Lightweight & Fast — optimized to avoid heavy load on charts. 👉 With YBL – EMA Pro, you not only track the trend but also get visual confirmation and automatic alerts for actionable entries/exits.
مؤشر Pine Script®
من ‎YBL_YURIEL‎
YBL – LITE HUD (Vol/Δ + RVOL + Squeeze) + ADV📌 Description – YBL – LITE HUD (Vol/Δ + Heatmap RVOL + Squeeze) + ADV The YBL – LITE HUD is a compact yet powerful dashboard built for traders who value clarity and precision without sacrificing depth. 🔹 Vol/Δ (Volume Delta): highlights the imbalance between buyers and sellers in real time, exposing absorption and institutional pressure. 🔹 Heatmap RVOL: transforms relative volume into a dynamic heatmap, emphasizing unusual activity compared to historical averages. 🔹 Squeeze Momentum: detects volatility compression phases and signals potential breakout opportunities. 🔹 Normalization (z-score): all calculations are scaled for consistent comparison, with colors mapped to zVol intensity for intuitive reading. 🔹 ADV (Average Daily Volume): provides context with daily average volume benchmarks to validate the true strength of moves. 👉 The result is a lightweight, visual HUD designed for scalping and intraday trading, combining order flow, relative volume, and statistical context into a single glance.
مؤشر Pine Script®
من ‎YBL_YURIEL‎
Chanlun ZSLX pen czsc Channel Chan Theory🏆 Chanzhongshuochan (CZSC) ZSLX Recursive Version Indicator - Flagship Edition Background A Decade of Craftsmanship, a Major Upgrade This Chanzhongshuochan (Chan Theory) indicator has undergone nearly 10 years of meticulous refinement and continuous optimization. Since its initial design in 2015, it has been validated through real trading practice. Now, it makes its debut on TradingView with a brand-new upgrade, featuring more comprehensive functions and superior performance. It truly implements all core theories from the original Chan Theory texts, including a complete system covering candlestick containment processing, fractal identification, pen-segment analysis, pivot zone theory, trading signal (buy/sell point) positioning, and divergence analysis. It serves as a professional and all-encompassing technical analysis tool for Chan Theory enthusiasts in the industry. 🎯 Chinese Translation for User Indicator Interface: ● 顶背离: Top Divergence ● 底背离: Bottom Divergence ● 顶背驰: Top Divergence Including Trend Structure ● 底背驰: Bottom Divergence Including Trend Structure ● 趋势: Trend ● 盘整: Consolidation ● 扩张: Expansion ● 大级别盘整: Higher-Timeframe Consolidation ● 一买: First Buy Signal ● 二买: Second Buy Signal ● 类二买：Quasi-Second Buy Signal ● 三买: Third Buy Signal ● 一卖: First Sell Signal ● 二卖: Second Sell Signal ● 类二卖：Quasi-Second Sell Signal ● 三卖: Third Sell Signal 📦 Parameter Settings ● Number of Candlesticks for Calculation: Up to 5,000 candlesticks. Even free TradingView users are limited to this data volume. ● Pen Detail Parameters: Adjustable options include "Old Pen", "New Pen", "Pen Based on Secondary High/Low", "Pen Based on Absolute High/Low", and "Strict Pen". Select via numerical options corresponding to each type. ● MA (Moving Average) and Bollinger Bands Display Parameters: Check the box to enable display; uncheck to hide (configure as needed). ● Pivot Zone Price & Pen-Segment Price Display: Check the box to enable display; uncheck to hide (configure as needed). ● MACD Parameters: Adjustable according to personal needs (set the periods for fast EMA, slow EMA, and signal line). ● MA Parameters: Default values can be manually adjusted. ● Pen Divergence, Segment Divergence, & Pen-Segment Divergence Display: Check the box to enable display; uncheck to hide (configure as needed). ● Pen-Segment Divergence Alert: Disabled by default. Enable by checking the box. To set up alerts: a. Go to TradingView’s "Alerts" interface. b. Under "Condition", select this indicator. c. Choose "Any alert() function call". d. Set the notification type, then create the alert to receive notifications. 🔥 Integration of Other Common Indicators & Rationale ● ✅ Moving Average (MA): Chan Theory inherently uses MA overlay for analysis. Adding MA here allows users to analyze price trends from multiple perspectives, making it an essential inclusion. ● ✅ Bollinger Bands: Combining Bollinger Bands (a price channel tool) with Chan Theory provides additional perspectives for trend analysis and improves accuracy, hence its integration. ● ✅ MACD: A 不可或缺 (indispensable) indicator for analyzing trend strength in Chan Theory. It is integrated to facilitate seamless analysis. ● ✅ Rationale for Integrating Shared Core Code between Two Scripts: The scripts Chanlun ZSLX pen czsc Channel Chan Theory and Chanlun FBFD pen czsc Channel Chan Theory share some underlying code. Here’s why integration is necessary: ○ The core logics of the original Chan Theory — including candlestick containment processing, MACD divergence analysis, candlestick objects, and pen-related calculations — are identical in both scripts. ○ However, significant differences exist in the top-level logics for segment division, trend recursion, display effects, and functions. ○ Additionally, TradingView imposes limits on script code size, making it impossible to fully integrate the two versions’ line-drawing features into one. ○ Therefore, this indicator reuses shared code components, including: ■ The Start_kxian() function (returns candlestick objects), ■ The Start_bi() function (returns pen objects), ■ The showKxianzsfunc() function (draws candlestick-based pivot zones), ■ MACD divergence judgment code. 🎯 Technical Principles 🔥 1. Comprehensive Coverage of Original Chan Theory Functions ● ✅ Real-Time Candlestick Containment Processing – Intelligent Recognition of Containment Relationships ○ In uptrends: Select the higher high and higher low values (prioritize higher extremes). ○ In downtrends: Select the lower high and lower low values (prioritize lower extremes). ● ✅ Accurate Fractal Marking – Automatic Identification of Top/Bottom Fractals ○ When the "Old/New Pen" parameter is set to 2, the high/low points of each top/bottom fractal are plotted. ○ Top Fractal: Among three consecutive candlesticks, the middle candlestick has a higher high and a higher low than the adjacent two. ○ Bottom Fractal: Among three consecutive candlesticks, the middle candlestick has a lower low and a lower high than the adjacent two. ● ✅ Multi-Dimensional Pen-Segment Analysis – Complete System for Pens & Segments ○ Pens: ■ Old Pen: Strictly follows the traditional Chan Theory definition of "pen". ■ New Pen: Adheres to the more flexible "new pen" definition from the original Chan Theory. ■ Fractal Pen: Forms a pen directly from top/bottom fractals (responds to price changes; many peers misclassify this as a "sub-timeframe pen"). ○ Segments: This version adopts a recursive logic. While some single pens are treated as segments (differing from pure segment division), most segment-drawing results align with standard methods — the core difference lies in the line-drawing logic. ● ✅ Multi-Level Pivot Zone Integration – Candlestick-, Pen-, & Segment-Based Pivot Zones ○ Candlestick Pivot Zone: The smallest-level pivot zone in Chan Theory. ○ Pen Pivot Zone: Follows the original Chan Theory for division — the zone’s high is the lowest high of overlapping pens; the zone’s low is the highest low of overlapping pens. ○ Segment Pivot Zone: Follows the original Chan Theory for division — the zone’s high is the lowest high of overlapping segments; the zone’s low is the highest low of overlapping segments. ● ✅ Intelligent Trading Signals – Accurate Positioning of Three Types of Buy/Sell Points ○ Type 1 Buy/Sell Signal ■ Principle: Based on Chan Theory’s "trend divergence" — momentum weakens when a trend structure forms. ■ Analysis Method: Identify bottom divergence (for buys) or top divergence (for sells) in the final pivot zone of a trend structure. ■ Application: For reference only. Comprehensive analysis requires combining momentum decay across multiple timeframes. ○ Type 2 Buy/Sell Signal ■ Principle: The first pullback low (for buys) or rebound high (for sells) after a Type 1 signal concludes. ■ Analysis Method: After a Type 1 structure forms, prices may occasionally break previous lows/highs. ■ Application: For reference only. Comprehensive analysis requires combining momentum decay across multiple timeframes. ○ Type 3 Buy/Sell Signal ■ Principle: A standard Type 3 buy signal occurs when prices break above the first pivot zone after a Type 1 buy, then pull back to test the zone. For ease of monitoring, all "breakout + pullback" patterns are marked as Type 3 signals here. ■ Identification Method: Beginners are advised to trade standard Type 3 structures (post-Type 1 signals), though such structures are relatively rare. ■ Application: For reference only. Comprehensive analysis requires combining momentum decay across multiple timeframes. ● ✅ Divergence Alert – Exclusive Divergence Algorithm ○ Principle: Uses MACD momentum statistics from the original Chan Theory to distinguish between "pen-structure divergence" and "segment-structure divergence". Specific values are labeled on the chart to differentiate MACD momentum for pens vs. segments. More complex statistical features will be added in future updates. For reference on the current timeframe only — multi-timeframe momentum analysis is still required. ● ✅ Trend Structure Recursion – Exclusive Trend Recursion Function ○ Principle: This is a "same-timeframe decomposition" version of trend types. Trends end as close to absolute highs/lows as possible. Some single pens may be treated as segments, resulting in more natural-looking trend structures. ⚙️ Indicator Features 🌟 1. Diverse Pen-Segment Algorithm Engines ● 🎨 Three Pen Algorithms: "Traditional Old Pen", "New Pen", "Top/Bottom Fractal Pen". ● 🔧 Extensive Parameter Adjustments: Fine-grained control over "secondary high/low pens", "fractal range judgment", etc. ● 📊 Dual Recursive Division: Recursion starting from pens + higher-timeframe recursion. ● 🎯 Customizable Parameters: Adapts to different Chan Theory interpretations and trading styles. 🌟 2. Multi-Level Intelligent Integration System ● 📈 Synchronized Calculation & Display: Linked analysis of pens, segments, and advanced recursive segments. ● 🏗️ Exclusive Recursion Algorithm: Accurate identification of advanced recursive segments. ● 🎪 Multi-Level Pivot Zone System: Full coverage of three pivot zone levels (candlestick, pen, segment). ● 📊 Holistic Market Analysis: Provides comprehensive insights into real-time market dynamics. 🎨 3. Professional Visual Customization ● 🌈 Custom Color Schemes: Perfectly matches personal chart styles. ● 💰 Price Label Display: Marks key prices for pens, segments, and pivot zones. ● 📐 Professional Auxiliary Tools: Practical features like MA and Bollinger Bands. ● 🎁 Included MACD: A supporting indicator for the sub-chart. ⏰ 4. Seamless Candlestick Replay Support ● 🔄 Historical Data Review: Fully compatible with TradingView’s replay function. ● 📚 Powerful Market Research Tool: Enhances the ability to analyze historical price trends. ● 🧠 Improved Decision-Making: Deepens market insights and judgment. 📦 Feature & Interface Showcase by Product Version Advanced Recursive Version ⭐ Exclusive Algorithm Suitable for: Advanced Chan Theory users seeking precise trend analysis. Exclusive Features: ● 🚀 Rare Algorithm: A pure recursive version (most competitors only reach segment-level analysis, which is their limit). ● 🎯 Optimized High/Low Points: Trend endpoints are accurately positioned at absolute highs/lows. ● 🏆 Natural Trend Structures: More natural and rational distribution of high/low points. ● 💎 Complete Chan Theory Elements: Covers all core components of Chan Theory. ● ⚡ Rich Pen Details: Extremely detailed processing of pen structures. User Feedback: The preferred version among many senior Chan Theory users, with excellent real-trading performance. Screenshot: --- 🎊 Experience Now & Start Your Professional Chan Theory Analysis Journey! Pre-Purchase Notes Chanzhongshuochan (Chan Theory) is relatively complex. While this software strives to implement all functions from the original texts, minor imperfections or unaddressed details may exist — these will be gradually improved in future updates. ● Users with concerns are advised to test the indicator for a few days first. Purchase only if it meets your needs; otherwise, treat the test as a trial. ● Feedback on issues or bugs is welcome. The developer will update, modify, and optimize the indicator in their spare time. Key Function Screenshots 1. Multi-Level Recursion 2. Candlestick Containment Processing 3. Area Statistics + Pivot Zone High/Low Price Display 4. Multi-Level Consolidation Divergence 5. Overlaid Practical Moving Averages 6. Fractal Pens 7. Candlestick-Based Pivot Zones 8. Dozens of Adjustable Parameters 9. Optional Alert Functions (More to be Added in Future Updates) 10. Trend Structure Markers 🏆 缠中说禅ZSLX 递归版指标 - 旗舰版 背景 十年匠心，重磅升级 这套缠中说禅指标历经近10年的精心打磨与持续优化，从2015年初版设计至今，已经过实战验证。现全新升级登陆TradingView，功能更加完善，性能更加卓越。 真正实现了缠论原文中的所有核心理论，包括K线包含、分型识别、笔段分析、中枢理论、买卖点定位、背离背驰等完整体系，为缠论爱好者提供业界专业、全面的技术分析工具。 📦参数设置 - 计算K线数量：最多计算5000根，免费tradingview用户也只能这么多数据 - 笔细节参数：新旧笔、次高低点笔、最高低点笔、严格笔 可以调整可按标的数字选 - 显示均线和布林线参数：根据需要设置打钩就是显示，取消打钩就是隐藏 - 显示中枢价格和笔段价格：根据需要设置打钩就是显示，取消打钩就是隐藏 - macd参数：可以根据自己需要调整参数 快 慢 信号线周期设置 - 均线参数：默认参数可以自己手动调整 - 显示笔背离、段背离、笔段背驰：根据需要设置打钩就是显示，取消打钩就是隐藏 - 笔段背离背驰报警：默认关闭可以设置打钩开启，然后在tradingview的警报界面，条件里选择指标，设置：任何alert()函数调用，再设置通知类型，然后创建报警就可以收到报警 🔥整合其他常用指标和原因： ✅均线：因为缠论里是有使用均线叠加分析，这种更方便用户从不同视角分析走势，所以必须增加 ✅布林线：布林线通道，结合缠论，也可以更多的视角分析走势，提高准确度，所以也增加了 ✅macd:这个是缠论分析理论里必不可少的分析走势力度的指标，所以也必须整合进来方便分析 ✅Chanlun ZSLX pen czsc Channel Chan Theory这个脚本和Chanlun FBFD pen czsc Channel Chan Theory这个脚本有些底层的源码是一样的必须整合原因： 因为缠论原文底层的K线包含、macd背离背驰、K线对象和笔部分都是一样的，但是顶层的线段和走势递归两种划分原理和显示效果和功能是有很大区别，并且tradingview社区的源码量有限制，导致也无法两个版本划线完全整合成一个，所以，这个脚本里的Start_kxian函数返回的kxian对象、 Start_bi函数返回的bi 对象 、showKxianzsfunc画K线中枢部分的源码、macd背离背驰判断部分代码，会有一样的整合部分源码 🎯 技术原理 🔥 1. 全方位缠论原文功能覆盖 ● ✅ 实时K线包含处理 - 智能识别包含关系 ○ - 上涨K线中取高点高值、低点高值，高高取高 - 下跌K线中取高点低值、低点低值，低低取低 ● ✅ 精准分型标记 - 顶底分型自动识别 ○ - 通过新旧笔参数设置2，会画出每个顶底分型高低点 ○ - 顶分型：三根k线中，中间K线高点高于两侧K线，低点也高于两侧 - 底分型：三根k线中，中间K线低点低于两侧K线，高点也低于两侧 ● ✅ 多维笔段分析 - 笔、线段完整体系 ○ - 老笔：传统缠论严格笔定义，符合原文 - 新笔：传统缠论新笔定义，符合原文宽松笔条件 - 分型笔：顶底分型就成笔，价格反应比较敏锐（同行很多人把本周期分型笔标成次级别） ○ - 线段：这个版本采用递归思路，有的地方一笔处理成段，大部分地方跟纯分段一样，但是划线原理不同 ● ✅ 多级中枢联立 - K线中枢、笔中枢、线段中枢 ○ - K线中枢：最小级别的缠论中枢 - 笔中枢：采用原文的笔中枢划分区间，高点取重叠部分的最低点，低点取重叠部分最高点 - 线段中枢：采用原文的线段中枢划分区间，高点取线段重叠部分的最低点，低点取线段重叠部分最高点 ● ✅ 智能买卖点 - 三类买卖点精准定位 ○ 1类买卖点 - 原理：基于缠论趋势背驰原理，形成趋势结构时候，力度减弱 - 分析方法：通过判断趋势结构中最后中枢形成底背驰 - 应用：只是参考，具体分析需要多周期力度衰减结合判断 2类买卖点 - 原理：基于1类买卖点结束后，第一个回调低点 - 分析方法：在形成一类结构后，有时候也会破低点 - 应用：只是参考，具体分析需要多周期力度衰减结合判断 3类买卖点 - 原理：标准的三买是在1买形成后，突破第一个中枢的第一个回踩，这边为了方便看盘，统一突破回踩就标三买 - 识别方法：新手建议选择一类买点后的标准三买结构操作，但是可能这种结构比较少 - 应用：只是参考，具体分析需要多周期力度衰减结合判断 ● ✅ 背离背驰预警 - 独家背离背驰算法 ○ - 原理：采用缠论原文的macd力度统计，区分笔结构的背离背驰和段结构的背离背驰，并且在图形上标上具体数值，区分笔和段的macd力度，后期这个还要继续升级更复杂的统计，仅做本周期的参考，具体也是要多周期力度分析 ● ✅ 走势结构递归 - 独家走势递归功能 - 原理：走势类型版本，这个是同级别分解版本，走势尽量结束在最高最低点，有的地方可能一笔成段，· 走势更为自然 ⚙️ 指标特点 🌟1. 多样化笔段算法引擎 ○ 🎨 三大笔算法: "传统旧笔"、"新笔"、"顶底分型笔" ○ 🔧 海量参数调节: 次高低笔、分型区间判断等精细化控制 ○ 📊 双重递归划分: 笔开始递归 + 大级别递归 ○ 🎯 个性化参数调整: 满足不同缠友的理解需求与交易风格 🌟 2. 多级别智能联立系统 ○ 📈 同步计算显示: 笔、线段、递归高级段联动分析 ○ 🏗️ 独家递归算法: 高级递归段精准识别 ○ 🎪 多级中枢体系: 三重中枢级别完整覆盖 ○ 📊 全景市场分析: 提供全面的市场动态洞察 🎨 3. 专业视觉定制 ○ 🌈 自定义配色方案 - 完美匹配个人图表风格 ○ 💰 价格标识显示 - 笔、段、中枢关键价位标注 ○ 📐 专业辅助工具 - 均线、布林线等实用功能 ○ 🎁 附带MACD - 配套附图指标 ⏰ 4. 完美K线回放支持 ○ 🔄 历史数据回顾 - 完美支持TradingView回放功能 ○ 📚 市场研究利器 - 提升历史走势分析能力 ○ 🧠 决策能力增强 - 深化市场洞察与判断水平 📦 产品版本功能界面展示 高级递归版 ⭐ 独家算法 适合：高阶缠友，追求极致走势分析 独家特色： ● 🚀 全网罕见算法: 纯递归版本，其他家最多只能设计到分段级别就是极限了 ● 🎯 优化高低点: 走势结束点精准定位最高最低点 ● 🏆 自然走势结构: 高低点分布更加自然合理 ● 💎 完整缠论元素: 涵盖所有缠论核心要素 ● ⚡ 超丰富笔细节: 笔的处理细节极其丰富 🌟 客户反馈: 众多资深缠友首选版本，实战效果卓越 快照 --- 🎊 立即体验，开启专业缠论分析之旅！ 售前说明：缠中说禅理论，相对比较复杂，软件尽量实现原文的功能，但是也难免有些瑕疵地方，无法处理到位，这边后期会陆续完善，介意的客户可以先试用几天，觉得合适再买，不合适就当测试下,欢迎大家反馈问题和bug，掌柜有空会后期更新修改和优化 1.多级别递归 2.K线包含 3.面积统计+中枢高低点价格显示 4.多级别盘整背离背驰 5.叠加实用均线 6.分型笔 7。k线中枢 8.几十个可选参数调整 9，可选的一些报警功能，后期陆续完善，更丰富 10.走势结构标志
مؤشر Pine Script®
من ‎gaoguifeng‎
1
Sortino Ratio -> PROFABIGHI_CAPITAL🌟 Overview This Sortino Ratio → PROFABIGHI_CAPITAL implements advanced risk-adjusted performance measurement focusing specifically on downside volatility for superior portfolio evaluation. It provides Enhanced Sortino Ratio calculation with downside deviation analysis , Customizable risk-free rate benchmarking for different market environments , EMA smoothing for trend clarity and noise reduction , and Dynamic threshold-based visualization with performance classification for comprehensive risk-adjusted return analysis. 🔧 Advanced Risk Measurement Architecture - Professional Sortino Ratio implementation focusing exclusively on downside risk measurement for accurate performance evaluation - Source Selection Framework with customizable price input allowing close, high, low, or other price sources for flexible analysis adaptation - Calculation Period Management with adjustable lookback period for statistical significance balancing responsiveness versus stability - Annual Risk-Free Rate Configuration enabling benchmark comparison against government bonds, treasury rates, or other risk-free instruments - EMA Smoothing System reducing noise and providing clearer trend identification through exponential moving average filtering - Dynamic Threshold Framework with strong and weak performance classification levels for objective performance assessment - Cryptocurrency Annualization using 365-day factor for proper crypto market risk-adjusted return calculation 📊 Sortino Ratio Calculation Engine - Periodic Returns Computation calculating bar-to-bar percentage changes for accurate return measurement across different timeframes - Risk-Free Rate Conversion transforming annual risk-free rates into period-appropriate benchmarks for proper comparison - Mean Return Analysis using Simple Moving Average over calculation period for statistical trend identification - Downside Deviation Framework measuring only negative deviations below risk-free rate for true downside risk assessment - Mathematical Precision implementing squared deviation calculations for proper statistical variance measurement - Zero-Division Protection preventing calculation errors through proper mathematical validation and edge case handling - Annualization Factor Application scaling periodic calculations to annual equivalents for standardized performance comparison 🔬 Advanced Statistical Implementation - Downside-Only Risk Measurement focusing exclusively on negative returns below risk-free threshold for accurate risk assessment - Squared Deviation Accumulation using proper statistical methodology for variance calculation with mathematical precision - Mean Downside Squared Calculation averaging squared negative deviations over calculation period for statistical accuracy - Square Root Standard Deviation converting variance to standard deviation for proper risk measurement units - Excess Return Calculation measuring portfolio performance above risk-free rate for true alpha generation assessment - Mathematical Validation Framework ensuring proper handling of edge cases and preventing division by zero errors - Statistical Significance using sufficient calculation periods for reliable Sortino Ratio measurement and trend identification 📈 EMA Smoothing and Trend Analysis - Exponential Moving Average Application reducing short-term noise while preserving trend direction for clearer signal interpretation - Smoothing Period Configuration balancing responsiveness versus stability through adjustable EMA length parameters - Trend Persistence Analysis identifying sustained performance improvements or deteriorations through smoothed ratio tracking - Signal Quality Enhancement filtering market noise while maintaining sensitivity to genuine performance changes - Null Value Protection using nz() function to handle missing values and ensure continuous ratio calculation - Real-Time Updates providing current smoothed Sortino values for immediate performance assessment and decision making 🎨 Dynamic Visualization Framework - Performance-Based Color Coding using green for strong performance above upper threshold and red for weak performance below lower threshold - Neutral Zone Visualization displaying gray coloring for performance between thresholds indicating moderate risk-adjusted returns - Threshold Reference Lines showing strong and weak performance boundaries through horizontal dashed lines for clear performance classification - Dynamic Line Width using prominent line display for clear trend identification and professional chart presentation - Real-Time Color Updates adjusting visualization based on current performance relative to threshold settings - Professional Styling implementing institutional-grade visual elements for serious portfolio analysis and performance tracking ⚖️ Risk-Adjusted Performance Assessment - Downside Risk Focus measuring only negative volatility for more accurate risk assessment compared to traditional Sharpe ratio - Asymmetric Risk Recognition acknowledging that upside volatility is desirable while downside volatility represents true risk - Benchmark Relative Performance comparing returns against risk-free alternatives for absolute performance measurement - Statistical Robustness using proper mathematical formulation for reliable risk-adjusted return calculation - Performance Classification providing objective strong/weak performance thresholds for systematic evaluation - Trend Analysis Capability identifying improving or deteriorating risk-adjusted performance through smoothed trending 🔍 Advanced Configuration Options - Flexible Source Selection accommodating different price sources for various analysis requirements and asset characteristics - Adaptive Calculation Periods allowing adjustment for different market conditions and analysis timeframes - Risk-Free Rate Customization enabling comparison against various benchmarks including government bonds and treasury rates - Smoothing Parameter Control balancing signal clarity versus responsiveness through adjustable EMA periods - Performance Threshold Management setting custom strong and weak performance boundaries for specific strategy requirements - Precision Control using three decimal places for accurate ratio measurement and detailed performance tracking 📊 Professional Portfolio Analysis Applications - Strategy Performance Evaluation measuring risk-adjusted returns for trading strategy assessment and optimization - Portfolio Comparison comparing multiple strategies or assets using standardized Sortino measurements - Risk Management Integration identifying periods of poor risk-adjusted performance for strategy adjustment - Benchmark Outperformance tracking excess returns above risk-free alternatives for alpha generation measurement - Performance Monitoring continuous assessment of strategy effectiveness through smoothed ratio trending - Institutional-Grade Analysis providing professional portfolio management metrics for serious investment analysis 🔧 Technical Implementation Features - Mathematical Accuracy implementing proper Sortino formula with correct statistical methodology and precision handling - Computational Efficiency using optimized loops and calculations for real-time performance measurement - Error Prevention incorporating comprehensive validation and edge case handling for reliable operation - Memory Management efficient variable usage and calculation methods for optimal indicator performance - Real-Time Processing providing immediate updates with each new bar for current performance assessment - Professional Standards following institutional portfolio analysis methodology for serious risk management applications ✅ Key Takeaways - Advanced Sortino Ratio implementation focusing exclusively on downside risk for superior portfolio performance measurement - Customizable risk-free rate benchmarking enabling comparison against various market alternatives and investment environments - EMA smoothing system reducing noise while preserving trend identification for clearer performance signal interpretation - Dynamic threshold-based visualization providing objective performance classification through color-coded strong/weak boundaries - Professional statistical implementation using proper mathematical methodology for institutional-grade risk-adjusted return analysis - Flexible configuration options accommodating different analysis requirements, timeframes, and market conditions - Comprehensive risk management integration enabling continuous strategy performance monitoring and optimization for superior portfolio management
مؤشر Pine Script®
من ‎PROFABIGHI_CAPITAL‎
YBL – PAC PREMIUM COMPACT MEDIUM (6 filas, 1 col. derecha) 📑 Document Structure: Cover Page → YBL logo + Indicator title. General Description → What the panel is and its purpose. Row-by-Row Explanation (6 modules): Volume with Delta Power vs USD NY Session Climax Trend / Momentum Correlation Visual Example → How to interpret values when green, red, or neutral. Practical Tips → Quick trading rules (e.g., “if strong Δ + Climax rejection = watch for reversal”). ⚡ Now the same question for you: Do you prefer the PDF in a technical style (with formulas and detailed calculations), or in a practical style (quick guide for traders, with examples and short phrases)?
مؤشر Pine Script®
من ‎YBL_YURIEL‎
RED: MomentumRED: Momentum Panel This indicator is designed to track the balance of buying and selling pressure in the market and highlight key momentum phases. It simplifies complex conditions into clear momentum states, helping traders quickly understand whether the market is in a strong zone or transitioning. - Top zones → when selling pressure reaches extreme levels. - Bottom zones → when buying pressure reaches extreme levels. - Momentum Bearish → when momentum shifts down after a strong top. - Momentum Bullish → when momentum shifts up after a strong bottom. The panel uses a scoring system in the background to filter noise and show only the dominant side (Buy vs Sell). Horizontal thresholds make it easy to spot when the market enters or exits extreme conditions. This tool is not meant to give signals by itself but to provide an intuitive view of where momentum stands right now, top, bottom, bullish, or bearish, at a glance.
مؤشر Pine Script®
من ‎REDexplorateur‎
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.
مؤشر Pine Script®
من ‎singhxgurjit‎
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.
مؤشر Pine Script®
من ‎guilleli‎
Lanxang V6 – Trend FollowingLanxang V6 – Trend Following The Lanxang V6 is a clean and simple trend-following tool that helps traders stay aligned with the market’s direction and catch key momentum shifts. 🔑 Features - Trend Direction – The system colors moving averages and chart areas to make bullish and bearish trends easy to spot at a glance. - Clear Buy/Sell Tags – When the market shifts direction, the indicator plots Buy or Sell tags directly on the chart for quick confirmation. - Pullback Highlights – Bars are marked to signal potential continuation setups during trending conditions. - Custom Visuals – Traders can adjust tag size, padding, and colors to match their chart style. - Alerts – Real-time alerts for Buy/Sell signals keep you notified of trend changes without watching the screen all the time. 📈 How to Use - Follow the Trend: Use the trend color as your main directional bias (green for bullish, red for bearish). - Entry Signals: Take Buy/Sell tags as confirmation points when the trend shifts. - Pullback Opportunities: Highlighted bars may indicate continuation trades within the existing trend. - Risk Management: Always confirm with your own analysis and manage risk properly. ⚠️ Disclaimer: This tool is for educational purposes only and does not guarantee results. Always test on demo before applying to live trading. Lao Version below: Lanxang V6 ແມ່ນເຄື່ອງມື ຕິດຕາມແນວໂນ້ມ ທີ່ອອກແບບມາໃຫ້ຊ່ວຍນັກລົງທຶນມອງເຫັນທິດທາງຂອງຕະຫຼາດ ແລະ ຈັບໂອກາດໃນການເຄື່ອນໄຫວສໍາຄັນໄດ້ຊັດເຈນຂຶ້ນ. 🔑 ຄຸນນະສົມບັດ - ການກໍານົດແນວໂນ້ມ – ລະບົບຈະສະແດງສີເສັ້ນ Moving Average ແລະ ພື້ນຫຼັງໃນການຊັດເຈນທັນທີ (ຂຽວ = ແນວໂນ້ມຂຶ້ນ, ແດງ = ແນວໂນ້ມລົງ). - ສັນຍານ Buy/Sell ຊັດເຈນ – ເມື່ອຕະຫຼາດປ່ຽນທິດທາງ ໂຕຊີ້ Buy ຫຼື Sell ຈະປາກົດໃນກາຟ. - ການເນັ້ນແທ່ງ Pullback – ກ່ອນຈະໄປຕໍ່ແນວໂນ້ມ ບາງແທ່ງຈະຖືກເນັ້ນເພື່ອໃຫ້ເຫັນໂອກາດໃນການເຂົ້າ. - ການປັບແຕ່ງຮູບແບບ – ປັບຂະໜາດ ແລະ ສີຂອງສັນຍານໄດ້ຕາມຄວາມຕ້ອງການ. - Alert ແບບ Real-time – ຮັບແຈ້ງເຕືອນທັນທີເມື່ອມີສັນຍານ Buy/Sell. 📈 ວິທີໃຊ້ - ຕິດຕາມແນວໂນ້ມ: ໃຊ້ສີຂອງເສັ້ນເພື່ອກໍານົດທິດທາງ (ຂຽວ = ຂຶ້ນ, ແດງ = ລົງ). - ສັນຍານເຂົ້າ: ຕິດຕາມສັນຍານ Buy/Sell ທີ່ປາກົດໃນກາຟ. - ໂອກາດ Pullback: ແທ່ງທີ່ເນັ້ນອາດຈະບອກໂອກາດໃນການເຂົ້າຕໍ່ຕາມແນວໂນ້ມ. - ຈັດການຄວາມສ່ຽງ: ຢ່າລືມກວດສອບກັບການວິເຄາະຂອງຕົນເອງ ແລະ ຈັດການຄວາມສ່ຽງໃຫ້ດີ. ⚠️ ຄໍາເຕືອນ: ເຄື່ອງມືນີ້ເປັນໄວ້ໃຊ້ເພື່ອການສຶກສາ ແລະ ບໍ່ຮັບປະກັນຜົນກໍາໄລ. ກ່ອນນໍາໃຊ້ໃນບັນຊີຈິງ ຄວນທົດສອບໃນ Demo ກ່ອນ.
مؤشر Pine Script®
من ‎Lanxang-Dev‎
Chanlun clmacd MACDThe commonly used MACD version in China has default parameters of 12, 26, 9. It is slightly different from the built-in MACD on the official TradingView website but generally similar. This MACD version is tailored to the usage habits of domestic users and is mainly designed to be used in conjunction with my Chanlun Theory indicators. 国内常用的macd版本，默认参数12,26,9，跟tradingview官网自带的有些不同，总体差不多，适合国内用户习惯的版本的macd,主要是配套我这边缠论指标使用
مؤشر Pine Script®
من ‎gaoguifeng‎
High Probability Order Blocks [AlgoAlpha]🟠 OVERVIEW This script detects and visualizes high-probability order blocks by combining a volatility-based z-score trigger with a statistical survival model inspired by Kaplan-Meier estimation. It builds and manages bullish and bearish order blocks dynamically on the chart, displays live survival probabilities per block, and plots optional rejection signals. What makes this tool unique is its use of historical mitigation behavior to estimate and plot how likely each zone is to persist, offering traders a probabilistic perspective on order block strength—something rarely seen in retail indicators. 🟠 CONCEPTS Order blocks are regions of strong institutional interest, often marked by large imbalances between buying and selling. This script identifies those areas using z-score thresholds on directional distance (up or down candles), detecting statistically significant moves that signal potential smart money footprints. A bullish block is drawn when a strong up-move (zUp > 4) follows a down candle, and vice versa for bearish blocks. Over time, each block is evaluated: if price “mitigates” it (i.e., closes cleanly past the opposite side and confirmed with a 1 bar delay), it’s considered resolved and logged. These resolved blocks then inform a Kaplan-Meier-like survival curve, estimating the likelihood that future blocks of a given age will remain unbroken. The indicator then draws a probability curve for each side (bull/bear), updating it in real time. 🟠 FEATURES Live label inside each block showing survival probability or “N.E.D.” if insufficient data. Kaplan-Meier survival curves drawn directly on the chart to show estimated strength decay. Rejection markers (▲ ▼) if price bounces cleanly off an active order block. Alerts for zone creation and rejection signals, supporting rule-based trading workflows. 🟠 USAGE Read the label inside each block for Age | Survival% (or N.E.D. if there aren’t enough samples yet); higher survival % suggests blocks of that age have historically lasted longer. Use the right-side survival curves to gauge how probability decays with age for bull vs bear blocks, and align entries with the side showing stronger survival at current age. Treat ▲ (bullish rejection) and ▼ (bearish rejection) as optional confluence when price tests a boundary and fails to break. Turn on alerts for “Bullish Zone Created,” “Bearish Zone Created,” and rejection signals so you don’t need to watch constantly. If your chart gets crowded, enable Prevent Overlap ; tune Max Box Age to your timeframe; and adjust KM Training Window / Minimum Samples to trade off responsiveness vs stability.
مؤشر Pine Script®
من ‎AlgoAlpha‎
10
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.
مؤشر Pine Script®
من ‎EdgeTools‎
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.
مؤشر Pine Script®
من ‎KyoshiroG‎
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.
مؤشر Pine Script®
من ‎Mirach35‎
ETFs Sector PerformanceDisplays a table of the Top 8 performing ETFs over a selected period (1M / 2M / 3M / 6M) to quickly identify industry strength. Pre-Set Universe (39 ETFs) ITA — iShares U.S. Aerospace & Defense ETF DBA — Invesco DB Agriculture Fund BOTZ — Global X Robotics & Artificial Intelligence ETF JETS — U.S. Global Jets ETF XLB — Materials Select Sector SPDR Fund XBI — SPDR S&P Biotech ETF PKB — Invesco Dynamic Building & Construction ETF ICLN — iShares Global Clean Energy ETF SKYY — First Trust Cloud Computing ETF DBC — Invesco DB Commodity Index Tracking Fund XLY — Consumer Discretionary Select Sector SPDR Fund XLP — Consumer Staples Select Sector SPDR Fund BLOK — Amplify Transformational Data Sharing ETF KARS — KraneShares Electric Vehicles & Future Mobility ETF XLE — Energy Select Sector SPDR Fund ESPO — VanEck Video Gaming and eSports ETF XLF — Financial Select Sector SPDR Fund PBJ — Invesco Dynamic Food & Beverage ETF ITB — iShares U.S. Home Construction ETF XLI — Industrial Select Sector SPDR Fund PAVE — Global X U.S. Infrastructure Development ETF PEJ — Invesco Dynamic Leisure & Entertainment ETF LIT — Global X Lithium & Battery Tech ETF IHI — iShares U.S. Medical Devices ETF XME — SPDR S&P Metals & Mining ETF FCG — First Trust Natural Gas ETF URA — Global X Uranium ETF PPH — VanEck Pharmaceutical ETF QTUM — Defiance Quantum Computing & Machine Learning ETF IYR — iShares U.S. Real Estate ETF XRT — SPDR S&P Retail ETF SOXX — iShares Semiconductor ETF BOAT — SonicShares Global Shipping ETF IGV — iShares Expanded Tech-Software Sector ETF TAN — Invesco Solar ETF SLX — VanEck Steel ETF IYZ — iShares U.S. Telecommunications ETF IYT — iShares U.S. Transportation ETF XLU — Utilities Select Sector SPDR Fund
مؤشر Pine Script®
من ‎Al_Rasbi‎