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Index Weighted Returns [SS]This is the index weighted return indicator. It supports a few ETFs, including: SPY/SPX QQQ/NDX ARKK SMH UFO XBI QTUM What it does is it takes the top, approximately 40, of the most heavily weighted tickers on the ETF, monitors their returns using the request security function, and then uses their weight to calculate the synthetic returns of the ETF of interest. For example, in the chart we have SMH. The indicator is looking at the top weighted tickers of SMH, calculating their returns, adjusting it for their individual weight on SMH and then predicting the expected return of SMH based on the weighing and holding's returns themselves. How to Use it The indicator is pretty straight forward, you select which ever index you are on and your desired timeframe (you can do as low as 30-Minutes or as high as monthly or quarterly). The indicator will then retrieve the top holdings for that ticker, their corresponding weights and calculate the expected daily return based on the weight and return of these tickers. It will plot this return for you on the chart. Other Options There is an optional table for you to view the actual weight, ticker composition and period returns for each of the top x tickers for an index. You can simply toggle "Show Table" in the settings menu, and it will show you the list of all tickers included, their period returns and their weight on the ETF. Tips for Use Works well to see when an index may be over the actual top weighted tickers, implying a pullback/sell, or under. For example: SPY today fell well below its top tickers and is currently rallying back up to the expected close range. You can see in the primary chart, SMH fell below and returned to its balance, being at the expected close range based on its component tickers. That is the indicator! Its simple but powerful! Hope you enjoy and as always, safe trades!
Pine Script® indicator
by Steversteves
Smart Flow Tracker [The_lurker] Smart Flow Tracker (SFT): Advanced Order Flow Tracking Indicator Overview Smart Flow Tracker (SFT) is an advanced indicator designed for real-time tracking and analysis of order flows. It focuses on detecting institutional patterns, massive orders, and potential reversals through analysis of lower timeframes (Lower Timeframe) or live ticks. It provides deep insights into market behavior using a multi-layered intelligent detection system and a clear visual interface, giving traders a competitive edge. SFT focuses on trade volumes, directions, and frequencies to uncover unusual activity that may indicate institutional intervention, massive orders, or manipulation attempts (traps). Indicator Operation Levels SFT operates on three main levels: 1. Microscopic Monitoring: Tracks every trade at precise timeframes (down to one second), providing visibility not available in standard timeframes. 2. Advanced Statistical Analysis: Calculates averages, deviations, patterns, and anomalies using precise mathematical algorithms. 3. Behavioral Artificial Intelligence: Recognizes behavioral patterns such as hidden institutional accumulation, manipulation attempts and traps, and potential reversal points. Key Features SFT features a set of advanced functions to enhance the trader's experience: 1. Intelligent Order Classification System: Classifies orders into six categories based on size and pattern: - Standard: Normal orders with typical size. - Significant 💎: Orders larger than average by 1.5 times. - Major 🔥: Orders larger than average by 2.5 times. - Massive 🐋: Orders larger than average by 3 times. - Institutional 🏛️: Consistent patterns indicating institutional activity. - Reversal 🔄: Large orders indicating direction change. - Trap ⚠️: Patterns that may be price traps. 2. Institutional Patterns Detection: Tracks sequences of similar-sized orders, detects organized institutional activity, and is customizable (number of trades, variance ratio). 3. Reversals Detection: Compares recent flows with previous ones, detects direction shifts from up to down or vice versa, and operates only on large orders (Major/Massive/Institutional). 4. Traps Detection: Identifies sequences of large orders in one direction, followed by an institutional order in the opposite direction, with early alerts for false moves. 5. Flow Delta Bar: Displays the difference between buy and sell volumes as a percentage for balance, with instant updates per trade. 6. Dynamic Statistics Panel: Displays overall buy and sell ratios with real-time updates and interactive colors. How It Works and Understanding SFT relies on logical sequential stages for data processing: A. Data Collection: Uses the `request.security_lower_tf()` function to extract data from a lower timeframe (like 1S) even on a higher timeframe (like 5D). For each time unit, it calculates: - Adjusted Volume: Either normal volume or "price-weighted volume" (hlc3 * volume) based on user choice. - Trade Direction: Compared to previous close (rise → buy, fall → sell). B. Building Temporary Memory: Maintains a dynamic list (sizeHistory) of the last 100 trade sizes, continuously calculating the moving average (meanSize). C. Intelligent Classification: Compares each new trade to the average: - > 1.5 × average → Significant. - > 2.5 × average → Major. - > 3.0 × average → Massive. - Institutional Patterns Check: A certain number of trades (e.g., 5) with a specified variance ratio (±5%) → Institutional. D. Advanced Detection: - Reversal: Compares buy/sell totals in two consecutive periods. - Trap: Sequence of large trades in one direction followed by an opposite institutional trade. E. Display and Alerts: Results displayed in an automatically updated table, with option to enable alerts for notable events. Settings (Fully Customizable) SFT offers extensive options to adapt to the trader's needs: A. Display Settings: - Language: English / Arabic. - Table Position: 9 options (e.g., Top Right, Middle Right, Bottom Left). - Display Size: Tiny / Small / Normal / Large. - Max Rows: 10–100. - Enable Flow Delta Bar: Yes / No. - Enable Statistics Panel: Yes / No (displays buy/sell % ratio). B.- Technical Settings: - Data Source: Lower Timeframe / Live Tick (simulation). - Timeframe: Optional (e.g., 1S, 5S, 1). - Calculation Type: Volume / Price Volume. C. Intelligent Detection System: - Enable Institutional Patterns Detection. - Pattern Length: 3–20 trades. - Allowed Variance Ratio: 1%–20%. - Massive Orders Detection Factor: 2.0–10.0. D. Classification Criteria: - Significant Orders Factor: 1.2–3.0. - Major Orders Factor: 2.0–5.0. E. **Advanced Detection**: - Enable Reversals Detection (with review period). - Enable Traps Detection (with minimum sequence limit). F. Alerts System: - Enable for each type: Massive orders, institutional patterns, reversals, traps, severe imbalance (60%–90%). G. Color System: Manual customization for each category: - Standard Buy 🟢: Dark gray green. - Standard Sell 🔴: Dark gray red. - Significant Buy 🟢: Medium green. - Significant Sell 🔴: Medium red. - Major Orders 🟣: Purple. - Massive Orders 🟠: Orange. - Institutional 🟦: Sky blue. - Reversal 🔵: Blue. - Trap 🟣: Pink-purple. Target Audiences SFT benefits a wide range of traders and investors: 1. Scalpers: Instant detection of large orders, liquidity points identification, avoiding traps in critical moments. 2. Day Traders: Tracking smart money footprint, determining real session direction, early reversals detection. 3. Swing Traders: Confirming trend strength, detecting institutional accumulation/distribution, identifying optimal entry points. 4. Investors: Understanding true market sentiments, avoiding entry at false peaks, identifying real value zones. ⚠️ Disclaimer: This indicator is for educational and analytical purposes only. It does not constitute financial, investment, or trading advice. Use it in conjunction with your own strategy and risk management. Neither TradingView nor the developer is liable for any financial decisions or losses. Smart Flow Tracker (SFT): مؤشر متقدم لتتبع تدفقات الأوامر نظرة عامة Smart Flow Tracker (SFT) مؤشر متقدم مصمم لتتبع وتحليل تدفقات الأوامر في الوقت الفعلي. يركز على كشف الأنماط المؤسسية، الأوامر الضخمة، والانعكاسات المحتملة من خلال تحليل الأطر الزمنية الأقل (Lower Timeframe) أو التيك الحي. يوفر رؤية عميقة لسلوك السوق باستخدام نظام كشف ذكي متعدد الطبقات وواجهة مرئية واضحة، مما يمنح المتداولين ميزة تنافسية. يركز SFT على حجم الصفقات، اتجاهها، وتكرارها لكشف النشاط غير العادي الذي قد يشير إلى تدخل مؤسسات، أوامر ضخمة، أو محاولات تلاعب (فخاخ). مستويات عمل المؤشر يعمل SFT على ثلاثة مستويات رئيسية: 1. المراقبة المجهرية: يتتبع كل صفقة على مستوى الأطر الزمنية الدقيقة (حتى الثانية الواحدة)، مما يوفر رؤية غير متوفرة في الأطر الزمنية العادية. 2. التحليل الإحصائي المتقدم: يحسب المتوسطات، الانحرافات، الأنماط، والشذوذات باستخدام خوارزميات رياضية دقيقة. 3. الذكاء الاصطناعي السلوكي: يتعرف على أنماط سلوكية مثل التراكم المؤسسي المخفي، محاولات التلاعب والفخاخ، ونقاط الانعكاس المحتملة. الميزات الرئيسية يتميز SFT بمجموعة من الوظائف المتقدمة لتحسين تجربة المتداول: 1. نظام تصنيف الأوامر الذكي: يصنف الأوامر إلى ست فئات بناءً على الحجم والنمط: - Standard (قياسي)**: أوامر عادية بحجم طبيعي. - Significant 💎 (مهم)**: أوامر أكبر من المتوسط بـ1.5 ضعف. - Major 🔥 (كبير)**: أوامر أكبر من المتوسط بـ2.5 ضعف. - Massive 🐋 (ضخم)**: أوامر أكبر من المتوسط بـ3 أضعاف. - Institutional 🏛️ (مؤسسي)**: أنماط متسقة تشير إلى نشاط مؤسسي. - Reversal 🔄 (انعكاس)**: أوامر كبيرة تشير إلى تغيير اتجاه. - Trap ⚠️ (فخ)**: أنماط قد تكون فخاخًا سعرية. 2. كشف الأنماط المؤسسية: يتتبع تسلسل الأوامر المتشابهة في الحجم، يكشف النشاط المؤسسي المنظم، وقابل للتخصيص (عدد الصفقات، نسبة التباين). 3. كشف الانعكاسات: يقارن التدفقات الأخيرة بالسابقة، يكشف تحول الاتجاه من صعود إلى هبوط أو العكس، ويعمل فقط على الأوامر الكبيرة (Major/Massive/Institutional). 4. كشف الفخاخ: يحدد تسلسل أوامر كبيرة في اتجاه واحد، يليها أمر مؤسسي في الاتجاه المعاكس، مع تنبيه مبكر للحركات الكاذبة. 5. شريط دلتا التدفق: يعرض الفرق بين حجم الشراء والبيع كنسبة مئوية للتوازن، مع تحديث فوري لكل صفقة. 6. لوحة إحصائيات ديناميكية: تعرض نسبة الشراء والبيع الإجمالية مع تحديث لحظي وألوان تفاعلية. طريقة العمل والفهم يعتمد SFT على مراحل منطقية متسلسلة لمعالجة البيانات: أ. جمع البيانات: يستخدم دالة `request.security_lower_tf()` لاستخراج بيانات من إطار زمني أدنى (مثل 1S) حتى على إطار زمني أعلى (مثل 5D). لكل وحدة زمنية، يحسب: - الحجم المعدّل: إما الحجم العادي (volume) أو "الحجم المرجّح بالسعر" (hlc3 * volume) حسب الاختيار. - اتجاه الصفقة: مقارنة الإغلاق الحالي بالسابق (ارتفاع → شراء، انخفاض → بيع). ب. بناء الذاكرة المؤقتة: يحتفظ بقائمة ديناميكية (sizeHistory) لآخر 100 حجم صفقة، ويحسب المتوسط المتحرك (meanSize) باستمرار. ج. التصنيف الذكي: يقارن كل صفقة جديدة بالمتوسط: - > 1.5 × المتوسط → Significant. - > 2.5 × المتوسط → Major. - > 3.0 × المتوسط → Massive. - فحص الأنماط المؤسسية: عدد معين من الصفقات (مثل 5) بنسبة تباين محددة (±5%) → Institutional. د. الكشف المتقدم: - الانعكاس: مقارنة مجموع الشراء/البيع في فترتين متتاليتين. - الفخ: تسلسل صفقات كبيرة في اتجاه واحد يتبعها صفقة مؤسسية معاكسة. هـ. العرض والتنبيه: عرض النتائج في جدول محدّث تلقائيًا، مع إمكانية تفعيل تنبيهات للأحداث المميزة. لإعدادات (قابلة للتخصيص بالكامل) يوفر SFT خيارات واسعة للتكييف مع احتياجات المتداول: أ. إعدادات العرض: - اللغة: English / العربية. - موقع الجدول: 9 خيارات (مثل Top Right, Middle Right, Bottom Left). - حجم العرض: Tiny / Small / Normal / Large. - الحد الأقصى للصفوف: 10–100. - تفعيل شريط دلتا التدفق: نعم / لا. - تفعيل لوحة الإحصائيات: نعم / لا (تعرض نسبة الشراء/البيع %). ب. الإعدادات التقنية: - مصدر البيانات: Lower Timeframe / Live Tick (محاكاة). - الإطار الزمني: اختياري (مثل 1S, 5S, 1). - نوع الحساب: Volume / Price Volume. ج. نظام الكشف الذكي: - تفعيل كشف الأنماط المؤسسية. - طول النمط: 3–20 صفقة. - نسبة التباين: 1%–20%. - عامل كشف الأوامر الضخمة: 2.0–10.0. د. معايير التصنيف: - عامل الأوامر المهمة: 1.2–3.0. - عامل الأوامر الكبرى: 2.0–5.0. هـ. الكشف المتقدم: - تفعيل كشف الانعكاسات (مع فترة مراجعة). - تفعيل كشف الفخاخ (مع حد أدنى للتسلسل). و. نظام التنبيهات: - تفعيل لكل نوع: أوامر ضخمة، أنماط مؤسسية، انعكاسات، فخاخ، عدم توازن شديد (60%–90%). ز. نظام الألوان**: تخصيص يدوي لكل فئة: - شراء قياسي 🟢: أخضر رمادي داكن. - بيع قياسي 🔴: أحمر رمادي داكن. - شراء مهم 🟢: أخضر متوسط. - بيع مهم 🔴: أحمر متوسط. - أوامر كبرى 🟣: بنفسجي. - أوامر ضخمة 🟠: برتقالي. - مؤسسي 🟦: أزرق سماوي. - انعكاس 🔵: أزرق. - فخ 🟣: وردي-أرجواني. الفئات المستهدفة يستفيد من SFT مجموعة واسعة من المتداولين والمستثمرين: 1. السكالبرز (Scalpers): كشف لحظي للأوامر الكبيرة، تحديد نقاط السيولة، تجنب الفخاخ في اللحظات الحرجة. 2. المتداولون اليوميون (Day Traders): تتبع بصمة الأموال الذكية، تحديد اتجاه الجلسة الحقيقي، كشف الانعكاسات المبكرة. 3. المتداولون المتأرجحون (Swing Traders): تأكيد قوة الاتجاه، كشف التراكم/التوزيع المؤسسي، تحديد نقاط الدخول المثلى. 4. المستثمرون: فهم معنويات السوق الحقيقية، تجنب الدخول في قمم كاذبة، تحديد مناطق القيمة الحقيقية. ⚠️ إخلاء مسؤولية: هذا المؤشر لأغراض تعليمية وتحليلية فقط. لا يُمثل نصيحة مالية أو استثمارية أو تداولية. استخدمه بالتزامن مع استراتيجيتك الخاصة وإدارة المخاطر. لا يتحمل TradingView ولا المطور مسؤولية أي قرارات مالية أو خسائر.
Pine Script® indicator
by The_lurker
4
Risk & Position DashboardRisk & Position Dashboard Overview The Risk & Position Dashboard is a comprehensive trading tool designed to help traders calculate optimal position sizes, manage risk, and visualize potential profit/loss scenarios before entering trades. This indicator provides real-time calculations for position sizing based on account size, risk percentage, and stop-loss levels, while displaying multiple take-profit targets with customizable risk-reward ratios. Key Features Position Sizing & Risk Management: Automatic position size calculation based on account size and risk percentage Support for leveraged trading with maximum leverage limits Fractional shares support for brokers that allow partial share trading Real-time fee calculation including entry, stop-loss, and take-profit fees Break-even price calculation including trading fees Multi-Target Profit Management: Support for up to 3 take-profit levels with individual portion allocations Customizable risk-reward ratios for each take-profit target Visual profit/loss zones displayed as colored boxes on the chart Individual profit calculations for each take-profit level Visual Dashboard: Clean, customizable table display showing all key metrics Configurable label positioning and styling options Real-time tracking of whether stop-loss or take-profit levels have been reached Color-coded visual zones for easy identification of risk and reward areas Advanced Configuration: Comprehensive input validation and error handling Support for different chart timeframes and symbols Customizable colors, fonts, and display options Hide/show individual data fields for personalized dashboard views How to Use Set Account Parameters: Configure your account size, maximum risk percentage per trade, and trading fees in the "Account Settings" section. Define Trade Setup: Use the "Entry" time picker to select your entry point on the chart, then input your entry price and stop-loss level. Configure Take Profits: Set your desired risk-reward ratios and portion allocations for each take-profit level. The script supports 1-3 take-profit targets. Analyze Results: The dashboard will automatically calculate and display position size, number of shares, potential profits/losses, fees, and break-even levels. Visual Confirmation: Colored boxes on the chart show profit zones (green) and loss zones (red), with lines extending to current price levels. Reset Entry and SL: You can easily reset the entry and stop-loss by clicking the "Reset points..." button from the script's "More" menu. This is useful if you want to quickly clear your current trade setup and start fresh without manually adjusting the points on the chart. Calculations The script performs sophisticated calculations including: Position size based on risk amount and price difference between entry and stop-loss Leverage requirements and position amount calculations Fee-adjusted risk-reward ratios for realistic profit expectations Break-even price including all trading costs Individual profit calculations for partial position closures Detailed Take-Profit Calculation Formula: The take-profit prices are calculated using the following mathematical formula: // Core variables: // risk_amount = account_size * (risk_percentage / 100) // total_risk_per_share = |entry_price - sl_price| + (entry_price * fee%) + (sl_price * fee%) // shares = risk_amount / total_risk_per_share // direction_factor = 1 for long positions, -1 for short positions // Take-profit calculation: net_win = total_risk_per_share * shares * RR_ratio tp_price = (net_win + (direction_factor * entry_price * shares) + (entry_price * fee% * shares)) / (direction_factor * shares - fee% * shares) Step-by-step example for a long position (based on screenshot): Account Size: 2,000 USDT, Risk: 2% = 40 USDT Entry: 102,062.9 USDT, Stop Loss: 102,178.4 USDT, Fee: 0.06% Risk per share: |102,062.9 - 102,178.4| + (102,062.9 × 0.0006) + (102,178.4 × 0.0006) = 115.5 + 61.24 + 61.31 = 238.05 USDT Shares: 40 ÷ 238.05 = 0.168 shares (rounded to 0.17 in display) Position Size: 0.17 × 102,062.9 = 17,350.69 USDT Position Amount (with 9x leverage): 17,350.69 ÷ 9 = 1,927.85 USDT For 2:1 RR: Net win = 238.05 × 0.17 × 2 = 80.94 USDT TP1 price = (80.94 + (1 × 102,062.9 × 0.17) + (102,062.9 × 0.0006 × 0.17)) ÷ (1 × 0.17 - 0.0006 × 0.17) = 101,464.7 USDT For 3:1 RR: TP2 price = 101,226.7 USDT (following same formula with RR=3) This ensures that after accounting for all fees, the actual risk-reward ratio matches the specified target ratio. Risk Management Features Maximum Trade Amount: Optional setting to limit position size regardless of account size Leverage Limits: Built-in maximum leverage protection Fee Integration: All calculations include realistic trading fees for accurate expectations Validation: Automatic checking that take-profit portions sum to 100% Historical Tracking: Visual indication when stop-loss or take-profit levels are reached (within last 5000 bars) Understanding Max Trade Amount - Multiple Simultaneous Trades: The "Max Trade Amount" feature is designed for traders who want to open multiple positions simultaneously while maintaining proper risk management. Here's how it works: Key Concept: - Risk percentage (2%) always applies to your full Account Size - Max Trade Amount limits the capital allocated per individual trade - This allows multiple trades with full risk on each trade Example from Screenshot: Account Size: 2,000 USDT Max Trade Amount: 500 USDT Risk per Trade: 2% × 2,000 = 40 USDT per trade Stop Loss Distance: 0.11% from entry Result: Position Size = 17,350.69 USDT with 35x leverage Total Risk (including fees): 40.46 USDT Multiple Trades Strategy: With this setup, you can open: Trade 1: 40 USDT risk, 495.73 USDT position amount (35x leverage) Trade 2: 40 USDT risk, 495.73 USDT position amount (35x leverage) Trade 3: 40 USDT risk, 495.73 USDT position amount (35x leverage) Trade 4: 40 USDT risk, 495.73 USDT position amount (35x leverage) Total Portfolio Exposure: - 4 simultaneous trades = 4 × 495.73 = 1,982.92 USDT position amount - Total risk exposure = 4 × 40 = 160 USDT (8% of account)
Pine Script® indicator
by HllN
Position Size & Drawdown ManagerThis tool is designed to help traders dynamically adjust their position size and drawdown expectations as their trading capital changes over time. It provides a simple and intuitive way to translate backtest results into real-world position sizing decisions. Purpose and Functionality The indicator uses your original backtest parameters — including base capital, base drawdown percentage, and base position size — and your current account balance to calculate how your risk profile changes. It presents two main scenarios: Lock Drawdown %: Keeps your original drawdown percentage fixed and calculates the new position size required. Lock Position Size: Keeps your position size unchanged and shows how your drawdown percentage will shift. Why it’s useful Many traders face the challenge of scaling their strategies as their account grows or shrinks. This tool makes it easy to visualize the relationship between position sizing, capital, and drawdown. It’s particularly valuable for risk management, portfolio rebalancing, and maintaining consistent exposure when transitioning from backtest conditions to live trading. How it works The calculations are displayed in a clean, color-coded table that updates dynamically. This allows you to instantly see how capital fluctuations impact your expected drawdown or position size. You can toggle between light and dark themes and highlight important cells for clarity. Practical use case Combine this tool with your TradingView strategy results to better interpret your backtests and adjust your real-world trade sizes accordingly. It bridges the gap between simulated performance and actual account management. Chart example The chart included focuses only on this indicator, showing the output table and visual layout clearly without additional scripts or overlays.
Pine Script® indicator
by tranholenguyen
Machine Learning Moving Average [BackQuant]Machine Learning Moving Average A powerful tool combining clustering, pseudo-machine learning, and adaptive prediction, enabling traders to understand and react to price behavior across multiple market regimes (Bullish, Neutral, Bearish). This script uses a dynamic clustering approach based on percentile thresholds and calculates an adaptive moving average, ideal for forecasting price movements with enhanced confidence levels. What is Percentile Clustering? Percentile clustering is a method that sorts and categorizes data into distinct groups based on its statistical distribution. In this script, the clustering process relies on the percentile values of a composite feature (based on technical indicators like RSI, CCI, ATR, etc.). By identifying key thresholds (lower and upper percentiles), the script assigns each data point (price movement) to a cluster (Bullish, Neutral, or Bearish), based on its proximity to these thresholds. This approach mimics aspects of machine learning, where we “train” the model on past price behavior to predict future movements. The key difference is that this is not true machine learning; rather, it uses data-driven statistical techniques to "cluster" the market into patterns. Why Percentile Clustering is Useful Clustering price data into meaningful patterns (Bullish, Neutral, Bearish) helps traders visualize how price behavior can be grouped over time. By leveraging past price behavior and technical indicators, percentile clustering adapts dynamically to evolving market conditions. It helps you understand whether price behavior today aligns with past bullish or bearish trends, improving market context. Clusters can be used to predict upcoming market conditions by identifying regimes with high confidence, improving entry/exit timing. What This Script Does Clustering Based on Percentiles : The script uses historical price data and various technical features to compute a "composite feature" for each bar. This feature is then sorted and clustered based on predefined percentile thresholds (e.g., 10th percentile for lower, 90th percentile for upper). Cluster-Based Prediction : Once clustered, the script uses a weighted average, cluster momentum, or regime transition model to predict future price behavior over a specified number of bars. Dynamic Moving Average : The script calculates a machine-learning-inspired moving average (MLMA) based on the current cluster, adjusting its behavior according to the cluster regime (Bullish, Neutral, Bearish). Adaptive Confidence Levels : Confidence in the predicted return is calculated based on the distance between the current value and the other clusters. The further it is from the next closest cluster, the higher the confidence. Visual Cluster Mapping : The script visually highlights different clusters on the chart with distinct colors for Bullish, Neutral, and Bearish regimes, and plots the MLMA line. Prediction Output : It projects the predicted price based on the selected method and shows both predicted price and confidence percentage for each prediction horizon. Trend Identification : Using the clustering output, the script colors the bars based on the current cluster to reflect whether the market is trending Bullish (green), Bearish (red), or is Neutral (gray). How Traders Use It Predicting Price Movements : The script provides traders with an idea of where prices might go based on past market behavior. Traders can use this forecast for short-term and long-term predictions, guiding their trades. Clustering for Regime Analysis : Traders can identify whether the market is in a Bullish, Neutral, or Bearish regime, using that information to adjust trading strategies. Adaptive Moving Average for Trend Following : The adaptive moving average can be used as a trend-following indicator, helping traders stay in the market when it’s aligned with the current trend (Bullish or Bearish). Entry/Exit Strategy : By understanding the current cluster and its associated trend, traders can time entries and exits with higher precision, taking advantage of favorable conditions when the confidence in the predicted price is high. Confidence for Risk Management : The confidence level associated with the predicted returns allows traders to manage risk better. Higher confidence levels indicate stronger market conditions, which can lead to higher position sizes. Pseudo Machine Learning Aspect While the script does not use conventional machine learning models (e.g., neural networks or decision trees), it mimics certain aspects of machine learning in its approach. By using clustering and the dynamic adjustment of a moving average, the model learns from historical data to adjust predictions for future price behavior. The "learning" comes from how the script uses past price data (and technical indicators) to create patterns (clusters) and predict future market movements based on those patterns. Why This Is Important for Traders Understanding market regimes helps to adjust trading strategies in a way that adapts to current market conditions. Forecasting price behavior provides an additional edge, enabling traders to time entries and exits based on predicted price movements. By leveraging the clustering technique, traders can separate noise from signal, improving the reliability of trading signals. The combination of clustering and predictive modeling in one tool reduces the complexity for traders, allowing them to focus on actionable insights rather than manual analysis. How to Interpret the Output Bullish (Green) Zone : When the price behavior clusters into the Bullish zone, expect upward price movement. The MLMA line will help confirm if the trend remains upward. Bearish (Red) Zone : When the price behavior clusters into the Bearish zone, expect downward price movement. The MLMA line will assist in tracking any downward trends. Neutral (Gray) Zone : A neutral market condition signals indecision or range-bound behavior. The MLMA line can help track any potential breakouts or trend reversals. Predicted Price : The projected price is shown on the chart, based on the cluster's predicted behavior. This provides a useful reference for where the price might move in the near future. Prediction Confidence : The confidence percentage helps you gauge the reliability of the predicted price. A higher percentage indicates stronger market confidence in the forecasted move. Tips for Use Combining with Other Indicators : Use the output of this indicator in combination with your existing strategy (e.g., RSI, MACD, or moving averages) to enhance signal accuracy. Position Sizing with Confidence : Increase position size when the prediction confidence is high, and decrease size when it’s low, based on the confidence interval. Regime-Based Strategy : Consider developing a multi-strategy approach where you use this tool for Bullish or Bearish regimes and a separate strategy for Neutral markets. Optimization : Adjust the lookback period and percentile settings to optimize the clustering algorithm based on your asset’s characteristics. Conclusion The Machine Learning Moving Average offers a novel approach to price prediction by leveraging percentile clustering and a dynamically adapting moving average. While not a traditional machine learning model, this tool mimics the adaptive behavior of machine learning by adjusting to evolving market conditions, helping traders predict price movements and identify trends with improved confidence and accuracy.
Pine Script® indicator
by BackQuant
8
Intraday Perpetual Premium & Z-ScoreThis indicator measures the real-time premium of a perpetual futures contract relative to its spot market and interprets it through a statistical lens. It helps traders detect when funding pressure is building, when leverage is being unwound, and when crowding in the futures market may precede volatility. How it works • Premium (%) = (Perp – Spot) ÷ Spot × 100 The script fetches both spot and perpetual prices and calculates their percentage difference each minute. • Rolling Mean & Z-Score Over a 4-hour look-back, it computes the average premium and standard deviation to derive a Z-Score, showing how stretched current sentiment is. • Dynamic ±2σ Bands highlight statistically extreme premiums or discounts. • Rate of Change (ROC) over one hour gauges the short-term directional acceleration of funding flows. Colour & Label Interpretation Visual cue Meaning Trading Implication 🟢 Green bars + “BULL Pressure” Premium rising faster than mean Leverage inflows → momentum strengthening 🔴 Red bars + “BEAR Pressure” Premium shrinking Leverage unwind → pull-back or consolidation ⚠️ Orange “EXTREME Premium/Discount” Crowded trade → heightened reversal risk ⚪ Grey bars Neutral Balanced conditions Alerts • Bull Pressure Alert → funding & premium rising (momentum building) • Bear Pressure Alert → premium falling (deleveraging) • Extreme Premium Alert → crowded longs; potential top • Extreme Discount Alert → capitulation; possible bottom Use case Combine this indicator with your Heikin-Ashi, RSI, and MACD confluence rules: • Enter only when your oscillators are low → curling up and Bull Pressure triggers. • Trim or exit when Bear Pressure or Extreme Premium appears. • Watch for Extreme Discount during flushes as an early bottoming clue.
Pine Script® indicator
by TokenTrendTracker
India Vix based Strangle StrikesA clean Nifty–VIX dashboard that converts India VIX into expected daily moves, price ranges, and suggested strangle strikes. Includes VIX %, expanded 1.2× range, and smart rounded strike levels for options trading. This script provides a professional on-chart dashboard that converts India VIX into actionable trading levels for Nifty. It calculates the VIX-based expected daily move, projected price ranges, expanded 1.2× ranges, and suggested strangle strike prices. Includes clean formatting, color-coded sections, and real-time updates. Ideal for traders using straddles, strangles, intraday volatility models, range-bound setups, and options-based risk management. 1.2x expanded range is better success probability, may keep 20% of strangle value as stop loss. The vix based system is intended to give approx. 70%+ success rate.
Pine Script® indicator
by ipuneet
JiNFOJiNFO is a clean, data-driven overlay that displays key information about the current symbol directly on your chart — without clutter. 🧭 What it shows Company & Symbol Info – Name, ticker, sector, industry, market cap Timeframe Label – Current chart timeframe (auto-formatted) ATR (14) & % Volatility – With color dots for low 🟢 / medium 🟡 / high 🔴 volatility Moving Average Status – Indicates if price is above or below the selected MA (default 150) RSI & RSI-SMA (14) – Compact line with live values and color dot for overbought/neutral/oversold zones Distance from SMA (50) – Shows how far price is from the 50 MA (+/- %) and grades it A–D by distance 🟢🟠🔴 Earnings Countdown – Days remaining until the next earnings date (if available) ⚙️ Customization Position (top/middle/bottom, left/center/right) Text size (default Small), color, opacity (100 %) Toggle any data row on or off Choose compact or verbose labels 🧩 Purpose JiNFO replaces bulky data panels with a lightweight, transparent information layer — perfect for traders who want essential fundamentals, volatility, and technical context at a glance.
Pine Script® indicator
by yoni_ag
Custom Horizontal Lines | Trade Symmetry📊 Custom Horizontal Lines 🔍 Overview The Custom Horizontal Lines is a precision utility designed for traders who perform manual higher-timeframe analysis and want to preserve their marked price levels directly on the chart. It doesn’t calculate or detect anything automatically — instead, it acts as your personal level memory, preserving your analyzed zones and reference prices throughout the session. Ideal for traders who manually mark the High, Low, Open, Close, Mean Thresholds, and Quarter Levels of Order Blocks, Fair Value Gaps, Inversion Fair Value Gaps and Wicks before the trading day begins. ⚙️ Key Features ✅ Manual Level Entry — Input your analyzed price levels (OB, FVG, WICK,etc) directly into the indicator settings. ✅ Preserved Levels — Once entered, your lines stay visible and consistent — even after switching symbols, timeframes, or reloading the chart. ✅ Supports All Level Types — Store any kind of manually defined level: OB highs/lows, FVG boundaries, Wicks, Mean Thresholds, Quarter levels, or custom reference prices. ✅ Clean Visualization — Customize line color, style, and labels for easy visual organization. ✅ Session-Ready Workflow — Built for pre-market preparation — enter your HTF levels once, and trade around them all day. ✅ No Auto Calculations — 100% manual by design — ensuring only your analyzed levels are shown, exactly as you defined them. 💡 How to Use Open the indicator’s settings and manually enter those price values. The indicator will plot and preserve those exact levels on your chart. Switch to your lower timeframe and observe how price reacts around them — without ever needing to redraw. 🎯 Why It’s Useful Keeps your HTF levels organized and persistent across sessions. Saves time by avoiding redrawing. Fits perfectly into ICT / Smart Money trading workflows. Ensures full manual control and precision over what’s displayed on your chart. 🧩 Ideal For ICT and Smart Money traders Institutional-style manual analysts Traders marking Mean Thresholds, or Quarter Levels of OBs, FVGs, Wicks etc Anyone who wants a clean, reliable way to preserve their manual analysis
Pine Script® indicator
by TradeSymmetry
Time & Sales , Volume Delta and CVD, Volume imbalance , Tick This Pine Script (version 6) creates a comprehensive TradingView indicator combining Time & Sales (Tape) with Volume Delta, Order Flow Pressure Indicator (OFPI), Volume Imbalance detection, Volume Delta (VD) histogram, Cumulative Volume Delta (CVD), TICK.US histogram, and a summary gauge table. It overlays on the chart with customizable tables, boxes, lines, and labels for real-time trade analysis, momentum, imbalances, and volume metrics. Key Features and Components: Time & Sales Table: A dynamic table showing recent trades (up to user-defined rows). Columns include Time, Side (▲/▼), Last Price, Volume (or Price-Weighted Volume). Trades below a volume threshold are hidden. Includes a buy/sell scale bar with percentages. Supports timeframe-based or live tick data fetching. OFPI with Gauge: Calculates net aggressive volume pressure using bar body position, smoothed with T3 moving average. Displays a centered gauge bar (e.g., "░░░|███░░") indicating bullish/bearish momentum or shifts. Volume Imbalance (VI): Detects bullish/bearish gaps between bars. Draws semi-transparent boxes with labels (e.g., "5 tks (vi)") for imbalances or gaps. Limits display to a max number, removes filled ones, and uses magnets (🧲) for gaps. Volume Delta (VD): Approximates buy/sell delta via intrabar pressure or polarity. Displays as unipolar/bipolar histogram, optionally overlapping with regular volume or TICK.US. Shows numerical values (green/red/orange for divergences) and price/VD divergences. Cumulative Volume Delta (CVD): Cumulates VD, reset on anchor timeframe (e.g., daily). Displays as line, area, baseline, or candles. Includes optional EMA smoothing and background fills. Detects divergences with price. TICK.US Histogram: Overlays US Tick index (from symbol "TICK.US") as positive/negative bars during US market hours (9:30-16:00 ET, Mon-Fri). Replaces regular volume in some modes. Gauge Summary Table: Bottom-left table with momentum text, OFPI gauge, CVD value, current Tick, and last bar's volume breakdown (total/buy/sell/delta). Customization Options: General: Timezone, date format, table position/size, colors (gradients for up/down), calculation mode (timeframe/live tick), volume type (volume/price-volume), thresholds, lengths (e.g., lookback, smoothing). Display: Heights/offsets for histograms, line widths/styles, transparencies, label sizes/alignments, divergences, MA on volume, CVD smoothing/background. Technical: Lower timeframe precision (auto or custom), anchor for CVD reset, max VIs to show. Other: Toggles for VI, TICK.US, numerical values, divergences. Credit // FuturesCall @ fcalgobot.com //Time & Sales (Tape) // CVD base on Luxalgo CVD indicator // Momentum Gauge by DskyzInvestments // volume imbalance by ...
Pine Script® indicator
by FC_ALGO
2
Scientific Correlation Testing FrameworkScientific Correlation Testing Framework - Comprehensive Guide Introduction to Correlation Analysis What is Correlation? Correlation is a statistical measure that describes the degree to which two assets move in relation to each other. Think of it like measuring how closely two dancers move together on a dance floor. Perfect Positive Correlation (+1.0): Both dancers move in perfect sync, same direction, same speed Perfect Negative Correlation (-1.0): Both dancers move in perfect sync but in opposite directions Zero Correlation (0): The dancers move completely independently of each other In financial markets, correlation helps us understand relationships between different assets, which is crucial for: Portfolio diversification Risk management Pairs trading strategies Hedging positions Market analysis Why This Script is Special This script goes beyond simple correlation calculations by providing: Two different correlation methods (Pearson and Spearman) Statistical significance testing to ensure results are meaningful Rolling correlation analysis to track how relationships change over time Visual representation for easy interpretation Comprehensive statistics table with detailed metrics Deep Dive into the Script's Components 1. Input Parameters Explained- Symbol Selection: This allows you to select the second asset to compare with the chart's primary asset Default is Apple (NASDAQ:AAPL), but you can change this to any symbol Example: If you're viewing a Bitcoin chart, you might set this to "NASDAQ:TSLA" to see if Bitcoin and Tesla are correlated Correlation Window (60): This is the number of periods used to calculate the main correlation Larger values (e.g., 100-500) provide more stable, long-term correlation measures Smaller values (e.g., 10-50) are more responsive to recent price movements 60 is a good balance for most daily charts (about 3 months of trading days) Rolling Correlation Window (20): A shorter window to detect recent changes in correlation This helps identify when the relationship between assets is strengthening or weakening Default of 20 is roughly one month of trading days Return Type: This determines how price changes are calculated Simple Returns: (Today's Price - Yesterday's Price) / Yesterday's Price Easy to understand: "The asset went up 2% today" Log Returns: Natural logarithm of (Today's Price / Yesterday's Price) More mathematically elegant for statistical analysis Better for time-additive properties (returns over multiple periods) Less sensitive to extreme values. Confidence Level (95%): This determines how certain we want to be about our results 95% confidence means we accept a 5% chance of being wrong (false positive) Higher confidence (e.g., 99%) makes the test more strict Lower confidence (e.g., 90%) makes the test more lenient 95% is the standard in most scientific research Show Statistical Significance: When enabled, the script will test if the correlation is statistically significant or just due to random chance. Display options control what you see on the chart: Show Pearson/Spearman/Rolling Correlation: Toggle each correlation type on/off Show Scatter Plot: Displays a scatter plot of returns (limited to recent points to avoid performance issues) Show Statistical Tests: Enables the detailed statistics table Table Text Size: Adjusts the size of text in the statistics table 2.Functions explained- calcReturns(): This function calculates price returns based on your selected method: Log Returns: Formula: ln(Price_t / Price_t-1) Example: If a stock goes from $100 to $101, the log return is ln(101/100) = ln(1.01) ≈ 0.00995 or 0.995% Benefits: More symmetric, time-additive, and better for statistical modeling Simple Returns: Formula: (Price_t - Price_t-1) / Price_t-1 Example: If a stock goes from $100 to $101, the simple return is (101-100)/100 = 0.01 or 1% Benefits: More intuitive and easier to understand rankArray(): This function calculates the rank of each value in an array, which is used for Spearman correlation: How ranking works: The smallest value gets rank 1 The second smallest gets rank 2, and so on For ties (equal values), they get the average of their ranks Example: For values Sorted: Ranks: (the two 2s tie for ranks 1 and 2, so they both get 1.5) Why this matters: Spearman correlation uses ranks instead of actual values, making it less sensitive to outliers and non-linear relationships. pearsonCorr(): This function calculates the Pearson correlation coefficient: Mathematical Formula: r = (nΣxy - ΣxΣy) / √ Where x and y are the two variables, and n is the sample size What it measures: The strength and direction of the linear relationship between two variables Values range from -1 (perfect negative linear relationship) to +1 (perfect positive linear relationship) 0 indicates no linear relationship Example: If two stocks have a Pearson correlation of 0.8, they have a strong positive linear relationship When one stock goes up, the other tends to go up in a fairly consistent proportion spearmanCorr(): This function calculates the Spearman rank correlation: How it works: Convert each value in both datasets to its rank Calculate the Pearson correlation on the ranks instead of the original values What it measures: The strength and direction of the monotonic relationship between two variables A monotonic relationship is one where as one variable increases, the other either consistently increases or decreases It doesn't require the relationship to be linear When to use it instead of Pearson: When the relationship is monotonic but not linear When there are significant outliers in the data When the data is ordinal (ranked) rather than interval/ratio Example: If two stocks have a Spearman correlation of 0.7, they have a strong positive monotonic relationship When one stock goes up, the other tends to go up, but not necessarily in a straight-line relationship tStatistic(): This function calculates the t-statistic for correlation: Mathematical Formula: t = r × √((n-2)/(1-r²)) Where r is the correlation coefficient and n is the sample size What it measures: How many standard errors the correlation is away from zero Used to test the null hypothesis that the true correlation is zero Interpretation: Larger absolute t-values indicate stronger evidence against the null hypothesis Generally, a t-value greater than 2 (in absolute terms) is considered statistically significant at the 95% confidence level criticalT() and pValue(): These functions provide approximations for statistical significance testing: criticalT(): Returns the critical t-value for a given degrees of freedom (df) and significance level The critical value is the threshold that the t-statistic must exceed to be considered statistically significant Uses approximations since Pine Script doesn't have built-in statistical distribution functions pValue(): Estimates the p-value for a given t-statistic and degrees of freedom The p-value is the probability of observing a correlation as strong as the one calculated, assuming the true correlation is zero Smaller p-values indicate stronger evidence against the null hypothesis Standard interpretation: p < 0.01: Very strong evidence (marked with **) p < 0.05: Strong evidence (marked with *) p ≥ 0.05: Weak evidence, not statistically significant stdev(): This function calculates the standard deviation of a dataset: Mathematical Formula: σ = √(Σ(x-μ)²/(n-1)) Where x is each value, μ is the mean, and n is the sample size What it measures: The amount of variation or dispersion in a set of values A low standard deviation indicates that the values tend to be close to the mean A high standard deviation indicates that the values are spread out over a wider range Why it matters for correlation: Standard deviation is used in calculating the correlation coefficient It also provides information about the volatility of each asset's returns Comparing standard deviations helps understand the relative riskiness of the two assets. 3.Getting Price Data- price1: The closing price of the primary asset (the chart you're viewing) price2: The closing price of the secondary asset (the one you selected in the input parameters) Returns are used instead of raw prices because: Returns are typically stationary (mean and variance stay constant over time) Returns normalize for price levels, allowing comparison between assets of different values Returns represent what investors actually care about: percentage changes in value 4.Information Table- Creates a table to display statistics Only shows on the last bar to avoid performance issues Positioned in the top right of the chart Has 2 columns and 15 rows Populating the Table The script then populates the table with various statistics: Header Row: "Metric" and "Value" Sample Information: Sample size and return type Pearson Correlation: Value, t-statistic, p-value, and significance Spearman Correlation: Value, t-statistic, p-value, and significance Rolling Correlation: Current value Standard Deviations: For both assets Interpretation: Text description of the correlation strength The table uses color coding to highlight important information: Green for significant positive results Red for significant negative results Yellow for borderline significance Color-coded headers for each section => Practical Applications and Interpretation How to Interpret the Results Correlation Strength 0.0 to 0.3 (or 0.0 to -0.3): Weak or no correlation The assets move mostly independently of each other Good for diversification purposes 0.3 to 0.7 (or -0.3 to -0.7): Moderate correlation The assets show some tendency to move together (or in opposite directions) May be useful for certain trading strategies but not extremely reliable 0.7 to 1.0 (or -0.7 to -1.0): Strong correlation The assets show a strong tendency to move together (or in opposite directions) Can be useful for pairs trading, hedging, or as a market indicator Statistical Significance p < 0.01: Very strong evidence that the correlation is real Marked with ** in the table Very unlikely to be due to random chance p < 0.05: Strong evidence that the correlation is real Marked with * in the table Unlikely to be due to random chance p ≥ 0.05: Weak evidence that the correlation is real Not marked in the table Could easily be due to random chance Rolling Correlation The rolling correlation shows how the relationship between assets changes over time If the rolling correlation is much different from the long-term correlation, it suggests the relationship is changing This can indicate: A shift in market regime Changing fundamentals of one or both assets Temporary market dislocations that might present trading opportunities Trading Applications 1. Portfolio Diversification Goal: Reduce overall portfolio risk by combining assets that don't move together Strategy: Look for assets with low or negative correlations Example: If you hold tech stocks, you might add some utilities or bonds that have low correlation with tech 2. Pairs Trading Goal: Profit from the relative price movements of two correlated assets Strategy: Find two assets with strong historical correlation When their prices diverge (one goes up while the other goes down) Buy the underperforming asset and short the outperforming asset Close the positions when they converge back to their normal relationship Example: If Coca-Cola and Pepsi are highly correlated but Coca-Cola drops while Pepsi rises, you might buy Coca-Cola and short Pepsi 3. Hedging Goal: Reduce risk by taking an offsetting position in a negatively correlated asset Strategy: Find assets that tend to move in opposite directions Example: If you hold a portfolio of stocks, you might buy some gold or government bonds that tend to rise when stocks fall 4. Market Analysis Goal: Understand market dynamics and interrelationships Strategy: Analyze correlations between different sectors or asset classes Example: If tech stocks and semiconductor stocks are highly correlated, movements in one might predict movements in the other If the correlation between stocks and bonds changes, it might signal a shift in market expectations 5. Risk Management Goal: Understand and manage portfolio risk Strategy: Monitor correlations to identify when diversification benefits might be breaking down Example: During market crises, many assets that normally have low correlations can become highly correlated (correlation convergence), reducing diversification benefits Advanced Interpretation and Caveats Correlation vs. Causation Important Note: Correlation does not imply causation Example: Ice cream sales and drowning incidents are correlated (both increase in summer), but one doesn't cause the other Implication: Just because two assets move together doesn't mean one causes the other to move Solution: Look for fundamental economic reasons why assets might be correlated Non-Stationary Correlations Problem: Correlations between assets can change over time Causes: Changing market conditions Shifts in monetary policy Structural changes in the economy Changes in the underlying businesses Solution: Use rolling correlations to monitor how relationships change over time Outliers and Extreme Events Problem: Extreme market events can distort correlation measurements Example: During a market crash, many assets may move in the same direction regardless of their normal relationship Solution: Use Spearman correlation, which is less sensitive to outliers Be cautious when interpreting correlations during extreme market conditions Sample Size Considerations Problem: Small sample sizes can produce unreliable correlation estimates Rule of Thumb: Use at least 30 data points for a rough estimate, 60+ for more reliable results Solution: Use the default correlation length of 60 or higher Be skeptical of correlations calculated with small samples Timeframe Considerations Problem: Correlations can vary across different timeframes Example: Two assets might be positively correlated on a daily basis but negatively correlated on a weekly basis Solution: Test correlations on multiple timeframes Use the timeframe that matches your trading horizon Look-Ahead Bias Problem: Using information that wouldn't have been available at the time of trading Example: Calculating correlation using future data Solution: This script avoids look-ahead bias by using only historical data Best Practices for Using This Script 1. Appropriate Parameter Selection Correlation Window: For short-term trading: 20-50 periods For medium-term analysis: 50-100 periods For long-term analysis: 100-500 periods Rolling Window: Should be shorter than the main correlation window Typically 1/3 to 1/2 of the main window Return Type: For most applications: Log Returns (better statistical properties) For simplicity: Simple Returns (easier to interpret) 2. Validation and Testing Out-of-Sample Testing: Calculate correlations on one time period Test if they hold in a different time period Multiple Timeframes: Check if correlations are consistent across different timeframes Economic Rationale: Ensure there's a logical reason why assets should be correlated 3. Monitoring and Maintenance Regular Review: Correlations can change, so review them regularly Alerts: Set up alerts for significant correlation changes Documentation: Keep notes on why certain assets are correlated and what might change that relationship 4. Integration with Other Analysis Fundamental Analysis: Combine correlation analysis with fundamental factors Technical Analysis: Use correlation analysis alongside technical indicators Market Context: Consider how market conditions might affect correlations Conclusion This Scientific Correlation Testing Framework provides a comprehensive tool for analyzing relationships between financial assets. By offering both Pearson and Spearman correlation methods, statistical significance testing, and rolling correlation analysis, it goes beyond simple correlation measures to provide deeper insights. For beginners, this script might seem complex, but it's built on fundamental statistical concepts that become clearer with use. Start with the default settings and focus on interpreting the main correlation lines and the statistics table. As you become more comfortable, you can adjust the parameters and explore more advanced applications. Remember that correlation analysis is just one tool in a trader's toolkit. It should be used in conjunction with other forms of analysis and with a clear understanding of its limitations. When used properly, it can provide valuable insights for portfolio construction, risk management, and pair trading strategy development.
Pine Script® indicator
by kingshukghosh71
Central Limit Theorem Reversion IndicatorDear TV community, let me introduce you to the first-ever Central Limit Theorem indicator on TradingView. The Central Limit Theorem is used in statistics and it can be quite useful in quant trading and understanding market behaviors. In short, the CLT states: "When you take repeated samples from any population and calculate their averages, those averages will form a normal (bell curve) distribution—no matter what the original data looks like." In this CLT indicator, I use statistical theory to identify high-probability mean reversion opportunities in the markets. It calculates statistical confidence bands and z-scores to identify when price movements deviate significantly from their expected distribution, signaling potential reversion opportunities with quantifiable probability levels. Mathematical Foundation The Central Limit Theorem (CLT) says that when you average many data points together, those averages will form a predictable bell-curve pattern, even if the original data is completely random and unpredictable (which often is in the markets). This works no matter what you're measuring, and it gets more reliable as you use more data points. Why using it for trading? Individual price movements seem random and chaotic, but when we look at the average of many price movements, we can actually predict how they should behave statistically. This lets us spot when prices have moved "too far" from what's normal—and those extreme moves tend to snap back (mean reversion). Key Formula: Z = (X̄ - μ) / (σ / √n) Where: - X̄ = Sample mean (average return over n periods) - μ = Population mean (long-term expected return) - σ = Population standard deviation (volatility) - n = Sample size - σ/√n = Standard error of the mean How I Apply CLT Step 1: Calculate Returns Measures how much price changed from one bar to the next (using logarithms for better statistical properties) Step 2: Average Recent Returns Takes the average of the last n returns (e.g., last 100 bars). This is your "sample mean." Step 3: Find What's "Normal" Looks at historical data to determine: a) What the typical average return should be (the long-term mean) and b) How volatile the market usually is (standard deviation) Step 4: Calculate Standard Error Determines how much sample averages naturally vary. Larger samples = smaller expected variation. Step 5: Calculate Z-Score Measures how unusual the current situation is. Step 6: Draw Confidence Bands Converts these statistical boundaries into actual price levels on your chart, showing where price is statistically expected to stay 95% and 99% of the time. Interpretation & Usage The Z-Score: The z-score tells you how statistically unusual the current price deviation is: |Z| < 1.0 → Normal behavior, no action |Z| = 1.0 to 1.96 → Moderate deviation, watch closely |Z| = 1.96 to 2.58 → Significant deviation (95%+), consider entry |Z| > 2.58 → Extreme deviation (99%+), high probability setup The Confidence Bands - Upper Red Bands: 95% and 99% overbought zones → Expect mean reversion downward as the price is not likely to cross these lines. - Center Gray Line: Statistical expectation (fair value) - Lower Blue Bands: 95% and 99% oversold zones → Expect mean reversion upward Trading Logic: - When price exceeds the upper 95% band (z-score > +1.96), there's only a 5% probability this is random noise → Strong sell/short signal - When price falls below the lower 95% band (z-score < -1.96), there's a 95% statistical expectation of upward reversion → Strong buy/long signal Background Gradient The background color provides real-time visual feedback: - Blue shades: Oversold conditions, expect upward reversion - Red shades: Overbought conditions, expect downward reversion - Intensity: Darker colors indicate stronger statistical significance Trading Strategy Examples Hypothetically, this is how the indicator could be used: - Long: Z-score < -1.96 (below 95% confidence band) - Short: Z-score > +1.96 (above 95% confidence band) - Take profit when price returns to center line (Z ≈ 0) Input Parameters Sample Size (n) - Default: 100 Lookback Period (m) - Default: 100 You can also create alerts based on the indicator. Final notes: - The indicator uses logarithmic returns for better statistical properties - Converts statistical bands back to price space for practical use - Adaptive volatility: Bands automatically widen in high volatility, narrow in low volatility - No repainting: yay! All calculations use historical data only Feedback is more than welcome! Henri
Pine Script® indicator
by HenriqueCentieiro
2
Stablecoin Liquidity Delta (Aggregate Market Cap Flow)Hi All, This indicator visualizes the bar-to-bar change in the aggregate market capitalization of major stablecoins, including USDT, USDC, DAI, and others. It serves as a proxy for monitoring on-chain liquidity and measuring capital inflows or outflows across the crypto market. Stablecoins are the primary liquidity layer of the crypto economy. Their combined market capitalization acts as a mirror of the available fiat-denominated liquidity in digital markets: 🟩 An increase in the total stablecoin market capitalization indicates new issuance (capital entering the market). 🟥 A decrease reflects redemption or burning (liquidity exiting the system). Tracking these flows helps anticipate macro-level liquidity trends that often lead overall market direction, providing context for broader price movements. All values are derived from TradingView’s public CRYPTOCAP tickers, which represent the market capitalization of each stablecoin. While minor deviations can occur due to small price fluctuations around the $1 peg, these figures serve as a proxy for circulating supply and net issuance across the stablecoin ecosystem.
Pine Script® indicator
by steenman
1
Simple FloatFloat Display Indicator A simple, clean indicator that displays the current float (shares outstanding float) for any stock directly in your indicator status line at the top left of the chart. Features: - Shows the float value with automatic K/M formatting for thousands and millions - No chart clutter - value only appears in the status line, nothing plotted on the chart - Works on any stock that has float data available - Lightweight and efficient Perfect for traders who want quick access to float information without switching between windows or cluttering their charts. Note: Float data availability depends on TradingView's financial data for the specific ticker. Some tickers may not have this data available.
Pine Script® indicator
by maxiejazz