Candle Tick Size + Value TableCandle Tick Size + Value Table
This indicator calculates and displays the tick size and monetary value of each candle for the selected instrument. It shows a vertical table in the top-right corner of your chart with the following columns:
Time – the timestamp of each candle (HH:MM)
Ticks – the candle’s range converted to whole ticks
Value – total value in dollars based on your cost per tick and number of contracts
Features:
Configurable number of recent candles to display
Supports custom point-to-tick ratio, cost per tick, and contracts
Table colors and font are fully customizable
Provides a quick visual reference of the most recent candle sizes and values
Use Case:
Ideal for futures traders, scalpers, or day traders who want to quickly see candle ranges in ticks and monetary value per trade, helping with position sizing and risk management.
Statistics
Monthly Trend Heatmap – Price Change by MonthThis indicator analyzes multi-year monthly price seasonality and displays it as a clear table of percentage returns for each month, from 2013 to the current year. By calculating the monthly open-to-close percentage change, it helps traders quickly identify recurring seasonal trends, positive or negative months, and long-term behavioral patterns of the selected market.
The goal of this tool is to make seasonal analysis accessible to everyday traders by presenting the data visually in a simple, structured, and easy-to-interpret format.
How It Works
The script must be used on a 1-Month chart.
For each month and each year, the indicator calculates:
Monthly return = (Monthly Close – Monthly Open) / Monthly Open × 100
The result is plotted inside a table, with green for positive months and red for negative months.
Data auto-updates as new monthly candles form.
This tool is not a signal generator and does not tell you when to buy or sell. It is a statistical seasonality visualizer meant to enhance decision-making.
The information provided is for educational and informational purposes only and should not be interpreted as financial, investment, or trading advice. Trading and investing in the stock market involve a high level of risk, including the potential loss of capital. Past performance does not guarantee future results, and no strategy or analysis can assure profits or prevent losses.
All examples, charts, scripts, indicators, or market discussions are strictly for demonstration, learning, and analytical purposes. No warranties or guarantees are made regarding accuracy, completeness, or future performance.
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.
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)
CB Spot v BN Futs Premium by Chop324Coinbase Spot vs Binance Futures Premium Tracker
What This Indicator Does:
This indicator automatically tracks the price premium or discount between Coinbase spot prices and Binance perpetual futures for any cryptocurrency you're viewing. It works dynamically with whatever ticker you load it on - no manual configuration needed.
How It Works:
The script extracts the base currency from your current chart (BTC, ETH, SOL, etc.) and automatically constructs the corresponding tickers:
Coinbase Spot: COINBASE: USD
Binance Perpetual Futures: BINANCE: USDT.P
It then calculates the simple price difference: Coinbase Spot - Binance Futures
Visual Display:
The premium/discount is plotted as a histogram:
Green columns: Coinbase trading at a premium (higher than Binance)
Red columns: Coinbase trading at a discount (lower than Binance)
Baseline at 0: Represents price parity between exchanges
Why This Matters:
Coinbase premium is a useful market sentiment indicator, particularly for institutional/US retail activity:
Positive premium: Often indicates strong US-based buying pressure
Negative premium: May suggest selling pressure or capital flowing to offshore exchanges
Extreme deviations: Can signal localized supply/demand imbalances or arbitrage opportunities
Usage:
Simply load the indicator on any crypto chart (BTCUSDT, ETHUSDT, SOLUSDT, etc.) and it will automatically display the premium/discount for that asset.
Note: Requires both Coinbase spot and Binance perpetual futures data to be available for the symbol you're viewing.
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.
Kalman Adaptive Score Overlay [BackQuant]Kalman Adaptive Score Overlay
A powerful indicator that uses adaptive scoring to assess market conditions and trends, utilizing advanced filtering techniques to smooth price data, enhance trend-following precision, and predict future price movements based on past data. It is ideal for traders who need a dynamic and responsive trend analysis tool that adjusts to market fluctuations.
What is Adaptive Scoring?
Adaptive scoring is a technique that adjusts the weight or importance of certain price movements over time based on an ongoing assessment of market behavior. This indicator uses dynamic scoring to assess the strength and direction of price movements, providing insight into whether a trend is likely to continue or reverse. The score is recalculated continuously to reflect the most up-to-date market conditions, offering a responsive approach to trend-following.
How It Works
The core of this indicator is built on advanced filtering methods that smooth price data, adjusting the response to recent price changes. The filtering mechanism incorporates a Kalman filter to reduce noise and improve the accuracy of price signals. Combined with adaptive scoring, this creates a robust framework that automatically adjusts to both short-term fluctuations and long-term trends.
The indicator also uses a dynamic trend-following component that updates its analysis based on the direction of the market, with the option to visualize it through colored candles. When a strong trend is identified, the candles are painted to reflect the prevailing trend, helping traders quickly identify whether the market is in a bullish or bearish state.
Why Adaptive Scoring Is Important
Dynamic Response: Adaptive scoring allows the indicator to respond to changing market conditions. By adjusting its sensitivity to price fluctuations, it ensures that trends are captured accurately, without being overly influenced by short-term noise.
Trend Precision: By combining Kalman filtering with adaptive scoring, the indicator offers a precise and smooth trend-following mechanism. It helps traders stay aligned with the market direction and avoid false signals.
Versatility: The indicator works across multiple timeframes, making it adaptable to different trading strategies, from scalping to long-term trend-following.
Confidence in Market Moves: The adaptive scoring component provides traders with confidence in the strength of the trend, helping them determine when to enter or exit positions with greater certainty.
How Traders Use It
Trend-Following Strategy: Traders can use this indicator to confirm trends and refine their entries and exits. The colored candles and adaptive scoring offer a visual cue of trend strength and direction, making it easier to follow the prevailing market movement.
Multi-Timeframe Analysis: The script supports multi-timeframe analysis, allowing traders to analyze trends and scores across different timeframes (e.g., 1m, 5m, 15m, 30m, 1h, 4h, 12h). This is useful for traders who want to confirm trends on both short and long-term charts before making a trade.
Refining Entry Points: By utilizing the adaptive scoring, traders can identify potential entry points where the score indicates a high probability of trend continuation. Higher scores signal stronger trends, guiding decision-making.
Managing Risk: Traders can use the adaptive scoring system to assess trend stability and adjust their risk management strategies accordingly. For example, higher confidence in the trend allows for larger positions, while lower confidence may require smaller, more cautious trades.
Key Features and Benefits
Kalman Filter for Noise Reduction: The Kalman filter helps to smooth out market noise and allows for a clearer understanding of the underlying price movements. This is particularly useful in volatile markets where short-term fluctuations can cloud trend analysis.
Adaptive Scoring for Flexibility: Adaptive scoring ensures that the indicator remains responsive to changing market conditions. It automatically adjusts to the strength of price movements, enabling better detection of trends and reversals.
Visual Trend Signals: The indicator provides visual signals through candle coloring, making it easier to identify whether the market is in a bullish, neutral, or bearish phase.
Multi-Timeframe Display: The indicator’s multi-timeframe feature allows traders to see the trend and adaptive score on different timeframes simultaneously, providing a comprehensive view of the market.
Customizable Settings: Traders can customize the indicator’s settings, such as the filter parameters, scoring thresholds, and visualization options, tailoring it to their specific trading style and strategy.
Why This is Important for Traders
Improved Decision Making: The adaptive nature of the scoring system allows traders to make more informed decisions based on real-time market data, without being influenced by past volatility.
Market Clarity: By smoothing out price movements and scoring trends adaptively, the indicator provides a clearer picture of market behavior, which is essential for effective trend-following and timing entries and exits.
Increased Confidence in Signals: Adaptive scoring ensures that signals are based on the current market structure, reducing the likelihood of false positives. This boosts traders' confidence when acting on signals.
Conclusion
The Kalman Adaptive Score Overlay offers a dynamic and responsive trend-following tool that integrates Kalman filtering with adaptive scoring. By adjusting to market fluctuations in real time, it allows traders to identify and follow trends with greater precision. Whether you are trading on short or long timeframes, this tool helps you stay aligned with market momentum, ensuring that your entries and exits are based on the most up-to-date and reliable data available.
7D Historical Volatility (Regimes + Stats) - ChrrizzyHere’s what that indicator does—at a glance:
### Core idea
It computes **7-day Historical Volatility (HV)** from **daily** log returns (annualized), then shows:
* the **HV line** and its **30-day average**,
* colored **volatility regimes** (Low / Normal / High / Extreme) with thresholds you set,
* a compact **status panel** (top-right, nudged left) with current stats and time-in-zone.
### Calculations
* **HV (7D)**: `stdev(log(close/close ), 7) * sqrt(365) * 100`, always from **daily data** via `request.security`, so it’s consistent on any chart timeframe.
* **Regimes** (defaults):
Low < 25% • Normal 25–50% • High 50–70% • Extreme > 70% (all editable).
* **30-day avg**: SMA of HV.
* **Time in zone (% over window)**: SMA of boolean flags (e.g., in Low=1 else 0) over `statsWin` days (default 300).
* **Rolling median HV**: 50th percentile over `statsWin`.
### What you see on the chart
* **HV line** (bold) + **30-day HV** (lighter).
* **Horizontal dashed lines** at your regime thresholds.
* **Background shading** that changes with the current regime (green/blue/orange/red).
### Panel (top-right)
Shows:
* BTC Price (daily close)
* Current HV
* 30-day Avg HV
* Median HV (over window)
* Current **Regime**
* A two-line summary: **% of time spent** in Low / Normal / High / Extreme over the chosen window.
The panel is shifted slightly left using a hidden spacer column; tweak the **“Panel right padding (chars)”** input to move it.
### Alerts (ready to use)
* **HV crossed up Low**
* **HV crossed down Low**
* **HV crossed up High**
* **HV crossed up Extreme**
### Inputs you can tune
* `HV Lookback (days)` (default 7)
* `Average HV (days)` (default 30)
* Thresholds: Low/High/Extreme
* `Stats Window (days)` (default 300)
* Panel padding, toggle table/zones on/off.
### How to use it
* **Context**: quickly see if BTC is in **compressed** (Low) or **stressed** (High/Extreme) volatility.
* **Regime cross alerts**: get notified when volatility **expands** from Low (potential breakout conditions) or pushes into High/Extreme (risk increases).
* **Stats/median**: compare today’s HV to its typical level over your lookback window.
If you want, I can add an **HV percentile rank** (e.g., “Current HV is at the 38th percentile over 300d”) or mirror the **low-vol breakout signal** from Script A into this panel.
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.
Session Engine — Market Opens, Killzones & Levels — SMC/ICTSession Engine — Market Opens, Killzones & Institutional Levels (Tokyo • London • New York) — SMC/ICT — TradingATH (PueblaATH)
Precision. Sessions. Structure.
Session Engine maps the institutional heartbeat of the day across Tokyo , London , and New York . It draws timezone-accurate Market Open Lines , clean Killzones (incl. London–NY overlap), and a rock-solid, timeframe-safe suite of Previous High/Low Levels (PDH/PDL/PWH/PWL/PMH/PML). On top, a compact Session Comparison Table with an integrated ADR panel shows extension, momentum context, and distance to key levels — at a glance.
Designed for SMC/ICT Traders who demand clarity and reliability, this tool stays stable when you change timeframe, reload, or zoom.
Map the day like a Pro : timezone-true Opens, configurable Killzones, TF-safe PDH/PDL/PWH/PWL/PMH/PML , and a sleek ADR panel beneath a Session Comparison Table . Built for precision SMC/ICT Execution . Zero flicker, full control.
Why Traders Love It
Timezone-Accurate Session Engine — Tokyo, London, New York opens and the London–NY overlap, all resolved to bar-time for precise plotting on any symbol.
Killzones you can trust — choose full-column height or price-bounded height with custom top/bottom tick offsets and label placement.
Bulletproof Previous Levels — PDH, PDL, PWH, PWL, PMH, PML are cached and only refresh on true D/W/M boundaries, eliminating the classic “levels disappear on TF change” problem.
Actionable Context — a compact Session Comparison Table (vs previous session & vs previous day) plus an ADR panel with extension thresholds, distance to PDH/PDL, and current H-L range.
Serious Customization — dotted/solid lines, widths, label size & alignment, auto label backgrounds, block transparency, weekend & timeframe filters, and more.
Performance-Minded — persistent objects are updated in place (not spam-created) to keep your chart crisp and responsive.
What You’ll See
Market Opens — Vertical opens for TOK/LDN/NY with dotted/solid styling, width control, infinite or bounded height, and optional labels.
Killzones + Overlap — Transparent time boxes for session windows (and London–NY overlap). Optional labels, adjustable transparency, and height mode.
Institutional Levels — PDH / PDL / PWH / PWL / PMH / PML with length modes: Infinite, N bars, or End of day. Optional labels with typographic control.
Session Comparison Table — For each session: bias vs previous session and previous day, with optional Δ% column.
ADR Panel — 24h rolling ADR% consumption with two attention thresholds, distance to PDH/PDL (price units), and current H-L range.
How It Works
Session Timing uses explicit IANA timezones (Asia/Tokyo, Europe/London, America/New_York) then anchors to bar_time for pixel-perfect placement.
Killzones are persistent boxes that reset only on daily change, preventing redundant object creation.
Previous Levels are requested once per true period roll (D/W/M) and stored locally; this cache keeps lines stable when switching TFs or reloading charts.
Level Line Length is enforced per-object (Infinite, N bars, End of day) with dynamic x2 handling — no redraw flicker.
ADR uses a timeframe-agnostic 24h rolling window for H/L/range; ADR length is defined in “days” and mapped to bars for any timeframe.
How to Use
Set Session Times (defaults are standard). Adjust the London–NY overlap if your venue differs.
Style your Opens & Killzones — line width, dotted/solid, infinite or bounded height, label font size/align/background.
Choose Level Behavior — Infinite, N bars, or End of day for PD/ PW / PM lines; toggle labels as needed.
Read the Table and ADR — quick bias vs previous session/day, Δ% if you enable it; ADR panel highlights extension with blink thresholds and shows live distance to PDH/PDL.
Inputs
Schedules — Open times + killzone windows for TOK/LDN/NY, and London–NY overlap.
Style — Line width, dotted/solid, label sizes & alignment, auto backgrounds.
Heights — Infinite or tick-bounded line height; full-column or tick-bounded killzones.
Levels — Show/hide PDH/PDL/PWH/PWL/PMH/PML; length mode; label options.
Table & ADR — Font size, arrows, Δ% column, ADR length (days), blink thresholds, show/hide rows.
Filters — Hide visuals on specified timeframe ranges; optional weekend suppression.
Best Practices
Use “End of day” for tidy level lines that still convey right-hand context.
Set ADR thresholds to your instrument’s personality (e.g., 80/120 for FX, 100/150 for crypto).
On exotic trading sessions, verify the IANA timezone alignment and tweak inputs accordingly.
If you stack many tools, consider disabling unused sessions/rows to stay within object limits.
What Makes It Original
A cohesive Session Engine architecture that unifies timezone-true Opens, configurable Killzones/Overlap, and TF-safe previous levels — tailored for SMC/ICT execution.
Robust caching that eliminates TF-switch flicker and preserves dependent calculations (distance to PDH/PDL, ADR%) without gaps.
A unified ADR panel directly under the session table with real-time extension signaling and distance-to-PDH/PDL — pragmatic, trade-ready context you won’t find in generic session scripts.
Deep length & typography controls so visuals are informative and elegant.
Notes & Disclaimer (Originality & Rights)
Original Work Notice — Please read — This script/indicator is an original work created exclusively by TradingATH ( PueblaATH ). It is not derived from, copied from, or authored by any other person or entity. Any resemblance to other scripts is coincidental and limited to the use of public and widely known trading concepts.
Usage & Publication — Redistribution, cloning, or republishing this script (in whole or in part) without the explicit written permission of TradingATH ( PueblaATH ) is prohibited. By using this tool, you acknowledge the author’s exclusive authorship and associated rights.
No Financial Advice — This tool is for educational/informational purposes only and does not constitute financial advice. Markets carry risk; manage your risk and make your own decisions.
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.
MomentumQ Ratio MatrixMomentumQ Ratio Matrix — Intermarket Risk & Sector Relationship Dashboard
The MomentumQ Ratio Matrix is a compact, on-chart dashboard designed to help traders quickly interpret intermarket relationships and sector leadership through key ETF ratios.
It visualizes the balance between risk-on vs. risk-off sentiment , growth vs. value rotation , and defensive vs. cyclical behavior — giving you an instant read of where capital is flowing in the U.S. market.
What It Does
The indicator compares weekly and daily percentage returns for five critical sector ETF pairs. Each pair represents a specific aspect of market structure or investor preference.
When a ratio is rising , it means the first sector is outperforming the second — signaling increased risk appetite or leadership from growth sectors.
When a ratio is falling , it indicates defensiveness, capital rotation, or weakening momentum in risk-oriented areas.
Examples:
XLY/XLP ↑ → Consumers are spending more on discretionary items (risk-on).
XLY/XLP ↓ → Money shifts into staples (risk-off, defensive tone).
XLK/XLF ↑ → Technology leads Financials (growth leadership).
XLK/XLF ↓ → Financials lead, signaling preference for value or cyclicals.
XLI/XLU ↑ → Industrials outperform Utilities (economic optimism).
XLI/XLU ↓ → Utilities outperform (defensive capital rotation).
XLE/XLB ↑ → Energy leading Materials (inflation or commodity strength).
XLE/XLB ↓ → Materials outperform (cooling inflationary trends).
XLV/XLU ↑ → Healthcare stronger than Utilities (mild defensiveness, but stable risk appetite).
XLV/XLU ↓ → Utilities lead (risk aversion, defensive positioning).
Color-coded cells highlight each ratio’s short-term and medium-term performance:
Green → Ratio rising (risk-on, cyclical, or growth leadership).
Red → Ratio falling (risk-off, defensive, or value rotation).
Gray → Neutral performance.
Key Features
Essential Ratio Coverage — Tracks the five most meaningful ETF ratios for intermarket and sentiment analysis.
Multi-Timeframe Analysis — Displays both Weekly and Daily (or Previous Day) changes for each ratio.
Adaptive Table Layout — Adjustable size, position, and decimal precision to fit any chart.
Light / Dark Mode Support — Automatically adapts to match your TradingView theme.
Performance-Based Coloring — Green for strength, red for weakness, and gray for neutral.
How to Use
Add the indicator to any chart (symbol-independent).
Choose your table position and size from the settings.
Toggle between Today and PrevD mode for different time comparisons.
Use the color-coded returns to gauge where capital is flowing.
Watch for shifts across multiple ratios to confirm changing market regimes.
When most ratios are green, the market generally favors growth and higher risk assets (risk-on).
When most are red, defensive sectors and value stocks tend to lead (risk-off).
Why It’s Valuable
Condenses intermarket and macro relationships into one visual dashboard.
Helps identify leadership shifts between risk, growth, and defensive sectors.
Provides a real-time snapshot of market sentiment without switching charts.
Supports both short-term tactical and long-term trend confirmation.
Disclaimer
The MomentumQ Ratio Matrix is designed for educational and analytical purposes only.
It does not constitute financial advice or guarantee profitability.
Always conduct independent analysis and apply proper risk management when trading.
HTF Ranges - AWR/AMR/AYR [bilal]📊 Overview
Professional higher timeframe range indicator for swing and position traders. Calculate Average Weekly Range (AWR), Average Monthly Range (AMR), and Average Yearly Range (AYR) with precision projection levels.
✨ Key Features
📅 Three Timeframe Modes
AWR (Average Weekly Range): Weekly swing targets - Default 4 weeks
AMR (Average Monthly Range): Monthly position targets - Default 6 months
AYR (Average Yearly Range): Yearly extremes - Default 9 years
🎯 Dual Anchor Options
Period Open: Week/Month/Year opening price
RTH Open: First RTH session (09:30 NY) of the period
📐 Projection Levels
100% Range Levels: Upper and lower targets from anchor
Fractional Levels: 33% and 66% zones for partial targets
Custom Mirrored Levels: Set any percentage (0-200%) with automatic mirroring
Example: 25% shows both 25% and 75%
Example: 150% shows both 150% and -50%
📊 Information Table
Active range type (AWR/AMR/AYR)
Average range value for selected period
Current period range and percentage used
Distance remaining to targets (up/down)
Color-coded progress (green/orange/red)
🎨 Fully Customizable
Orange theme by default (differentiates from daily indicators)
Line colors, styles (solid/dashed/dotted), and widths
Toggle labels on/off
Adjustable lookback periods for each timeframe
Independent settings for each range type
⚡ Smart Features
Lines start at actual period open (not fixed lookback)
Automatically tracks current period high/low
Works on any chart timeframe
Real-time range tracking
Alert conditions when targets reached or exceeded
🎯 Use Cases
AWR (Weekly Ranges):
Swing trade targets (3-7 day holds)
Weekly support/resistance zones
Identify weekly trend vs rotation
Compare daily moves to weekly context
AMR (Monthly Ranges):
Position trade targets (2-4 week holds)
Monthly breakout levels
Institutional-level zones
Earnings play targets
AYR (Yearly Ranges):
Major reversal zones
Long-term support/resistance
Identify macro trend strength
Annual high/low projections
💡 Trading Strategies
AWR Strategy (Swing Trading):
Week opens near AWR lower level = potential long setup
Target AWR 66% and 100% levels
Week hits AWR upper in first 2 days = watch for reversal
Use fractional levels as scale-in/scale-out points
AMR Strategy (Position Trading):
Month opens near AMR extremes = fade setup
Month breaks AMR in week 1 = expansion (trend) month
Target opposite AMR extreme for swing positions
Use 33%/66% for partial profit taking
AYR Strategy (Long-term Context):
Price near AYR extremes = major reversal zones
Breaking AYR levels = historic moves (rare)
Use for macro trend confirmation
Great for yearly forecasting and planning
📊 Range Interpretation
<33% Range Used: Early in period, room for expansion
33-66% Range Used: Normal progression
66-100% Range Used: Extended, approaching extremes
>100% Range Used: Expansion period - trending or high volatility
⚙️ Settings Guide
Lookback Periods:
AWR: 4 weeks (standard) - adjust to 8-12 for smoother average
AMR: 6 months (standard) - seasonal patterns
AYR: 9 years (standard) - captures full cycles
Anchor Type:
Period Open: Use for clean week/month/year open reference
RTH Open: Use if you only trade day session, ignores overnight gaps
Custom Levels:
25% = quartile targets
75% = three-quarter targets
80% = "danger zone" for reversals
111% = extended breakout target
🔄 Combine with ADR Indicator
Run both indicators together for complete multi-timeframe analysis:
ADR for intraday precision
AWR/AMR/AYR for swing/position context
See if today's ADR move is significant in weekly/monthly context
Multi-timeframe confluence = highest probability setups
💼 Ideal For
Swing Traders: Use AWR for 3-10 day holds
Position Traders: Use AMR for 2-8 week holds
Long-term Investors: Use AYR for macro context
Index Futures Traders: ES, NQ, YM, RTY
Multi-timeframe Analysis: Combine with daily ADR
Advanced ICT ADR Projections [bilal]📊 Overview
Professional ADR indicator designed specifically for index futures traders. Calculate and visualize Average Daily Range with multiple session options, fractional levels, and higher timeframe context.
✨ Key Features
🎯 Multiple Session Types
Full Day: Standard calendar day calculation
Midnight: Anchored to 00:00 NY time open
RTH (Regular Trading Hours): 09:30-16:00 NY session
Custom: Define your own session hours and anchor point
📐 Projection Levels
100% ADR Levels: Upper and lower range targets from anchor
Fractional Levels: 33% and 66% zones for partial targets
Custom Mirrored Levels: Set any percentage (0-200%) with automatic mirroring
Example: 25% shows both 25% and 75%
Example: 111% shows both 111% and -11%
📅 Higher Timeframe Context (Optional)
AWR: Average Weekly Range overlay
AMR: Average Monthly Range overlay
AYR: Average Yearly Range overlay
All HTF ranges use same anchor as daily session
📊 Information Table
Current session type and anchor time
ADR value for selected period
Current range and percentage used
Distance remaining to ADR targets (up/down)
Color-coded range percentage (green/orange/red)
🎨 Fully Customizable
Line colors, styles (solid/dashed/dotted), and widths
Toggle labels on/off
Adjustable ADR lookback period (1-100 days)
All HTF periods customizable
⚡ Smart Features
Lines start at actual session open (not fixed lookback)
Works on any timeframe
Real-time range tracking
Alert conditions when ADR reached or exceeded
🎯 Use Cases
For Day Traders:
Set profit targets at ADR extremes
Identify range expansion vs rotation days
Know when you've used 75%+ of daily range (possible reversal)
Compare RTH vs full day ranges
For Swing Traders:
Use AWR/AMR for weekly/monthly targets
Understand if today's move is significant in weekly context
Multi-timeframe confluence
Risk Management:
Size positions based on % of ADR remaining
Avoid trading when ADR exhausted (>100%)
Better stop placement using fractional levels
💡 Trading Tips
<50% ADR used = Room to run (continuation trades)
50-75% ADR used = Getting extended (scale out)
75-100% ADR used = Near extremes (reversal setups)
>100% ADR = Expansion day (trend day or volatility spike)
Use fractional levels (33%, 66%) as:
Partial profit targets
Re-entry zones on pullbacks
Confluence with other support/resistance
Compare RTH vs Full Day ADR to see if overnight or day session drives volatility.
⚙️ Settings Guide
ADR Period: 5 days is standard, adjust for different market regimes
Session Types:
Use Midnight for crypto or 24hr markets
Use RTH for pure day session analysis
Use Custom for specific session times (London, Asia, etc.)
Custom Levels:
Set 25% for quartile levels
Set 111% for extended targets beyond ADR
Experiment with 50%, 75%, 80% for your strategy
Perfect for ES, NQ, YM, RTY futures traders who need precise intraday range analysis with higher timeframe context!
korea time with 200 korea time
start time
08
09
17
18
23
00
This script makes it easier to look at the charts
The time automatically displays even if you don't bother to bring the mouse by hand
Now you can see the time intuitively
Run a very happy trading session
HIPA - High IRL Probability Areas [Pro]Overview
HIPA (High IRL Probability Areas) visualizes statistically derived Internal Range Liquidity (IRL) zones — price areas that have historically shown a higher tendency for revisit or interaction within an intraday session.
When a candle breaks a higher-timeframe reference bar’s high or low, HIPA plots a draw line at the opposite extreme of that bar, highlighting where liquidity is most likely to rest.
Each line displays a live, time-conditioned probability (CH) of being revisited before session end, plus an optional End-of-Day (EOD) probability.
HIPA provides statistical context on how current price interacts with prior structural ranges. It does not issue trade signals or forecasts.
Key Features
• Tracks higher-timeframe reference bars across the session.
• Plots draw lines at the opposite extreme once a break occurs.
• Displays dynamic CH and optional EOD probabilities derived from embedded historical statistics.
• Tested lines change style once price interacts; expired levels are automatically removed.
• Fully customizable palette, text size, and visibility options.
Signal Area Context
HIPA includes an optional Signal Area overlay that blends IRL probabilities with higher-timeframe (HTF) structure.
You can reference candle highs/lows or pivots from selected HTF intervals and display a subtle gradient between these external liquidity points and active IRL zones.
The shaded area is intended to help visualize where intraday behavior aligns with multi-timeframe liquidity zones — not to produce entries or exits.
Traders may use it to study how price develops around these confluence regions.
Methodology (transparency)
HIPA uses embedded statistical references derived from long-term historical market behavior to estimate revisit probabilities conditioned by time-of-day and break direction.
These references are stored directly within the script for deterministic plotting — no external data is fetched or required.
Session data is internally organized into consistent time segments to reflect how revisit tendencies vary through the day.
HIPA is descriptive: it visualizes empirically observed behavior, not predictive outcomes.
How to Use
- Apply HIPA to any intraday chart.
- Observe when price breaks a higher-timeframe candle’s high or low — a draw line appears at the opposite extreme with CH/EOD labels.
- Optionally enable the Signal Area to visualize overlap between HTF liquidity and current IRL zones.
- Use HIPA as structural and statistical context only; combine with your own framework for decision-making.
- Treat draw lines and gradients as reference areas rather than precise trade levels.
Notes
• Historical behavior can evolve under different market conditions.
• CH/EOD probabilities adjust with session time; interpret contextually.
• Rendering many lines simultaneously may affect performance — enable pruning if needed.
• Works on any symbol or timeframe supported by TradingView.
Disclaimers
Educational use only. Past performance does not guarantee future results.
HIPA visualizes statistical context based on historical behavior and does not predict or recommend trades.
ATR Position SizerFound the substack from Ryan Wright (raen prop trading) which has amazing insights into the real prop trading world.
In his post Your Trading Edge Isn't Your Setup . It's Your Knowledge he shared a few interesting nuggets of knowledge.
Especially the part about Risk according to the 20-day ATR caught my eye, so i reconstructed a version of that formula directly for Tradingview. It works with ES (MES), NQ (MNQ), GC (MGC), YM and can be extended.
Additionally I implemented a function that tracks the chart ATR (automatically on the chosen time frame) on a defined period. This can of course be disable.
Hope it helps
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.
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.
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
NSE Pairs Screener-20 pair This advanced Pine Script screener is designed for pairs trading on the National Stock Exchange (NSE) of India. It simultaneously monitors up to 20 stock pairs, calculates key statistical metrics, and provides real-time trading signals based on mean reversion strategies.
Key Features
1. Multi-Pair Analysis
Monitor up to 20 stock pairs simultaneously
Customizable number of pairs to display (1-20)
Pre-configured with popular NSE stock pairs across various sectors
2. Statistical Calculations
Correlation Analysis: Measures the strength of relationship between paired stocks
Z-Score Calculation: Identifies extreme deviations from the mean spread
Cointegration Score: Validates long-term equilibrium relationships
Dynamic Hedge Ratio: Calculates optimal position sizing between pairs
3. Trading Signals
Long Signal: When spread is oversold (Z-score ≤ -2.0)
Short Signal: When spread is overbought (Z-score ≥ 2.0)
Exit Signal: When spread returns to mean (Z-score ≤ 0.5)
Watch Status: Pairs requiring monitoring
4. Automated Alert System
Single comprehensive alert for all qualifying pairs
Customizable alert thresholds for correlation, Z-score, and cointegration
On-chart visual alerts with detailed information
Notification support via TradingView's alert system
5. Visual Display
Clean, color-coded table interface
Adjustable table position (9 positions available)
Highlighted trading opportunities
Real-time metric updates
Configuration Parameters
Screener Settings
Number of Pairs to Display: 1-20 pairs (default: 20)
Calculation Parameters
Parameter Default Range Description Correlation Lookback Period25220-500Historical period for correlation calculation Z-Score SMA Length205-100Moving average length for spread calculation Hedge Ratio Length205-100Period for hedge ratio smoothing Minimum Correlation0.70.5-1.0Threshold for pair validation
Alert Settings
Parameter Default Range Description Alert Correlation Threshold0.70.5-1.0Minimum correlation for alerts Alert Z-Score Threshold2.01.0-3.0Z-score trigger level Alert Cointegration Threshold90%80-99%Minimum cointegration percentage
Display Settings
Table Position: 9 position options (default: middle_center)
Table Background Color: Customizable
Highlight Opportunities: Toggle visual highlighting of trading signals
Pre-Configured Stock Pairs
The script includes 20 carefully selected NSE pairs across various sectors:
Financial Services
RELIANCE / ONGC
HDFCBANK / ICICIBANK
SBIN / PNB
KOTAKBANK / AXISBANK
BAJFINANCE / BAJAJFINSV
Information Technology
TCS / INFY
WIPRO / HCLTECH
TECHM / LTIM
Consumer Goods
ITC / HINDUNILVR
TITAN / TANLA
ASIANPAINT / BERGEPAINT
Telecommunications
BHARTIARTL / IDEA
Automotive
MARUTI / TATAMOTORS
Infrastructure & Industrials
LT / UBL
POWERGRID / NTPC
Pharmaceuticals
SUNPHARMA / CIPLA
DIVISLAB / DRREDDY
Materials
ULTRACEMCO / ACC
UPL / JSWSTEEL
Energy
ADANIENT / ADANIPOWER
🎨 Color-Coded Metrics
Correlation
🟢 Green: ≥ Minimum threshold (strong relationship)
🔴 Red: < Minimum threshold (weak relationship)
Z-Score
🔴 Red: |Z| ≥ 2.0 (extreme deviation - trading opportunity)
🟡 Yellow: 0.5 < |Z| < 2.0 (normal range - watch)
🟢 Green: |Z| ≤ 0.5 (mean reversion - exit signal)
Cointegration
🟢 Green: ≥ 70% (strong cointegration)
🟡 Yellow: 50-70% (moderate cointegration)
🔴 Red: < 50% (weak cointegration)
Status
🟢 Green: Long (buy spread)
🔴 Red: Short (sell spread)
🔵 Blue: Exit (close positions)
⚪ Gray: Watch (monitor)
Validation
🟢 Green: Pass (meets all criteria)
🔴 Red: Fail (doesn't meet criteria)
How It Works
1. Data Collection
The script fetches real-time closing prices for all 20 stock pairs from NSE.
2. Statistical Analysis
For each pair, the script calculates:
Log Returns: Natural logarithm of price changes
Correlation: Pearson correlation coefficient between returns
Hedge Ratio: Price ratio smoothed over specified period
Spread: Price difference adjusted by hedge ratio
Z-Score: Standardized spread deviation
3. Signal Generation
Based on Z-score thresholds:
Z ≥ 2.0: Short spread (short overvalued, long undervalued)
Z ≤ -2.0: Long spread (long overvalued, short undervalued)
|Z| ≤ 0.5: Exit positions (spread reverted to mean)
4. Validation
Pairs must meet criteria:
Correlation ≥ minimum threshold
Valid trading signal (entry or exit)
5. Alert Triggering
Alerts fire when pairs simultaneously meet:
Correlation ≥ alert threshold
|Z-score| ≥ alert threshold
Cointegration ≥ alert threshold
Alert System
The script features a single comprehensive alert that monitors all pairs:
Consolidated Notifications: One alert for all qualifying pairs
Detailed Information: Includes pair names, signal type, and key metrics
Visual Indicators: Red label on chart with complete details
Customizable Thresholds: Adjust sensitivity based on trading style
Alert Message Format
PAIR TRADING OPPORTUNITIES
Pair X: STOCK1/STOCK2
Signal: LONG/SHORT Spread
Z-Score: X.XX
Correlation: X.XXX
Cointegration: XX.X%
Trading Strategy Guide
Entry Rules
Long Spread (Z-score ≤ -2.0):
Buy Stock Y
Sell Stock X (in ratio of hedge ratio)
Short Spread (Z-score ≥ 2.0):
Sell Stock Y
Buy Stock X (in ratio of hedge ratio)
Exit Rules
Close positions when Z-score returns to ±0.5
Set stop-loss at Z-score ±3.0 (extreme deviations)
Risk Management
Only trade pairs with correlation ≥ 0.7
Prefer cointegration scores ≥ 90%
Monitor hedge ratio changes
Diversify across multiple pairs
Customization Options
Adding New Pairs
Simply modify the stock symbol inputs in the respective pair groups (Pair 1 through Pair 20).
Adjusting Sensitivity
Conservative: Increase Z-score threshold to 2.5-3.0
Aggressive: Decrease Z-score threshold to 1.5-2.0
Long-term: Increase lookback period to 500
Short-term: Decrease lookback period to 50-100
Visual Preferences
Change table position to suit your layout
Adjust background colors for better contrast
Toggle opportunity highlighting on/off
Technical Notes
Calculation Method
Uses logarithmic returns for correlation (better statistical properties)
Z-score normalized by standard deviation
Cointegration approximated using correlation strength
Hedge ratio smoothed using simple moving average
Performance Considerations
Calculations update on every bar close
Table displays only on the last bar
Alert checks occur at bar close
Maximum 500 labels supported (more than sufficient)
Limitations
Does not account for transaction costs
Assumes linear relationships between pairs
Historical correlation doesn't guarantee future behaviour
Requires sufficient liquidity in both stocks
Best Practices
Back test Thoroughly: Test parameters on historical data before live trading
Monitor Regularly: Check pairs daily for validation changes
Diversify: Trade multiple pairs to reduce risk
Stay Informed: Be aware of corporate actions, news affecting pairs
Adjust Parameters: Optimize for current market conditions
Use Stop-Losses: Protect against extreme divergences
Track Performance: Maintain trading journal for continuous improvement
Indicator Information
Version: Pine Script v5
Overlay: False (separate pane)
Max Labels: 500
Update Frequency: Every bar close
Compatible Timeframes: All (works best on daily or higher)
Getting Started
Add to Chart: Apply indicator to any NSE stock
Configure Pairs: Adjust stock symbols as needed
Set Parameters: Customize calculation and alert settings
Create Alert: Set up Trading View alert for notifications
Monitor: Watch the table for trading opportunities
Execute: Trade based on validated signals
📞Support & Updates
This script is designed for educational and research purposes. Always:
Conduct thorough back testing
Use proper risk management
Consider transaction costs
Consult with financial advisors
Trade responsibly
Disclaimer: This indicator is for educational purposes only. Past performance does not guarantee future results. Always conduct your own research and risk assessment before trading.
Manifold Singularity EngineManifold Singularity Engine: Catastrophe Theory Detection Through Multi-Dimensional Topology Analysis
The Manifold Singularity Engine applies catastrophe theory from mathematical topology to multi-dimensional price space analysis, identifying potential reversal conditions by measuring manifold curvature, topological complexity, and fractal regime states. Unlike traditional reversal indicators that rely on price pattern recognition or momentum oscillators, this system reconstructs the underlying geometric surface (manifold) that price evolves upon and detects points where this topology undergoes catastrophic folding—mathematical singularities that correspond to forced directional changes in price dynamics.
The indicator combines three analytical frameworks: phase space reconstruction that embeds price data into a multi-dimensional coordinate system, catastrophe detection that measures when this embedded manifold reaches critical curvature thresholds indicating topology breaks, and Hurst exponent calculation that classifies the current fractal regime to adaptively weight detection sensitivity. This creates a geometry-based reversal detection system with visual feedback showing topology state, manifold distortion fields, and directional probability projections.
What Makes This Approach Different
Phase Space Embedding Construction
The core analytical method reconstructs price evolution as movement through a three-dimensional coordinate system rather than analyzing price as a one-dimensional time series. The system calculates normalized embedding coordinates: X = normalize(price_velocity, window) , Y = normalize(momentum_acceleration, window) , and Z = normalize(volume_weighted_returns, window) . These coordinates create a trajectory through phase space where price movement traces a path across a geometric surface—the market manifold.
This embedding approach differs fundamentally from traditional technical analysis by treating price not as a sequential data stream but as a dynamical system evolving on a curved surface in multi-dimensional space. The trajectory's geometric properties (curvature, complexity, folding) contain information about impending directional changes that single-dimension analysis cannot capture. When this manifold undergoes rapid topological deformation, price must respond with directional change—this is the mathematical basis for catastrophe detection.
Statistical normalization using z-score transformation (subtracting mean, dividing by standard deviation over a rolling window) ensures the coordinate system remains scale-invariant across different instruments and volatility regimes, allowing identical detection logic to function on forex, crypto, stocks, or indices without recalibration.
Catastrophe Score Calculation
The catastrophe detection formula implements a composite anomaly measurement combining multiple topology metrics: Catastrophe_Score = 0.45×Curvature_Percentile + 0.25×Complexity_Ratio + 0.20×Condition_Percentile + 0.10×Gradient_Percentile . Each component measures a distinct aspect of manifold distortion:
Curvature (κ) is computed using the discrete Laplacian operator: κ = √ , which measures how sharply the manifold surface bends at the current point. High curvature values indicate the surface is folding or developing a sharp corner—geometric precursors to catastrophic topology breaks. The Laplacian measures second derivatives (rate of change of rate of change), capturing acceleration in the trajectory's path through phase space.
Topological Complexity counts sign changes in the curvature field over the embedding window, measuring how chaotically the manifold twists and oscillates. A smooth, stable surface produces low complexity; a highly contorted, unstable surface produces high complexity. This metric detects when the geometric structure becomes informationally dense with multiple local extrema, suggesting an imminent topology simplification event (catastrophe).
Condition Number measures the Jacobian matrix's sensitivity: Condition = |Trace| / |Determinant|, where the Jacobian describes how small changes in price produce changes in the embedding coordinates. High condition numbers indicate numerical instability—points where the coordinate transformation becomes ill-conditioned, suggesting the manifold mapping is approaching a singularity.
Each metric is converted to percentile rank within a rolling window, then combined using weighted sum. The percentile transformation creates adaptive thresholds that automatically adjust to each instrument's characteristic topology without manual recalibration. The resulting 0-100% catastrophe score represents the current bar's position in the distribution of historical manifold distortion—values above the threshold (default 65%) indicate statistically extreme topology states where reversals become geometrically probable.
This multi-metric ensemble approach prevents false signals from isolated anomalies: all four geometric features must simultaneously indicate distortion for a high catastrophe score, ensuring only true manifold breaks trigger detection.
Hurst Exponent Regime Classification
The Hurst exponent calculation implements rescaled range (R/S) analysis to measure the fractal dimension of price returns: H = log(R/S) / log(n) , where R is the range of cumulative deviations from mean and S is the standard deviation. The resulting value classifies market behavior into three fractal regimes:
Trending Regime (H > 0.55) : Persistent price movement where future changes are positively correlated with past changes. The manifold exhibits directional momentum with smooth topology evolution. In this regime, catastrophe signals receive 1.2× confidence multiplier because manifold breaks in trending conditions produce high-magnitude directional changes.
Mean-Reverting Regime (H < 0.45) : Anti-persistent price movement where future changes tend to oppose past changes. The manifold exhibits oscillatory topology with frequent small-scale distortions. Catastrophe signals receive 0.8× confidence multiplier because reversal significance is diminished in choppy conditions where the manifold constantly folds at minor scales.
Random Walk Regime (H ≈ 0.50) : No statistical correlation in returns. The manifold evolution is geometrically neutral with moderate topology stability. Standard 1.0× confidence multiplier applies.
This adaptive weighting system solves a critical problem in reversal detection: the same geometric catastrophe has different trading implications depending on the fractal regime. A manifold fold in a strong trend suggests a significant reversal opportunity; the same fold in mean-reversion suggests a minor oscillation. The Hurst-based regime filter ensures detection sensitivity automatically adjusts to market character without requiring trader intervention.
The implementation uses logarithmic price returns rather than raw prices to ensure
stationarity, and applies the calculation over a configurable window (default 5 bars) to balance responsiveness with statistical validity. The Hurst value is then smoothed using exponential moving average to reduce noise while maintaining regime transition detection.
Multi-Layer Confirmation Architecture
The system implements five independent confirmation filters that must simultaneously validate
before any singularity signal generates:
1. Catastrophe Threshold : The composite anomaly score must exceed the configured threshold (default 0.65 on 0-1 scale), ensuring the manifold distortion is statistically extreme relative to recent history.
2. Pivot Structure Confirmation : Traditional swing high/low patterns (using ta.pivothigh and ta.pivotlow with configurable lookback) must form at the catastrophe bar. This ensures the geometric singularity coincides with observable price structure rather than occurring mid-swing where interpretation is ambiguous.
3. Swing Size Validation : The pivot magnitude must exceed a minimum threshold measured in ATR units (default 1.5× Average True Range). This filter prevents signals on insignificant price jiggles that lack meaningful reversal potential, ensuring only substantial swings with adequate risk/reward ratios generate signals.
4. Volume Confirmation : Current volume must exceed 1.3× the 20-period moving average, confirming genuine market participation rather than low-liquidity price noise. Manifold catastrophes without volume support often represent false topology breaks that don't translate to sustained directional change.
5. Regime Validity : The market must be classified as either trending (ADX > configured threshold, default 30) or volatile (ATR expansion > configured threshold, default 40% above 30-bar average), and must NOT be in choppy/ranging state. This critical filter prevents trading during geometrically unfavorable conditions where edge deteriorates.
All five conditions must evaluate true simultaneously for a signal to generate. This conjunction-based logic (AND not OR) dramatically reduces false positives while preserving true reversal detection. The architecture recognizes that geometric catastrophes occur frequently in noisy data, but only those catastrophes that align with confirming evidence across price structure, participation, and regime characteristics represent tradable opportunities.
A cooldown mechanism (default 8 bars between signals) prevents signal clustering at extended pivot zones where the manifold may undergo multiple small catastrophes during a single reversal process.
Direction Classification System
Unlike binary bull/bear systems, the indicator implements a voting mechanism combining four
directional indicators to classify each catastrophe:
Pivot Vote : +1 if pivot low, -1 if pivot high, 0 otherwise
Trend Vote : Based on slow frequency (55-period EMA) slope—+1 if rising, -1 if falling, 0 if flat
Flow Vote : Based on Y-gradient (momentum acceleration)—+1 if positive, -1 if negative, 0 if neutral
Mid-Band Vote : Based on price position relative to medium frequency (21-period EMA)—+1 if above, -1 if below, 0 if at
The total vote sum classifies the singularity: ≥2 votes = Bullish , ≤-2 votes = Bearish , -1 to +1 votes = Neutral (skip) . This majority-consensus approach ensures directional classification requires alignment across multiple timeframes and analysis dimensions rather than relying on a single indicator. Neutral signals (mixed voting) are displayed but should not be traded, as they represent geometric catastrophes without clear directional resolution.
Core Calculation Methodology
Embedding Coordinate Generation
Three normalized phase space coordinates are constructed from price data:
X-Dimension (Velocity Space):
price_velocity = close - close
X = (price_velocity - mean) / stdev over hurstWindow
Y-Dimension (Acceleration Space):
momentum = close - close
momentum_accel = momentum - momentum
Y = (momentum_accel - mean) / stdev over hurstWindow
Z-Dimension (Volume-Weighted Space):
vol_normalized = (volume - mean) / stdev over embedLength
roc = (close - close ) / close
Z = (roc × vol_normalized - mean) / stdev over hurstWindow
These coordinates define a point in 3D phase space for each bar. The trajectory connecting these points is the reconstructed manifold.
Gradient Field Calculation
First derivatives measure local manifold slope:
dX/dt = X - X
dY/dt = Y - Y
Gradient_Magnitude = √
The gradient direction indicates where the manifold is "pushing" price. Positive Y-gradient suggests upward topological pressure; negative Y-gradient suggests downward pressure.
Curvature Tensor Components
Second derivatives measure manifold bending using discrete Laplacian:
Laplacian_X = X - 2×X + X
Laplacian_Y = Y - 2×Y + Y
Laplacian_Magnitude = √
This is then normalized:
Curvature_Normalized = (Laplacian_Magnitude - mean) / stdev over embedLength
High normalized curvature (>1.5) indicates sharp manifold folding.
Complexity Accumulation
Sign changes in curvature field are counted:
Sign_Flip = 1 if sign(Curvature ) ≠ sign(Curvature ), else 0
Topological_Complexity = sum(Sign_Flip) over embedLength window
This measures oscillation frequency in the geometry. Complexity >5 indicates chaotic topology.
Condition Number Stability Analysis
Jacobian matrix sensitivity is approximated:
dX/dp = dX/dt / (price_change + epsilon)
dY/dp = dY/dt / (price_change + epsilon)
Jacobian_Determinant = (dX/dt × dY/dp) - (dX/dp × dY/dt)
Jacobian_Trace = dX/dt + dY/dp
Condition_Number = |Trace| / (|Determinant| + epsilon)
High condition numbers indicate numerical instability near singularities.
Catastrophe Score Assembly
Each metric is converted to percentile rank over embedLength window, then combined:
Curvature_Percentile = percentrank(abs(Curvature_Normalized), embedLength)
Gradient_Percentile = percentrank(Gradient_Magnitude, embedLength)
Condition_Percentile = percentrank(abs(Condition_Z_Score), embedLength)
Complexity_Ratio = clamp(Topological_Complexity / embedLength, 0, 1)
Final score:
Raw_Anomaly = 0.45×Curvature_P + 0.25×Complexity_R + 0.20×Condition_P + 0.10×Gradient_P
Catastrophe_Score = Raw_Anomaly × Hurst_Multiplier
Values are clamped to range.
Hurst Exponent Calculation
Rescaled range analysis on log returns:
Calculate log returns: r = log(close) - log(close )
Compute cumulative deviations from mean
Find range: R = max(cumulative_dev) - min(cumulative_dev)
Calculate standard deviation: S = stdev(r, hurstWindow)
Compute R/S ratio
Hurst = log(R/S) / log(hurstWindow)
Clamp to and smooth with 5-period EMA
Regime Classification Logic
Volatility Regime:
ATR_MA = SMA(ATR(14), 30)
Vol_Expansion = ATR / ATR_MA
Is_Volatile = Vol_Expansion > (1.0 + minVolExpansion)
Trend Regime (Corrected ADX):
Calculate directional movement (DM+, DM-)
Smooth with Wilder's RMA(14)
Compute DI+ and DI- as percentages
Calculate DX = |DI+ - DI-| / (DI+ + DI-) × 100
ADX = RMA(DX, 14)
Is_Trending = ADX > (trendStrength × 100)
Chop Detection:
Is_Chopping = NOT Is_Trending AND NOT Is_Volatile
Regime Validity:
Regime_Valid = (Is_Trending OR Is_Volatile) AND NOT Is_Chopping
Signal Generation Logic
For each bar:
Check if catastrophe score > topologyStrength threshold
Verify regime is valid
Confirm Hurst alignment (trending or mean-reverting with pivot)
Validate pivot quality (price extended outside spectral bands then re-entered)
Confirm volume/volatility participation
Check cooldown period has elapsed
If all true: compute directional vote
If vote ≥2: Bullish Singularity
If vote ≤-2: Bearish Singularity
If -1 to +1: Neutral (display but skip)
All conditions must be true for signal generation.
Visual System Architecture
Spectral Decomposition Layers
Three harmonic frequency bands visualize entropy state:
Layer 1 (Surface Frequency):
Center: EMA(8)
Width: ±0.3 × 0.5 × ATR
Transparency: 75% (most visible)
Represents fast oscillations
Layer 2 (Mid Frequency):
Center: EMA(21)
Width: ±0.5 × 0.5 × ATR
Transparency: 85%
Represents medium cycles
Layer 3 (Deep Frequency):
Center: EMA(55)
Width: ±0.7 × 0.5 × ATR
Transparency: 92% (most transparent)
Represents slow baseline
Convergence of layers indicates low entropy (stable topology). Divergence indicates high entropy (catastrophe building). This decomposition reveals how different frequency components of price movement interact—when all three align, the manifold is in equilibrium; when they separate, topology is unstable.
Energy Radiance Fields
Concentric boxes emanate from each singularity bar:
For each singularity, 5 layers are generated:
Layer n: bar_index ± (n × 1.5 bars), close ± (n × 0.4 × ATR)
Transparency gradient: inner 75% → outer 95%
Color matches signal direction
These fields visualize the "energy well" of the catastrophe—wider fields indicate stronger topology distortion. The exponential expansion creates a natural radiance effect.
Singularity Node Geometry
N-sided polygon (default hexagon) at each signal bar:
Vertices calculated using polar coordinates
Rotation angle: bar_index × 0.1 (creates animation)
Radius: ATR × singularity_strength × 2
Connects vertices with colored lines
The rotating geometric primitive marks the exact catastrophe bar with visual prominence.
Gradient Flow Field
Directional arrows display manifold slope:
Spawns every 3 bars when gradient_magnitude > 0.1
Symbol: "↗" if dY/dt > 0.1, "↘" if dY/dt < -0.1, "→" if neutral
Color: Bull/bear/neutral based on direction
Density limited to flowDensity parameter
Arrows cluster when gradient is strong, creating intuitive topology visualization.
Probability Projection Cones
Forward trajectory from each singularity:
Projects 10 bars forward
Direction based on vote classification
Center line: close + (direction × ATR × 3)
Uncertainty width: ATR × singularity_strength × 2
Dashed boundaries, solid center
These are mathematical projections based on current gradient, not price targets. They visualize expected manifold evolution if topology continues current trajectory.
Dashboard Metrics Explanation
The real-time control panel displays six core metrics plus regime status:
H (Hurst Exponent):
Value: Current Hurst (0-1 scale)
Label: TREND (>0.55), REVERT (<0.45), or RANDOM (0.45-0.55)
Icon: Direction arrow based on regime
Purpose: Shows fractal character—only trade when favorable
Σ (Catastrophe Score):
Value: Current composite anomaly (0-100%)
Bar gauge shows relative strength
Icon: ◆ if above threshold, ○ if below
Purpose: Primary signal strength indicator
κ (Curvature):
Value: Normalized Laplacian magnitude
Direction arrow shows sign
Color codes severity (green<0.8, yellow<1.5, red≥1.5)
Purpose: Shows manifold bending intensity
⟳ (Topology Complexity):
Value: Count of sign flips in curvature
Icon: ◆ if >3, ○ otherwise
Color codes chaos level
Purpose: Indicates geometric instability
V (Volatility Expansion):
Value: ATR expansion percentage above 30-bar average
Icon: ● if volatile, ○ otherwise
Purpose: Confirms energy present for reversal
T (Trend Strength):
Value: ADX reading (0-100)
Icon: ● if trending, ○ otherwise
Purpose: Shows directional bias strength
R (Regime):
Label: EXPLOSIVE / TREND / VOLATILE / CHOP / NEUTRAL
Icon: ✓ if valid, ✗ if invalid
Purpose: Go/no-go filter for trading
STATE (Bottom Display):
Shows: "◆ BULL SINGULARITY" (green), "◆ BEAR SINGULARITY" (red), "◆ WEAK/NEUTRAL" (orange), or "— Monitoring —" (gray)
Purpose: Current signal status at a glance
How to Use This Indicator
Initial Setup and Configuration
Apply the indicator to your chart with default settings as a starting point. The default parameters (21-bar embedding, 5-bar Hurst window, 2.5σ singularity threshold, 0.65 topology confirmation) are optimized for balanced detection across most instruments and timeframes. For very fast markets (scalping crypto, 1-5min charts), consider reducing embedding depth to 13-15 bars and Hurst window to 3 bars for more responsive detection. For slower markets (swing trading stocks, 4H-Daily charts), increase embedding depth to 34-55 bars and Hurst window to 8-10 bars for more stable topology measurement.
Enable the dashboard (top right recommended) to monitor real-time metrics. The control panel is your primary decision interface—glancing at the dashboard should instantly communicate whether conditions favor trading and what the current topology state is. Position and size the dashboard to remain visible but not obscure price action.
Enable regime filtering (strongly recommended) to prevent trading during choppy/ranging conditions where geometric edge deteriorates. This single setting can dramatically improve overall performance by eliminating low-probability environments.
Reading Dashboard Metrics for Trade Readiness
Before considering any trade, verify the dashboard shows favorable conditions:
Hurst (H) Check:
The Hurst Exponent reading is your first filter. Only consider trades when H > 0.50 . Ideal conditions show H > 0.60 with "TREND" label—this indicates persistent directional price movement where manifold catastrophes produce significant reversals. When H < 0.45 (REVERT label), the market is mean-reverting and catastrophes represent minor oscillations rather than substantial pivots. Do not trade in mean-reverting regimes unless you're explicitly using range-bound strategies (which this indicator is not optimized for). When H ≈ 0.50 (RANDOM label), edge is neutral—acceptable but not ideal.
Catastrophe (Σ) Monitoring:
Watch the Σ percentage build over time. Readings consistently below 50% indicate stable topology with no imminent reversals. When Σ rises above 60-65%, manifold distortion is approaching critical levels. Signals only fire when Σ exceeds the configured threshold (default 65%), so this metric pre-warns you of potential upcoming catastrophes. High-conviction setups show Σ > 75%.
Regime (R) Validation:
The regime classification must read TREND, VOLATILE, or EXPLOSIVE—never trade when it reads CHOP or NEUTRAL. The checkmark (✓) must be present in the regime cell for trading conditions to be valid. If you see an X (✗), skip all signals until regime improves. This filter alone eliminates most losing trades by avoiding geometrically unfavorable environments.
Combined High-Conviction Profile:
The strongest trading opportunities show simultaneously:
H > 0.60 (strong trending regime)
Σ > 75% (extreme topology distortion)
R = EXPLOSIVE or TREND with ✓
κ (Curvature) > 1.5 (sharp manifold fold)
⟳ (Complexity) > 4 (chaotic geometry)
V (Volatility) showing elevated ATR expansion
When all metrics align in this configuration, the manifold is undergoing severe distortion in a favorable fractal regime—these represent maximum-conviction reversal opportunities.
Signal Interpretation and Entry Logic
Bullish Singularity (▲ Green Triangle Below Bar):
This marker appears when the system detects a manifold catastrophe at a price low with bullish directional consensus. All five confirmation filters have aligned: topology score exceeded threshold, pivot low structure formed, swing size was significant, volume/volatility confirmed participation, and regime was valid. The green color indicates the directional vote totaled +2 or higher (majority bullish).
Trading Approach: Consider long entry on the bar immediately following the signal (bar after the triangle). The singularity bar itself is where the geometric catastrophe occurred—entering after allows you to see if price confirms the reversal. Place stop loss below the singularity bar's low (with buffer of 0.5-1.0 ATR for volatility). Initial target can be the previous swing high, or use the probability cone projection as a guide (though not a guarantee). Monitor the dashboard STATE—if it flips to "◆ BEAR SINGULARITY" or Hurst drops significantly, consider exiting even if target not reached.
Bearish Singularity (▼ Red Triangle Above Bar):
This marker appears when the system detects a manifold catastrophe at a price high with bearish directional consensus. Same five-filter confirmation process as bullish signals. The red color indicates directional vote totaled -2 or lower (majority bearish).
Trading Approach: Consider short entry on the bar following the signal. Place stop loss above the singularity bar's high (with buffer). Target previous swing low or use cone projection as reference. Exit if opposite signal fires or Hurst deteriorates.
Neutral Signal (● Orange Circle at Price Level):
This marker indicates the catastrophe detection system identified a topology break that passed catastrophe threshold and regime filters, but the directional voting system produced a mixed result (vote between -1 and +1). This means the four directional components (pivot, trend, flow, mid-band) are not in agreement about which way the reversal should resolve.
Trading Approach: Skip these signals. Neutral markers are displayed for analytical completeness but should not be traded. They represent geometric catastrophes without clear directional resolution—essentially, the manifold is breaking but the direction of the break is ambiguous. Trading neutral signals dramatically increases false signal rate. Only trade green (bullish) or red (bearish) singularities.
Visual Confirmation Using Spectral Layers
The three colored ribbons (spectral decomposition layers) provide entropy visualization that helps confirm signal quality:
Divergent Layers (High Entropy State):
When the three frequency bands (fast 8-period, medium 21-period, slow 55-period) are separated with significant gaps between them, the manifold is in high entropy state—different frequency components of price movement are pulling in different directions. This geometric tension precedes catastrophes. Strong signals often occur when layers are divergent before the signal, then begin reconverging immediately after.
Convergent Layers (Low Entropy State):
When all three ribbons are tightly clustered or overlapping, the manifold is in equilibrium—all frequency components agree. This stable geometry makes catastrophe detection more reliable because topology breaks clearly stand out against the baseline stability. If you see layers converge, then a singularity fires, then layers diverge, this pattern suggests a genuine regime transition.
Signal Quality Assessment:
High-quality singularity signals should show:
Divergent layers (high entropy) in the 5-10 bars before signal
Singularity bar occurs when price has extended outside at least one of the spectral bands (shows pivot extended beyond equilibrium)
Close of singularity bar re-enters the spectral band zone (shows mean reversion starting)
Layers begin reconverging in 3-5 bars after signal (shows new equilibrium forming)
This pattern visually confirms the geometric narrative: manifold became unstable (divergence), reached critical distortion (extended outside equilibrium), broke catastrophically (singularity), and is now stabilizing in new direction (reconvergence).
Using Energy Fields for Trade Management
The concentric glowing boxes around each singularity visualize the topology distortion
magnitude:
Wide Energy Fields (5+ Layers Visible):
Large radiance indicates strong catastrophe with high manifold curvature. These represent significant topology breaks and typically precede larger price moves. Wide fields justify wider profit targets and longer hold times. The outer edge of the largest box can serve as a dynamic support/resistance zone—price often respects these geometric boundaries.
Narrow Energy Fields (2-3 Layers):
Smaller radiance indicates moderate catastrophe. While still valid signals (all filters passed), expect smaller follow-through. Use tighter profit targets and be prepared for quicker exit if momentum doesn't develop. These are valid but lower-conviction trades.
Field Interaction Zones:
When energy fields from consecutive signals overlap or touch, this indicates a prolonged topology distortion region—often corresponds to consolidation zones or complex reversal patterns (head-and-shoulders, double tops/bottoms). Be more cautious in these areas as the manifold is undergoing extended restructuring rather than a clean catastrophe.
Probability Cone Projections
The dashed cone extending forward from each singularity is a mathematical projection, not a
price target:
Cone Direction:
The center line direction (upward for bullish, downward for bearish, flat for neutral) shows the expected trajectory based on current manifold gradient and singularity direction. This is where the topology suggests price "should" go if the catastrophe completes normally.
Cone Width:
The uncertainty band (upper and lower dashed boundaries) represents the range of outcomes given current volatility (ATR-based). Wider cones indicate higher uncertainty—expect more price volatility even if direction is correct. Narrower cones suggest more constrained movement.
Price-Cone Interaction:
Price following near the center line = catastrophe resolving as expected, geometric projection accurate
Price breaking above upper cone = stronger-than-expected reversal, consider holding for larger targets
Price breaking below lower cone (for bullish signal) = catastrophe failing, manifold may be re-folding in opposite direction, consider exit
Price oscillating within cone = normal reversal process, hold position
The 10-bar projection length means cones show expected behavior over the next ~10 bars. Don't confuse this with longer-term price targets.
Gradient Flow Field Interpretation
The directional arrows (↗, ↘, →) scattered across the chart show the manifold's Y-gradient (vertical acceleration dimension):
Upward Arrows (↗):
Positive Y-gradient indicates the momentum acceleration dimension is pushing upward—the manifold topology has upward "slope" at this location. Clusters of upward arrows suggest bullish topological pressure building. These often appear before bullish singularities fire.
Downward Arrows (↘):
Negative Y-gradient indicates downward topological pressure. Clusters precede bearish singularities.
Horizontal Arrows (→):
Neutral gradient indicates balanced topology with no strong directional pressure.
Using Flow Field:
The arrows provide real-time topology state information even between singularity signals. If you're in a long position from a bullish singularity and begin seeing increasing downward arrows appearing, this suggests manifold gradient is shifting—consider tightening stops. Conversely, if arrows remain upward or neutral, topology supports continuation.
Don't confuse arrow direction with immediate price direction—arrows show geometric slope, not price prediction. They're confirmatory context, not entry signals themselves.
Parameter Optimization for Your Trading Style
For Scalping / Fast Trading (1m-15m charts):
Embedding Depth: 13-15 bars (faster topology reconstruction)
Hurst Window: 3 bars (responsive fractal detection)
Singularity Threshold: 2.0-2.3σ (more sensitive)
Topology Confirmation: 0.55-0.60 (lower barrier)
Min Swing Size: 0.8-1.2 ATR (accepts smaller moves)
Pivot Lookback: 3-4 bars (quick pivot detection)
This configuration increases signal frequency for active trading but requires diligent monitoring as false signal rate increases. Use tighter stops.
For Day Trading / Standard Approach (15m-4H charts):
Keep default settings (21 embed, 5 Hurst, 2.5σ, 0.65 confirmation, 1.5 ATR, 5 pivot)
These are balanced for quality over quantity
Best win rate and risk/reward ratio
Recommended for most traders
For Swing Trading / Position Trading (4H-Daily charts):
Embedding Depth: 34-55 bars (stable long-term topology)
Hurst Window: 8-10 bars (smooth fractal measurement)
Singularity Threshold: 3.0-3.5σ (only extreme catastrophes)
Topology Confirmation: 0.75-0.85 (high conviction only)
Min Swing Size: 2.5-4.0 ATR (major moves only)
Pivot Lookback: 8-13 bars (confirmed swings)
This configuration produces infrequent but highly reliable signals suitable for position sizing and longer hold times.
Volatility Adaptation:
In extremely volatile instruments (crypto, penny stocks), increase Min Volatility Expansion to 0.6-0.8 to avoid over-signaling during "always volatile" conditions. In stable instruments (major forex pairs, blue-chip stocks), decrease to 0.3 to allow signals during moderate volatility spikes.
Trend vs Range Preference:
If you prefer trading only strong trends, increase Min Trend Strength to 0.5-0.6 (ADX > 50-60). If you're comfortable with volatility-based trading in weaker trends, decrease to 0.2 (ADX > 20). The default 0.3 balances both approaches.
Complete Trading Workflow Example
Step 1 - Pre-Session Setup:
Load chart with MSE indicator. Check dashboard position is visible. Verify regime filter is enabled. Review recent signals to gauge current instrument behavior.
Step 2 - Market Assessment:
Observe dashboard Hurst reading. If H < 0.45 (mean-reverting), consider skipping this session or using other strategies. If H > 0.50, proceed. Check regime shows TREND, VOLATILE, or EXPLOSIVE with checkmark—if CHOP, wait for regime shift alert.
Step 3 - Signal Wait:
Monitor catastrophe score (Σ). Watch for it climbing above 60%. Observe spectral layers—look for divergence building. If you see curvature (κ) rising above 1.0 and complexity (⟳) increasing, catastrophe is building. Do not anticipate—wait for the actual signal marker.
Step 4 - Signal Recognition:
▲ Bullish or ▼ Bearish triangle appears at a bar. Dashboard STATE changes to "◆ BULL/BEAR SINGULARITY". Energy field appears around the signal bar. Check signal quality:
Was Σ > 70% at signal? (Higher quality)
Are energy fields wide? (Stronger catastrophe)
Did layers diverge before and reconverge after? (Clean break)
Is Hurst still > 0.55? (Good regime)
Step 5 - Entry Decision:
If signal is green/red (not orange neutral), all confirmations look strong, and no immediate contradicting factors appear, prepare entry on next bar open. Wait for confirmation bar to form—ideally it should close in the signal direction (bullish signal → bar closes higher, bearish signal → bar closes lower).
Step 6 - Position Entry:
Enter at open or shortly after open of bar following signal bar. Set stop loss: for bullish signals, place stop at singularity_bar_low - (0.75 × ATR); for bearish signals, place stop at singularity_bar_high + (0.75 × ATR). The buffer accommodates volatility while protecting against catastrophe failure.
Step 7 - Trade Management:
Monitor dashboard continuously:
If Hurst drops below 0.45, consider reducing position
If opposite singularity fires, exit immediately (manifold has re-folded)
If catastrophe score drops below 40% and stays there, topology has stabilized—consider partial profit taking
Watch gradient flow arrows—if they shift to opposite direction persistently, tighten stops
Step 8 - Profit Taking:
Use probability cone as a guide—if price reaches outer cone boundary, consider taking partial profits. If price follows center line cleanly, hold for larger target. Traditional technical targets work well: previous swing high/low, round numbers, Fibonacci extensions. Don't expect precision—manifold projections give direction and magnitude estimates, not exact prices.
Step 9 - Exit:
Exit on: (a) opposite signal appears, (b) dashboard shows regime became invalid (checkmark changes to X), (c) technical target reached, (d) Hurst deteriorates significantly, (e) stop loss hit, or (f) time-based exit if using session limits. Never hold through opposite singularity signals—the manifold has broken in the other direction and your trade thesis is invalidated.
Step 10 - Post-Trade Review:
After exit, review: Did the probability cone projection align with actual price movement? Were the energy fields proportional to move size? Did spectral layers show expected reconvergence? Use these observations to calibrate your interpretation of signal quality over time.
Best Performance Conditions
This topology-based approach performs optimally in specific market environments:
Favorable Conditions:
Well-Developed Swing Structure: Markets with clear rhythm of advances and declines where pivots form at regular intervals. The manifold reconstruction depends on swing formation, so instruments that trend in clear waves work best. Stocks, major forex pairs during active sessions, and established crypto assets typically exhibit this characteristic.
Sufficient Volatility for Topology Development: The embedding process requires meaningful price movement to construct multi-dimensional coordinates. Extremely quiet markets (tight consolidations, holiday trading, after-hours) lack the volatility needed for manifold differentiation. Look for ATR expansion above average—when volatility is present, geometry becomes meaningful.
Trending with Periodic Reversals: The ideal environment is not pure trend (which rarely reverses) nor pure range (which reverses constantly at small scale), but rather trending behavior punctuated by occasional significant counter-trend reversals. This creates the catastrophe conditions the system is designed to detect: manifold building directional momentum, then undergoing sharp topology break at extremes.
Liquid Instruments Where EMAs Reflect True Flow: The spectral layers and frequency decomposition require that moving averages genuinely represent market consensus. Thinly traded instruments with sporadic orders don't create smooth manifold topology. Prefer instruments with consistent volume where EMA calculations reflect actual capital flow rather than random tick sequences.
Challenging Conditions:
Extremely Choppy / Whipsaw Markets: When price oscillates rapidly with no directional persistence (Hurst < 0.40), the manifold undergoes constant micro-catastrophes that don't translate to tradable reversals. The regime filter helps avoid these, but awareness is important. If you see multiple neutral signals clustering with no follow-through, market is too chaotic for this approach.
Very Low Volatility Consolidation: Tight ranges with ATR below average cause the embedding coordinates to compress into a small region of phase space, reducing geometric differentiation. The manifold becomes nearly flat, and catastrophe detection loses sensitivity. The regime filter's volatility component addresses this, but manually avoiding dead markets improves results.
Gap-Heavy Instruments: Stocks that gap frequently (opening outside previous close) create discontinuities in the manifold trajectory. The embedding process assumes continuous evolution, so gaps introduce artifacts. Most gaps don't invalidate the approach, but instruments with daily gaps >2% regularly may show degraded performance. Consider using higher timeframes (4H, Daily) where gaps are less proportionally significant.
Parabolic Moves / Blowoff Tops: When price enters an exponential acceleration phase (vertical rally or crash), the manifold evolves too rapidly for the standard embedding window to track. Catastrophe detection may lag or produce false signals mid-move. These conditions are rare but identifiable by Hurst > 0.75 combined with ATR expansion >2.0× average. If detected, consider sitting out or using very tight stops as geometry is in extreme distortion.
The system adapts by reducing signal frequency in poor conditions—if you notice long periods with no signals, the topology likely lacks the geometric structure needed for reliable catastrophe detection. This is a feature, not a bug: it prevents forced trading during unfavorable environments.
Theoretical Justification for Approach
Why Manifold Embedding?
Traditional technical analysis treats price as a one-dimensional time series: current price is predicted from past prices in sequential order. This approach ignores the structure of price dynamics—the relationships between velocity, acceleration, and participation that govern how price actually evolves.
Dynamical systems theory (from physics and mathematics) provides an alternative framework: treat price as a state variable in a multi-dimensional phase space. In this view, each market condition corresponds to a point in N-dimensional space, and market evolution is a trajectory through this space. The geometry of this space (its topology) constrains what trajectories are possible.
Manifold embedding reconstructs this hidden geometric structure from observable price data. By creating coordinates from velocity, momentum acceleration, and volume-weighted returns, we map price evolution onto a 3D surface. This surface—the manifold—reveals geometric relationships that aren't visible in price charts alone.
The mathematical theorem underlying this approach (Takens' Embedding Theorem from dynamical systems theory) proves that for deterministic or weakly stochastic systems, a state space reconstruction from time-delayed observations of a single variable captures the essential dynamics of the full system. We apply this principle: even though we only observe price, the embedded coordinates (derivatives of price) reconstruct the underlying dynamical structure.
Why Catastrophe Theory?
Catastrophe theory, developed by mathematician René Thom (Fields Medal 1958), describes how continuous systems can undergo sudden discontinuous changes when control parameters reach critical values. A classic example: gradually increasing force on a beam causes smooth bending, then sudden catastrophic buckling. The beam's geometry reaches a critical curvature where topology must break.
Markets exhibit analogous behavior: gradual price changes build tension in the manifold topology until critical distortion is reached, then abrupt directional change occurs (reversal). Catastrophes aren't random—they're mathematically necessary when geometric constraints are violated.
The indicator detects these geometric precursors: high curvature (manifold bending sharply), high complexity (topology oscillating chaotically), high condition number (coordinate mapping becoming singular). These metrics quantify how close the manifold is to a catastrophic fold. When all simultaneously reach extreme values, topology break is imminent.
This provides a logical foundation for reversal detection that doesn't rely on pattern recognition or historical correlation. We're measuring geometric properties that mathematically must change when systems reach critical states. This is why the approach works across different instruments and timeframes—the underlying geometry is universal.
Why Hurst Exponent?
Markets exhibit fractal behavior: patterns at different time scales show statistical self-similarity. The Hurst exponent quantifies this fractal structure by measuring long-range dependence in returns.
Critically for trading, Hurst determines whether recent price movement predicts future direction (H > 0.5) or predicts the opposite (H < 0.5). This is regime detection: trending vs mean-reverting behavior.
The same manifold catastrophe has different trading implications depending on regime. In trending regime (high Hurst), catastrophes represent significant reversal opportunities because the manifold has been building directional momentum that suddenly breaks. In mean-reverting regime (low Hurst), catastrophes represent minor oscillations because the manifold constantly folds at small scales.
By weighting catastrophe signals based on Hurst, the system adapts detection sensitivity to the current fractal regime. This is a form of meta-analysis: not just detecting geometric breaks, but evaluating whether those breaks are meaningful in the current fractal context.
Why Multi-Layer Confirmation?
Geometric anomalies occur frequently in noisy market data. Not every high-curvature point represents a tradable reversal—many are artifacts of microstructure noise, order flow imbalances, or low-liquidity ticks.
The five-filter confirmation system (catastrophe threshold, pivot structure, swing size, volume, regime) addresses this by requiring geometric anomalies to align with observable market evidence. This conjunction-based logic implements the principle: extraordinary claims require extraordinary evidence .
A manifold catastrophe (extraordinary geometric event) alone is not sufficient. We additionally require: price formed a pivot (visible structure), swing was significant (adequate magnitude), volume confirmed participation (capital backed the move), and regime was favorable (trending or volatile, not chopping). Only when all five dimensions agree do we have sufficient evidence that the geometric anomaly represents a genuine reversal opportunity rather than noise.
This multi-dimensional approach is analogous to medical diagnosis: no single test is conclusive, but when multiple independent tests all suggest the same condition, confidence increases dramatically. Each filter removes a different category of false signals, and their combination creates a robust detection system.
The result is a signal set with dramatically improved reliability compared to any single metric alone. This is the power of ensemble methods applied to geometric analysis.
Important Disclaimers
This indicator applies mathematical topology and catastrophe theory to multi-dimensional price space reconstruction. It identifies geometric conditions where manifold curvature, topological complexity, and coordinate singularities suggest potential reversal zones based on phase space analysis. It should not be used as a standalone trading system.
The embedding coordinates, catastrophe scores, and Hurst calculations are deterministic mathematical formulas applied to historical price data. These measurements describe current and recent geometric relationships in the reconstructed manifold but do not predict future price movements. Past geometric patterns and singularity markers do not guarantee future market behavior will follow similar topology evolution.
The manifold reconstruction assumes certain mathematical properties (sufficient embedding dimension, quasi-stationarity, continuous dynamics) that may not hold in all market conditions. Gaps, flash crashes, circuit breakers, news events, and other discontinuities can violate these assumptions. The system attempts to filter problematic conditions through regime classification, but cannot eliminate all edge cases.
The spectral decomposition, energy fields, and probability cones are visualization aids that represent mathematical constructs, not price predictions. The probability cone projects current gradient forward assuming topology continues current trajectory—this is a mathematical "if-then" statement, not a forecast. Market topology can and does change unexpectedly.
All trading involves substantial risk. The singularity markers represent analytical conditions where geometric mathematics align with threshold criteria, not certainty of directional change. Use appropriate risk management for every trade: position sizing based on account risk tolerance (typically 1-2% maximum risk per trade), stop losses placed beyond recent structure plus volatility buffer, and never risk capital needed for living expenses.
The confirmation filters (pivot, swing size, volume, regime) are designed to reduce false signals but cannot eliminate them entirely. Markets can produce geometric anomalies that pass all filters yet fail to develop into sustained reversals. This is inherent to probabilistic systems operating on noisy real-world data.
No indicator can guarantee profitable trades or eliminate losses. The catastrophe detection provides an analytical framework for identifying potential reversal conditions, but actual trading outcomes depend on numerous factors including execution, slippage, spreads, position sizing, risk management, psychological discipline, and market conditions that may change after signal generation.
Use this tool as one component of a comprehensive trading plan that includes multiple forms of analysis, proper risk management, emotional discipline, and realistic expectations about win rates and drawdowns. Combine catastrophe signals with additional confirmation methods such as support/resistance analysis, volume patterns, multi-timeframe alignment, and broader market context.
The spacing filter, cooldown mechanism, and regime validation are designed to reduce noise and over-signaling, but market conditions can change rapidly and render any analytical signal invalid. Always use stop losses and never risk capital you cannot afford to lose. Past performance of detection accuracy does not guarantee future results.
Technical Implementation Notes
All calculations execute on closed bars only—signals and metric values do not repaint after bar close. The indicator does not use any lookahead bias in its calculations. However, the pivot detection mechanism (ta.pivothigh and ta.pivotlow) inherently identifies pivots with a lag equal to the lookback parameter, meaning the actual pivot occurred at bar but is recognized at bar . This is standard behavior for pivot functions and is not repainting—once recognized, the pivot bar never changes.
The normalization system (z-score transformation over rolling windows) requires approximately 30-50 bars of historical data to establish stable statistics. Values in the first 30-50 bars after adding the indicator may show instability as the rolling means and standard deviations converge. Allow adequate warmup period before relying on signals.
The spectral layer arrays, energy field boxes, gradient flow labels, and node geometry lines are subject to TradingView drawing object limits (500 lines, 500 boxes, 500 labels per indicator as specified in settings). The system implements automatic cleanup by deleting oldest objects when limits approach, but on very long charts with many signals, some historical visual elements may be removed to stay within limits. This does not affect signal generation or dashboard metrics—only historical visual artifacts.
Dashboard and visual rendering update only on the last bar to minimize computational overhead. The catastrophe detection logic executes on every bar, but table cells and drawing objects refresh conditionally to optimize performance. If experiencing chart lag, reduce visual complexity: disable spectral layers, energy fields, or flow field to improve rendering speed. Core signal detection continues to function with all visual elements disabled.
The Hurst calculation uses logarithmic returns rather than raw price to ensure stationarity, and implements clipping to range to handle edge cases where R/S analysis produces invalid values (which can occur during extended periods of identical prices or numerical overflow). The 5-period EMA smoothing reduces noise while maintaining responsiveness to regime transitions.
The condition number calculation adds epsilon (1e-10) to denominators to prevent division by zero when Jacobian determinant approaches zero—which is precisely the singularity condition we're detecting. This numerical stability measure ensures the indicator doesn't crash when detecting the very phenomena it's designed to identify.
The indicator has been tested across multiple timeframes (5-minute through daily) and multiple asset classes (forex majors, stock indices, individual equities, cryptocurrencies, commodities, futures). It functions identically across all instruments due to the adaptive normalization approach and percentage-based metrics. No instrument-specific code or parameter sets are required.
The color scheme system implements seven preset themes plus custom mode. Color assignments are applied globally and affect all visual elements simultaneously. The opacity calculation system multiplies component-specific transparency with master opacity to create hierarchical control—adjusting master opacity affects all visuals proportionally while maintaining their relative transparency relationships.
All alert conditions trigger only on bar close to prevent false alerts from intrabar fluctuations. The regime transition alerts (VALID/INVALID) are particularly useful for knowing when trading edge appears or disappears, allowing traders to adjust activity levels accordingly.
— Dskyz, Trade with insight. Trade with anticipation.
ICT ADR/AWR/AMR Levels | Trade Symmetry🌟 ICT ADR/AWR/AMR Levels
📋 Overview
This advanced technical analysis tool calculates and displays Average Daily Range (ADR), Average Weekly Range (AWR), and Average Monthly Range (AMR) levels. The indicator incorporates smart detection technology that automatically maintains monthly level visibility when historical data becomes unavailable.
✨ Key Features
🕒 Precise Time Alignment
True Daily Opens (TDO) aligned with 00:00 UTC
True Weekly Opens (TWO) at 00:00 UTC (configurable Monday/Sunday start)
True Monthly Opens (TMO) at 00:00 UTC on month start
Customizable period start times and parameters
📊 Comprehensive Multi-Timeframe Analysis
Daily Levels (ADR): Base level with multiple extensions including Fibonacci ratios
Weekly Levels (AWR): Weekly range projections and key levels
Monthly Levels (AMR): Monthly range calculations with automatic fallback system
🔄 Intelligent Level Management
Smart Detection: Automatically switches between historical and current monthly levels
Continuous Visibility: Ensures reference levels remain visible regardless of data availability
Seamless Operation: No manual adjustment needed for level transitions
⚙️ Extensive Customization
Adjustable lookback periods for all timeframes
Independent control over each level type and extension
Complete visual customization (colors, styles, widths)
Flexible labeling and display options
Configurable vertical separation lines
🏷️ Advanced Display Options
Clean, organized label placement
Optional price display in labels
Historical period tracking
Overlapping label merging capability
Adjustable label sizing and positioning
🚀 How to Use
Initial Setup: Enable desired timeframes (Daily/Weekly/Monthly)
Range Configuration: Set appropriate averaging periods for each timeframe
Level Selection: Choose which extension levels to display
Visual Settings: Customize colors and styles to match your trading workspace
Automatic Operation: The indicator intelligently manages level transitions
💡 Practical Applications
Identify potential support and resistance areas across multiple timeframes
Establish realistic profit targets based on historical volatility
Plan trade entries and exits around significant time-based levels
Analyze market volatility patterns across different time horizons
Incorporate institutional trading concepts into your analysis
