LIT - Timings Fx MartinThe Asia Liquidity Points Indicator is a powerful tool designed for traders to identify key liquidity points during the Asia trading session. This script is tailored specifically to aid traders in capitalizing on the unique characteristics of Asian markets, providing invaluable insights into liquidity zones that can significantly enhance trading decisions.
Key Features:
Asia Session Focus: The indicator focuses exclusively on the Asia trading session, which encompasses the trading activity primarily in the Asian markets such as Tokyo, Hong Kong, Singapore, and others.
Liquidity Zones Identification: The script utilizes advanced algorithms to identify and map out liquidity zones within the Asia trading session. These zones represent areas where significant buying or selling pressure is likely to occur, thus presenting lucrative trading opportunities.
Customizable Parameters: Traders have the flexibility to customize various parameters such as time frame, sensitivity, and display options to suit their trading preferences and strategies.
Visual Alerts: The indicator provides visual alerts on the trading chart, clearly indicating the location and strength of liquidity points. This feature enables traders to quickly identify potential entry or exit points based on the liquidity dynamics in the market.
Real-Time Updates: The script continuously monitors market activity during the Asia session, providing real-time updates on liquidity points as they evolve. This ensures traders stay informed and adaptable to changing market conditions.
Integration with Trading Strategies: The Asia Liquidity Points Indicator seamlessly integrates with various trading strategies, serving as a valuable tool for both discretionary and algorithmic traders. Whether used in isolation or in combination with other technical analysis tools, this indicator can enhance trading performance and profitability.
User-Friendly Interface: The indicator boasts a user-friendly interface, making it accessible to traders of all levels of experience. Whether you are a novice trader or a seasoned professional, you can easily incorporate this tool into your trading arsenal.
In conclusion, the Asia Liquidity Points Indicator offers traders a strategic advantage in navigating the nuances of the Asia trading session. By identifying key liquidity zones and providing real-time insights, this script empowers traders to make informed decisions and capitalize on lucrative trading opportunities in the dynamic Asian markets.
在腳本中搜尋"liquidity"
Support & Resistance PROHi Traders!
The Support & Resistance PRO
A simple and effective indicator that helped me a bunch!
This indicator will chart simple support and resistance zones on 2 time frames of your choice.
It uses a 30 day lookback period and will find the last high and low.
Each zone is built from the highest/lowest closure, and the highest/lowest wick, creating a liquid zone between the 2.
It is perfect for people trading support and resistance, watching key areas, scalping zones and much more!
*You can change the time frames you are looking at and the lookback period.
*The example in the picture is looking at the Daily and Weekly zones on BTC.
UNITY[ALGO] PO3 V3Of course. Here is a complete and professional description in English for the indicator we have built, detailing all of its features and functionalities.
Indicator: UNITY PO3 V7.2
Overview
The UNITY PO3 is an advanced, multi-faceted technical analysis tool designed to identify high-probability reversal setups based on the Swing Failure Pattern (SFP). It combines real-time SFP detection on the current timeframe with a sophisticated analysis of key institutional liquidity zones from the H4 timeframe, presenting all information in a clear, dynamic, and interactive visual interface.
This indicator is built for traders who use liquidity concepts, providing a complete dashboard of entries, targets, and invalidation levels directly on the chart.
Core Features & Functionality
1. Swing Failure Pattern (SFP) Detection (Current Timeframe)
The indicator's primary engine identifies SFPs on the chart's active timeframe with two layers of logic:
Standard SFP: Detects a classic liquidity sweep where the current candle's wick takes out the high or low of the previous candle and the body closes back within the previous candle's range.
Outside Bar SFP Logic: Intelligently analyzes engulfing candles that sweep both the high and low of the previous candle. A valid signal is only generated if the candle has a clear directional close:
Bullish Signal: If the outside bar closes higher than its open.
Bearish Signal: If the outside bar closes lower than its open.
Neutral (doji-like) outside bars are ignored to filter for indecision.
2. Comprehensive On-Chart SFP Markings
When a valid SFP is detected, a full suite of dynamic drawings appears on the chart:
Failure Line: A dashed line (red for bearish, green for bullish) marking the precise price level of the liquidity sweep.
PREMIUM ZONE (SFP Candle Wick): A transparent, colored rectangle highlighting the rejection wick of the signal candle (the upper wick for bearish SFPs, the lower wick for bullish SFPs). This zone automatically extends to the right, following the current price, until the DOL is hit.
CRT BOX (Reference Candle): A transparent box with a colored border drawn around the entire range of the candle that was swept (Candle 1). This highlights the full liquidity zone and also extends dynamically until the DOL is hit.
Dynamic Target Line: A blue dashed line marking the primary objective (the low of the signal candle for shorts, the high for longs).
The line begins with a "⏳ Target" label and extends with the current price.
Upon being touched by price, the line freezes, and its label permanently changes to "✅ Target".
Dynamic DOL (Draw on Liquidity) Line: An orange dashed line marking the invalidation level, defined as the opposite extremity of the swept candle (Candle 1).
It begins with a "⏳ dol" label and extends with the price.
Upon being touched, it freezes, and its label changes to "✅ dol".
3. Multi-Session Killzone Liquidity Levels (H4 Analysis)
The indicator automatically analyzes the H4 timeframe in the background to identify and plot key liquidity levels from three major trading sessions, based on their UTC opening times.
1am Killzone (London Lunch): Tracks the high/low of the 05:00 UTC H4 candle.
5am Killzone (London Open): Tracks the high/low of the 09:00 UTC H4 candle.
9am Killzone (NY Open): Tracks the high/low of the 13:00 UTC H4 candle.
For each of these Killzones, the indicator provides two types of analysis:
Last KZ Lines: Plots the high and low of the most recent qualifying Killzone candle. These lines are dynamic, extending with price and showing a ⏳/✅ status when touched.
Fresh Zones: A powerful feature that scans the entire available history of Killzones to find and display the closest untouched high (above the current price) and the closest untouched low (below the current price). These "Fresh" lines are also fully dynamic and provide a real-time view of the most relevant nearby liquidity targets.
4. Advanced User Settings & Chart Management
The indicator is designed for a clean and user-centric experience with powerful customization:
Show Only Last SFP: Keeps the chart clean by automatically deleting the previous SFP setup when a new one appears.
Hide SFP on DOL Reset: When checked, automatically removes all drawings related to an SFP setup the moment its invalidation level (DOL line) is touched. This leaves only active, valid setups on the chart.
Hide Consumed KZ: When checked, automatically removes any Killzone or Fresh Zone line from the chart as soon as it is touched by the price.
Independent Toggles: Every visual element—SFP signals, each of the three Killzones, and their respective "Fresh" zone counterparts—can be turned on or off independently from the settings menu for complete control over the visual display.
Z-Order Priority: All indicator drawings are rendered in front of the chart candles, ensuring they are always clearly visible and never hidden from view.
TCP | Market Session | Session Analyzer📌 TCP | Market Session Indicator | Crypto Version
A powerful, real-time market session visualization tool tailored for crypto traders. Track the heartbeat of Asia, Europe, and US trading hours directly on your chart with live session boxes, behavioral analysis, liquidity grab detection, and countdown timers. Know when the action starts, how the market behaves, and where the traps lie.
🔰 Introduction:
Trade the Right Hours with the Right Tools
Time matters in trading. Most significant moves happen during key sessions—and knowing when and how each session unfolds can give you a sharp edge. The TCP Market Session Indicator, developed by Trade City Pro (TCP), puts professional session tracking and behavioral insights at your fingertips.
Whether you're a scalper or swing trader, this indicator gives you the timing context to enter and exit trades with greater confidence and clarity.
🕒 Core Features
• Live Session Boxes :
Highlight active ranges during Asia, Europe, and US sessions with dynamic high/low updates.
• Session Start/End Labels :
Know exactly when each session begins and ends plotted clearly on your chart with context.
• Session Behavior Analysis :
At the end of each session, the indicator classifies the price action as:
- Trend Up
- Trend Down
- Consolidation
- Manipulation
• Liquidity Grab Detection: Automatically detects possible stop hunts (fake breakouts) and marks them on the chart with precision filters (volume, ATR, reversal).
• Session Countdown Table: A live dashboard showing:
- Current active session
- Time left in session
- Upcoming session and how many minutes until it starts
- Utility time converter (e.g. 90 min = 01:30)
• Vertical Session Lines: Visualize past and upcoming session boundaries with customizable history and future range.
• Multi-Day Support: Draw session ranges for previous, current, and future days for better backtesting and forecasting.
⚙️ Settings Panel
Customize everything to fit your trading style and schedule:
• Session Time Settings:
Set the opening and closing time for each session manually using UTC-based minute inputs.
→ For example, enter Asia Start: 0, Asia End: 480 for 00:00–08:00 UTC.
This gives full flexibility to adjust session hours to match your preferred market behavior.
• Enable or Disable Elements:
Toggle the visibility of each session (Asia, Europe, US), as well as:
- Session Boxes
- Countdown Table
- Session Lines
- Liquidity Grab Labels
• Timezone Selection:
Choose between using UTC or your chart’s local timezone for session calculations.
• Customization Options:
Select number of past and future days to draw session data
Adjust vertical line transparency
Fine-tune label offset and spacing for clean layout
📊 Smart Session Boxes
Each session box tracks high, low, open, and close in real time, providing visual clarity on market structure. Once a session ends, the box closes, and the behavior type is saved and labeled ideal for spotting patterns across sessions.
• Asia: Green Box
• Europe: Orange Box
• US: Blue Box
💡 Why Use This Tool?
• Perfect Timing: Don’t get chopped in low-liquidity hours. Focus on sessions where volume and volatility align.
• Pattern Recognition: Study how price behaves session-to-session to build better strategies.
• Trap Detection: Spot manipulation moves (liquidity grabs) early and avoid common retail pitfalls.
• Macro Session Mapping: Use as a foundational layer to align trades with market structure and news cycles.
🔍 Example Use Case
You're watching BTC at 12:45 UTC. The indicator tells you:
The Asia session just ended (label shows “Asia Session End: Trend Up”)
Europe session starts in 15 minutes
A liquidity grab just triggered at the previous high—label confirmed
Now you know who’s active, what the market just did, and what’s about to start—all in one glance.
✅ Why Traders Trust It
• Visual & Intuitive: Fully chart-based, no clutter, no guessing
• Crypto-Focused: Designed specifically for 24/7 crypto markets (not outdated forex models)
• Non-Repainting: All labels and boxes stay as printed—no tricks
• Reliable: Tested across multiple exchanges, pairs, and timeframes
🧩 Built by Trade City Pro (TCP)
The TCP Market Session Indicator is part of a suite of professional tools used by over 150,000 traders. It’s coded in Pine Script v6 for full compatibility with TradingView’s latest capabilities.
🔗 Resources
• Tutorial: Learn how to analyze sessions like a pro in our TradingView guide:
"TradeCityPro Academy: Session Mapping & Liquidity Traps"
• More Tools: Explore our full library of indicators on
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
REFERENCES
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.
Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education.
Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education.
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press.
Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
Dealing rangeHi all!
This indicator will show you the current dealing range. The concept of dealing range comes from the inner circle trader (ICT) and gives you a range between an established swing high and an established swing low (the length of these pivots can be changed in settings parameter Length and defaults to 5/2 (left/right)). These swing points must have taken out liquidity to be considered "established". The liquidity that must be grabbed by the swing point has to be a pivot of left length of 1 and a right length of 1.
The dealing range that's created should be used in conjunction with market structure. This could be done through scripts (maybe the Market structure script that I published ()) or manually. It's a common approach to look for long opportunities when the trend is bullish and price is currently in the discount zone of the dealing range. If the trend is bearish then short opportunities are presented when the price is currently in the premium zone of the dealing range.
The zones within the dealing range are premium and discount that are split on the 50% level of the dealing range. These zones can be split into 3 zone with a Fair price (also called Fair value ) zone in between premium and discount. This makes the premium zone to be in the upper third of the dealing range, fair price in the middle third and discount in the lower third. This can be enabled in the settings through the Fair price parameter.
Enabled:
You can choose to enable/disable the visualisation of liquidity grabs and the External liquidity available above and below the swing points that created the dealing range.
Enabled:
Disabled:
Enabled on a higher timeframe (will display a box of the liquidity grab price instead of a label):
This dealing range is configurable to be created by a higher timeframe then the visible charts. Use the setting Higher timeframe to change this.
You can force candles to be closed (for liquidity and swing points). Please note that if you use a higher timeframe then the visible charts the candles must be closed on this timeframe.
Lastly you can also change the transparency of liquidity grabs and external liquidity outside of the dealing range. Use the Transparency setting to change this (a lower value will lead to stronger visuals).
If you have any input or suggestions on future features or bugs, don't hesitate to let me know!
Best of trading luck!
ICT Turtle Soup Ultimate V2📜 ICT Turtle Soup Ultimate V2 — Advanced Liquidity Reversal System
Overview:
The ICT Turtle Soup Ultimate V2 is a next-generation liquidity reversal indicator built on the principles of smart money concepts (SMC) and the classic ICT Turtle Soup setup. It is designed to detect false breakouts (liquidity grabs) at key swing points, enhanced by proprietary logic that filters out low-quality signals using a combination of trend context, kill zone timing, candle wick behavior, and multi-timeframe imbalance zones.
This tool is ideal for intraday traders seeking high-probability entry signals near liquidity pools and imbalance zones — where smart money makes its move.
🔍 What This Script Does
🧠 Liquidity Grab Detection (Turtle Soup Core Logic)
The script scans for recent swing highs/lows using a user-defined lookback.
A signal is generated when price breaks above/below a previous swing level but closes back inside — indicating a liquidity run and likely reversal.
A special Wick Trap Mode enhances this logic by detecting long-wick fakeouts — where the wick grabs stops but the candle body closes opposite the breakout direction.
📉 Trend Filter with ATR Buffer
Optional trend filter uses a simple moving average (SMA) to gauge market direction.
Instead of hard filtering, it applies an ATR-based buffer to allow for entries near the trend line, reducing signal suppression from micro-fluctuations.
🕰️ Kill Zone Session Filtering
Only show signals during institutional trading hours:
London Session
New York AM
Or any custom user-defined session
Helps traders avoid low-volume hours and focus on where stop hunts and price expansions typically occur.
🧱 Multi-Timeframe FVG Confluence (Optional)
Signal validation is strengthened by checking if price is within a higher timeframe Fair Value Gap — commonly used to identify imbalances or inefficiencies.
Filters out setups that lack underlying displacement or order flow justification.
🎨 Visual Feedback
Plots 🔺 bullish and 🔻 bearish markers at signal candles.
Optionally displays:
Swing High/Low Labels (SH / SL)
Reversal distance labels
Background color shading on valid signals
Includes built-in alerts for automated trade notification.
🔑 Unique Benefits
Wick Trap Detection: A proprietary approach to detecting stop hunts via wick behavior, not just candle closes.
ATR-based trend filtering: Avoids unnecessary filtering while still maintaining directional bias.
All-in-one system: No need to stack multiple indicators — swing detection, reversal logic, session filtering, and imbalance confirmation are all integrated.
💡 How to Use
Enable Wick Trap Mode to detect stealthy liquidity grabs with strong wicks.
Use Kill Zone filters to trade only when institutions are active.
Optionally enable FVG confluence to improve confidence in reversal zones.
Watch for Bullish signals near SL levels and Bearish signals near SH levels.
Combine with your own execution strategy or other SMC tools for optimal results.
🔗 Best Used With:
Maximize your edge by combining this script with complementary SMC-based tools:
✅ First FVG — Opening Range Fair Value Gap Detector
✅ ICT SMC Liquidity Grabs + OB + Fibonacci OTE Levels
✅ Liquidity Levels — Smart Swing Highs and Lows with horizontal line projections
Pivot S/R with Volatility Filter## *📌 Indicator Purpose*
This indicator identifies *key support/resistance levels* using pivot points while also:
✅ Detecting *high-volume liquidity traps* (stop hunts)
✅ Filtering insignificant pivots via *ATR (Average True Range) volatility*
✅ Tracking *test counts and breakouts* to measure level strength
---
## *⚙ SETTINGS – Detailed Breakdown*
### *1️⃣ ◆ General Settings*
#### *🔹 Pivot Length*
- *Purpose:* Determines how many bars to analyze when identifying pivots.
- *Usage:*
- *Low values (5-20):* More pivots, better for scalping.
- *High values (50-200):* Fewer but stronger levels for swing trading.
- *Example:*
- Pivot Length = 50 → Only the most significant highs/lows over 50 bars are marked.
#### *🔹 Test Threshold (Max Test Count)*
- *Purpose:* Sets how many times a level can be tested before being invalidated.
- *Example:*
- Test Threshold = 3 → After 3 tests, the level is ignored (likely to break).
#### *🔹 Zone Range*
- *Purpose:* Creates a price buffer around pivots (±0.001 by default).
- *Why?* Markets often respect "zones" rather than exact prices.
---
### *2️⃣ ◆ Volatility Filter (ATR)*
#### *🔹 ATR Period*
- *Purpose:* Smoothing period for Average True Range calculation.
- *Default:* 14 (standard for volatility measurement).
#### *🔹 ATR Multiplier (Min Move)*
- *Purpose:* Requires pivots to show *meaningful price movement*.
- *Formula:* Min Move = ATR × Multiplier
- *Example:*
- ATR = 10 pips, Multiplier = 1.5 → Only pivots with *15+ pip swings* are valid.
#### *🔹 Show ATR Filter Info*
- Displays current ATR and minimum move requirements on the chart.
---
### *3️⃣ ◆ Volume Analysis*
#### *🔹 Volume Change Threshold (%)*
- *Purpose:* Filters for *unusual volume spikes* (institutional activity).
- *Example:*
- Threshold = 1.2 → Requires *120% of average volume* to confirm signals.
#### *🔹 Volume MA Period*
- *Purpose:* Lookback period for "normal" volume calculation.
---
### *4️⃣ ◆ Wick Analysis*
#### *🔹 Wick Length Threshold (Ratio)*
- *Purpose:* Ensures rejection candles have *long wicks* (strong reversals).
- *Formula:* Wick Ratio = (Upper Wick + Lower Wick) / Candle Range
- *Example:*
- Threshold = 0.6 → 60% of the candle must be wicks.
#### *🔹 Min Wick Size (ATR %)*
- *Purpose:* Filters out small wicks in volatile markets.
- *Example:*
- ATR = 20 pips, MinWickSize = 1% → Wicks under *0.2 pips* are ignored.
---
### *5️⃣ ◆ Display Settings*
- *Show Zones:* Toggles support/resistance shaded areas.
- *Show Traps:* Highlights liquidity traps (▲/▼ symbols).
- *Show Tests:* Displays how many times levels were tested.
- *Zone Transparency:* Adjusts opacity of zones.
---
## *🎯 Practical Use Cases*
### *1️⃣ Liquidity Trap Detection*
- *Scenario:* Price spikes *above resistance* then reverses sharply.
- *Requirements:*
- Long wick (Wick Ratio > 0.6)
- High volume (Volume > Threshold)
- *Outcome:* *Short Trap* signal (▼) appears.
### *2️⃣ Strong Support Level*
- *Scenario:* Price bounces *3 times* from the same level.
- *Indicator Action:*
- Labels the level with test count (3/5 = 3 tests out of max 5).
- Turns *red* if broken (Break Count > 0).
Deep Dive: How This Indicator Works*
This indicator combines *four professional trading concepts* into one powerful tool:
1. *Classic Pivot Point Theory*
- Identifies swing highs/lows where price previously reversed
- Unlike basic pivot indicators, ours uses *confirmed pivots only* (filtered by ATR)
2. *Volume-Weighted Validation*
- Requires unusual trading volume to confirm levels
- Filters out "phantom" levels with low participation
3. *ATR Volatility Filtering*
- Eliminates insignificant price swings in choppy markets
- Ensures only meaningful levels are plotted
4. *Liquidity Trap Detection*
- Spots institutional stop hunts where markets fake out traders
- Uses wick analysis + volume spikes for high-probability signals
---
Deep Dive: How This Indicator Works*
This indicator combines *four professional trading concepts* into one powerful tool:
1. *Classic Pivot Point Theory*
- Identifies swing highs/lows where price previously reversed
- Unlike basic pivot indicators, ours uses *confirmed pivots only* (filtered by ATR)
2. *Volume-Weighted Validation*
- Requires unusual trading volume to confirm levels
- Filters out "phantom" levels with low participation
3. *ATR Volatility Filtering*
- Eliminates insignificant price swings in choppy markets
- Ensures only meaningful levels are plotted
4. *Liquidity Trap Detection*
- Spots institutional stop hunts where markets fake out traders
- Uses wick analysis + volume spikes for high-probability signals
---
## *📊 Parameter Encyclopedia (Expanded)*
### *1️⃣ Pivot Engine Settings*
#### *Pivot Length (50)*
- *What It Does:*
Determines how many bars to analyze when searching for swing highs/lows.
- *Professional Adjustment Guide:*
| Trading Style | Recommended Value | Why? |
|--------------|------------------|------|
| Scalping | 10-20 | Captures short-term levels |
| Day Trading | 30-50 | Balanced approach |
| Swing Trading| 50-200 | Focuses on major levels |
- *Real Market Example:*
On NASDAQ 5-minute chart:
- Length=20: Identifies levels holding for ~2 hours
- Length=50: Finds levels respected for entire trading day
#### *Test Threshold (5)*
- *Advanced Insight:*
Institutions often test levels 3-5 times before breaking them. This setting mimics the "probe and push" strategy used by smart money.
- *Psychology Behind It:*
Retail traders typically give up after 2-3 tests, while institutions keep testing until stops are run.
---
### *2️⃣ Volatility Filter System*
#### *ATR Multiplier (1.0)*
- *Professional Formula:*
Minimum Valid Swing = ATR(14) × Multiplier
- *Market-Specific Recommendations:*
| Market Type | Optimal Multiplier |
|------------------|--------------------|
| Forex Majors | 0.8-1.2 |
| Crypto (BTC/ETH) | 1.5-2.5 |
| SP500 Stocks | 1.0-1.5 |
- *Why It Matters:*
In EUR/USD (ATR=10 pips):
- Multiplier=1.0 → Requires 10 pip swings
- Multiplier=1.5 → Requires 15 pip swings (fewer but higher quality levels)
---
### *3️⃣ Volume Confirmation System*
#### *Volume Threshold (1.2)*
- *Institutional Benchmark:*
- 1.2x = Moderate institutional interest
- 1.5x+ = Strong smart money activity
- *Volume Spike Case Study:*
*Before Apple Earnings:*
- Normal volume: 2M shares
- Spike threshold (1.2): 2.4M shares
- Actual volume: 3.1M shares → STRONG confirmation
---
### *4️⃣ Liquidity Trap Detection*
#### *Wick Analysis System*
- *Two-Filter Verification:*
1. *Wick Ratio (0.6):*
- Ensures majority of candle shows rejection
- Formula: (UpperWick + LowerWick) / Total Range > 0.6
2. *Min Wick Size (1% ATR):*
- Prevents false signals in flat markets
- Example: ATR=20 pips → Min wick=0.2 pips
- *Trap Identification Flowchart:*
Price Enters Zone →
Spikes Beyond Level →
Shows Long Wick →
Volume > Threshold →
TRAP CONFIRMED
---
## *💡 Master-Level Usage Techniques*
### *Institutional Order Flow Analysis*
1. *Step 1:* Identify pivot levels with ≥3 tests
2. *Step 2:* Watch for volume contraction near levels
3. *Step 3:* Enter when trap signal appears with:
- Wick > 2×ATR
- Volume > 1.5× average
### *Multi-Timeframe Confirmation*
1. *Higher TF:* Find weekly/monthly pivots
2. *Lower TF:* Use this indicator for precise entries
3. *Example:*
- Weekly pivot at $180
- 4H shows liquidity trap → High-probability reversal
---
## *⚠ Critical Mistakes to Avoid*
1. *Using Default Settings Everywhere*
- Crude oil needs higher ATR multiplier than bonds
2. *Ignoring Trap Context*
- Traps work best at:
- All-time highs/lows
- Major psychological numbers (00/50 levels)
3. *Overlooking Cumulative Volume*
- Check if volume is building over multiple tests
Money Flow Divergence IndicatorOverview
The Money Flow Divergence Indicator is designed to help traders and investors identify key macroeconomic turning points by analyzing the relationship between U.S. M2 money supply growth and the S&P 500 Index (SPX). By comparing these two crucial economic indicators, the script highlights periods where market liquidity is outpacing or lagging behind stock market growth, offering potential buy and sell signals based on macroeconomic trends.
How It Works
1. Data Sources
S&P 500 Index (SPX500USD): Tracks the stock market performance.
U.S. M2 Money Supply (M2SL - Federal Reserve Economic Data): Represents available liquidity in the economy.
2. Growth Rate Calculation
SPX Growth: Percentage change in the S&P 500 index over time.
M2 Growth: Percentage change in M2 money supply over time.
Growth Gap (Delta): The difference between M2 growth and SPX growth, showing whether liquidity is fueling or lagging behind market performance.
3. Visualization
A histogram displays the growth gap over time:
Green Bars: M2 growth exceeds SPX growth (potential bullish signal).
Red Bars: SPX growth exceeds M2 growth (potential bearish signal).
A zero line helps distinguish between positive and negative growth gaps.
How to Use It
✅ Bullish Signal: When green bars appear consistently, indicating that liquidity is outpacing stock market growth. This suggests a favorable environment for buying or holding positions.
❌ Bearish Signal: When red bars appear consistently, meaning stock market growth outpaces liquidity expansion, signaling potential overvaluation or a market correction.
Best Timeframes for Analysis
This indicator works best on monthly timeframes (M) since it is designed for long-term investors and macro traders who focus on broad economic cycles.
Who Should Use This Indicator?
📈 Long-term investors looking for macroeconomic trends.
📊 Swing traders who incorporate liquidity analysis in their strategies.
💰 Portfolio managers assessing market liquidity conditions.
🚀 Use this indicator to stay ahead of market trends and make informed investment decisions based on macroeconomic liquidity shifts! 🚀
High Volume Points [BigBeluga]High Volume Points is a unique volume-based indicator designed to highlight key liquidity zones where significant market activity occurs. By visualizing high-volume pivots with dynamically sized markers and optional support/resistance levels, traders can easily identify areas of interest for potential breakouts, liquidity grabs, and trend reversals.
🔵 Key Features:
High Volume Points Visualization:
The indicator detects pivot highs and lows with exceptionally high trading volume.
Each high-volume point is displayed as a concentric circle, with its size dynamically increasing based on the volume magnitude.
The exact volume at the pivot is shown within the circle.
Dynamic Levels from Volume Pivots:
Horizontal levels are drawn from detected high-volume pivots to act as support or resistance.
Traders can use these levels to anticipate potential liquidity zones and market reactions.
Liquidity Grabs Detection:
If price crosses a high-volume level and grabs liquidity, the level automatically changes to a dashed line.
This feature helps traders track areas where institutional activity may have occurred.
Volume-Based Filtering:
Users can filter volume points by a customizable threshold from 0 to 6, allowing them to focus only on the most significant high-volume pivots.
Lower thresholds capture more volume points, while higher thresholds highlight only the most extreme liquidity events.
🔵 Usage:
Identify strong support/resistance zones based on high-volume pivots.
Track liquidity grabs when price crosses a high-volume level and converts it into a dashed line.
Filter volume points based on significance to remove noise and focus on key areas.
Use volume circles to gauge the intensity of market interest at specific price points.
High Volume Points is an essential tool for traders looking to track institutional activity, analyze liquidity zones, and refine their entries based on volume-driven market structure.
SL Hunting Detector📌 Step 1: Identify Liquidity Zones
The script plots high-liquidity zones (red) and low-liquidity zones (green).
These are areas where big players target stop-losses before reversing the price.
Example:
If price is near a red liquidity zone, expect a potential stop-loss hunt & reversal downward.
If price is near a green liquidity zone, expect a potential stop-loss hunt & reversal upward.
📌 Step 2: Watch for Stop-Loss Hunts (Fakeouts)
The indicator marks stop-loss hunts with red (bearish) or green (bullish) arrows.
When do stop-loss hunts occur?
✅ A long wick below support (with high volume) = Stop hunt before reversal upward.
✅ A long wick above resistance (with high volume) = Stop hunt before reversal downward.
Confirmation:
Volume must spike (volume > 1.5x the average volume).
ATR-based wicks must be longer than usual (showing a stop-hunt trap).
📌 Step 3: Enter a Trade After a Stop-Hunt
🔹 Bullish Trade (Buying a Dip)
If a green arrow appears (stop-hunt below support):
✅ Enter a long (buy) trade at or just above the wick’s recovery level.
✅ Stop-loss: Below the wick’s low (avoid getting hunted again).
✅ Take-profit: Next resistance level or mid-range of the liquidity zone.
🔹 Bearish Trade (Shorting a Fakeout)
If a red arrow appears (stop-hunt above resistance):
✅ Enter a short (sell) trade at or just below the wick’s rejection level.
✅ Stop-loss: Above the wick’s high (avoid getting stopped out).
✅ Take-profit: Next support level or mid-range of the liquidity zone.
📌 Step 4: Set Alerts & Automate
✅ The indicator triggers alerts when a stop-hunt is detected.
✅ You can set TradingView to notify you instantly when:
A bullish stop-hunt occurs → Look for long entry.
A bearish stop-hunt occurs → Look for short entry.
📌 Example Trade Setup
Example (BTC Long Trade on Stop-Hunt)
BTC is near $40,000 support (green liquidity zone).
A long wick drops to $39,800 with a green arrow (bullish stop-hunt signal).
Volume spikes, and price recovers quickly back above $40,000.
Trade entry: Buy at $40,050.
Stop-loss: Below wick ($39,700).
Take-profit: $41,500 (next resistance).
Result: BTC pumps, stop-loss remains safe, and trade profits.
🔥 Final Tips
Always wait for confirmation (don’t enter blindly on signals).
Use higher timeframes (15m, 1H, 4H) for better accuracy.
Combine with Order Flow tools (like Bookmap) to see real liquidity zones.
🚀 Now try it on TradingView! Let me know if you need adjustments. 📈🔥
Swing Breakout System (SBS)The Swing Breakout Sequence (SBS) is a trading strategy that focuses on identifying high-probability entry points based on a specific pattern of price swings. This indicator will identify these patterns, then draw lines and labels to show confirmation.
How To Use:
The indicator will show both Bullish and Bearish SBS patterns.
Bullish Pattern is made up of 6 points: Low (0), HH (1), LL (2 | but higher than initial Low), New HH (3), LL (5), LL again (5)
Bearish Patten is made up of 6 points: High (0), LL (1), HH (2 | but lower than initial high), New LL (3), HH (5), HH again (5)
A label with an arrow will appear at the end, showing the completion of a successful sequence
Idea behind the strategy:
The idea behind this strategy, is the accumulation and then manipulation of liquidity throughout the sequence. For example, during SBS sequence, liquidity is accumulated during step (2), then price will push away to make a new high/low (step 3), after making a minor new high/low, price will retrace breaking the key level set up in step (2). This is price manipulating taking liquidity from behind high/low from step (2). After taking liquidity price the idea is price will continue in the original direction.
Step 0 - Setting up initial direction
Step 1 - Setting up initial direction
Step 2 - Key low/high establishing liquidity
Step 3 - Failed New high/low
Step 4 - Taking liquidity from step (2)
Step 5 - Taking liquidity from step 2 and 4
Pattern Detection:
- Uses pivot high/low points to identify swing patterns
- Stores 6 consecutive swing points in arrays
- Identifies two types of patterns:
1. Bullish Pattern: A specific sequence of higher lows and higher highs
2. Bearish Pattern: A specific sequence of lower highs and lower lows
Note: Because the indicator is identifying a perfect sequence of 6 steps, set ups may not appear frequently.
Visualization:
- Draws connecting lines between swing points
- Labels each point numerically (optional)
- Shows breakout arrows (↑ for bullish, ↓ for bearish)
- Generates alerts on valid breakouts
User Input Settings:
Core Parameters
1. Pivot Lookback Period (default: 2)
- Controls how many bars to look back/forward for pivot point detection
- Higher values create fewer but more significant pivot points
2. Minimum Pattern Height % (default: 0.1)
- Minimum required height of the pattern as a percentage of price
- Filters out insignificant patterns
3. Maximum Pattern Width (bars) (default: 50)
- Maximum allowed width of the pattern in bars
- Helps exclude patterns that form over too long a period
LRLR [TakingProphets]LRLR (Low Resistance Liquidity Run) Indicator
This indicator identifies potential liquidity runs in areas of low resistance, based on ICT (Inner Circle Trader) concepts. It specifically looks for a series of unmitigated swing highs in a downtrend that form without any bearish fair value gaps (FVGs) between them.
What is an LRLR?
- A Low Resistance Liquidity Run occurs when price creates a series of lower highs without any bearish fair value gaps in between
- The absence of bearish FVGs indicates there is no significant resistance in the area
- These formations often become targets for smart money to collect liquidity above the swing highs
How to Use the Indicator:
1. The indicator will draw a diagonal line connecting a series of qualifying swing highs
2. A small "LRLR" label appears to mark the pattern
3. These areas often become targets for future price moves, as they represent zones of accumulated liquidity with minimal resistance
Key Points:
- Minimum of 4 consecutive lower swing highs
- No bearish fair value gaps can exist between these swing highs
- The diagonal line helps visualize the liquidity run formation
- Can be used for trade planning and identifying potential reversal zones
Settings:
- Show Labels: Toggle the "LRLR" label visibility
- LRLR Line Color: Customize the appearance of the diagonal line
Best Practices:
1. Use in conjunction with other ICT concepts and market structure analysis
2. Pay attention to how price reacts when returning to these levels
3. Consider these areas as potential targets for smart money liquidity grabs
4. Most effective when used on higher timeframes (4H and above)
Note: This is an educational tool and should be used as part of a complete trading strategy, not in isolation.
RSI Divergence + Sweep + Signal + Alerts Toolkit [TrendX_]The RSI Toolkit is a powerful set of tools designed to enhance the functionality of the traditional Relative Strength Index (RSI) indicator. By integrating advanced features such as Moving Averages, Divergences, and Sweeps, it helps traders identify key market dynamics, potential reversals, and newly-approach trading stragies.
The toolkit expands on standard RSI usage by incorporating features from smart money concepts (Just try to be creative 🤣 Hope you like it), providing a deeper understanding of momentum, liquidity sweeps, and trend reversals. It is suitable for RSI traders who want to make more informed and effective trading decisions.
💎 FEATURES
RSI Moving Average
The RSI Moving Average (RSI MA) is the moving average of the RSI itself. It can be customized to use various types of moving averages, including Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Moving Average (RMA), and Volume-Weighted Moving Average (VWMA).
The RSI MA smooths out the RSI fluctuations, making it easier to identify trends and crossovers. It helps traders spot momentum shifts and potential entry/exit points by observing when the RSI crosses above or below its moving average.
RSI Divergence
RSI Divergence identifies discrepancies between price action and RSI momentum. There are two types of divergences: Regular Divergence - Indicates a potential trend reversal; Hidden Divergence - Suggests the continuation of the current trend.
Divergence is a critical signal for spotting weakness or strength in a trend. Regular divergence highlights potential trend reversals, while hidden divergence confirms trend continuation, offering traders valuable insights into market momentum and possible trade setups.
RSI Sweep
RSI Sweep detects moments when the RSI removes liquidity from a trend structure by sweeping above or below the price at key momentum level crossing. These sweeps are overlaid on the RSI chart for easier visualized.
RSI Sweeps are significant because they indicate potential turning points in the market. When RSI sweeps occur: In an uptrend - they suggest buyers' momentum has peaked, possibly leading to a reversal; In a downtrend - they indicate sellers’ momentum has peaked, also hinting at a reversal.
(Note: This feature incorporates Liquidity Sweep concepts from Smart Money Concepts into RSI analysis, helping RSI traders identify areas where liquidity has been removed, which often precedes a trend reversal)
🔎 BREAKDOWN
RSI Moving Average
How MA created: The RSI value is calculated first using the standard RSI formula. The MA is then applied to the RSI values using the trader’s chosen type of MA (SMA, EMA, RMA, or VWMA). The flexibility to choose the type of MA allows traders to adjust the smoothing effect based on their trading style.
Why use MA: RSI by itself can be noisy and difficult to interpret in volatile markets. Applying moving average would provide a smoother, more reliable view of RSI trends.
RSI Divergence
How Regular Divergence created: Regular Divergence is detected when price forms HIGHER highs while RSI forms LOWER highs (bearish divergence) or when price forms LOWER lows while RSI forms HIGHER lows (bullish divergence).
How Hidden Divergence created: Hidden Divergence is identified when price forms HIGHER lows while RSI forms LOWER lows (bullish hidden divergence) or when price forms LOWER highs while RSI forms HIGHER highs (bearish hidden divergence).
Why use Divergence: Divergences provide early warning signals of a potential trend change. Regular divergence helps traders anticipate reversals, while hidden divergence supports trend continuation, enabling traders to align their trades with market momentum.
RSI Sweep
How Sweep created: Trend Structure Shift are identified based on the RSI crossing key momentum level of 50. To track these sweeps, the indicator pinpoints moments when liquidity is removed from the Trend Structure Shift. This is a direct application of Liquidity Sweep concepts used in Smart Money theories, adapted to RSI.
Why use Sweep: RSI Sweeps are created to help traders detect potential trend reversals. By identifying areas where momentum has exhausted during a certain trend direction, the indicator highlights opportunities for traders to enter trades early in a reversal or continuation phase.
⚙️ USAGES
Divergence + Sweep
This is an example of combining Devergence & Sweep in BTCUSDT (1 hour)
Wait for a divergence (regular or hidden) to form on the RSI. After the divergence is complete, look for a sweep to occur. A potential entry might be formed at the end of the sweep.
Divergences indicate a potential trend change, but confirmation is required to ensure the setup is valid. The RSI Sweep provides that confirmation by signaling a liquidity event, increasing the likelihood of a successful trade.
Sweep + MA Cross
This is an example of combining Devergence & Sweep in BTCUSDT (1 hour)
Wait for an RSI Sweep to form then a potential entry might be formed when the RSI crosses its MA.
The RSI Sweep highlights a potential turning point in the market. The MA cross serves as additional confirmation that momentum has shifted, providing a more reliable and more potential entry signal for trend continuations.
DISCLAIMER
This indicator is not financial advice, it can only help traders make better decisions. There are many factors and uncertainties that can affect the outcome of any endeavor, and no one can guarantee or predict with certainty what will occur. Therefore, one should always exercise caution and judgment when making decisions based on past performance.
Engulfing bar detectorHere’s the updated description with the added step about using Fibonacci levels across timeframes for confirmation:
Liquidity Engulfing Bar Detector
The **Liquidity Engulfing Bar Detector** is a powerful tool designed for traders who want to identify high-probability reversal patterns in the market based on liquidity grabbing and price action. This indicator highlights **Bullish Engulfing** and **Bearish Engulfing** bars that fulfill specific liquidity criteria, helping you spot potential trend reversals and trading opportunities.
**Features**:
1. **Bullish Engulfing Bars**:
- The current candle's low dips below the previous candle's low (grabs liquidity).
- The current candle closes above the previous candle's open.
- A green label is plotted above the engulfing bar for easy identification.
2. **Bearish Engulfing Bars**:
- The current candle's high exceeds the previous candle's high (grabs liquidity).
- The current candle closes below the previous candle's open.
- A red label is plotted below the engulfing bar for clear visibility.
3. **Customizable Alerts**:
- Receive instant notifications via TradingView alerts when a bullish or bearish engulfing pattern is detected.
- Alerts are fully customizable, allowing you to stay updated without actively monitoring the chart.
4. **Visual Markers**:
- Clear and intuitive labels make it easy to spot key patterns directly on your chart.
- Fully integrated with any timeframe and market, ensuring versatility for all trading styles.
---
### **How to Use**:
1. **Add the Indicator**:
- Apply the Liquidity Engulfing Bar Detector to your chart to automatically highlight bullish and bearish engulfing bars.
2. **Enable Alerts**:
- Set up TradingView alerts to get notified of potential setups in real-time.
3. **Analyze with Fibonacci Levels**:
- Draw a Fibonacci retracement tool over the identified engulfing bar, from its low to its high (for bullish patterns) or high to low (for bearish patterns).
- Use the following Fibonacci levels as key zones of interest:
- **0.0 (start)**, **0.25**, **0.5 (midpoint)**, **0.75**, and **1.0 (end)**.
- These levels often act as critical support or resistance zones for price action.
4. **Use Multi-Timeframe Confirmation**:
- Validate zones from higher timeframes using lower timeframe candles:
- **1-minute candles** for confirming zones on the **15-minute chart**.
- **5-minute candles** for confirming zones on the **1-hour chart**.
- **15-minute candles** for confirming zones on the **4-hour chart**.
- This approach ensures precision in your entry points and aligns intraday movements with higher timeframe setups.
5. **Integrate with Your Strategy**:
- Combine the indicator with other tools (e.g., trendlines, moving averages, or volume analysis) for confirmation.
- Use proper risk management to maximize your trading edge.
---
### **Why Use This Indicator?**
Liquidity grabs often signal the participation of major market players, which can lead to significant reversals or continuations. By combining liquidity concepts with engulfing bar patterns and Fibonacci analysis, this indicator helps you:
- Identify key market turning points.
- Improve your entries and exits with multi-timeframe precision.
- Enhance your trading strategy with an edge rooted in smart money concepts.
---
**Note**: This indicator is best used with proper risk management and alongside other technical or fundamental analyses.
---
Let me know if there's anything more you'd like to include!
Market Structure & Session Alerts### Market Structure & Session Alerts Indicator
#### Overview
The "Market Structure & Session Alerts" indicator is a comprehensive tool designed to assist traders in identifying key market structure levels, detecting liquidity sweeps, and receiving alerts for specific trading sessions. This indicator is particularly useful for traders who want to keep an eye on previous high and low levels and be alerted during pre-London and pre-New York sessions.
#### Features
1. **Previous High/Low Levels:**
- **Daily, Weekly, and Monthly Highs and Lows:** The indicator plots the previous day, week, and month high and low levels on the chart. These levels can be crucial for identifying support and resistance zones.
- **Toggle Display:** Users can choose to show or hide these levels using the "Show Previous Day/Week/Month High/Low" option.
2. **Liquidity Sweep Detection:**
- **Liquidity Sweep Identification:** The indicator detects liquidity sweeps when the current price closes above the previous day's high. This can signal potential reversals or continuations in the market.
- **Visual Alerts:** When a liquidity sweep is detected, a green triangle is plotted below the bar.
3. **Session Alerts:**
- **Session Timings:** Users can set specific start and end times for the pre-London and pre-New York sessions to match their timezone.
- **Visual Background Highlight:** The background of the chart is highlighted in yellow during the defined session times to provide a visual cue.
- **Alert Messages:** The indicator can generate alerts to notify traders when the market enters the pre-London or pre-New York session.
4. **Current Price Line:**
- The current price is plotted as a black line, providing a clear visual reference for the current market price.
#### How to Use
1. **Input Parameters:**
- `Show Previous Day/Week/Month High/Low`: Enable or disable the display of previous high/low levels.
- `Show Liquidity Sweep`: Enable or disable the detection and display of liquidity sweeps.
- `Show Session Alerts`: Enable or disable session alerts and background highlights.
2. **Session Timing Adjustments:**
- Set the `Pre-London Start`, `Pre-London End`, `Pre-New York Start`, and `Pre-New York End` times according to your timezone to ensure accurate session alerts.
3. **Alerts:**
- Make sure alerts are enabled in your TradingView settings to receive notifications when the market enters the pre-London or pre-New York sessions.
#### Example Use Cases
- **Day Traders:** Identify potential support and resistance levels using the previous day's high and low.
- **Swing Traders:** Use weekly and monthly high and low levels to determine significant market structure points.
- **Scalpers:** Detect liquidity sweeps to identify potential quick trades.
- **Session Traders:** Be alerted when the market enters key trading sessions to align your trading strategy with major market activities.
This indicator combines multiple market analysis tools into one, providing a robust system for traders to enhance their trading decisions and market awareness.
Uptrick: Volume StrengthPurpose:
The "Uptrick: Volume Strength" indicator, known by its short title 'VolStrength,' is meticulously designed to evaluate the strength of volume activity within a market, providing traders with valuable insights into liquidity dynamics. By visualizing volume bars and comparing them to a predefined threshold, traders can gauge the intensity of buying or selling pressure, thereby assessing market liquidity and potential price movements.
Explanation:
Input Parameters:
Traders benefit from the ability to customize the threshold for high volume, allowing them to adapt the indicator to varying market conditions and trading strategies.
The calculation of the average volume over a specified period adds depth to the analysis, offering traders a reference point for assessing current volume levels relative to historical averages and evaluating liquidity trends.
Volume Analysis:
The script discerns between bars where the closing price exceeds the opening price (up bars) and bars where the closing price is lower than the opening price (down bars), facilitating the identification of bullish or bearish market sentiment.
High-volume bars that surpass the predefined threshold are prominently highlighted, serving as indicators of increased trading activity and enhanced liquidity levels.
Average Volume Visualization:
A line representing the average volume over the specified period is plotted on the chart, providing traders with a visual reference for evaluating current volume levels against historical averages. This aids in assessing the overall liquidity conditions in the market.
Volume Bar Representation:
The colorization of volume bars is contingent upon their direction (up or down) and whether they exceed the high volume threshold.
Up bars, symbolizing buying pressure, are typically depicted in green, while down bars, indicative of selling pressure, are rendered in red.
Notably, when volume surpasses the high volume threshold, the respective bar color is applied, accentuating significant volume spikes and their potential impact on liquidity and price dynamics.
Through its meticulous design and comprehensive features, the "Uptrick: Volume Strength" indicator equips traders with actionable insights into market liquidity dynamics. By integrating volume analysis into their trading strategies, traders can effectively assess liquidity conditions, identify potential price movements, and make informed trading decisions.
ICT - GAPs and Volume Imbalance
GAPs
Gaps are areas on chart where the price have moved sharply up or down, with no trading in between. Gaps often fill, but they don't have to.
Volume Imbalance
Volume imbalance - determined using 2 candles
Bullish Volume Imbalance - area between the close of 1st candle and the open of 2nd candle
Bearish Volume Imbalance - area between the close of 1st candle and the open of 2nd candle
How to use the indicator:-
When you find imbalance in volume or a GAP in the chart, you may expect price to rebalance it before continuation.
Importantly, GAPs/Imbalances do not always fill. Traders should never assume that a gap/imbalance will fill without understanding the reasons for the gap and monitoring trading activity around the gap.
Pair it with your current bias for better results.
FX Mini-Day/Index Dividers V2This is a combination of the Mini-Day Separator Indicator, timings based off the research by Tom Henstridge/@LiquiditySniper and additional Index KZ delineations, based on ICT's 2022 Youtube Mentorship.
*It borrows some minor code from Enricoamato997 . Credit where it is due!
This is a joint effort by myself, @vbwilkes / Offseason Vince and @Tom_FOREX / TraderTom on the Index/Index Future portion.
Index Future Example
Forex Example
Crypto Macro CockpitCrypto Macro Cockpit — Institutional Liquidity Regime Detection
🔍 Overview
This script introduces a modern macro framework for crypto market regime detection, leveraging newly added stablecoin market data on TradingView. It’s designed to guide traders through the evolving institutional era of crypto — where liquidity, not just price, is king.
🌐 Why This Matters
Historically, traditional proxies like M2 money supply or bond yields were referenced to infer macro liquidity shifts. But with the regulatory green light and institutional embrace of stablecoins, on-chain fiat liquidity is now directly observable.
Stablecoins = The new M2 for crypto.
This script utilizes real-time data from:
📊 CRYPTOCAP:STABLE.C (Total Stablecoin Market Cap)
📊 CRYPTOCAP:STABLE.C.D (Stablecoin Dominance)
to assess dry powder, risk appetite, and macro regime transitions.
📋 How to Read the Crypto Macro Cockpit
This dashboard updates every few bars and is organized into four actionable segments:
1️⃣ Macro Spreads
Metric --> Interpretation
Risk Flow --> Measures capital flow between stablecoins and total crypto market cap. → Green = risk deploying.
ETH vs BTC --> Shift in dominance between ETH and BTC → rotation gauge.
ETHBTC --> Price ratio movement → confirms leadership tilt.
ALTs (TOTAL3ES) --> Momentum in altcoin market, excluding BTC/ETH/stables → key for alt season timing.
2️⃣ Liquidity & Risk Appetite
Metric --> Interpretation
Liquidity --> Directional change in stablecoin cap → more stables = more dry powder.
Risk Appetite --> Inverse of stablecoin dominance → falling dominance = capital rotating into risk.
3️⃣ Stablecoin Context
Metric --> Interpretation
StableCap ROC --> Growth rate of stablecoin market cap → proxy for fiat inflows.
StableDom ROC --> Change in stablecoin dominance → reflects market caution or aggression.
4️⃣ Composite Labels
Label --> Interpretation
Rotation --> Sector tilt (BTC-led vs ETH/Alts)
Regime --> Synthesized macro environment → "Risk-ON", "Caution", "Waiting", or "Risk-OFF"
Background Color --> Optional tint reflecting regime for quick glance validation
All metrics are evaluated with directional arrows (▲/▼/•) and acceleration overlays, using user-defined thresholds scaled by timeframe for precision.
🔔 Built-in Alerts
Predefined, non-repainting alerts include:
Regime transitions
Sector rotations
Confirmed ETH/ALT rotations
Stablecoin market cap spikes
Risk Flow acceleration
You can use these alerts for discretionary trading or automated system triggers.
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice. Trading cryptocurrencies involves risk, and past performance does not guarantee future results. Always do your own research and manage risk responsibly.
✅ Ready to Use
No configuration needed — just load the script
Works on all timeframes (optimized for 1D)
Thresholds and smoothing are customizable
Table positioning and sizing is user-controlled
If you find this helpful, feel free to ⭐️ favorite or leave feedback. Questions welcome in the comments.
Let’s trade with macro awareness in this new era.
Fibs Has Lied 🌟 Fibs Has Lied - Indicator Overview 🌟
Designed for indices like US30, NQ, and SPX, this indicator highlights setups where price interacts with key EMA levels during specific trading sessions (default: 6:30–11:30 AM EST).
🌟 Key Features & Levels 🌟
🔹EMA Crossover Setups
The indicator uses the 100-period and 200-period EMAs to identify bullish and bearish setups:
- Bullish Setup: Triggers when the 100 EMA crosses above the 200 EMA, followed by two consecutive candles opening above the 100 EMA, with the low within a specified point distance (e.g., 20 points for US30).
- Bearish Setup: Triggers when the 100 EMA crosses below the 200 EMA, followed by two consecutive candles opening below the 100 EMA, with the high within the point distance.
- Signals are marked with green (buy) or red (sell) triangles and text, ensuring you don’t miss a setup. 📈
🔹 Reset Conditions for Re-Entries
After an initial setup, the indicator watches for “reset” opportunities:
- Buy Reset: If price moves below the 200 EMA after a bullish crossover, then returns with two consecutive candles where lows are above the 100 EMA (within point distance), a new buy signal is plotted.
- Sell Reset: If price moves above the 200 EMA after a bearish crossover, then returns with two consecutive candles where highs are below the 100 EMA (within point distance), a new sell signal is plotted.
This feature captures additional entries after liquidity grabs or fakeouts, aligning with ICT’s manipulation concepts. 🔄
🔹 Session-Based Filtering
Focus your trades during high-liquidity windows! The default session (6:30–11:30 AM EST, New York timezone) targets the London/NY overlap, where price often seeks liquidity or sets up for reversals. Toggle the time filter off for 24/7 signals if desired. 🕒
🔹Symbol-Specific Point Distance
Customizable entry zones based on your chosen index:
- US30: 20 points from the 100 EMA.
- NQ: 3 points from the 100 EMA.
- SPX: 2.5 points from the 100 EMA.
This ensures setups are tailored to the volatility of your market, maximizing relevance. 🎯
🔹 Market Structure Markers (Optional)
Visualize swing points with pivot-based labels:
- HH (Higher High): Signals uptrend continuation.
- HL (Higher Low): Indicates potential bullish support.
- LH (Lower High): Suggests weakening uptrend or reversal.
- LL (Lower Low): Points to downtrend continuation.
- Toggle these on/off to keep your chart clean while analyzing trend direction. 📊
🔹 EMA Visualization
Optionally plot the 100 EMA (blue) and 200 EMA (red) to see key levels where price reacts. These act as dynamic support/resistance, perfect for spotting liquidity pools or ICT’s Power of 3 setups. ⚖️
🌟 Customization Options 🌟
- Symbol Selection: Choose US30, NQ, or SPX to adjust point distance for entries.
- Time Filter: Enable/disable the 6:30–11:30 AM EST session to focus on high-liquidity periods.
- EMA Display: Toggle 100/200 EMAs on/off to reduce chart clutter.
- Market Structure: Show/hide HH/HL/LH/LL labels for cleaner analysis.
- Signal Markers: Green (buy) and red (sell) triangles with text are auto-plotted for easy identification.
🌟 Usage Tips 🌟
- Best Timeframes: Use on 3m for intraday scalping and 30m for swing trades.
- Combine with ICT Tools: Pair with order blocks, fair value gaps, or kill zones for stronger setups.
- Focus on Session: The default 6:30–11:30 AM EST session captures London/NY volatility—perfect for liquidity-driven moves.
- Avoid Overcrowding: Disable market structure or EMAs if you only want setup signals.
Multi-Timeframe SFP + SMTImportant: Please Read First
This indicator is not a "one size fits all" solution. It is a professional and complex tool that requires you to learn how to use it, in addition to backtesting different settings to discover what works best for your specific trading style and the assets you trade. The default settings provided are my personal preferences for trading higher-timeframe setups, but you are encouraged to experiment and find your own optimal configuration.
Please note that while this initial version is solid, it may still contain small errors or bugs. I will be actively working on improving the indicator over time. Also, be aware that the script is not written for maximum efficiency and may be resource-intensive, but this should not pose a problem for most users.
The source code for this indicator is open. If you truly want to understand precisely how all the logic works, you can copy and paste the code into an AI assistant like Gemini or ChatGPT and ask it to explain any part of the script to you.
Author's Preferred Settings (Guideline)
As a starting point, here are the settings I personally use for my trading:
SFP Timeframe: 4-Hour (Strength: 5-5)
Max Lookback: 35 Bars
Raid Expiration: 1 Bar
SFP Lines Limit: 1
SMT Timeframe 1: 30-Minute (Strength: 2-2) with 3-Minute LTF Detection.
SMT Timeframe 2: 15-Minute (Strength: 3-3) with 3-Minute LTF Detection.
SMT Timeframe 3: 1-Hour (Strength: 1-1) with 3-Minute LTF Detection.
SMT Timeframe 4: 15-Minute (Strength: 1-1) with 3-Minute LTF Detection.
Multi-Timeframe SMT: An Overview
This indicator is a powerful tool designed to identify high-probability trading setups by combining two key institutional concepts: Swing Failure Patterns (SFP) on a higher timeframe and Smart Money Technique (SMT) divergences on a lower timeframe. A key feature is the ability to configure and run up to four independent SMT analyses simultaneously, allowing you to monitor for divergences across multiple timeframes (e.g., 15m, 1H, 4H) from a single indicator.
Its primary purpose is to generate automated signals through TradingView's alert system. By setting up alerts, the script runs server-side, monitoring the market for you. When a setup presents itself, it will send a push notification to your device, allowing you to personally evaluate the trade without being tied to your screen.
The Strategy: HTF Liquidity Sweeps into LTF SMT
The core strategy is built on a classic institutional trading model:
Wait for a liquidity sweep on a significant high timeframe (e.g., 4-hour, Daily).
Once liquidity is taken, look for a confirmation of a shift in market structure on a lower timeframe.
This indicator uses an SMT divergence as that confirmation signal, indicating that smart money may be stepping in to reverse the price.
How It Works: The Two-Step Process
The indicator's logic follows a precise two-step process to generate a signal:
Step 1: The Swing Failure Pattern (SFP)
First, the indicator identifies a high-timeframe liquidity sweep. This is configured in the "Swing Failure Pattern (SFP) Timeframe" settings.
It looks for a candle that wicks above a previous high (or below a previous low) but then closes back within the range of that pivot. This action is known as a "raid" or a "swing failure," suggesting the move failed to find genuine momentum.
Step 2: The SMT Divergence
The moment a valid SFP is confirmed, the indicator's multiple SMT engines activate.
Each engine begins monitoring the specific SMT timeframe you have configured (e.g., "SMT Timeframe 1," "SMT Timeframe 2," etc.) for a Smart Money Technique (SMT) divergence.
An SMT divergence occurs when two closely correlated assets fail to move in sync. For example, after a raid on a high, Asset A makes a new high, but Asset B fails to do so. This disagreement suggests weakness and a potential reversal.
When the script finds this divergence, it plots the SMT line and triggers an alert.
The Power of Alerts
The true strength of this indicator lies in its alert capabilities. You can create alerts for both unconfirmed and confirmed SMTs.
Enable Alerts LTF Detection: These alerts trigger when an unconfirmed, potential SMT is spotted on the lower "LTF Detection" timeframe. While not yet confirmed, these early alerts can notify you of a potential move before it fully happens, allowing you to be ahead of the curve and find the best possible trade entries.
Enable Alerts Confirmed SMT: These alerts trigger only when a permanent, confirmed SMT line is plotted on your chosen SMT timeframe. These signals are more reliable but occur later than the early detection alerts.
Key Concepts Explained
What is Pivot Strength?
Pivot Strength determines how significant a high or low needs to be to qualify as a valid structural point. A setting of 5-5, for example, means that for a candle's high to be considered a valid pivot high, its high must be higher than the highs of the 5 candles to its left and the 5 candles to its right.
Higher Strength (e.g., 5-5, 8-8): Creates fewer, but more significant, pivots. This is ideal for identifying major structural highs and lows on higher timeframes.
Lower Strength (e.g., 2-2, 3-3): Creates more pivots, making it suitable for identifying the smaller shifts in momentum on lower timeframes.
Raid Expiration & Validity
An SFP signal is not valid forever. The "Raid Expiration" setting determines how many SFP timeframe bars can pass after a raid before that signal is considered "stale" and can no longer be used to validate an SMT. This ensures your SMT divergences are always in response to recent liquidity sweeps.
Why You Must Be on the Right Chart Timeframe to See SMT Lines
Pine Script™ has a fundamental rule: an indicator running on a chart can only "see" the bars of that chart's timeframe or higher.
When the SMT logic is set to the 15-minute timeframe, it calculates its pivots based on 15-minute data. To accurately plot lines connecting these pivots, you must be on a 15-minute chart or lower (e.g., 5-minute, 1-minute).
If you are on a higher timeframe chart, like the 1-hour, the 15-minute bars do not exist on that chart, so the indicator has no bars to draw the lines on.
This is precisely why the alert system is so powerful. You can set your alert to run on the 15-minute timeframe, and TradingView's servers will monitor that timeframe for you, sending a notification regardless of what chart you are currently viewing.
Mig Trade Model - Kill Zones
Key features:
Liquidity Hunt Detection: Spots aggressive moves that "hunt" stops beyond recent swing highs/lows.
Consolidation Filter: Requires 1-3 small-range candles after a hunt before confirming with a strong candle.
Bias Application: Uses daily open/close to auto-detect bias or allows manual override.
Kill Zone Restriction: Limits signals to London (default: 7-10 AM UTC) and NY (default: 12-3 PM UTC) sessions for better relevance in active markets.
This strategy is inspired by smart money concepts (SMC) and ICT (Inner Circle Trader) methodologies, aiming to capture venom-like "stings" in price action where liquidity is grabbed before reversals.
How It Works
ATR Calculation: Uses a user-defined ATR length (default: 14) to measure volatility, which scales candle body and range thresholds.
Bias Determination:
Auto: Compares daily close to open (bullish if close > open).
Manual: User selects "Bullish" or "Bearish."
Strong Candles:
Bullish: Green candle with body > 2x ATR (configurable).
Bearish: Red candle with body > 2x ATR.
Small Range Candles:
Candles where high-low < 0.5x ATR (configurable).
Liquidity Hunt:
Bullish Hunt: Strong bearish candle making a new low below the past swing low (default: 10 bars).
Bearish Hunt: Strong bullish candle making a new high above the past swing high.
Signal Generation:
After a hunt, counts 1-3 small-range candles.
Confirms with a strong candle in the opposite direction (e.g., strong bullish after bearish hunt).
Resets if >3 small candles or an opposing strong candle appears.
Kill Zone Filter:
Checks if the current bar's time (in UTC) falls within London or NY Kill Zones.
Only allows final "Buy" (bullish entry) or "Sell" (bearish entry) if bias matches and in Kill Zone.
Plots:
Yellow circle (below): Bullish liquidity hunt.
Orange circle (above): Bearish liquidity hunt.
Blue diamond (below): Raw bullish signal.
Purple diamond (above): Raw bearish signal.
Green triangle up ("Buy"): Filtered bullish entry.
Red triangle down ("Sell"): Filtered bearish entry.
Inputs
Bias: "Auto" (default), "Bullish", or "Bearish" – Controls signal direction based on daily trend.
ATR Length: 14 (default) – Period for ATR calculation.
Swing Length for Liquidity Hunt: 10 (default) – Bars to look back for swing highs/lows.
Strong Candle Body Multiplier (x ATR): 2.0 (default) – Threshold for strong candle bodies.
Small Range Multiplier (x ATR): 0.5 (default) – Threshold for small-range candles.
London Kill Zone Start/End Hour (UTC): 7/10 (default) – Customize London session hours.
NY Kill Zone Start/End Hour (UTC): 12/15 (default) – Customize New York session hours.
Usage Tips
Timeframe: Best on lower timeframes (e.g., 5-15 min) for intraday trading, especially forex pairs like EURUSD or GBPUSD.
Timezone Adjustment: Inputs are in UTC. If your chart is in a different timezone (e.g., EST = UTC-5), adjust hours accordingly (e.g., London: 2-5 AM EST → 7-10 UTC).
Risk Management: Use with stop-loss (e.g., beyond the hunt low/high) and take-profit based on ATR multiples. Not financial advice—backtest thoroughly.
Customization: Tweak multipliers for different assets; higher for volatile cryptos, lower for stocks.
Limitations: Relies on historical data; may generate false signals in ranging markets. Combine with other indicators like volume or support/resistance.
This indicator is for educational purposes. Always use discretion and proper risk management in live trading. If you find it useful, feel free to share feedback or suggestions!
