P3 Weekly Goldbach levelsP3 Weekly Session Projections
Originality and Uniqueness:
Novel Time-Based Approach:
This indicator uniquely combines the previous weeks range analysis with mathematical Goldbach number sequences
Unlike standard Fibonacci retracements that use swing highs/lows, this script uses a specific weekly session window for consistent anchor points
The weekly reset mechanism ensures levels are always based on the most recent Sunday session, providing fresh, relevant levels
2. Mathematical Innovation:
First-of-its-kind application weekly Goldbach numbers (100, 97, 89, 83, 71, 59, 50, 47, 41, 29, 17, 11, 3, 0) as support/resistance levels
Dual-range projection system: Projects both standard deviations internally and overlays Goldbach levels for precise mathematical alignment
Auto-extending ranges when price breaks beyond 100/0 levels – automatically adds upper and lower GB ranges
3. Advanced Technical Features:
Dynamic label positioning with 4 different modes (Right Edge, Left of Line, Right of Line, Fixed Position)
Color-coded level hierarchy: Red (G:100), Green (G:0), Yellow (G:111/-111) for instant visual recognition
Session-based calculations using real market hours rather than arbitrary chart points
Clean weekly management – automatically removes previous levels and draws fresh ones each Sunday
Practical Usefulness:
1. Professional Trading Application:
Institutional session timing: plots when major institutions begin weekly positioning
Objective level placement: Eliminates subjective swing high/low selection - uses concrete session data
Multi-market applicability: Works on forex, indices, commodities, and crypto that trade during this session
2. Risk Management Benefits:
Predefined support/resistance zones based on mathematical progression rather than subjective analysis
Extension levels provide targets when price moves beyond normal ranges
Weekly refresh ensures levels remain relevant to current market structure
3. Unique Market Insights:
Goldbach number spacing provides mathematically-derived levels that often align with natural market movements
Session-based anchoring captures institutional weekly bias and positioning
Visual clarity with customizable labels and positioning for different trading styles
How It Differs from Existing Scripts:
Not a standard Fibonacci tool - uses specific mathematical sequence with weekly session anchoring
Not a generic pivot indicator - focuses on Sunday institutional session range
Not a simple support/resistance script - combines time-based analysis with mathematical projections
Not a rehash of existing indicators - genuinely novel approach combining session analysis with Goldbach mathematics
Target Audience:
Institutional traders using weekly analysis
Mathematical traders interested in number theory applications
Session-based analysts focusing on specific market opening periods
Risk management specialists needing objective level placement
This script represents genuine innovation in combining specific market session analysis with mathematical number theory, providing traders with a unique tool that doesn't exist elsewhere in the TradingView library.
Educational
Liquidation Levels V3Uh similar to liq lvls v2 , but this is useful for backtesting as the lvls dont erase. so you can see how price reacted in the past backtest in specific market conditions and be ready for whats ahead.
BCbc script using everything to detect the thing we need and using every volume % wise to see dry wet
Gold Power Queen StrategyTrade XAUUSD (Gold) or XAUEUR LIKE A CHAMP!!!! Only during the most volatile hours of the New York session, using momentum and trend confirmation, with session-specific risk/reward profiles.
✅ Strategy Rules
🕒 Valid Trading Times ("Power Hours"):
Trades are only taken during high-probability time windows on Tuesdays, Wednesdays, and Thursdays, corresponding to key New York session activity:
Morning Session:
08:00 – 12:00 (NY time)
Afternoon Session:
12:00 – 15:00
These times align with institutional activity and economic news releases.
📊 Technical Indicators:
50-period Simple Moving Average (SMA50):
Identifies the dominant market trend.
14-period Relative Strength Index (RSI):
Measures market momentum with session-adjusted thresholds.
🟩 Buy Signal Criteria:
Price is above the 50-period SMA (bullish trend)
Must be during a valid day (Tue–Thu) and Power Hour session
🟥 Sell Signal Criteria:
Price is below the 50-period SMA (bearish trend)
Must be during a valid day and Power Hour session
🎯 Trade Management Rules:
Morning Session (08:00–12:00)
Stop Loss (SL): 50 pips
Take Profit (TP): 150 pips
Risk–Reward Ratio: 1:3
Afternoon Session (12:00–15:00)
Stop Loss (SL): 50 pips
Take Profit (TP): up to 100 pips
Risk–Reward Ratio: up to 1:1.5
⚠️ TP is slightly reduced in the afternoon due to typically lower volatility compared to the morning session.
📺 Visuals & Alerts:
Buy signals: Green triangle plotted below the bar
Sell signals: Red triangle plotted above the bar
SMA50 line: Orange
Valid session background: Light pink
Alerts: Automatic alerts for buy/sell signals
Sat Stacking Strategies Simulation (SSSS)Sat Stacking Strategies Simulation (SSSS)
This indicator simulates and compares different Bitcoin stacking strategies over time, allowing you to visualize performance, cost basis, and stacking behavior directly on your chart.
Core Features:
Three Stacking Strategies
• Trend-Based – Stack only when price is above/below a long-term SMA.
• Stack the Dip – Buy during sharp pullbacks or oversold conditions.
• Price Zone – Stack only in “cheap”, “fair”, or “expensive” zones based on a simulated Short-Term Holder (STH) cost basis.
Always Stack Benchmark
Compare your chosen strategy against a simple “Always Stack” approach for a real-world DCA reference.
Performance Metrics Table
Track:
• Total Fiat Added
• Total BTC Accumulated
• Current Value
• Average Cost per BTC
• PnL %
• CAGR
• Sharpe Ratio & Stdev
• Stack Events & Time Underwater
Advanced Options
• Simulate cash-secured puts on unused fiat.
• Simulate covered calls on BTC holdings.
• Roll over unused stacking amounts for future buys.
This tool is designed for Bitcoiners, stackers, and DCA enthusiasts who want to backtest and visualize their stacking plan—whether you keep it simple or go full quant.
Sometimes the best alpha is just showing up every week with your wallet open… and occasionally wearing a helmet. 🪖💰
Dark Pool Block Trades - Institutional Volume📊 Dark Pool Block Trades - Institutional Volume
Visualize where institutional money positions before major price moves occur. This indicator reveals hidden dark pool block trades that often precede significant price movements - because when smart money deploys millions and billions in strategic accumulation or distribution, retail traders need to see where it's happening.
🎯 WHY DARK POOL DATA MATTERS:
Institutions don't move large capital randomly. Dark pool block trades represent strategic positioning by sophisticated money managers with superior research and conviction. These trades create hidden support/resistance levels that often predict future price action.
The key principle: Follow institutional flow, don't fight it. When institutions get involved, they create high-probability trading opportunities.
💰 HOW INSTITUTIONS INFLUENCE PRICE:
- Large block trades establish hidden accumulation/distribution zones
- Smart money builds positions BEFORE retail awareness increases
- Institutional activity creates "footprints" at key technical levels
- These trades often signal conviction plays ahead of major moves
- Institutions typically add to winning positions throughout trends
🔍 WHAT THIS INDICATOR SHOWS:
- Visual overlay of dark pool block trades directly on price charts
- Track institutional positioning across major stocks and ETFs
- Identify accumulation/distribution zones before they become obvious to retail
- Spot high-conviction institutional trades in real-time visualization
- Customizable block trade size filters and timeframe selection
- Historical institutional activity up to 5 years or custom ranges
💡 THE TRADING ADVANTAGE:
Instead of guessing price direction, see where institutions are already positioning. When large block trades appear in dark pools, you're witnessing strategic institutional commitment that frequently leads to significant price movements.
⚡ HOW IT WORKS:
This Pine Script displays institutional dark pool transactions as visual markers on your charts. The script comes with sample data for immediate use. For expanded ticker coverage and real-time updates, external data services are available.
🎯 IDEAL FOR:
- Swing traders following institutional footprints
- Traders seeking setups backed by smart money conviction
- Position traders looking for accumulation zones
- Anyone wanting to align with institutional flow rather than fight it
🔄 SAMPLE DATA INCLUDED:
Pre-loaded with institutional activity data across popular tickers, updated daily to demonstrate how dark pool activity correlates with future price movements.
The script initially covers these tickers going back 6 months showing the top 10 trades by volume over 400,000 shares: AAPL, AMD, AMZN, ARKK, ARKW, BAC, BITO, COIN, COST, DIA, ETHA, GLD, GOOGL, HD, HYG, IBB, IWM, JNJ, JPM, LQD, MA, META, MSFT, NVDA, PG, QQQ, RIOT, SLV, SMCI, SMH, SOXX, SPY, TLT, TSLA, UNH, USO, V, VEA, VNQ, VOO, VTI, VWO, WMT, XLE, XLF, XLK, XLU, XLV, XLY
Moby Tick Prints - version 1.0.0Prints are aggregated by date and price. If there are multiple trades on the same day at the same price, they are added and represented in the Shares column
MTPI SUI | JeffreyTimmermansMedium-Term Trend Probability Indicator
The "Medium-Term Trend Probability Indicator" on SUI is a custom-designed tool created to analyze SUI from a medium-term perspective. While short-term indicators often respond to quick fluctuations and long-term models focus on broader macro cycles, the MTPI sits perfectly in between—detecting trend shifts over multiple weeks and helping traders and analysts stay ahead of the curve.
This specific version of the MTPI is applied to SUI, making it a dedicated trend-following tool for this unique digital asset, tuned to reflect its own volatility and structural behavior.
Key Features
Medium-Term Focus:
The MTPI is optimized for trend tracking over medium horizons—typically weeks to a few months. It filters out noise while remaining responsive to meaningful directional changes.
6 Input Signals:
The model combines 6 carefully selected input trend-following indicators, each targeting different dimensions of trend strength and continuation.
Market Regimes:
The MTPI classifies market conditions into:
Bullish → Strong upward momentum and trend confirmation
Bearish → Sustained downward pressure and breakdown signals
Neutral → Mixed signals or transition phases, often seen in consolidations or early reversals
Visual Background:
The chart background shifts based on the active regime. This provides instant visual clarity on whether the asset is trending, reversing, or consolidating.
Indicator Dashboard:
At the bottom of the chart, the MTPI includes a live dashboard showing:
The state of all 6 inputs (Bullish, Bearish, Neutral)
The composite MTPI Score
The resulting Market Trend classification
How It Works
Input Signal Logic:
Each input returns one of three possible scores:
+1 = Bullish
-1 = Bearish
0 = Neutral
Score Aggregation:
The MTPI Score is calculated as the average of all 6 input values:
Score > +0.1 → Bullish regime
Score < -0.1 → Bearish regime
Between -0.1 and +0.1 → Neutral regime
Background Coloring:
The background changes automatically to match the current trend regime, making it visually easy to interpret the dominant market environment.
Use Cases
Mid-Term Strategy Alignment:
Use the MTPI to align with the dominant medium-term market direction on SUI.
Rotation & Momentum Detection:
Catch early signs of reversals, breakout expansions, or trend exhaustion.
Multi-Timeframe Integration:
Combine MTPI with short-term tools (STPI) or long-term indicators (LTPI) for a complete market overview.
Dynamic Alerts:
Bullish Alert: MTPI Score crosses above +0.1
Bearish Alert: MTPI Score crosses below -0.1
Neutral Zone: MTPI Score enters between -0.1 and +0.1
Conclusion
The MTPI – SUI is a reliable medium-term probability model that simplifies complex market structure into an actionable, color-coded signal system. By distilling 6 intelligent inputs into one combined trend score, it offers clear directional bias and regime classification—crucial for positioning in a volatile asset like SUI. Whether used standalone or as part of a broader trend framework, this indicator enhances clarity, discipline, and precision in your medium-term trading decisions.
MTPI OTHERS.D | JeffreyTimmermansMedium-Term Trend Probability Indicator
The "Medium-Term Trend Probability Indicator" on OTHERS.D is a custom-built model designed to measure the medium-term trend strength of the entire crypto market excluding the Top 10 assets. By focusing on the performance of smaller-cap and emerging cryptocurrencies, this indicator offers a refined view of risk appetite and capital rotation beyond the major players like BTC, ETH, and other top coins.
OTHERS.D (Total Crypto Market Cap Dominance excluding the Top 10) serves as a proxy for altcoin speculation cycles, market breadth, and rotational momentum. The MTPI leverages this by applying 8 carefully selected trend-following indicators to generate a composite probability score that reflects the directional bias of the broader altcoin market.
Key Features
Mid-Term Trend Orientation:
The MTPI focuses on multi-week to multi-month trend phases, filtering out short-term volatility while responding faster than long-term macro models.
8 Input Signals:
Built using 8 trend-following indicators, each measuring trend strength, direction, and persistence within the "OTHERS" segment.
Market Regime Detection:
The MTPI identifies three distinct market states:
Bullish → Clear upward trend in the altcoin market (excluding top 10)
Bearish → Persistent downward movement or weakness in the broader altcoin segment
Neutral → Choppy or indecisive behavior
Background Coloring:
The background dynamically adapts based on the current regime, making it easy to visually identify dominant conditions.
Trend Dashboard:
A dashboard displays:
The current state of all 8 trend signals
The overall MTPI score
The interpreted market regime
How It Works
Trend Signal Evaluation:
Each of the 8 inputs outputs a discrete signal:
+1 → Bullish
-1 → Bearish
0 → Neutral
Composite Score Calculation:
The MTPI score is computed as the average of the 8 inputs:
Score > +0.1 → Bullish regime
Score < -0.1 → Bearish regime
Between -0.1 and +0.1 → Neutral regime
This produces a normalized score from -1 to +1, helping quantify trend confidence and detect early shifts in momentum.
Color-Coded Background:
The score automatically drives the background color:
Green tones for bullish phases
Red tones for bearish phases
Gray/orange tones for sideways conditions
Use Cases
Altcoin Rotation Tracking:
Use MTPI – OTHERS.D to monitor when capital is rotating into or out of smaller-cap cryptocurrencies — a key signal for risk-on or risk-off sentiment.
Medium-Term Positioning:
Perfect for swing traders or trend followers looking to align positions with the dominant trend in the non-top-10 market segment.
Multi-Timeframe Confirmation:
Combine MTPI with other tools like STPI (Short-Term) or LTPI (Long-Term) for enhanced decision-making and better timing across timeframes.
Dynamic Alerts:
Bullish Entry: MTPI score crosses above +0.1
Bearish Entry: MTPI score crosses below -0.1
Neutral Zone: MTPI score moves between -0.1 and +0.1
These alerts help you react quickly to regime shifts in the altcoin market outside the top 10.
Conclusion
The MTPI – OTHERS.D is a focused, probability-based trend tool built for analyzing the non-top-10 segment of the crypto market. By merging 8 independent trend signals into a single composite score and regime model, it provides a clear lens into where capital is flowing and how smaller-cap crypto assets are behaving. An essential tool for anyone active in altcoin trading, rotational strategies, or full-spectrum crypto market analysis.
swing_fun_advancedThis indicator is similar to my free open-source swing_fun indicator, but it contains sell signals and sell alerts too.
Design to be used on the indexes with the 4hr chart. It gives alerts whenever a long or short signal is found.
I have tested it with US100, UK100, DE40, US30, US500, J225.
TRI - Quick Analysis"TRI - Quick Analysis" is a multi-indicator dashboard designed to give traders an immediate overview of market momentum, trend strength, volume flow, and volatility.
It visually summarizes key technical indicators in a compact table, including:
RSI (momentum)
MACD Histogram (trend momentum)
ADX + SuperTrend (trend strength & direction)
StochRSI (oversold/overbought)
CCI (price deviation)
CMF (volume flow)
MFI (volume-weighted momentum)
OBV (cumulative volume pressure)
ATR (volatility)
%B Bollinger (position within Bollinger Bands)
Each value is color-coded (green, red, blue) based on whether it's favorable, unfavorable, or neutral for a potential long position.
At the bottom of the table, a summary score dynamically aggregates signals from all indicators and provides a simple trading score.
This tool is designed for discretionary traders looking for a quick, color-coded insight into current market conditions without relying on a single signal.
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.
SMC Pro - Smart Money Concepts🎯 SMC Pro - Complete Smart Money Concepts Trading System with Trade Alerts
The Most Comprehensive SMC/ICT Indicator Built for Real Traders
After extensive research into what retail and prop firm traders actually need, I've created SMC Pro - a complete Smart Money Concepts indicator that solves the biggest problems with existing SMC tools.
🚀 What Makes This Different:
✅ COMPLETE TRADE SETUP ALERTS - Not just structure breaks! Get full trade setups with:
* Entry, Stop Loss & Target Prices
* Risk/Reward Calculations
* 5-Point Confluence Scoring
* Visual Trade Labels on Chart
✅ INTELLIGENT FILTERING - No more chart spam:
* Minimum structure size filter (ATR-based)
* Minimum bars between signals
* Volume confirmation for order blocks
* Clean, actionable signals only
📊 Core Features:
1. Market Structure Analysis
* Break of Structure (BOS) with smart filtering
* Change of Character (CHoCH) detection
* Clear directional bias identification
* Prevents excessive signal clustering
2. Order Blocks
* Volume-confirmed institutional zones
* Automatic mitigation tracking
* Entry points for trade setups
3. Fair Value Gaps
* ATR-based size filtering
* Automatic fill detection
* Confluence factor for trades
4. Liquidity Zones
* Buy-side & Sell-side liquidity mapping
* Sweep detection with alerts
* Target zones for trades
5. Risk Management Integration
* Automatic R:R calculation
* Position sizing guidance
* Minimum R:R filtering (default 2:1)
🎯 5-Point Trade Confluence System:
1. Market structure alignment
2. Recent structure break (BOS/CHoCH)
3. Order block at current price
4. Fair value gap support
5. Liquidity target available
Minimum score of 3/5 required for trade alerts (adjustable)
⚙️ Smart Settings:
* Swing Length: 10 (default) - adjust for sensitivity
* Min Bars Between Signals: 20 - prevents clustering
* Min Structure Size: 1.0 ATR - filters noise
* Min Confluence Score: 3/5 - quality control
* Target R:R: 2:1 minimum - proper risk management
📱 Alert Types:
* 🎯 Trade Setup Alerts - Complete entry/exit plans
* ✅ Structure Breaks - BOS & CHoCH notifications
* 📊 Order Block Touch - Price at key zones
* 💧 Liquidity Sweeps - Stop hunts detected
💡 Pro Tips:
* Start with default settings
* Use on 15m+ timeframes for cleaner signals
* Increase confluence requirement for prop firm trading
* Enable volume confirmation for higher quality OBs
* Dashboard shows real-time setup status
🔧 If You Get Too Many Signals:
* Increase Swing Length to 15-20
* Increase Min Bars Between to 30-50
* Increase Min Structure Size to 1.5 ATR
* Raise Min Confluence Score to 4 or 5
This indicator is the result of solving real problems traders face with SMC/ICT concepts. It's designed to give you clean, actionable trade setups - not just mark up your chart with zones.
Built with Pine Script v6 for maximum performance and reliability.
Trade with confluence. Trade with confidence. Trade smart. 🎯
EPS, Revenue & NPM Growth Marker v2This indicator visually tracks fundamental growth by plotting smart labels on earnings dates, showing three key metrics:
EPS (Earnings Per Share)
Revenue (Sales)
Net Profit Margin (NPM)
It overlays compact, readable labels on the chart allowing you to evaluate a company's financial performance — both sequentially (QoQ) and annually (YoY).
EPS, Revenue & NPM Growth Marker v2 uses both official earnings data and fallback logic (EPS/Revenue/NPM changes) to ensure consistent detection of earnings events.
⭐ Key Features
Three Metrics: Displays EPS, Revenue, and Net Profit Margin
Growth Breakdown: Shows Absolute, Quarter-over-Quarter %, and Year-over-Year % change
Smart Arrows: Trend direction shown with emoji arrows (🡩🡫)
Earnings Detection: Aligns labels to official earnings events or fallback data
Highly Customizable: Full control over visible metrics, growth types, label style, placement, and max label count
Clean Display: Keeps the chart clean with a limit on max labels
⚙️ Customization Options
You can personalize the indicator to match your charting style:
Select metrics to display: EPS, Revenue, NPM
Toggle row types: Absolute, QoQ %, YoY %
Choose label position: Above Bar, Below Bar, Top, or Bottom
Customize line style, colors, and label text color
Set the maximum number of labels shown on chart
🧠How It Works
Absolute Value: Latest reported figure for each metric (e.g., EPS = ₹3.20)
QoQ % Change: (Current - Previous Quarter) / |Previous| * 100
YoY % Change: (Current - Same Quarter Last Year) / |Previous Year| * 100
The script uses TradingView’s request.earnings() data when available. If official earnings data is missing, it intelligently detects earnings events based on changes in EPS, Revenue, or NPM figures.
This is a major update to EPS & Sales/Revenue Growth Marker script, with expanded metrics, enhanced logic, and greater customization.
Developed & Published by: @learningvitals
MTPI SOL | JeffreyTimmermansMedium-Term Trend Probability Indicator
The "Medium-Term Trend Probability Indicator" on SOL (Solana) is a custom-built tool designed to analyze Solana (SOL) from a medium-term perspective. Unlike short-term indicators that react quickly to intraday volatility or long-term models that focus on macro cycles, the MTPI is optimized to detect medium-term market trends—capturing key turning points and momentum shifts that unfold over multiple weeks.
This version of the MTPI is applied to SOL, making it a Solana-specific trend-following tool with particular sensitivity to its price behavior and structural dynamics.
Key Features
Medium-Term Focus:
Built to monitor price action over several weeks to months, the MTPI filters out short-term noise while remaining responsive to meaningful trend changes.
8 Input Signals:
The MTPI aggregates 8 carefully selected trend-following inputs, each tuned to reflect mid-cycle behavior in SOL’s price movements.
Market Regimes:
The MTPI classifies market behavior into one of three clear regimes:
Bullish → Momentum and structure align to support a continued uptrend
Bearish → Majority of signals point to trend deterioration or downside momentum
Neutral → Mixed signals, often during consolidation or early transition phases
Visual Background:
The background color shifts dynamically to reflect the active regime—making it easy to visually interpret the prevailing market condition.
Comprehensive Dashboard:
The lower panel displays:
The state of each individual input (Bullish, Bearish, Neutral)
The numerical MTPI Score (average of all 8 signals)
The final Trend Classification (Bullish / Bearish / Neutral)
How It Works
Input Analysis:
Each of the 8 inputs outputs a score based on its internal signal:
+1 = Bullish condition
-1 = Bearish condition
0 = Neutral / indecisive
Score Calculation:
The MTPI Score is calculated as the average of all 8 input signals:
Score > +0.1 = Bullish regime
Score < -0.1 = Bearish regime
Score between -0.1 and +0.1 = Neutral regime
Background Coloring:
Color-coded backgrounds instantly reflect the current trend classification based on the MTPI Score, helping traders stay aligned with the market direction at all times.
Use Cases
Mid-Term Positioning:
Identify strong trend phases on SOL with reduced noise and increased directional clarity.
Confirmation Layer:
Use MTPI as a mid-term confirmation tool alongside short-term setups or long-term macro models (like LTPI).
Rotation or Transition Detection:
Spot key moments when SOL transitions from expansion to contraction phases (and vice versa).
Dynamic Alerts:
Bullish Signal: MTPI Score crosses above +0.1
Bearish Signal: MTPI Score crosses below -0.1
Neutral Zone: MTPI Score enters between -0.1 and +0.1
Conclusion
The Medium-Term Trend Probability Indicator (MTPI – SOL) provides a powerful framework for identifying trend phases on Solana with mid-term relevance. By combining 8 intelligent inputs into a single score and market classification, it offers clarity in times of uncertainty and confidence in times of momentum. Whether used alone or as part of a broader multi-timeframe strategy, the MTPI helps refine entries, exits, and macro alignment in the dynamic world of Solana trading.
Supply & Demand Pro [Institutional]🎯 Overview
The most comprehensive Supply & Demand indicator on TradingView, designed for serious traders and prop firm professionals. Unlike traditional S&D indicators that just draw pretty zones, this system tracks actual performance metrics, provides entry/exit signals, and includes professional risk management tools.
❓ Why This Indicator?
After extensive research into what traders actually need (not just want), this indicator addresses the TOP complaints about Supply & Demand trading:
- ❌ "I don't know which zones to trust" → ✅ Each zone shows historical win rate
- ❌ "No clear entry/exit rules" → ✅ Multiple entry methods with visual R:R
- ❌ "Can't backtest effectiveness" → ✅ Full performance tracking
- ❌ "Too many false signals" → ✅ Quality filters and volume validation
🚀 Key Features
🎯 Professional Zone Detection
- Volume Profile Analysis (finds institutional accumulation/distribution)
- Swing Point Detection (classic pivot-based zones)
- Order Flow Analysis (coming in v2)
- Hybrid Mode (combines multiple methods)
📊 Performance Analytics
- Individual zone win rates
- Daily P&L tracking
- Account balance simulation
- Success/failure ratio for each zone
- Historical performance data
💼 Prop Firm Tools
- Daily loss limits (auto-stops trading)
- Position sizing controls
- Maximum concurrent positions
- Daily profit targets
- Clean reporting for evaluations
🎨 Entry & Risk Management
- Zone Edge entry (immediate)
- 50% Retracement entry (patient)
- Momentum Confirmation entry
- Visual Risk:Reward boxes
- Multiple stop loss methods (ATR, Fixed %, Zone-based)
📈 Advanced Features
- Auto-removes failed zones
- Volume confirmation requirements
- Strength-based zone ranking
- Smart alerts for high-probability setups
- Multi-timeframe compatibility
📋 How It Works
1. Zone Creation: Continuously scans for high-quality supply/demand zones using your selected method
2. Quality Filtering: Each zone must pass strength, volume, and historical performance filters
3. Visual Feedback: Zones display strength %, test count, and win rate directly on chart
4. Trade Signals: When price touches a zone, the system calculates entry, stop, and target
5. Performance Tracking: Every zone touch is tracked to build historical win rates
⚙️ Quick Settings Guide
For Beginners:
- Detection Method: "Swing Points"
- Min Zone Strength: 15%
- Risk:Reward: 2:1
- Entry Method: "Zone Edge"
For Advanced Traders:
- Detection Method: "Volume Profile"
- Min Zone Strength: 20%
- Min Win Rate: 50%
- Entry Method: "Momentum Confirm"
For Prop Firm Traders:
- Enable all Prop Firm Tools
- Set Daily Loss Limit to your drawdown rules
- Max Positions: 2-3
- Use "Professional" theme for screenshots
📊 What Makes This Different?
Traditional S&D Indicators:
- Draw zones based on one method
- No performance tracking
- No entry/exit rules
- Can't verify effectiveness
Supply & Demand Pro:
- Multiple detection methods
- Tracks win rate for EVERY zone
- Clear entry/exit signals
- Full backtesting capability
- Risk management built-in
🎓 Best Practices
1. Start Conservative: Use higher strength requirements (20%+) until familiar
2. Trust the Data: Zones with 3+ tests and 60%+ win rate are golden
3. Respect Risk Limits: The daily loss limit feature will save your account
4. Volume Matters: Zones with volume confirmation are significantly stronger
5. Be Patient: Wait for high-probability setups (check the win rate!)
🔔 Alert Options
- Zone Touch Alerts (with strength & win rate)
- High Probability Setups (60%+ win rate zones)
- Daily Limit Warnings
- Risk Management Alerts
💡 Pro Tips
- Combine with market structure for best results
- Higher timeframe zones are more reliable
- Watch for zones that align with round numbers
- Use partial profits feature to lock in gains
- Review daily performance to improve
🐛 Troubleshooting
- No zones appearing? → Lower Min Zone Strength to 10%
- Too many zones? → Increase strength requirement or enable filters
- Win rates not updating? → Zones need multiple tests to calculate
⚡ Performance Note
This indicator uses advanced calculations and may take a moment to load on lower-end devices. The comprehensive analytics are worth the wait!
🎁 Bonus Features
- 4 Professional themes
- Customizable dashboard
- R:R visualization
- Zone strength ranking
- Session-based filtering (coming soon)
📧 Support & Updates
This is an actively maintained indicator. Updates include:
- New detection methods
- Enhanced analytics
- Community-requested features
- Performance optimizations
⭐ If you find this indicator helpful, please leave a rating and comment with your results!
📌 Remember: No indicator is perfect. Always use proper risk management and never risk more than you can afford to lose.
Liquidity Hours By HH🚦 Liquidity Hours By HH 🚦
This script highlights the major trading sessions on your chart — Asia, London KTW, and New York KTW — so you always know when the markets are buzzing! 🌏🕒
✨ Asia Session
Shows a colored box marking the entire session 🟣
Tracks the high and low with clear lines 📈📉
Optional midline that you can toggle ON/OFF 🔀 — perfect for spotting the session’s midpoint without cluttering your chart!
✨ London KTW & New York KTW Sessions
Displays clean boxes marking session duration 🟦🟩
No distracting high/low lines — just simple, neat session highlights
⏰ London session starts 1 hour earlier ⏰ — so you get an advanced heads-up for European market action! 🇬🇧
⏳ Boxes automatically hide on higher timeframes for a cleaner look 👀
Customize colors, durations, and toggle what you want to see — your chart, your rules! 🎨⚙️
Stay sharp and trade smarter with clear liquidity session zones! 💹🔥
Futures Risk Contract TableFutures risk table for NQ MNQ YM MYM ES and MES
changeable capital and risk percentage along with points.
Daily High/Low Close Breakout - GOLD### **Daily High/Low Close Breakout Indicator**
This indicator is a powerful tool for identifying potential breakout opportunities based on the previous day's price action. It's built on a unique time-based logic that defines key support and resistance levels for the trading day.
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### **How the Indicator Works**
The indicator operates in two main phases:
1. **Calculation Period (00:00 to 16:30 Tehran Time):** The indicator first observes the price action from the start of the day until 16:30. During this time, it records the highest and lowest **closing prices** of all candles. The chart background is shaded gray to visually mark this period.
2. **Trading Period (16:30 to 16:30 the next day):** At 16:30, the highest and lowest close levels are finalized and drawn as horizontal lines. These levels then become the primary breakout zones for the next 24 hours. The indicator will generate signals whenever the price crosses these lines.
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### **Trading Signals**
The indicator uses a simple and effective crossover logic for its signals:
* **BUY Signal:** A signal is generated when a candle's closing price **crosses above** the high close line.
* **SELL Signal:** A signal is generated when a candle's closing price **crosses below** the low close line.
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### **Important Usage Guidelines**
For optimal performance, please follow these specific recommendations:
* **Timeframe:** This indicator is designed and optimized to be used exclusively on the **15-minute timeframe**. Using it on other timeframes may produce inconsistent or unreliable results.
* **Primary Asset:** The logic for this indicator was developed and backtested primarily for **Gold (XAUUSD)**. Its performance and win rate have been observed to be the most consistent on this asset.
* **Asset Restriction:** It is strongly recommended to **avoid using this indicator on other currency pairs or assets**, as it has not been optimized for their specific market behavior.
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### **Disclaimer**
*This indicator is provided for informational and educational purposes only. It is not financial advice. Past performance is not a guarantee of future results. All trading decisions should be based on your own research and risk analysis. Always use proper risk management.*
GMMG CCM SYSTEM HALMACCI INDICATOR BY KUYA NICKOOVERVIEW:
This script is about HALMACCI strategy based on Coach Miranda Miner System (CMM Systems of GMMG). It's an indicator to help traders decide when to enter and exit. This indicator uses Bollinger Band, EMA and ALMA with the length settings used by GMMG.
USAGE:
Apply the indicator to any chart. Best use in lower timeframes (Ex: 5m and 1m). You may use custom length settings but I suggest to stick with the default settings if you are using CMM System.
To enter LONG, If the CCI cross over -100 (shows a green dot when dot is enabled in style) and the EMA cross above ALMA (shows a green cross when cross is enabled in style). You may enter long. Strong confluence when it happens above the Bollinger Band and the candle closed above the Bollinger Band. You may exit when the CCI cross under -100 or immediate resistance.
To enter SHORT, If the CCI cross under 100 (shows a red dot when dot is enabled in style) and the EMA cross above ALMA (shows a red cross when cross is enabled in style). You may enter short. Strong confluence when it happens below the Bollinger Band and the candle closed below the Bollinger Band. You may exit when the CCI cross over 100 or immediate support.
Use may use alerts to catch breakout events so you would not need to monitor the chart continuously
M2 Global Liquidity Index [Extended + Empirical BTC Offset]M2 Global Liquidity Index
This script visualizes global M2 liquidity based on major economic zones (USA, China, Eurozone, Japan, UK), with the option to include extended countries such as Switzerland, Canada, India, Russia, Brazil, South Korea, Mexico, and South Africa.
The indicator includes an empirically derived offset to reflect how Bitcoin historically reacts with a time lag—typically around 12 weeks—after shifts in global liquidity.
Features:
Predefined empirical offset options ranging from 12 to 120 days
Automatic offset adjustment when applied to the weekly chart
Optional inclusion of extended global M2 sources
Important:
This indicator is intended only for use on the weekly chart. It provides meaningful and accurate results exclusively in this time frame, due to the nature of the offset-based correlation logic.
Use cases:
Macro-level analysis of Bitcoin’s price movements
Identifying early signs of potential market tops or bottoms in relation to liquidity flows
SwingSignal RSI Overlay AdvancedSwingSignal RSI Overlay Advanced
By BFAS
This advanced indicator leverages the Relative Strength Index (RSI) to pinpoint critical market reversal points by highlighting key swing levels with intuitive visual markers.
Key Features:
Detects overbought and oversold levels with customizable RSI period and threshold settings.
Visually marks swing points:
Red star (HH) for Higher Highs.
Yellow star (LH) for Lower Highs.
Blue star (HL) for Higher Lows.
Green star (LL) for Lower Lows.
Connects swings with lines, aiding in the analysis of market structure.
Optimized for use on the main chart (overlay), tracking candles in real time.
This indicator provides robust visual support for traders aiming to identify price patterns related to RSI momentum, facilitating entry and exit decisions based on clear swing signals.
US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation.
Introduction and Motivation
Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment.
The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data.
Theoretical Framework and Methodology
Composite Index Construction
The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures.
The mathematical formulation of the composite index follows:
USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t)
Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011).
Weighting Methodology
The weighting scheme incorporates findings from economic research:
Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points.
Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019).
Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004).
Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology).
Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000).
Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology.
Data Normalisation and Scaling
Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows:
Normalized_i,t = (R_i,t / 252) × 100
Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution.
Implementation and Technical Architecture
The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation.
User Interface Design and Customization Framework
The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments.
Visual Theme System
The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions.
Glow Effect Implementation
The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability.
Table Display Architecture
The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability.
The table displays twenty economic indicators with the following information architecture:
- Category classification for cognitive grouping
- Indicator names with standard economic nomenclature
- Current values with intelligent number formatting
- Percentage change calculations with directional indicators
- Cross-asset market implications using standardised notation
- Risk assessment using three-tier classification (HIGH/MED/LOW)
- Data update timestamps for temporal reference
Index Customisation Parameters
The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices.
Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification.
Cross-Asset Market Implications
The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012).
The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation:
Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions.
Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications.
Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators.
Aggregated Market Impact Analysis
The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes.
Alert System Architecture
The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes:
Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period.
Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation.
Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability.
Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration.
Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods.
Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions.
Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points.
Performance Optimization and Technical Implementation
The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications.
Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration.
Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016).
Risk Assessment Framework
Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns.
Empirical Validation and Performance
Business Cycle Correspondence
Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods.
The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications.
Cross-Asset Predictive Ability
The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements.
These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation.
Practical Applications and Use Cases
Institutional Asset Allocation
The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction.
The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies.
Risk Management Applications
The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment.
The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions.
Economic Research Applications
The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions.
The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations.
Enhanced User Experience and Operational Features
The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments.
Multi-Screen and Multi-User Adaptability
The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings.
Cognitive Load Management
The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006).
Real-Time Decision Support
The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods.
Professional Alert Management
The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels.
Data Quality and Reliability Features
The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity.
Customisation for Institutional Workflows
The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances.
Limitations and Constraints
Data Dependency
The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications.
Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases.
Structural Relationship Stability
The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights.
The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies.
Frequency Limitations
Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies.
The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources.
Future Research Directions
Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach.
The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors.
Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications.
Conclusion
The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications.
The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation.
The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools.
References
Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417-427.
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