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Professional GBP/JPY Analysis ToolThe foundation of professional trading begins with analyzing individual currencies first, not just currency pairs. By understanding the relative strength of each currency in the pair, traders can anticipate potential market moves with greater accuracy.
This indicator simplifies that process by:
Analyzing Individual Currency Strength:
The strength of GBP is calculated by averaging its performance across seven major GBP currency pairs:
GBP/EUR
GBP/USD
GBP/CAD
GBP/CHF
GBP/AUD
GBP/NZD
GBP/JPY
The strength of JPY is calculated by averaging its performance across seven major JPY currency pairs:
JPY/USD
JPY/CAD
JPY/EUR
JPY/GBP
JPY/AUD
JPY/NZD
JPY/CHF
The values are normalized to allow direct comparison on the same scale.
Identifying Correlation Between GBP and JPY:
The histogram displays the correlation between GBP and JPY strength:
Positive Correlation (Green): Both GBP and JPY are trending up or down together, indicating a less strong trend. This is a market condition to avoid, as both currencies are strengthening or weakening simultaneously.
Negative Correlation (Red): One currency is strong while the other is weak, indicating a stronger trend in GBP/JPY. This scenario presents a better trading opportunity, as you are trading one strong currency against one weak currency, amplifying the potential for a clearer price movement in GBP/JPY.
Visualizing Long/Short Bias:
GBP Strength > JPY Strength: Bullish bias for GBP/JPY (green background).
JPY Strength > GBP Strength: Bearish bias for GBP/JPY (red background).
This indicator equips traders with a deeper understanding of GBP/JPY dynamics by first breaking down the individual currencies. With insights into currency strength, their correlation, and the optimal conditions for trading, it provides a solid foundation for making informed trading decisions.
How to Use:
Check the Histogram for Correlation:
Wait for the histogram to be red. This indicates that GBP and JPY are moving in opposite directions, signaling a stronger trend where you're trading a strong currency against a weak one—a more favorable setup.
Align with Background Color for Confirmation:
Wait for the background color to match your trade plan:
Green Background: Confirms a bullish bias, supporting long positions on the GBP/JPY pair.
Red Background: Confirms a bearish bias, supporting short positions on the GBP/JPY pair.
By following these steps, you can identify stronger trade opportunities and align them with your strategy.
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.
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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.
Dual Custom Index with SpreadDual Custom Index with Spread
Create powerful custom indices from any instruments and analyze their relative strength dynamics
Overview
This advanced indicator allows you to build two completely customizable indices from your choice of instruments and analyze their spread relationship. Perfect for inter-market analysis, sector rotation strategies, currency strength comparisons, and sophisticated relative performance studies.
Key Features
🔧 Fully Customizable Index Construction
Build each index from up to 6 instruments with individual weightings
Enable/disable instruments on the fly without losing settings
Automatic weight validation ensures mathematically accurate calculations
Invert functionality for instruments that move opposite to index strength
📊 Advanced ADX-Based Methodology
Uses sophisticated ADX +DI/-DI directional bias calculations
Normalized bias calculation for consistent scaling across different instruments
Optimized default settings for intraday trading with full customization options
Professional-grade smoothing and filtering options
📈 Dual Analysis Modes
Difference Mode: Shows absolute strength difference (Index1 - Index2)
Ratio Mode: Shows relative performance ratio (Index1 / Index2)
Additional spread smoothing for cleaner signals
🎨 Professional Display Options
Custom labels with full color, size, and positioning control
Dynamic "Follow Line" labels that move with your data
Static corner positioning for reference displays
Clean error messaging and validation feedback
Use Cases
Gold Trading: Create gold strength vs USD strength indices for precise market timing
Sector Analysis: Compare technology vs financial sector strength for rotation strategies
Currency Strength: Build custom currency baskets for advanced forex analysis
Commodity Spreads: Analyze relative strength between different commodity groups
Regional Markets: Compare strength between different geographical market indices
Crypto Analysis: Track relative performance between different cryptocurrency sectors
Technical Specifications
Instruments per Index: Up to 6 with individual enable/disable
Weight Validation: Automatic 100% total weight enforcement
Calculation Method: ADX-based directional bias with trend strength weighting
Smoothing Options: Multiple levels of customizable smoothing
Error Handling: Professional validation with clear user feedback
Optimization Tips
Intraday Trading: Use DI Length 3-7 for faster response
Daily Analysis: Use DI Length 10-14 for smoother signals
Noisy Markets: Increase Final Smoothing for cleaner signals
Trending Markets: Lower smoothing values for faster reaction
Perfect for traders who need sophisticated inter-market analysis tools beyond standard indicators. Whether you're analyzing gold vs dollar dynamics, sector rotation opportunities, or custom currency strength relationships, this indicator provides institutional-grade analysis capabilities with complete customization flexibility.
Session-Based Sentiment Oscillator [TradeDots]Track, analyze, and monitor market sentiment across global trading sessions with this advanced multi-session sentiment analysis tool. This script provides session-specific sentiment readings for Asian (Tokyo), European (London), and US (New York) markets, combining price action, volume analysis, and volatility factors into a comprehensive sentiment oscillator. It is an original indicator designed to help traders understand regional market psychology and capitalize on cross-session sentiment shifts directly on TradingView.
📝 HOW IT WORKS
1. Multi-Component Sentiment Engine
Price Action Momentum : Calculates normalized price movement relative to recent trading ranges, providing directional sentiment readings.
Volume-Weighted Analysis : When volume data is available, incorporates volume flow direction to validate price-based sentiment signals.
Volatility-Adjusted Factors : Accounts for changing market volatility conditions by comparing current ATR against historical averages.
Weighted Combination : Merges all components using optimized weightings (Price: 1.0, Volume: 0.3, Volatility: 0.2) for balanced sentiment readings.
2. Session-Segregated Tracking
Automatic Session Detection : Precisely identifies active trading sessions based on user-configured time parameters.
Independent Calculations : Maintains separate sentiment accumulation for each major session, updated only during respective active hours.
Historical Preservation : Stores session-specific sentiment values even when sessions are closed, enabling cross-session comparison.
Real-Time Updates : Continuously processes sentiment during active sessions while preserving inactive session data.
3. Cross-Session Transition Analysis
Sentiment Differential Detection : Monitors sentiment changes when transitioning between trading sessions.
Configurable Thresholds : Generates signals only when sentiment shifts exceed user-defined minimum thresholds.
Directional Signals : Provides distinct bullish and bearish transition alerts with visual markers.
Smart Filtering : Applies smoothing algorithms to reduce false signals from minor sentiment variations.
⚙️ KEY FEATURES
1. Session-Specific Dashboard
Real-Time Status Display : Shows current session activity (ACTIVE/CLOSED) for all three major sessions.
Sentiment Percentages : Displays precise sentiment readings as percentages for easy interpretation.
Strength Classification : Automatically categorizes sentiment as HIGH (>50%), MEDIUM (20-50%), or LOW (<20%).
Customizable Positioning : Place dashboard in any corner with adjustable size options.
2. Advanced Signal Generation
Transition Alerts : Triangle markers indicate significant sentiment shifts between sessions.
Extreme Conditions : Diamond markers highlight overbought/oversold threshold breaches.
Configurable Sensitivity : Adjust signal thresholds from 0.05 to 0.50 based on trading style.
Alert Integration : Built-in TradingView alert conditions for automated notifications.
3. Forex Currency Strength Analysis
Base/Quote Decomposition : For forex pairs, separates sentiment into individual currency strength components.
Major Currency Support : Analyzes USD, EUR, GBP, JPY, CHF, CAD, AUD, NZD strength relationships.
Relative Strength Display : Shows which currency is driving pair movement during active sessions.
4. Visual Enhancement System
Session Background Colors : Distinct background shading for each active trading session.
Overbought/Oversold Zones : Configurable extreme sentiment level visualization with colored zones.
Multi-Timeframe Compatibility : Works across all timeframes while maintaining session accuracy.
Customizable Color Schemes : Full color customization for dashboard, signals, and plot elements.
🚀 HOW TO USE IT
1. Add the Script
Search for "Session-Based Sentiment Oscillator " in the Indicators tab or manually add it to your chart. The indicator will appear in a separate pane below your main chart.
2. Configure Session Times
Asian Session : Set Tokyo market hours (default: 00:00-09:00) based on your chart timezone.
European Session : Configure London market hours (default: 07:00-16:00) for European analysis.
US Session : Define New York market hours (default: 13:00-22:00) for American markets.
Timezone Adjustment : Ensure session times match your broker's specifications and account for daylight saving changes.
3. Optimize Analysis Parameters
Sentiment Period : Choose 5-50 bars (default: 14) for sentiment calculation lookback period.
Smoothing Settings : Select 1-10 bars smoothing (default: 3) with SMA, EMA, or RMA options.
Component Selection : Enable/disable volume analysis, price action, and volatility factors based on available data.
Signal Sensitivity : Adjust threshold from 0.05-0.50 (default: 0.15) for transition signal generation.
4. Interpret Readings and Signals
Positive Values : Indicate bullish sentiment for the active session.
Negative Values : Suggest bearish sentiment conditions.
Dashboard Status : Monitor which session is currently active and their respective sentiment strengths.
Transition Signals : Watch for triangle markers indicating significant cross-session sentiment changes.
Extreme Alerts : Note diamond markers when sentiment reaches overbought (>70%) or oversold (<-70%) levels.
5. Set Up Alerts
Configure TradingView alerts for:
- Bullish session transitions
- Bearish session transitions
- Overbought condition alerts
- Oversold condition alerts
❗️LIMITATIONS
1. Data Dependency
Volume Requirements : Volume-based analysis only functions when volume data is provided by your broker. Many forex brokers do not supply reliable volume data.
Price Action Focus : In absence of volume data, sentiment calculations rely primarily on price movement and volatility factors.
2. Session Time Sensitivity
Manual Adjustment Required : Session times must be manually updated for daylight saving time changes.
Broker Variations : Different brokers may have slightly different session definitions requiring time parameter adjustments.
3. Ranging Market Limitations
Trend Bias : Sentiment calculations may be less reliable during extended sideways or low-volatility market conditions.
Lag Consideration : As with all sentiment indicators, readings may lag during rapid market transitions.
4. Regional Market Focus
Major Session Coverage : Designed primarily for major global sessions; may not capture sentiment from smaller regional markets.
Weekend Gaps : Does not account for weekend gap effects on sentiment calculations.
⚠️ RISK DISCLAIMER
Trading and investing carry significant risk and can result in financial loss. The "Session-Based Sentiment Oscillator " is provided for informational and educational purposes only. It does not constitute financial advice.
- Always conduct your own research and analysis
- Use proper risk management and position sizing in all trades
- Past sentiment patterns do not guarantee future market behavior
- Combine this indicator with other technical and fundamental analysis tools
- Consider overall market context and your personal risk tolerance
This script is an original creation by TradeDots, published under the Mozilla Public License 2.0.
Session-based sentiment analysis should be used as part of a comprehensive trading strategy. No single indicator can predict market movements with certainty. Exercise proper risk management and maintain realistic expectations about indicator performance across varying market conditions.
GBP Index vs CAD Index Currency OscillatorGBP vs CAD Currency Oscillator
This custom oscillator compares the relative strength of GBP (British Pound) and CAD (Canadian Dollar) against a basket of other currencies to determine potential overbought and oversold conditions. The indicator is designed to help traders evaluate momentum shifts and identify possible trend reversals between these two currencies, not just the GBPCAD pair.
How it Works:
Currency Index Calculation:
The oscillator calculates the average percentage change in 7 key GBP pairs (GBPUSD, EURGBP, GBPJPY, GBPAUD, GBPNZD, GBPCAD, and GBPCHF).
Similarly, it calculates the average percentage change for 7 key CAD pairs (USDCAD, EURCAD, CADJPY, AUDCAD, NZDCAD, GBPCAD, and CADCHF).
Stochastic Oscillator:
The indicator calculates a 0-100 oscillator for both the GBP and CAD currency indices based on the highest high and lowest low over a user-defined lookback period (default is 14 anlthough 60 works great on 1m chart).
The oscillator is smoothed using a simple moving average (default smoothing period is 3) to reduce noise and improve visual clarity.
Overbought/Oversold Conditions:
Overbought: When both the GBP and CAD oscillators exceed 80, the background turns red, indicating potential overbought conditions.
Oversold: When both oscillators fall below 20, the background turns green, signaling possible oversold conditions.
Crossovers:
When the GBP oscillator crosses above the CAD oscillator, a green dot appears at the bottom of the chart, signaling potential GBP strength.
When the GBP oscillator crosses below the CAD oscillator, a red dot appears, signaling potential CAD strength.
How to Use:
Overbought/Oversold Conditions: Use the red and green background highlights to spot potential overbought or oversold market conditions, helping you identify possible turning points.
Customization Options:
Lookback Period: You can adjust the lookback period for the stochastic calculation, allowing for sensitivity tuning (default: 14).
Smoothing Period: Control the degree of smoothing applied to the oscillators (default: 3).
This oscillator is ideal for traders focused on trading GBP and CAD pairs, offering a comparative analysis that can assist in better decision-making based on relative currency strength.
10-Year Yields Table for Major CurrenciesThe "10-Year Yields Table for Major Currencies" indicator provides a visual representation of the 10-year government bond yields for several major global economies, alongside their corresponding Rate of Change (ROC) values. This indicator is designed to help traders and analysts monitor the yields of key currencies—such as the US Dollar (USD), British Pound (GBP), Japanese Yen (JPY), and others—on a daily timeframe. The 10-year yield is a crucial economic indicator, often used to gauge investor sentiment, inflation expectations, and the overall health of a country's economy (Higgins, 2021).
Key Components:
10-Year Government Bond Yields: The indicator displays the daily closing values of 10-year government bond yields for major economies. These yields represent the return on investment for holding government bonds with a 10-year maturity and are often considered a benchmark for long-term interest rates. A rise in bond yields generally indicates that investors expect higher inflation and/or interest rates, while falling yields may signal deflationary pressures or lower expectations for future economic growth (Aizenman & Marion, 2020).
Rate of Change (ROC): The ROC for each bond yield is calculated using the formula:
ROC=Current Yield−Previous YieldPrevious Yield×100
ROC=Previous YieldCurrent Yield−Previous Yield×100
This percentage change over a one-day period helps to identify the momentum or trend of the bond yields. A positive ROC indicates an increase in yields, often linked to expectations of stronger economic performance or rising inflation, while a negative ROC suggests a decrease in yields, which could signal concerns about economic slowdown or deflation (Valls et al., 2019).
Table Format: The indicator presents the 10-year yields and their corresponding ROC values in a table format for easy comparison. The table is color-coded to differentiate between countries, enhancing readability. This structure is designed to provide a quick snapshot of global yield trends, aiding decision-making in currency and bond market strategies.
Plotting Yield Trends: In addition to the table, the indicator plots the 10-year yields as lines on the chart, allowing for immediate visual reference of yield movements across different currencies. The plotted lines provide a dynamic view of the yield curve, which is a vital tool for economic analysis and forecasting (Campbell et al., 2017).
Applications:
This indicator is particularly useful for currency traders, bond investors, and economic analysts who need to monitor the relationship between bond yields and currency strength. The 10-year yield can be a leading indicator of economic health and interest rate expectations, which often impact currency valuations. For instance, higher yields in the US tend to attract foreign investment, strengthening the USD, while declining yields in the Eurozone might signal economic weakness, leading to a depreciating Euro.
Conclusion:
The "10-Year Yields Table for Major Currencies" indicator combines essential economic data—10-year government bond yields and their rate of change—into a single, accessible tool. By tracking these yields, traders can better understand global economic trends, anticipate currency movements, and refine their trading strategies.
References:
Aizenman, J., & Marion, N. (2020). The High-Frequency Data of Global Bond Markets: An Analysis of Bond Yields. Journal of International Economics, 115, 26-45.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2017). The Econometrics of Financial Markets. Princeton University Press.
Higgins, M. (2021). Macroeconomic Analysis: Bond Markets and Inflation. Harvard Business Review, 99(5), 45-60.
Valls, A., Ferreira, M., & Lopes, M. (2019). Understanding Yield Curves and Economic Indicators. Financial Markets Review, 32(4), 72-91.
US Market Real Value Adjusted for CPI and Dollar IndexUS Market Real Value Adjusted for CPI and Dollar Index
Provides quick access to this formula: (SP:SPX+NASDAQ_DLY:IXIC+TVC:DJI+CAPITALCOM:RTY)/4/(ECONOMICS:USCPI*TVC:DXY*100)
Overview:
This indicator provides a dynamic view of the US stock market's real value, adjusted for inflation and currency strength. It combines major stock indices including the S&P 500, NASDAQ, Dow Jones, and Russell 2000, and adjusts the composite index using the US Consumer Price Index (CPI) and the US Dollar Index (DXY). This adjustment helps to reveal the true market performance, stripped of inflationary effects and currency valuation changes.
Key Features:
Composite Index Calculation: Averages the prices of SPX, IXIC, DJI, and RTY to create a broad market overview.
Inflation Adjustment: Uses the CPI to adjust for the effects of inflation, ensuring that the real value changes in the stock market are highlighted.
Currency Strength Adjustment: Applies the DXY to account for fluctuations in the strength of the US dollar, providing insights into how currency variations impact market valuation.
Dynamic Base Calculation: Utilizes a rolling window to dynamically update base values, allowing for continuous reassessment of the market’s adjusted value as new data becomes available.
This indicator provides:
Real Value Insights: By adjusting for both inflation and currency strength, this indicator offers a more accurate measure of the underlying market conditions.
Dynamic Updates: With a rolling window approach, the indicator continually adapts, providing up-to-date information.
Strategic Decisions: Helps in identifying true market growth or decline periods, aiding in strategic investment planning.
Usage:
To use this indicator, simply add it to your chart, and it will automatically display the adjusted composite index. This index can be particularly useful for investors looking to understand underlying market trends beyond nominal price movements, helping in making more informed investment decisions when comparing certain tickers to an average of the major US stock market indexes, adjusted for inflation and the strength of the US dollar.
Example Use Case:
A typical use case might involve comparing periods of high inflation to see how the overall US stock market performed in real terms, not just nominal terms. This can indicate whether the market growth was genuine or merely a reflection of inflation. By comparing this result to an average of these major indexes without adjusting for inflation or currency strength changes, you can see how significantly these forces can impact real gains or losses.
Currency Group Stochastic (Dual Timeframe)
This is a stochastic for an entire currency group (majors and crosses). So if you are wondering whether the entire group will reverse this might help. For example, if you are think the USD group will roll over you can see an amalgamated stochastic of AUDUSD, NZDUSD, USDJPY, USDCHF, EURUSD, GBPUSD, USDCAD (average stochastic of all of them). The concept is that it might give help to identify 2 opposing currencies - an overbought currency verses an oversold currency.
Also, if your 'classic' instrument specific stochastic is showing an entry, does the the entire currency group agree?
There's more! You can also see the stochastic of the timeframe above on the current timeframe. You're current period stochastic tells you you've an entry and the stochastic from the timeframe above can indicate there is momentum in your direction. (There is a classic stochastic version of this on my profile)
There is a limit to how much I can fit into a single indicator so if you want to see the current and timeframe above together (recommended) you need to overlap the indicator on itself. See below
You can create a dashboard combined with 'currency relative strengths' (that indicator is on my profile) as per below. You now have an idea of the currency strengths, which currencies are correlating and potential turning point to help you decide which currencies to focus on...
Example...
gbp group COULD be ready to buy
chf group COULD be ready to sell
gbpchf - wait for the 3 min chart to roll over and an its not a bad call (considering it took 60 secs to review the market and choose an entry with the possible backing of the entire currency groups :o) )
REMEMBER, YOU CAN'T THIS TRADE FROM THIS INDICATOR. LOOK AT IT TO UNDERSTAND WHAT THE MARKET MIGHT BE DOING AND FOCUS YOUR DETAILED ATTENTION BASED ON YOUR CONCLUSION.
Good luck
Global Inflation Indicator🔹 Overview:
The Global Inflation Indicator is a macro-analysis tool designed to track and compare inflation trends across major economies. It pulls Consumer Price Index (CPI) data from multiple regions, helping traders and investors analyze how inflation impacts global markets, particularly gold, forex, and commodities.
📊 Key Features:
✅ Tracks inflation in six major economies:
🇺🇸 USA (CPIAUCSL) – Key driver for USD and gold prices
🇪🇺 Eurozone (CPHPTT01EZM659N) – Euro inflation impact
🇬🇧 United Kingdom (GBRCPIALLMINMEI) – GBP & economic trends
🇨🇳 China (CHNCPIALLMINMEI) – Emerging market impact
🇯🇵 Japan (JPNCPIALLMINMEI) – Yen & inflation control policies
🇮🇳 India (INDCPIALLMINMEI) – Key gold-consuming economy
✅ Real-time Inflation Trends:
Provides a visual comparison of inflation levels in different regions.
Helps traders identify inflationary cycles & their effect on global assets.
✅ Macro-Driven Trading Decisions:
Gold & Forex Correlation: High inflation may increase demand for gold.
Interest Rate Expectations: Central banks respond to inflation shifts.
Currency Strength: Inflation impacts USD, EUR, GBP, JPY, CNY, INR.
📉 How to Use It:
Gold traders can assess inflation trends to predict potential price movements.
Forex traders can compare inflation effects on major currency pairs (EUR/USD, USD/JPY, GBP/USD, etc.).
Stock investors can evaluate how inflation affects central bank policies and interest rates.
📌 Conclusion:
The Global Inflation Indicator is a powerful tool for macroeconomic analysis, providing real-time insights into global inflation trends. By integrating this indicator into your gold, forex, and commodity trading strategies, you can make more informed investment decisions in response to economic changes.
Weekly currency strength indicatorThe indicator uses the SAXO feed for the currencies USD, EUR, GBP, JPY, AUD and CAD. This can easily be changed to your preferred feed and currencies by changing the code.
The overall idea is to get a clear picture of which currencies are strengthening and weakening. This indicator does not predict future price movements.
Session Range and Breakout Summary
This script presents the session range and post session movements relative to that range of all the majors and crosses on a single page. You can also set it to a daily range and weekly range (beta). It will even show you the pip value of the range. I made the indicator to easily stay on top of market movements at london open relative to the Asia session range. Its very easy to see which entire currency group is breaking its asia range WHIST ITS HAPPENING. Focus on NZD in the examples as it was the market lead today - I was able to get some of it when I saw the entire group breaking its range
Showing all the majors and crosses relative to the Asia range (00:00 - 07:00 GMT)
Active 'show on chart' to verify the indicator is measuring the range correctly. Compare below to the NZD box above - you can see how NZD had control of the market this morning and all NZD pairs broke out of their ranges.
'PIP MODE' - active pip mode to see what the pip range was of the session
Notes
The information is presented RELATIVELY - this means that all the ranges and movements are scaled to be the same size. You are therefore seeing the movements relative to their ranges. When you see a breakout it relative to the size of the range - for example, if GBPJPY had a range of 50pips and breaks out of the range by 100 pip and GBPEUR has a range of 20 pips and breaks out by 40 pips they have both broken out double the range and will be displayed as the same distance.
The indicator will show the movements whilst the range is forming. I did this so I can see what the groups are doing before Europe open and be ready - such as lingering at the top end of its INCOMPLETE asia range. Be aware through that if the lines are flat at the top of the range WHILST THE RANGE IS STILL FORMING this does not mean price was flat, it means that price was pushing up and growing the range. (Price can't breakout until the range has formed at the end of the session)
The currency pairs are organised to show the strength or weakness of the selected group - this means that the base currency is always the select group. This is to present the data with currencies moving in the same direction rather than some reversed but meaning the same in relation to currency strength. In the NZD example:
NZDAUD (not AUDNZD )
NZDCAD
NZDCHF
NZDEUR (not EURNZD )
NZDGBP (not GBPNZD )
NZDJPY
NZDUSD
I hope its useful. This is the most powerful indicator I've managed to write yet. It was difficult to make the code efficient enough to fit into the pinescript limit and still do everything.
Cryptocurrency StrengthMulti-Currency Analysis: Monitor up to 19 different currencies simultaneously, including major pairs like USD, EUR, JPY, and GBP, as well as emerging market currencies such as CNY, INR, and BRL.
Customizable Display: Easily toggle the visibility of each currency and personalize their colors to suit your preferences, allowing for a tailored analysis experience.
Real-Time Strength Measurement: The indicator calculates and displays the relative strength of each currency in real-time, helping you identify potential trends and trading opportunities.
Clear Visual Representation: With color-coded lines and a dynamic legend, the indicator presents complex currency relationships in an easy-to-understand format.
Advantages
Comprehensive Market View: Gain insights into the broader forex market dynamics by analyzing multiple currencies at once.
Trend Identification: Quickly spot strong and weak currencies, aiding in the identification of potential trending pairs.
Divergence Detection: Use the indicator to identify divergences between currency strength and price action, potentially signaling reversals or continuation patterns.
Flexible Time Frames: Apply the indicator across various time frames to align with your trading strategy, from intraday to long-term analysis.
Enhanced Decision Making: Make more informed trading decisions by understanding the relative strength of currencies involved in your trades.
Unique Qualities
TSI-Based Calculations: Utilizes the True Strength Index for a more nuanced and responsive measure of currency strength compared to simple price-based indicators.
Adaptive Legend: The indicator features a dynamic legend that updates automatically based on the selected currencies, ensuring a clutter-free and relevant display.
Emerging Market Inclusion: Unlike many standard currency strength indicators, this tool includes a wide range of emerging market currencies, providing a truly global perspective.
Whether you're a seasoned forex trader or just starting out, this Currency Strength Indicator offers valuable insights that can complement your existing strategy and potentially improve your trading outcomes. Its combination of comprehensive analysis, customization options, and clear visualization makes it an essential tool for navigating the complex world of currency trading.
Forex RadarForex Radar Indicator: A Powerful Tool for Analyzing Currency Strength and Weakness
Introduction
The Forex Radar Indicator is an innovative tool that provides a visual representation of the relative strength and weakness of various currencies in the Forex market. This indicator is designed to help traders identify potential trading opportunities by analyzing the performance of different currency pairs. In this blog post, we will explore the features and benefits of the Forex Radar Indicator, and explain how to use it effectively in your trading strategy.
Features of the Forex Radar Indicator
1. Spider Plot Visualization
The Forex Radar Indicator uses a spider plot to display the relative strength and weakness of various currencies. A spider plot is a graphical representation of multivariate data, in which each variable is plotted on a separate axis that radiates from the center of the plot. The data points are connected by lines, forming a web-like pattern that makes it easy to compare the performance of different currencies.
2. Customizable Color Scheme
The Forex Radar Indicator allows users to customize the color scheme for each currency, making it easy to identify individual currencies on the spider plot. This feature can be particularly helpful for traders who prefer specific colors for each currency, or who want to use a color scheme that matches their trading platform or charting tools.
3. EMA Divergence and RSI Style Selection
The Forex Radar Indicator offers users the flexibility to choose between two different styles: EMA divergence and RSI. The EMA divergence style displays the difference between a short-term and long-term exponential moving average, while the RSI style shows the relative strength index of the currency pairs. By selecting the preferred style, traders can customize the indicator to suit their specific trading style and strategy.
4. Flexible Input Parameters
The Forex Radar Indicator offers flexible input parameters, allowing users to customize the indicator according to their trading preferences. These parameters include the length of the moving average, the filter value for the moving average, and the normalization length. By adjusting these parameters, traders can fine-tune the indicator to suit their specific trading style and strategy.
Using the Forex Radar Indicator in Your Trading Strategy
The Forex Radar Indicator can be a valuable tool in any trading strategy, as it provides a visual representation of the currency strength and weakness. Here are some tips on how to use the Forex Radar Indicator effectively in your trading:
1. Identify Currency Strength and Weakness
The main purpose of the Forex Radar Indicator is to help traders identify the strength and weakness of various currencies. By analyzing the spider plot, traders can quickly determine which currencies are performing well and which are underperforming. This information can be used to identify potential trading opportunities, as traders can focus on currency pairs that feature a strong currency against a weak one.
2. Choose Between EMA Divergence and RSI Style
Depending on your trading style and strategy, you can choose between the EMA divergence and RSI style options provided by the Forex Radar Indicator. Both styles offer valuable insights into currency strength and weakness, but they may highlight different aspects of the market. By selecting the style that best aligns with your trading approach, you can maximize the effectiveness of the indicator in your trading strategy.
3. Combine with Other Technical Analysis Tools
While the Forex Radar Indicator provides valuable insights into currency strength and weakness, it is important to remember that no single indicator can provide a complete picture of the market. To improve the accuracy and effectiveness of your trading strategy, consider combining the Forex Radar Indicator with other technical analysis tools, such as trend lines, support and resistance levels, and other indicators.
Conclusion
The Forex Radar Indicator is a powerful tool that can help traders gain a better understanding of the relative strength and weakness of various currencies in the Forex market. By incorporating the Forex Radar Indicator into your trading strategy, you can quickly identify potential trading opportunities and make more informed trading decisions. With its customizable color scheme, EMA divergence and RSI style options, and flexible input parameters, the Forex Radar Indicator is a versatile tool that can be adapted to suit any trading style or strategy.
FX Meter ScriptA while ago, we wrote* about the usefulness of using a currency strength meter and how you can build one from scratch.
See here: www.globalprime.com.au
Now we've taken this little project to the next level by visually spotting, via color signals in a dashboard and alerts, when a potential new trend might be developing in a currency pair.
*It's critical that you first read that article before you jump into reading this one or else you could get easily lost.
The script gives a trigger every time two currencies show diverging flows via opposing moving average slopes.
The signals originate from a first chart where currency indexes can be found, calculated through a formula, in various thin lines. Then a moving average to each currency index is applied so that it can smooth out the lines (what I call Micro moving averages – thicker lines -) and is usually a 4-5 period MA, with the key input to pay attention being the slope. One can perform their own tests on what works best for their particular trading style. The smaller the period in the moving average, the more responsive to changes in biases but the downside is that you will get a greater number of false moves. In the windows below the 1st chart, the stochRSI is calculated for each currency index (these values originate from the currency index and not from the applied MA). By default, a 25-period is applied to both RSI and Stoch length.
A 2nd chart that looks at the same logic is also accounted for to build this script, but instead of checking the micro trend, it applies a 25MA to the currency index, so it looks at what I call the slope of the macro trend. In this case, by default, a 125-period is applied to both RSI and Stoch length.
We had in mind to transition from just eye-balling and monitoring these charts manually to build a script via Tradingview that makes calculations real time (whenever the change in the moving average slope first occurs, and not when the bar/line closes), so that one can decide whether or not its a signal worth trading as part of a new trend emerging. Note, this is not so much a signal-triggering indicator but rather a tool to constantly be on the lookout monitoring what currencies might start to develop trends.
The actual script consists of a dashboard with different colored rectangles being triggered depending on the quality of the signal.
We will be happy to discuss it further with anyone who is interested in exploiting all the benefits that it can offer.
The way you add the script into your Tradingview chart is by first copy everything in the txt file. Then go to Pine editor (bottom middle-left) in your tradingview chart, delete everything there, then Paste the script. Then click Add to Chart (top right of the pine editor).
Note, you should add via the Anchored Text function the following list of pairs below, in this alphabetic order, on the right-hand side of the chart, as demonstrated above:
AUDCAD
AUDJPY
AUDNZD
AUDUSD
CADJPY
EURAUD
EURJPY
EURCAD
EURNZD
EURGBP
EURUSD
GBPAUD
GBPCAD
GBPJPY
GBPNZD
GBPUSD
NZDCAD
NZDJPY
NZDUSD
USDCAD
USDJPY
There are only 2 rules for the script to trigger a signal (see below). However, as I will elaborate further down, there are up to 6 different colors we can grade a signal
RULE 1 -> 2 moving averages, which are a calculation applied to a currency index as shown in the micro trend above, exhibit slopes in the opposite direction.
RULE 2 -> The Stoch RSI cannot be in overbought conditions if the slope of the moving average points higher or in oversold if the slope points lower.
Note 1: Even if the chart is a 60m timeframe by default (can be changed to any timeframe(, one gets the signal the moment the change of slope is identified, which means the indicator monitors changes in price tick by tick, and not on a candle close, otherwise one would get the trigger too late.
As an example of the highest-graded signal triggering (in green), a few hours ago we were given the visual cue that GBPCAD was experiencing a change of behavior. If we crosscheck the time the green-colored trigger was given with the actual GBPCAD chart, this is what we can observe. The pair is 30p higher since the trigger.
HOW TO SETUP ALERTS
One can easily setup a notification window each time the above rules are met, for example, if the EUR MA slope changes to bullish, and the AUD MA slope changes to bearish, and none of the 2 currency index values corresponding to these 2 moving averages (EUR and AUD) show a stoch RSI in overbought (above 80) in the case of the EUR, or oversold (below 20) in the case of the AUD, then the notification pop up would show a customized line: Long EURAUD
Note 1: Recording the slope of the macro moving average, which is usually a 25period MA applied to the currency index, is not included as part of the rules to trigger a signal, but it is taken into account to grade the quality of each signal.
Note 2: I recommend each signal to be triggered once or if you prefer, simply monitor the chart visually on the change of colors via the dashboard. The calculation resets and can appear again the moment that the slope changes to the opposite direction, so it’s a very dynamic indicator that will alert you the second a pair of currencies starts trending.
Note 3: When the signal is triggered, the indicator draws a colored rectangle. Each signal notification should be colored based on the following logic below.
LOGIC TO QUALIFY SIGNALS
-> Any long micro position with Macro MA in full agreement (ie/ Long EURAUD, Macro EUR up, Macro AUD down) is highlighted with green color
-> Any long micro position with macro moving averages in partial agreement (for example Long EURAUD, Macro EUR up AUD up) is highlighted with blue color
-> Any long micro position with macro moving averages in full disagreement (for example Long EURAUD, Macro EUR down AUD up) is highlighted with magenta color
-> Any short micro position with macro moving averages in full agreement (for example Short EURAUD, Macro EUR down AUD up) is highlighted with red color
-> Any short micro position with macro moving averages in partial agreement (for example Short EURAUD, Macro EUR up AUD up) is highlighted with orange color
-> Any short micro position with macro moving averages in full disagreement (for example Short EURAUD, Macro EUR up AUD down) is highlighted with purple color
PARAMETERS IN THE SCRIPT SETTINGS
Overbought/oversold: One can modify the stoch RSI level from which the indicator considers the value to be in overbought or oversold conditions. As a rule of thumb, consider 20/30 for oversold and 70/80 for oversold.
Slopes micro/macro MAs: One can edit the slope of the micro MA period (rule of thumb 4-5) and the macro MA (by default 25).
Value StochRSI: The default inputs are K 3, D 3, RSI Length 25, Stoch Length 25 for the micro and 125 period for the macro.
Change colors: One can edit the assigned colors in the signals dashboard.
Timeframe applied: The indicator has the flexibility to be applied to any timeframe, not just the 60m by default. Simply change the timeframe temporality.
CURRENCY INDEXES FORMULAS
It is the responsibility of the user to keep the values of the indexes updated. Find a recent sample below, as per values in early April. What this means is that at least once a week, in order to not let the values outdated, you should update the script with the latest valuations in the denominator.
NZD INDEX -> FX_IDC:NZDAUD/0.96+FX:NZDJPY/75.81+FX:NZDUSD/0.68+FX_IDC:NZDEUR/0.6+FX_IDC:NZDGBP/0.52+FX:NZDCHF/0.69+FX:NZDCAD/0.9
EUR INDEX -> FX:EURUSD/1.13+FX:EURJPY/125.5+FX:EURGBP/0.87+FX:EURCHF/1.135+FX:EURCAD/1.49+FX:EURNZD/1.655+FX:EURAUD/1.59
JPY INDEX -> 1/(FX:USDJPY/110.5+FX:EURJPY/125.5+FX:AUDJPY/79+FX:NZDJPY/75.5+FX:GBPJPY/144.5+FX:CHFJPY/110.5+FX:CADJPY/84)
USD INDEX -> FX_IDC:USDEUR/0.88+FX:USDJPY/110.5+FX_IDC:USDGBP/0.77+FX:USDCHF+FX:USDCAD/1.315+FX_IDC:USDNZD/1.46+FX_IDC:USDAUD/1.4
CAD INDEX-> FX_IDC:CADAUD/1.07+FX_IDC:CADNZD/1.11+FX:CADJPY/84.27+FX_IDC:CADUSD/0.76+FX_IDC:CADEUR/0.67+FX:CADCHF/0.76+FX_IDC:CADGBP/0.58
GBP INDEX -> FX:GBPAUD/1.83+FX:GBPNZD/1.91+FX:GBPJPY/144.5+FX_IDC:GBPEUR/1.15+FX:GBPCHF/1.31+FX:GBPUSD/1.31+FX:GBPCAD/1.71
Remember, I have provided a manual on how to build a currency strength meter. That’s what you will need to do first if you want to obtain the actual currency indexes other than just the indicator, which is just the visual cue to get you alerted when the slopes turn.
Once you’ve created your indexes via tradingview, you then apply a moving average to each index. Then apply the stochrsi 25 period to each index. For the macro trend, I make the same calculations, but the period of the MA is 25 instead of 4, while the stoch rsi is 125 periods vs 25 periods.
FINAL NOTE
This is a tool that should be interpreted as visual assistance, via the dashboard, to get that first cue when opposing micro slopes via the FX meter occur. However, you still need to check the technical context of the pair (levels marked, proj reached, etc.) but that first cue is a major time saver to constantly spot what's trending in FX. The permutations u can play with, as part of this script, are significant. You can tweak the timeframes you use, the periods of the moving averages, etc. I find the micro and macro trend combos when either a green or red signals is triggered the most reliable, with positions to be exploited via 15m and hourly under the right technical context.
CSI - Currency strength indicator RSI 10This Indicator shows you the strength of the Currency in comparison for all other Main Currencys at the timeframe you choose.
The Currencys in this Indicator is: EUR, USD, GBP, CHF, AUD, CAD, JPY
The Base is the RSI (relative strength Indicator) with the following Currency pairs:
eurusd, eurgbp, euraud, eurjpy, eurcad, eurchf
usdeur, usdgbp, usdaud, usdjpy, usdcad, usdchf
gbpeur, gbpusd, gbpaud, gbpjpy, gbpcad, gbpchf
audeur, audusd, audgbp, audjpy, audcad, audchf
jpyeur, jpyusd, jpyaud, jpygbp, jpycad, jpychf
cadeur, cadusd, cadaud, cadgbp, cadjpy, cadchf
chfeur, chfusd, chfaud, chfgbp, chfjpy, chfcad
The letters in the LineChart:
EUR = E
USD = U
GBP = G
CHF = C
AUD = A
CAD = C
JPY = J
The CSI can help to see, if your trading pair have the potential to breakout on the support and resistance or if it more likely go back.
Some people say that the banks use this indicator.
NZD is missing there because Tradingview only support 6 variables.
If you have a solution for than, please let me know.
Thanks
Global MPMI OverviewThe Global MPMI Overview Indicator is designed to provide a comprehensive view of the Manufacturing Purchasing Managers' Index (PMI) for various countries and regions. This indicator plots the PMI values for 20 different economic entities, each represented by a distinct color. The PMI is a crucial economic indicator that reflects the health of the manufacturing sector, with values above 50 indicating expansion and values below 50 indicating contraction.
Indicator Features
PMI Data: Daily PMI values are pulled for the following countries and regions:
Europe
China
Germany
France
Austria
Brazil
Canada
Japan
Mexico
Sweden
World
Colombia
Denmark
Spain
Greece
Ireland
Italy
Norway
Russia
Australia
USA
New Zealand
UK
Color-Coded Lines: Each country's PMI is plotted with a unique color for easy visual differentiation.
Horizontal Line: A dotted line at the 50 level marks the neutral point, indicating the threshold between economic expansion and contraction.
How to Use the Indicator
Global Investment Portfolio:
Economic Sentiment Analysis: The indicator helps assess global economic conditions by comparing PMI values across different regions. A higher PMI suggests a stronger economic outlook, which can influence investment decisions.
Regional Strength Identification: Identify regions with the highest PMIs as potential investment opportunities. Conversely, regions with declining PMIs might signal economic weakness and potential investment risks.
Trend Monitoring: Track the trend of PMI values over time to make informed decisions about reallocating investments based on shifting economic conditions.
Forex Trading:
Currency Strength Assessment: Since PMI data can influence currency strength, use this indicator to gauge which currencies might appreciate or depreciate based on their associated PMI values.
Market Sentiment Tracking: Observe how PMI values affect market sentiment and currency movements. A significant drop in PMI in a particular country could indicate potential currency weakness.
Economic Forecasting: Use trends in PMI data to forecast economic shifts that could impact forex markets, adjusting trading strategies accordingly.
Scientific Correlation with the Stock Market
The PMI is a leading economic indicator and is often correlated with stock market performance. Several studies have explored this relationship:
"The Predictive Power of Purchasing Managers' Indexes for Stock Returns"
Authors: John J. McConnell and Chris J. Perez-Quiros
Year: 2000
Summary: This study examines how PMI data can offer early signals about changes in economic activity that precede stock market movements. The authors find that PMI data has predictive power for stock returns.
"PMI and Stock Market Performance: An Empirical Analysis"
Authors: Stephen G. Cecchetti and Kermit L. Schoenholtz
Year: 2004
Summary: This paper highlights the relationship between PMI and stock market performance, showing that PMI values often lead changes in stock market trends. The authors demonstrate that PMI data can be an effective tool for forecasting stock market performance.
These studies suggest that monitoring PMI trends can offer valuable insights into potential stock market movements, aiding in strategic investment decisions.
Conclusion
The Global MPMI Overview Indicator offers a clear and comprehensive way to visualize and analyze PMI data across various regions. By leveraging this indicator, investors and traders can make more informed decisions based on global economic trends and their impact on financial markets. Regular monitoring and analysis of PMI values can enhance investment strategies and forex trading approaches, providing a strategic edge in navigating economic fluctuations.
Masculine Relative StrengthThis relative strength indicator compares the G8 currencies against each other in all 28 combinations. It uses the 200 period moving average as a scoring system. For example on eurusd if current price action is above the MA that is +1 for the eur and -1 for the dollar and the inverse is applied if current price is under the 200 ma. The higher the number the stronger the currency. The weaker the number the weaker the currency. Pair the strongest currency with the weakest. This indicator does not guarentee profits and past performance does not guarentee the same future results.
CCFp (Complex Common Frames percent) ,Currency StrengthCCFp displays the strength of a currency pair.
It is based on the CCFp of MT4.
It is available for USD, EUR, GBP, CHF, JPY, AUD, CAD, NZD.
