Big Mo’s Glaskugel — Macro Drawdown Risk (v1.1.2)What it does / what you see
An at-a-glance drawdown-risk oscillator that blends several macro US signals.
• A smooth, color-blended line (green→orange→red) shows the scaled risk score (0–100).
• Subtle shading marks “re-steepen warning windows” (starts when the yield curve re-steepens after an inversion; ends on normalization/cool-down).
• A compact status table summarizes: overall risk level, Yield Curve (10y–3m), Credit Stress (Baa–10y), Economy (LEI), and Valuation (CAPE).
Data used & why
Yield Curve (10y–3m) — FRED:T10Y3M. Inversions and subsequent re-steepens often precede recessions/equity drawdowns.
Credit Stress — FRED:BAA10Y vs its 1-year average (deviation in bps). Widening credit spreads flag tightening financial conditions.
Economy (LEI) — ECONOMICS:USLEI. 6-month annualized growth below a cutoff highlights macro deterioration.
Valuation (CAPE) — SHILLER_PE_RATIO_MONTH. Elevated valuations can amplify downside risk.
VIX spikes — optional boost that recognizes sudden risk repricings.
Important disclaimer
This is not a reliable or predictive indicator in all regimes. No guarantees or warranties of any kind are provided. It is not financial advice. Signals can be early, late, or wrong.
That said, it leans on well-studied warning factors (yield-curve dynamics, credit spreads, LEI weakness, valuation extremes) that have flagged major market downturns in the past.
Key customization / tweaks
Weights for each component (Yield, Credit, LEI, VIX, CAPE).
Thresholds: yield inversion months, re-steepen lookback, credit-stress bps, LEI cutoff, CAPE level, VIX spike levels.
Re-steepen boost: enable/disable, base points, half-life decay.
Shading behavior: cool-down bars to “unwarn,” max warning duration, only shade when risk ≠ green.
Scaling & smoothing: dynamic rolling max, EMA length, yellow/red thresholds.
Status table: position, and a snapshot mode to view values at a chosen historical time.
Recherche dans les scripts pour "N+credit最新动态"
Risk MeterRisk Meter Indicator for TradingView
The Risk Meter is a powerful market risk assessment tool designed to help traders evaluate the current risk environment using a simple, data-driven score. By analyzing four critical market factors—VIX (volatility index), market breadth, trailing volatility, and credit spreads—the indicator generates a risk score between 0 and 4. This score empowers traders to make informed decisions about hedging, exiting positions, or re-entering the market, with clear visual cues and alerts for intraday monitoring.
What It Does
Calculates a Risk Score: Assigns a score from 0 to 4, where each point reflects an active risk condition based on four market indicators.
Identifies Risk Levels:
A score of 3 or higher indicates a high-risk environment, suggesting traders consider hedging or reducing exposure.
A score of 2 or lower for at least two consecutive days signals a potential opportunity to re-enter the market.
Provides Visual Feedback: Uses color-coded Columns, threshold markers, and a component table for quick interpretation.
Supports Decision-Making: Offers a structured approach to managing risk and timing trades.
How It Works
The Risk Meter aggregates four key risk conditions, each contributing 1 point to the total score when triggered:
Elevated and Rising VIX (Risk 1)
Condition: The VIX is above 18 and higher than it was 20 days ago.
Purpose: Detects increasing market fear or uncertainty.
Market Breadth Dropping (Risk 2)
Condition: Either:
Fewer than 50% of S&P 500 stocks are above their 200-day moving average and fewer than 70% are above their 50-day moving average, or
The 3-day EMA of the 200-day breadth falls below 80% of its 20-day SMA.
Purpose: Identifies weakening participation across the market.
Trailing Volatility (Risk 3)
Condition: The 30-day annualized volatility of the equal-weight S&P 500 (RSP) exceeds 35%.
Purpose: Highlights periods of heightened price instability.
Credit Spreads (Risk 4)
Condition: The price ratio of high-yield bonds (HYG) to Treasuries (TLT or IEF) is lower than it was 20 days ago, indicating widening credit spreads.
Purpose: Signals potential stress in credit markets.
The total risk score is the sum of these conditions (0 to 4). Additionally, the indicator tracks consecutive days with a score of 2 or lower to generate re-entry signals.
How to Read It Intraday
The Risk Meter is built on daily data but can be monitored intraday for real-time insights. Here’s how traders can interpret it:
Risk Score Plot:
Displayed as a step line ranging from 0 to 4.
Colors:
Red: High risk (score ≥ 3) – caution advised.
Green: Re-entry signal – score ≤ 2 for at least two consecutive days (triggered when the count increments from 1 to 2).
Blue: Neutral or low risk (score < 3 without a re-entry signal).
Threshold Lines:
Dashed Gray Line at 3: Marks the high-risk threshold.
Dotted Gray Line at 2: Indicates the low-risk threshold for re-entry signals.
Risk Component Table:
Located in the top-right corner, it lists:
VIX, Breadth, Volatility, and Credit Spreads.
Status: Shows "" (warning, red) if the risk condition is met, or "✓" (safe, blue) if not.
Helps traders pinpoint which factors are driving the score.
Alerts:
High Risk Alert: Triggers when the score moves from < 3 to ≥ 3.
Re-entry Signal Alert: Triggers when the score ≤ 2 for two consecutive days.
Intraday Usage Tips
Check the indicator throughout the day for early signs of risk shifts, especially if the score is near a threshold (e.g., 2 or 3).
Combine with other intraday tools (e.g., price action, volume) since the Risk Meter updates daily but reflects broader market conditions.
How Traders Can Use It
High-Risk Signal (Score ≥ 3):
Consider hedging positions (e.g., with options) or reducing equity exposure to protect against potential downturns.
Re-entry Signal (Score ≤ 2 for 2+ Days):
Look to re-enter the market or increase exposure, as it suggests stabilizing conditions.
Daily Risk Management:
Use the score and table to assess overall market health and adjust strategies accordingly.
Alert-Driven Trading:
Set up alerts to stay notified of critical risk changes without constant monitoring.
Why Use the Risk Meter?
This indicator offers a systematic, multi-factor approach to risk assessment, blending volatility, breadth, and credit market data into an easy-to-read score. Whether you’re an intraday trader or a longer-term investor, the Risk Meter helps you stay proactive, avoid surprises, and time your trades with greater confidence.
Financial Risk Disclaimer for the Risk Meter Tool
Important Notice: The Risk Meter is a market risk assessment tool designed to provide insights into current market conditions based on historical data and predefined indicators. It is intended for informational and educational purposes only and should not be considered financial advice, a recommendation to buy or sell any securities, or a guarantee of future market performance.
Key Considerations
No Guarantee of Accuracy: While the Risk Meter utilizes reliable data sources and established financial metrics, the creators do not guarantee the accuracy, completeness, or timeliness of the information provided. Financial markets are complex and subject to rapid, unpredictable changes, and the tool’s output may not fully reflect all market dynamics.
Market Risks: Trading and investing in financial markets carry significant risks, including the potential loss of principal. Market volatility, economic shifts, and other factors can lead to unexpected outcomes. Past performance is not a reliable indicator of future results, and the Risk Meter’s assessments are based on historical data, not future predictions.
Not a Substitute for Professional Advice: The Risk Meter is not intended to replace personalized financial guidance. Users are strongly encouraged to consult a qualified financial advisor, perform their own research, and evaluate their personal financial situation, risk tolerance, and investment objectives before making any trading or investment decisions.
Limitation of Liability: The creators of the Risk Meter, including any affiliates, developers, or contributors, are not liable for any direct, indirect, incidental, or consequential losses or damages arising from the use of this tool. This includes, but is not limited to, financial losses, missed opportunities, or decisions based on the tool’s output.
User Responsibility: By using the Risk Meter, you accept full responsibility for your trading and investment decisions. You acknowledge that you use the tool at your own risk and that the creators bear no responsibility for any outcomes resulting from its use.
Final Note
The Risk Meter is a supplementary tool designed to enhance your understanding of market risk. It is not a comprehensive solution for investment management. Approach trading and investing with caution, ensuring your decisions align with your personal financial strategy.
Financial Conditions Composite Z-Score1. Inputs and Data Sources
The script pulls data for the following financial metrics using TradingView's request.security function:
CBOE:VIX (Volatility Index): A measure of market volatility.
MOVE Index: A measure of bond market volatility (or Treasury volatility).
BAMLH0A0HYM2 (High-Yield Spread): The spread between high-yield corporate bonds and Treasury yields.
BAMLC0A0CM (Credit Spread): The spread for investment-grade corporate bonds.
Each of these metrics represents a key aspect of financial conditions:
VIX: Equity market risk.
MOVE: Bond market risk.
High-Yield Spread and Credit Spread: Perception of risk in corporate debt.
2. Z-Score Calculation
A z-score standardizes each metric to show how far it deviates from its average over a specified period (lookback = 160, or 160 days):
Positive z-scores indicate the metric is higher than average.
Negative z-scores indicate the metric is lower than average.
The formula for the z-score:
Z-Score = Metric − Mean
Standard Deviation Z-Score = Standard Deviation Metric−Mean
3. Combined Z-Score
The script combines the four individual z-scores into a single Composite Z-Score, equally weighted across the metrics:
Combined Z-Score = (Z VIX + Z MOVE + Z High-Yield Spread + Z Credit Spread) / 4
This Combined Z-Score provides an overall measure of financial conditions:
Positive combined z-scores indicate tighter or riskier financial conditions.
Negative combined z-scores indicate looser or less risky financial conditions.
4. Visual Elements on the Chart
A. Colorful Lines: Individual Z-Scores
Each of the four metrics is plotted as a separate line:
Red: Z-score of the VIX.
Green: Z-score of the MOVE index.
Orange: Z-score of the high-yield spread.
Purple: Z-score of the credit spread.
These lines show how each metric contributes to the overall financial conditions. For example:
A rising red line means increasing equity market volatility (risk).
A rising green line means increasing bond market volatility (risk).
B. Blue Line: Combined Z-Score
The blue line represents the Combined Z-Score. It aggregates the individual z-scores into a single measure:
A rising blue line suggests financial conditions are tightening (greater risk across markets).
A falling blue line suggests financial conditions are loosening (lower risk across markets).
C. Red and Green Background: Z-Score Regions
Red Background: When the Combined Z-Score is positive (>0), it indicates riskier or tighter financial conditions.
Green Background: When the Combined Z-Score is negative (<0), it indicates less risky or looser financial conditions.
This background coloring helps visually distinguish periods of riskier financial conditions from less risky ones.
5. Purpose of the Visualization
This indicator provides a comprehensive view of financial conditions across multiple asset classes:
Traders can use it to gauge the level of systemic market stress.
Investors can use it to assess when risk is elevated (positive z-scores) or subdued (negative z-scores).
It helps in decision-making for strategies that depend on market volatility or risk appetite.
Summary of What You See:
Colorful Lines (Red, Green, Orange, Purple): Individual z-scores for each metric (VIX, MOVE, high-yield spread, credit spread).
Blue Line: The aggregated Combined Z-Score that summarizes financial conditions.
Red and Green Background:
Red: Tight or risky financial conditions (Combined Z-Score > 0).
Green: Loose or low-risk financial conditions (Combined Z-Score < 0).
This visualization provides a multi-dimensional view of financial conditions at a glance, helping to identify periods of high or low risk in the markets.
Recession Warning Model [BackQuant]Recession Warning Model
Overview
The Recession Warning Model (RWM) is a Pine Script® indicator designed to estimate the probability of an economic recession by integrating multiple macroeconomic, market sentiment, and labor market indicators. It combines over a dozen data series into a transparent, adaptive, and actionable tool for traders, portfolio managers, and researchers. The model provides customizable complexity levels, display modes, and data processing options to accommodate various analytical requirements while ensuring robustness through dynamic weighting and regime-aware adjustments.
Purpose
The RWM fulfills the need for a concise yet comprehensive tool to monitor recession risk. Unlike approaches relying on a single metric, such as yield-curve inversion, or extensive economic reports, it consolidates multiple data sources into a single probability output. The model identifies active indicators, their confidence levels, and the current economic regime, enabling users to anticipate downturns and adjust strategies accordingly.
Core Features
- Indicator Families : Incorporates 13 indicators across five categories: Yield, Labor, Sentiment, Production, and Financial Stress.
- Dynamic Weighting : Adjusts indicator weights based on recent predictive accuracy, constrained within user-defined boundaries.
- Leading and Coincident Split : Separates early-warning (leading) and confirmatory (coincident) signals, with adjustable weighting (default 60/40 mix).
- Economic Regime Sensitivity : Modulates output sensitivity based on market conditions (Expansion, Late-Cycle, Stress, Crisis), using a composite of VIX, yield-curve, financial conditions, and credit spreads.
- Display Options : Supports four modes—Probability (0-100%), Binary (four risk bins), Lead/Coincident, and Ensemble (blended probability).
- Confidence Intervals : Reflects model stability, widening during high volatility or conflicting signals.
- Alerts : Configurable thresholds (Watch, Caution, Warning, Alert) with persistence filters to minimize false signals.
- Data Export : Enables CSV output for probabilities, signals, and regimes, facilitating external analysis in Python or R.
Model Complexity Levels
Users can select from four tiers to balance simplicity and depth:
1. Essential : Focuses on three core indicators—yield-curve spread, jobless claims, and unemployment change—for minimalistic monitoring.
2. Standard : Expands to nine indicators, adding consumer confidence, PMI, VIX, S&P 500 trend, money supply vs. GDP, and the Sahm Rule.
3. Professional : Includes all 13 indicators, incorporating financial conditions, credit spreads, JOLTS vacancies, and wage growth.
4. Research : Unlocks all indicators plus experimental settings for advanced users.
Key Indicators
Below is a summary of the 13 indicators, their data sources, and economic significance:
- Yield-Curve Spread : Difference between 10-year and 3-month Treasury yields. Negative spreads signal banking sector stress.
- Jobless Claims : Four-week moving average of unemployment claims. Sustained increases indicate rising layoffs.
- Unemployment Change : Three-month change in unemployment rate. Sharp rises often precede recessions.
- Sahm Rule : Triggers when unemployment rises 0.5% above its 12-month low, a reliable recession indicator.
- Consumer Confidence : University of Michigan survey. Declines reflect household pessimism, impacting spending.
- PMI : Purchasing Managers’ Index. Values below 50 indicate manufacturing contraction.
- VIX : CBOE Volatility Index. Elevated levels suggest market anticipation of economic distress.
- S&P 500 Growth : Weekly moving average trend. Declines reduce wealth effects, curbing consumption.
- M2 + GDP Trend : Monitors money supply and real GDP. Simultaneous declines signal credit contraction.
- NFCI : Chicago Fed’s National Financial Conditions Index. Positive values indicate tighter conditions.
- Credit Spreads : Proxy for corporate bond spreads using 10-year vs. 2-year Treasury yields. Widening spreads reflect stress.
- JOLTS Vacancies : Job openings data. Significant drops precede hiring slowdowns.
- Wage Growth : Year-over-year change in average hourly earnings. Late-cycle spikes often signal economic overheating.
Data Processing
- Rate of Change (ROC) : Optionally applied to capture momentum in data series (default: 21-bar period).
- Z-Score Normalization : Standardizes indicators to a common scale (default: 252-bar lookback).
- Smoothing : Applies a short moving average to final signals (default: 5-bar period) to reduce noise.
- Binary Signals : Generated for each indicator (e.g., yield-curve inverted or PMI below 50) based on thresholds or Z-score deviations.
Probability Calculation
1. Each indicator’s binary signal is weighted according to user settings or dynamic performance.
2. Weights are normalized to sum to 100% across active indicators.
3. Leading and coincident signals are aggregated separately (if split mode is enabled) and combined using the specified mix.
4. The probability is adjusted by a regime multiplier, amplifying risk during Stress or Crisis regimes.
5. Optional smoothing ensures stable outputs.
Display and Visualization
- Probability Mode : Plots a continuous 0-100% recession probability with color gradients and confidence bands.
- Binary Mode : Categorizes risk into four levels (Minimal, Watch, Caution, Alert) for simplified dashboards.
- Lead/Coincident Mode : Displays leading and coincident probabilities separately to track signal divergence.
- Ensemble Mode : Averages traditional and split probabilities for a balanced view.
- Regime Background : Color-coded overlays (green for Expansion, orange for Late-Cycle, amber for Stress, red for Crisis).
- Analytics Table : Optional dashboard showing probability, confidence, regime, and top indicator statuses.
Practical Applications
- Asset Allocation : Adjust equity or bond exposures based on sustained probability increases.
- Risk Management : Hedge portfolios with VIX futures or options during regime shifts to Stress or Crisis.
- Sector Rotation : Shift toward defensive sectors when coincident signals rise above 50%.
- Trading Filters : Disable short-term strategies during high-risk regimes.
- Event Timing : Scale positions ahead of high-impact data releases when probability and VIX are elevated.
Configuration Guidelines
- Enable ROC and Z-score for consistent indicator comparison unless raw data is preferred.
- Use dynamic weighting with at least one economic cycle of data for optimal performance.
- Monitor stress composite scores above 80 alongside probabilities above 70 for critical risk signals.
- Adjust adaptation speed (default: 0.1) to 0.2 during Crisis regimes for faster indicator prioritization.
- Combine RWM with complementary tools (e.g., liquidity metrics) for intraday or short-term trading.
Limitations
- Macro indicators lag intraday market moves, making RWM better suited for strategic rather than tactical trading.
- Historical data availability may constrain dynamic weighting on shorter timeframes.
- Model accuracy depends on the quality and timeliness of economic data feeds.
Final Note
The Recession Warning Model provides a disciplined framework for monitoring economic downturn risks. By integrating diverse indicators with transparent weighting and regime-aware adjustments, it empowers users to make informed decisions in portfolio management, risk hedging, or macroeconomic research. Regular review of model outputs alongside market-specific tools ensures its effective application across varying market conditions.
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis.
## 1. Introduction
Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017).
The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines:
- Classical monetary policy rules (Taylor Rule framework)
- Real-time macroeconomic indicators from FRED database
- Financial market conditions and term structure analysis
- Labor market dynamics and inflation expectations
- Regime-dependent parameter adjustments
This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes.
## 2. Literature Review and Theoretical Foundation
### 2.1 Taylor Rule Framework
The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals:
rt = r + πt + α(πt - π) + β(yt - y)
Where:
- rt = nominal federal funds rate
- r = equilibrium real interest rate
- πt = inflation rate
- π = inflation target
- yt - y = output gap
- α, β = policy response coefficients
Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions.
### 2.2 Data-Dependent Monetary Policy
The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions.
### 2.3 Financial Conditions and Monetary Transmission
The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010).
### 2.4 Labor Market Indicators
The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments.
## 3. Methodology
### 3.1 Model Architecture
The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index:
#### Component 1: Taylor Rule Analysis (Weight: 25%)
Implements real-time Taylor Rule calculation using FRED data:
- Core PCE inflation (Fed's preferred measure)
- Unemployment gap proxy for output gap
- Dynamic neutral rate estimation
- Regime-dependent parameter adjustments
#### Component 2: Employment Conditions (Weight: 20%)
Multi-dimensional labor market assessment:
- Unemployment gap relative to NAIRU estimates
- JOLTS job openings momentum
- Average hourly earnings growth
- Beveridge curve position analysis
#### Component 3: Financial Conditions (Weight: 18%)
Comprehensive financial market evaluation:
- Chicago Fed NFCI real-time data
- Yield curve shape and term structure
- Credit growth and lending conditions
- Market volatility and risk premia
#### Component 4: Inflation Expectations (Weight: 15%)
Forward-looking inflation analysis:
- TIPS breakeven inflation rates (5Y, 10Y)
- Market-based inflation expectations
- Inflation momentum and persistence measures
- Phillips curve relationship dynamics
#### Component 5: Growth Momentum (Weight: 12%)
Real economic activity assessment:
- Real GDP growth trends
- Economic momentum indicators
- Business cycle position analysis
- Sectoral growth distribution
#### Component 6: Liquidity Conditions (Weight: 10%)
Monetary aggregates and credit analysis:
- M2 money supply growth
- Commercial and industrial lending
- Bank lending standards surveys
- Quantitative easing effects assessment
### 3.2 Normalization and Scaling
Each component undergoes robust statistical normalization using rolling z-score methodology:
Zi,t = (Xi,t - μi,t-n) / σi,t-n
Where:
- Xi,t = raw indicator value
- μi,t-n = rolling mean over n periods
- σi,t-n = rolling standard deviation over n periods
- Z-scores bounded at ±3 to prevent outlier distortion
### 3.3 Regime Detection and Adaptation
The model incorporates dynamic regime detection based on:
- Policy volatility measures
- Market stress indicators (VIX-based)
- Fed communication tone analysis
- Crisis sensitivity parameters
Regime classifications:
1. Crisis: Emergency policy measures likely
2. Tightening: Restrictive monetary policy cycle
3. Easing: Accommodative monetary policy cycle
4. Neutral: Stable policy maintenance
### 3.4 Composite Index Construction
The final AFDFM index combines weighted components:
AFDFMt = Σ wi × Zi,t × Rt
Where:
- wi = component weights (research-calibrated)
- Zi,t = normalized component scores
- Rt = regime multiplier (1.0-1.5)
Index scaled to range for intuitive interpretation.
### 3.5 Decision Probability Calculation
Fed decision probabilities derived through empirical mapping:
P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100)
P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100)
P(Hold) = 100 - |AFDFMt| × 15
Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds).
## 4. Data Sources and Real-Time Implementation
### 4.1 FRED Database Integration
- Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted
- Unemployment Rate (UNRATE): Monthly, seasonally adjusted
- Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate
- Federal Funds Rate (FEDFUNDS): Monthly average
- Treasury Yields (GS2, GS10): Daily constant maturity
- TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data
### 4.2 High-Frequency Financial Data
- Chicago Fed NFCI: Weekly financial conditions
- JOLTS Job Openings (JTSJOL): Monthly labor market data
- Average Hourly Earnings (AHETPI): Monthly wage data
- M2 Money Supply (M2SL): Monthly monetary aggregates
- Commercial Loans (BUSLOANS): Weekly credit data
### 4.3 Market-Based Indicators
- VIX Index: Real-time volatility measure
- S&P; 500: Market sentiment proxy
- DXY Index: Dollar strength indicator
## 5. Model Validation and Performance
### 5.1 Historical Backtesting (2017-2024)
Comprehensive backtesting across multiple Fed policy cycles demonstrates:
- Signal Accuracy: 78% correct directional predictions
- Timing Precision: 2.3 meetings average lead time
- Crisis Detection: 100% accuracy in identifying emergency measures
- False Signal Rate: 12% (within acceptable research parameters)
### 5.2 Regime-Specific Performance
Tightening Cycles (2017-2018, 2022-2023):
- Hawkish signal accuracy: 82%
- Average prediction lead: 1.8 meetings
- False positive rate: 8%
Easing Cycles (2019, 2020, 2024):
- Dovish signal accuracy: 85%
- Average prediction lead: 2.1 meetings
- Crisis mode detection: 100%
Neutral Periods:
- Hold prediction accuracy: 73%
- Regime stability detection: 89%
### 5.3 Comparative Analysis
AFDFM performance compared to alternative methods:
- Fed Funds Futures: Similar accuracy, lower lead time
- Economic Surveys: Higher accuracy, comparable timing
- Simple Taylor Rule: Lower accuracy, insufficient complexity
- Market-Based Models: Similar performance, higher volatility
## 6. Practical Applications and Use Cases
### 6.1 Institutional Investment Management
- Fixed Income Portfolio Positioning: Duration and curve strategies
- Currency Trading: Dollar-based carry trade optimization
- Risk Management: Interest rate exposure hedging
- Asset Allocation: Regime-based tactical allocation
### 6.2 Corporate Treasury Management
- Debt Issuance Timing: Optimal financing windows
- Interest Rate Hedging: Derivative strategy implementation
- Cash Management: Short-term investment decisions
- Capital Structure Planning: Long-term financing optimization
### 6.3 Academic Research Applications
- Monetary Policy Analysis: Fed behavior studies
- Market Efficiency Research: Information incorporation speed
- Economic Forecasting: Multi-factor model validation
- Policy Impact Assessment: Transmission mechanism analysis
## 7. Model Limitations and Risk Factors
### 7.1 Data Dependency
- Revision Risk: Economic data subject to subsequent revisions
- Availability Lag: Some indicators released with delays
- Quality Variations: Market disruptions affect data reliability
- Structural Breaks: Economic relationship changes over time
### 7.2 Model Assumptions
- Linear Relationships: Complex non-linear dynamics simplified
- Parameter Stability: Component weights may require recalibration
- Regime Classification: Subjective threshold determinations
- Market Efficiency: Assumes rational information processing
### 7.3 Implementation Risks
- Technology Dependence: Real-time data feed requirements
- Complexity Management: Multi-component coordination challenges
- User Interpretation: Requires sophisticated economic understanding
- Regulatory Changes: Fed framework evolution may require updates
## 8. Future Research Directions
### 8.1 Machine Learning Integration
- Neural Network Enhancement: Deep learning pattern recognition
- Natural Language Processing: Fed communication sentiment analysis
- Ensemble Methods: Multiple model combination strategies
- Adaptive Learning: Dynamic parameter optimization
### 8.2 International Expansion
- Multi-Central Bank Models: ECB, BOJ, BOE integration
- Cross-Border Spillovers: International policy coordination
- Currency Impact Analysis: Global monetary policy effects
- Emerging Market Extensions: Developing economy applications
### 8.3 Alternative Data Sources
- Satellite Economic Data: Real-time activity measurement
- Social Media Sentiment: Public opinion incorporation
- Corporate Earnings Calls: Forward-looking indicator extraction
- High-Frequency Transaction Data: Market microstructure analysis
## References
Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404.
Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu
Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707.
Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52.
Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478.
Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26.
Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System.
Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150.
Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022.
Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City.
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley.
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Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model.
Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
Bear Market Probability Model# Bear Market Probability Model: A Multi-Factor Risk Assessment Framework
The Bear Market Probability Model represents a comprehensive quantitative framework for assessing systemic market risk through the integration of 13 distinct risk factors across four analytical categories: macroeconomic indicators, technical analysis factors, market sentiment measures, and market breadth metrics. This indicator synthesizes established financial research methodologies to provide real-time probabilistic assessments of impending bear market conditions, offering institutional-grade risk management capabilities to retail and professional traders alike.
## Theoretical Foundation
### Historical Context of Bear Market Prediction
Bear market prediction has been a central focus of financial research since the seminal work of Dow (1901) and the subsequent development of technical analysis theory. The challenge of predicting market downturns gained renewed academic attention following the market crashes of 1929, 1987, 2000, and 2008, leading to the development of sophisticated multi-factor models.
Fama and French (1989) demonstrated that certain financial variables possess predictive power for stock returns, particularly during market stress periods. Their three-factor model laid the groundwork for multi-dimensional risk assessment, which this indicator extends through the incorporation of real-time market microstructure data.
### Methodological Framework
The model employs a weighted composite scoring methodology based on the theoretical framework established by Campbell and Shiller (1998) for market valuation assessment, extended through the incorporation of high-frequency sentiment and technical indicators as proposed by Baker and Wurgler (2006) in their seminal work on investor sentiment.
The mathematical foundation follows the general form:
Bear Market Probability = Σ(Wi × Ci) / ΣWi × 100
Where:
- Wi = Category weight (i = 1,2,3,4)
- Ci = Normalized category score
- Categories: Macroeconomic, Technical, Sentiment, Breadth
## Component Analysis
### 1. Macroeconomic Risk Factors
#### Yield Curve Analysis
The inclusion of yield curve inversion as a primary predictor follows extensive research by Estrella and Mishkin (1998), who demonstrated that the term spread between 3-month and 10-year Treasury securities has historically preceded all major recessions since 1969. The model incorporates both the 2Y-10Y and 3M-10Y spreads to capture different aspects of monetary policy expectations.
Implementation:
- 2Y-10Y Spread: Captures market expectations of monetary policy trajectory
- 3M-10Y Spread: Traditional recession predictor with 12-18 month lead time
Scientific Basis: Harvey (1988) and subsequent research by Ang, Piazzesi, and Wei (2006) established the theoretical foundation linking yield curve inversions to economic contractions through the expectations hypothesis of the term structure.
#### Credit Risk Premium Assessment
High-yield credit spreads serve as a real-time gauge of systemic risk, following the methodology established by Gilchrist and Zakrajšek (2012) in their excess bond premium research. The model incorporates the ICE BofA High Yield Master II Option-Adjusted Spread as a proxy for credit market stress.
Threshold Calibration:
- Normal conditions: < 350 basis points
- Elevated risk: 350-500 basis points
- Severe stress: > 500 basis points
#### Currency and Commodity Stress Indicators
The US Dollar Index (DXY) momentum serves as a risk-off indicator, while the Gold-to-Oil ratio captures commodity market stress dynamics. This approach follows the methodology of Akram (2009) and Beckmann, Berger, and Czudaj (2015) in analyzing commodity-currency relationships during market stress.
### 2. Technical Analysis Factors
#### Multi-Timeframe Moving Average Analysis
The technical component incorporates the well-established moving average convergence methodology, drawing from the work of Brock, Lakonishok, and LeBaron (1992), who provided empirical evidence for the profitability of technical trading rules.
Implementation:
- Price relative to 50-day and 200-day simple moving averages
- Moving average convergence/divergence analysis
- Multi-timeframe MACD assessment (daily and weekly)
#### Momentum and Volatility Analysis
The model integrates Relative Strength Index (RSI) analysis following Wilder's (1978) original methodology, combined with maximum drawdown analysis based on the work of Magdon-Ismail and Atiya (2004) on optimal drawdown measurement.
### 3. Market Sentiment Factors
#### Volatility Index Analysis
The VIX component follows the established research of Whaley (2009) and subsequent work by Bekaert and Hoerova (2014) on VIX as a predictor of market stress. The model incorporates both absolute VIX levels and relative VIX spikes compared to the 20-day moving average.
Calibration:
- Low volatility: VIX < 20
- Elevated concern: VIX 20-25
- High fear: VIX > 25
- Panic conditions: VIX > 30
#### Put-Call Ratio Analysis
Options flow analysis through put-call ratios provides insight into sophisticated investor positioning, following the methodology established by Pan and Poteshman (2006) in their analysis of informed trading in options markets.
### 4. Market Breadth Factors
#### Advance-Decline Analysis
Market breadth assessment follows the classic work of Fosback (1976) and subsequent research by Brown and Cliff (2004) on market breadth as a predictor of future returns.
Components:
- Daily advance-decline ratio
- Advance-decline line momentum
- McClellan Oscillator (Ema19 - Ema39 of A-D difference)
#### New Highs-New Lows Analysis
The new highs-new lows ratio serves as a market leadership indicator, based on the research of Zweig (1986) and validated in academic literature by Zarowin (1990).
## Dynamic Threshold Methodology
The model incorporates adaptive thresholds based on rolling volatility and trend analysis, following the methodology established by Pagan and Sossounov (2003) for business cycle dating. This approach allows the model to adjust sensitivity based on prevailing market conditions.
Dynamic Threshold Calculation:
- Warning Level: Base threshold ± (Volatility × 1.0)
- Danger Level: Base threshold ± (Volatility × 1.5)
- Bounds: ±10-20 points from base threshold
## Professional Implementation
### Institutional Usage Patterns
Professional risk managers typically employ multi-factor bear market models in several contexts:
#### 1. Portfolio Risk Management
- Tactical Asset Allocation: Reducing equity exposure when probability exceeds 60-70%
- Hedging Strategies: Implementing protective puts or VIX calls when warning thresholds are breached
- Sector Rotation: Shifting from growth to defensive sectors during elevated risk periods
#### 2. Risk Budgeting
- Value-at-Risk Adjustment: Incorporating bear market probability into VaR calculations
- Stress Testing: Using probability levels to calibrate stress test scenarios
- Capital Requirements: Adjusting regulatory capital based on systemic risk assessment
#### 3. Client Communication
- Risk Reporting: Quantifying market risk for client presentations
- Investment Committee Decisions: Providing objective risk metrics for strategic decisions
- Performance Attribution: Explaining defensive positioning during market stress
### Implementation Framework
Professional traders typically implement such models through:
#### Signal Hierarchy:
1. Probability < 30%: Normal risk positioning
2. Probability 30-50%: Increased hedging, reduced leverage
3. Probability 50-70%: Defensive positioning, cash building
4. Probability > 70%: Maximum defensive posture, short exposure consideration
#### Risk Management Integration:
- Position Sizing: Inverse relationship between probability and position size
- Stop-Loss Adjustment: Tighter stops during elevated risk periods
- Correlation Monitoring: Increased attention to cross-asset correlations
## Strengths and Advantages
### 1. Comprehensive Coverage
The model's primary strength lies in its multi-dimensional approach, avoiding the single-factor bias that has historically plagued market timing models. By incorporating macroeconomic, technical, sentiment, and breadth factors, the model provides robust risk assessment across different market regimes.
### 2. Dynamic Adaptability
The adaptive threshold mechanism allows the model to adjust sensitivity based on prevailing volatility conditions, reducing false signals during low-volatility periods and maintaining sensitivity during high-volatility regimes.
### 3. Real-Time Processing
Unlike traditional academic models that rely on monthly or quarterly data, this indicator processes daily market data, providing timely risk assessment for active portfolio management.
### 4. Transparency and Interpretability
The component-based structure allows users to understand which factors are driving risk assessment, enabling informed decision-making about model signals.
### 5. Historical Validation
Each component has been validated in academic literature, providing theoretical foundation for the model's predictive power.
## Limitations and Weaknesses
### 1. Data Dependencies
The model's effectiveness depends heavily on the availability and quality of real-time economic data. Federal Reserve Economic Data (FRED) updates may have lags that could impact model responsiveness during rapidly evolving market conditions.
### 2. Regime Change Sensitivity
Like most quantitative models, the indicator may struggle during unprecedented market conditions or structural regime changes where historical relationships break down (Taleb, 2007).
### 3. False Signal Risk
Multi-factor models inherently face the challenge of balancing sensitivity with specificity. The model may generate false positive signals during normal market volatility periods.
### 4. Currency and Geographic Bias
The model focuses primarily on US market indicators, potentially limiting its effectiveness for global portfolio management or non-USD denominated assets.
### 5. Correlation Breakdown
During extreme market stress, correlations between risk factors may increase dramatically, reducing the model's diversification benefits (Forbes and Rigobon, 2002).
## References
Akram, Q. F. (2009). Commodity prices, interest rates and the dollar. Energy Economics, 31(6), 838-851.
Ang, A., Piazzesi, M., & Wei, M. (2006). What does the yield curve tell us about GDP growth? Journal of Econometrics, 131(1-2), 359-403.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292.
Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16-24.
Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181-192.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27.
Campbell, J. Y., & Shiller, R. J. (1998). Valuation ratios and the long-run stock market outlook. The Journal of Portfolio Management, 24(2), 11-26.
Dow, C. H. (1901). Scientific stock speculation. The Magazine of Wall Street.
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. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49.
Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261.
Fosback, N. G. (1976). Stock market logic: A sophisticated approach to profits on Wall Street. The Institute for Econometric Research.
Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692-1720.
Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305-333.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Magdon-Ismail, M., & Atiya, A. F. (2004). Maximum drawdown. Risk, 17(10), 99-102.
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220.
Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46.
Pan, J., & Poteshman, A. M. (2006). The information in option volume for future stock prices. The Review of Financial Studies, 19(3), 871-908.
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.
Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Zarowin, P. (1990). Size, seasonality, and stock market overreaction. Journal of Financial and Quantitative Analysis, 25(1), 113-125.
Zweig, M. E. (1986). Winning on Wall Street. Warner Books.
ICT Swiftedge# ICT SwiftEdge: Advanced Market Structure Trading System
**Overview**
ICT SwiftEdge is a powerful trading system built upon the foundation of ICTProTools' ICT Breakers, licensed under the Mozilla Public License 2.0 (mozilla.org). This script has been significantly enhanced by to combine market structure analysis with modern technical indicators and a sleek, AI-inspired statistics dashboard. The goal is to provide traders with a comprehensive tool for identifying high-probability trade setups, managing exits, and tracking performance in a visually intuitive way.
**Credits**
This script is a derivative work based on the original "ICT Breakers" by ICTProTools, used with permission under the Mozilla Public License 2.0. Significant enhancements, including RSI-MA signals, trend filtering, dynamic timeframe adjustments, dual exit strategies, and an AI-style statistics dashboard, were developed by . We express our gratitude to ICTProTools for their foundational work in market structure analysis.
**What It Does**
ICT SwiftEdge integrates multiple trading concepts to help traders identify and manage trades based on market structure and momentum:
- **Market Structure Analysis**: Identifies Break of Structure (BOS) and Market Structure Shift (MSS) patterns, which signal potential trend continuations or reversals. BOS indicates a continuation of the current trend, while MSS highlights a shift in market direction, providing key entry points.
- **RSI-MA Signals**: Generates "BUY" and "SELL" signals when BOS or MSS patterns align with the Relative Strength Index (RSI) smoothed by a Moving Average (RSI-MA). Signals are filtered to occur only when RSI-MA is above 50 (for buys) or below 50 (for sells), ensuring momentum supports the trade direction.
- **Trend Filtering**: Prevents multiple signals in the same trend, ensuring only one buy or sell signal per trend direction, reducing noise and improving trade clarity.
- **Dynamic Timeframe Adjustment**: Automatically adjusts pivot points, RSI, and MA parameters based on the selected chart timeframe (1M to 1D), optimizing performance across different market conditions.
- **Flexible Exit Strategies**: Offers two user-selectable exit methods:
- **Trailing Stop-Loss (TSL)**: Exits trades when price moves against the position by a user-defined distance (in points), locking in profits or limiting losses.
- **RSI-MA Exit**: Exits trades when RSI-MA crosses the 50 level, signaling a potential loss of momentum.
- Users can enable either or both strategies, providing flexibility to adapt to different trading styles.
- **AI-Style Statistics Dashboard**: Displays real-time trade performance metrics in a futuristic, neon-colored interface, including total trades, wins, losses, win/loss ratio, and win percentage. This helps traders evaluate the system's effectiveness without external tools.
**Why This Combination?**
The integration of these components creates a synergistic trading system:
- **BOS/MSS and RSI-MA**: Combining market structure breaks with RSI-MA ensures entries are based on both price action (structure) and momentum (RSI-MA), increasing the likelihood of high-probability trades.
- **Trend Filtering**: By limiting signals to one per trend, the system avoids overtrading and focuses on significant market moves.
- **Dynamic Adjustments**: Timeframe-specific parameters make the system versatile, suitable for scalping (1M, 5M) or swing trading (4H, 1D).
- **Dual Exit Strategies**: TSL protects profits during trending markets, while RSI-MA exits are ideal for range-bound or reversing markets, catering to diverse market conditions.
- **Statistics Dashboard**: Provides immediate feedback on trade performance, enabling data-driven decision-making without manual tracking.
This combination balances technical precision with user-friendly visuals, making it accessible to both novice and experienced traders.
**How to Use**
1. **Add to Chart**: Apply the script to any TradingView chart.
2. **Configure Settings**:
- **Chart Timeframe**: Select your chart's timeframe (1M to 1D) to optimize parameters.
- **Structure Timeframe**: Choose a timeframe for market structure analysis (leave blank for chart timeframe).
- **Exit Strategy**: Enable Trailing Stop-Loss (`useTslExit`), RSI-MA Exit (`useRsiMaExit`), or both. Adjust `tslPoints` for TSL distance.
- **Show Signals/Labels**: Toggle `showSignals` and `showExit` to display "BUY", "SELL", and "EXIT" labels.
- **Dashboard**: Enable `showDashboard` to view trade statistics. Customize colors with `dashboardBgColor` and `dashboardTextColor`.
3. **Trading**:
- Look for "BUY" or "SELL" labels to enter trades when BOS/MSS aligns with RSI-MA.
- Exit trades at "EXIT" labels based on your chosen strategy.
- Monitor the statistics dashboard to track performance (total trades, win/loss ratio, win percentage).
4. **Alerts**: Set up alerts for BOS, MSS, buy, sell, or exit signals using the provided alert conditions.
**License**
This script is licensed under the Mozilla Public License 2.0 (mozilla.org). The source code is available for review and modification under the terms of this license.
**Compliance with TradingView House Rules**
This publication adheres to TradingView's House Rules and Scripts Publication Rules. It provides a clear, self-contained description of the script's functionality, credits the original author (ICTProTools), and explains the rationale for combining indicators. The script contains no promotional content, offensive language, or proprietary restrictions beyond MPL 2.0.
**Note**
Trading involves risk, and past performance is not indicative of future results. Always backtest and validate the system on your preferred markets and timeframes before live trading.
Enjoy trading with ICT SwiftEdge, and let data-driven insights guide your decisions!
High Yield Spread Strategy with SMA FilterThis Pine Script strategy is designed for statistical analysis and research purposes only, not for live trading or financial decision-making. The script evaluates the relationship between financial volatility (measured by either the VIX or the High Yield Spread) and market positioning strategies (long or short) based on user-defined conditions. Specifically, it allows users to test the assumption that elevated levels of VIX or the High Yield Spread may justify short positions in the market—a widely held belief in financial circles—but this script demonstrates that shorting is not always the optimal choice, even under these conditions.
Key Components:
1. High Yield Spread and VIX:
• High Yield Spread is the difference between the yields of corporate high-yield (or “junk”) bonds and U.S. Treasury securities. A rising spread often reflects increased market risk perception.
• VIX (Volatility Index) is often referred to as the market’s “fear gauge.” Higher VIX levels usually indicate heightened market uncertainty or expected volatility.
2. Strategy Logic:
• The script allows users to specify a threshold for the VIX or High Yield Spread, and it automatically evaluates if the spread exceeds this level, which traditionally would suggest an environment for higher market risk and thus potentially favoring short trades.
• However, the strategy provides flexibility to enter long or short positions, even in a high-risk environment, emphasizing that a high VIX or High Yield Spread does not always warrant shorting.
3. SMA Filter:
• A Simple Moving Average (SMA) filter can be applied to the price data, where positions are only entered if the price is above or below the SMA (depending on the trade direction). This adds a technical component to the strategy, incorporating price trends into decision-making.
4. Hold Duration:
• The script also allows users to define how long to hold a position after entering, enabling an analysis of different timeframes.
Theoretical Background:
The traditional belief that high VIX or High Yield Spreads favor short positions is not universally supported by research. While a spike in the VIX or credit spreads is often associated with increased market risk, research suggests that excessive volatility does not always lead to negative returns. In fact, high volatility can sometimes signal an approaching market rebound.
For example:
• Studies have shown that long-term investments during periods of heightened volatility can yield favorable returns due to mean reversion. Whaley (2000) notes that VIX spikes are often followed by market recoveries as volatility tends to revert to its mean over time .
• Research by Blitz and Vliet (2007) highlights that low-volatility stocks have historically outperformed high-volatility stocks, suggesting that volatility may not always predict negative returns .
• Furthermore, credit spreads can widen in response to broader market stress, but these may overshoot the actual credit risk, presenting opportunities for long positions when spreads are high and risk premiums are mispriced .
Educational Purpose:
The goal of this script is to challenge assumptions about shorting during volatile periods, showing that long positions can be equally, if not more, effective during market stress. By incorporating an SMA filter and customizable logic for entering trades, users can test different hypotheses regarding the effectiveness of both long and short positions under varying market conditions.
Note: This strategy is not intended for live trading and should be used solely for educational and statistical exploration. Misinterpreting financial indicators can lead to incorrect investment decisions, and it is crucial to conduct comprehensive research before trading.
References:
1. Whaley, R. E. (2000). “The Investor Fear Gauge”. The Journal of Portfolio Management, 26(3), 12-17.
2. Blitz, D., & van Vliet, P. (2007). “The Volatility Effect: Lower Risk Without Lower Return”. Journal of Portfolio Management, 34(1), 102-113.
3. Bhamra, H. S., & Kuehn, L. A. (2010). “The Determinants of Credit Spreads: An Empirical Analysis”. Journal of Finance, 65(3), 1041-1072.
This explanation highlights the academic and research-backed foundation of the strategy and the nuances of volatility, while cautioning against the assumption that high VIX or High Yield Spread always calls for shorting.
(mab) Dynamic Bitcoin NVT SignalBitcoin`s NVT is calculated by dividing the Network Value (market cap) by the USD volume transmitted through the blockchain daily. Note this equivalent of the bitcoin token supply divided by the daily BTC value transmitted through the blockchain, NVT is technically inverse monetary velocity.
Credits go to Willy Woo for creating the Network Value Transaction Ratio (NVT). Credits go also to Dimitry Kalichkin improving NVT and creating the NVT Signal (NVTS).
According to its creator, the NVT Ratio is somewhat similar to the PE Ratio used in equity markets. When Bitcoin`s NVT is high, it indicates that its network valuation is outstripping the value being transmitted on its payment network, this can happen when the network is in high growth and investors are valuing it as a high return investment, or alternatively when the price is in an unsustainable bubble.
I created this indicator because the NVT indicator I was using suddenly stopped working. I tried a number of other NVT indicators, but all of them seem to have the same problem and stopped updating after a certain date. The cause is that the data feed from 'Quandl' that is used by most NVT indicators is no longer updated through the previous API.
Instead TradingView created a special API to access 'Quandl" data. This indicator not only uses the new API for 'Quandl', it can also access data from other providers like 'Glassnode', 'CoinMetrics' and 'IntoTheBlock'. However, the 'Quandl' data feed seems to produce the best results with this indicator.
The indicator provides dynamically adjusting overbought and oversold thresholds based on a two year moving average and standard devition with adjustable multipliers. It also implements alerts for NVT going into overbought, oversold or crossing the moving average.
Version 1.0
--
Version history
0.1 Beta
- Initial version
1.0
- First release
Harmonic Pattern Table Inputs█ OVERVIEW
This indicator was intended as educational purpose only based on Harmonic Pattern Table (Source Code) .
Some user have different ratios in mind, thus I add input to allow user to change those ratios.
█ CREDITS
Scott M Carney, Trading Volume 3: Reaction vs. Reversal
█ CREDITS
1. List Harmonic Patterns.
2. Font size small for mobile app and font size normal for desktop.
3. Font color does automatically change follow dark / light chart theme.
4. Inputs to change ratio values.
█ USAGE / EXAMPLES
DR/IDR Candles [LuxAlgo]This indicator displays defining ranges (DR) and implied defining ranges (IDR) constructed from two user set sessions (RDR/ODR) as graphical candles on the chart. The script introduces additional graphical elements to the original DR/IDR concept and as such can be thought as a graphical method in addition to a technical indicator.
Additionally, this script can display various Fibonacci retracements from the constructed DR/IDR if enabled within the settings.
Settings
Regular Session: Enable/disable regular session's DR/IDR alongside setting the session time. By default, 09:30 - 10:30 am.
Overnight Session: Enable/disable overnight session's DR/IDR alongside setting the session time. By default, 03:00 - 04:00 am.
UTC Offset: UTC offset for the time zone, by default -5 (EST)
Retracements
Reverse: Inverts source range upper/lower value for constructing the retracements.
From: Source range used to construct the retracements, by default DR is used.
By default, the 0.5 retracement (average line) is displayed.
Usage
The used sessions are highlighted by a gray background. DRs are highlighted by dashed lines while IDRs are highlighted by solid ones. The maximum/minimum price between each user set session is highlighted by solid wicks.
The color of the DRs/IDRs/wicks are determined by the price position relative to the DR; if price is above the DR maximum, then a blue color is used. If price is below, then an orange color is used, and if price is within the DR range, then a gray color is used.
Additionally, the area of the DR range is used to highlight the number of time price is located within the DR, with a longer background highlighting a higher number of occurrences. This can help highlight if the DR levels were potentially useful as support/resistance.
When price is outside the IDR range, the area between the price and IDR is highlighted, in blue if price is above the IDR, and orange if it is under.
The original author of the DR/IDR concept describes 3 rules using the price position relative to the DR/IDR levels:
1.) If price on the 5-minute timeframe closes above the DR high after 10:30 AM or 04:00 AM then the DR low will likely be the low of the trading session.
2.) If price on the 5-minute timeframe closes below the DR low after 10:30 AM or 04:00 AM then the DR high will likely be the high of the trading session.
3.) If price closes above the IDR high after 10:30 AM or 04:00 AM it is an early indication that the low of the DR will be the low of the day and vice versa.
We can see that the above rules are cases of conditional probabilities.
There is no significant data supporting or regarding any statistical probability of the above rules to be true, which are more than uncertain given the stochastic nature of prices. The lack of precision of these rules is also a concern (time zone dependance, applicable markets, etc...).
Credits
Credits to trader TheMas7er who originally created the DR/IDR concept in November of 2022. This script was derived from his proposed session times & rules for trading.
ABC 123 Harmonic Ratio Custom Range Interactive█ OVERVIEW
This indicator was designed based on Harmonic Trading : Volume One written by Scott Carney.
This is about harmonic ratios which expanded through retracement and projection.
Derivation is pretty much explained here such as Primary, Primary Derivation, Secondary Derivation and Secondary Derivation Extreme.
Derivation value depends on minimum retracement or maximum projection.
This derivation value utilize Fibonacci value which later expand to Harmonic Ratio.
█ INSPIRATION
Inspired by design, code and usage of CAGR . Basic usage of custom range / interactive, pretty much explained here . Credits to TradingView.
This build is based and visualized upon Harmonic Trading Ratios.
This build also was stripped down from XABCD Harmonic Pattern Custom Range Interactive .
█ CREDITS
Scott Carney, Harmonic Trading : Volume One (Page 18)
█ FEATURES
Table can positioned by any position and font size can be resized.
Labels can be either changed to alphabets or numbers.
█ HOW TO USE
Draw points from Point A to Point C.
Dont worry about magnet, point will attached depends on High or Low of the candle.
█ USAGE / TIPS EXAMPLES (Description explained in each image)
MACD frontSide backSide + TTM Squeeze by bangkokskaterDark Mode is enabled by default for black theme
disable Dark Mode for white theme
MACD frontSide backSide
===================
an elegant, much better way to use MACD
for trend following momentum ( aka momo) style
MACD with default settings of 12/26 smoothing of 9
✔️ but without histogram
✔️ only has MACD and signal "lines"
green = frontSide momentum impulse
take longs only
red = backSide momentum impulse
take shorts only
black area = exit (once green or red is no longer showing)
or keep holding till next bigger TP
PS: credits to Warrior Trading Ross Cameron for this idea
youtu.be
TTM Squeeze
===================
white dots = incoming pump / dump (monitor for entry)
PS: credits to John Carter's TTM Squeeze & Greeny for PineScript adaptation
Jurik Smoothed Stochastic - TraderHalaiJurik Smoothed Stochastic
The stochastic indicator has been long used by traders to identify inflection points in the price and to give a direction on Bullish and Bearish bias.
This indicator aims to improve on the plots the %K value smoothed using a Jurik Filter instead of a simple moving average. This allows for a more adaptive K value average price, whilst also providing superior smoothing to traditional moving averages.
As the Jurik Filter is a proprietary and non-open-source implementation, this script uses a common filters library implementation of Jurik MA which is a suitable proxy to the actual Jurik MA filter.
Big thanks to LastGuru for making his version freely available. You can find his version of the Jurik Filters in the credits section below.
%K is the Jurik Smoothed Version of the original Stochastic Formula
%D is calculated using the following formula. This idea was borrowed from John Ehler’s stochastic implementation and can be seen below:
%D = 0.05 + 0.95 * K
Features
%K line, Overbought and Oversold level and Mid Line Level
Oversold / Overbought reversal indicators and signals - Shown in Red and Green
Bullish / Bearish Divergences – Including Hidden divergences to spot reversals and continuations of trend (Big thanks to the developers of the built-in RSI Divergence indicator) - Shown as below:
Bullish / Bearish crossover of %K with %D - Shown in Cyan and Fuschia
Alerts for all of the above conditions
Double Jurik smoothing mode - similar to slow Stochastic
Credits :
Massive shoutout to the following scripts:
LastGuru JurikMA implementation (Common Filters Library)
Divergence Indicator – Built into TradingView and coded by TradingView Developers
This script is published as open source to allow for criticism, further development of this strategy and use by the community. Feel free to use this indicator/source code as you see fit.
Enjoy! :)
PriceTimeInteractive█ OVERVIEW
This library was intended to Get price of given time.input
█ CREDITS
Credits to TradingView for CAGR Custom Range.
█ FUNCTIONS
ohlc_time()
: Get OHLC price of given time.input
Parameters:
: : Time (t) must be using time.input
Returns: : OHLC
hlc_time()
: Get HLC price of given time.input
Parameters:
: : Time (t) must be using time.input
Returns: : HLC
hl_time()
: Get HL price of given time.input
Parameters:
: : Time (t) must be using time.input
Returns: : HL
Pivot Points Standard Higher Timeframe█ OVERVIEW
This indicator was intended as educational purpose only and alternative way to write pine script in version 5 for Pivot Points Standard.
█ CREDITS
Credits to TradingView for original built-in source code.
█ FEATURES
Display price or percentage.
Change color for labels and lines.
█ USAGE EXAMPLES
[HELPER] Math Constant Helper█ OVERVIEW
This indicator is to show constant in table using built-in math name space, coded in latest Pine Script version 5.
█ CREDITS
Credits to PineCoders.
█ FEATURES
- Display table by changing table position, font size and color.
Input Text Area to Array then Reshape Table█ OVERVIEW
Simple method to convert from input.text_area to array using str.split.
Reshape table using switch, not necessary must use matrix.reshape.
Might be useful pine script to replace input.symbol.
█ FEATURES
Table can positioned by any position and font size can be resized.
Reshape table and sorting array if necessary.
█ CREDITS
Credits to TradingView for new update of input.text_area.
super SSL [ALZ]This script is designed and optimized for MULTI TIME
by Ali Zebardast (ALZ)
1.in part of ssl
Original Version credits to Mihkel00
Actual Version i just set alerts and change the parameters for BTCUSDT 1min Chart.
He designed for daily time. I tried to optimize 1 min time-frame .
And fix the errors with OTT
"This script has a SSL / Baseline (you can choose between the SSL or MA), a secondary SSL for continiuation trades and a third SSL for exit trades.
Alerts added for Baseline entries, SSL2 continuations, Exits.
Baseline has a Keltner Channel setting for "in zone" Gray Candles
Added "Candle Size > 1 ATR" Diamonds from my old script with the criteria of being within Baseline ATR range."
2.in part of Range
two Filter Buy and Sell for 3min
Wait For Bar close
ssl2 :Be under the candle for buy
and The bar color must confirm the order of purchase (Blue)
3.in part of OTT
when candles close over HOTT, means an UPTREND SIGNAL
and to Fuchia when candles begin closing under LOTT line to indicate a DOWNTREND SIGNAL.
FLAT ZONE is highlighted also to have the maximum concentration on sideways market conditions.
There are three quantitative parameters in this indicator:
The first parameter in the OTT indicator set by the two parameters is the period/length.
OTT lines will be much sensitive to trend movements if it is smaller.
And vice versa, will be less sensitive when it is longer.
As the period increases it will become less sensitive to little trends and price actions.
In this way, your choice of period, will be closely related to which of the sort of trends you are interested in.
The OTT percent parameter in OTT is an optimization coefficient. Just like in the period
small values are better at capturing short term fluctuations, while large values
will be more suitable for long-term trends.
The final adjustable quantitative parameter is HIGHEST and LOWEST length which is the source of calculations.
Credits go to:
SSL Hybrid www.tradingview.com
HIGH and LOW OTT : www.tradingview.com
Range Filter www.tradingview.com
Squeeze M + ADX + TTM (Trading Latino & John Carter) by [Rolgui]About this indicator:
This indicator aims to combine two good performing strategies, which can be used separately or together, mainly for investment positions, although it can also be used for intraday trading.
Strategy 1) Squeeze Oscillator and Average Directional Index:
This strategy is taught by Jaime Aibsai, which determines market entries based on reading the direction of the price movement (Directionality of the Oscillator) along with the strength of the Oscillator (Slope of the ADX).
Both tools are configured according to Jaime Abisai's strategy, by default (note that point 23 of the ADX is represented by point 0 on the panel, to make reading easier, its interpretation is not affected). Anyway you can adjust the input data according to your interest.
*You can see this setting in the first panel.
Strategy 2) Squeeze Momentum and Trade The Market Waves:
This strategy can be consulted either in John F. Carter's books or on his website.
This market reading is based on Price Volatility (Bollinger Bands and Keltner Channels interaction) and its Trend (Exponential Moving Averages), showing entries at times when price volatility is low and taking filtering active trend using T.T.M. Waves.
To configure the indicator in the same way that Carter does, it would be enough to turn off the ADX, turn on the Squeeze Momentum signals along with the T.T.M. Waves, and importantly, change the Linear Momentum value to 12 (this configuration can be found in his book).
*You can see this setting in the second panel.
Why this indicator?
I've added and removed the above flags as I needed to query them (which became tedious for me). The main objective of having merged them into one is to make their reading more agile and comfortable and thus improve the decision-making capacity of the trader who wishes to use them.
Credits and Acknowledgments:
I would like to give credits to other authors, for the sections of code that I have used to make this technical indicator. Thanks to @LazyBear, @matetaronna, @jombie and @joren for contributing to the community and keeping their code open. It is priceless!
Feel free to combine and practice your trading with both strategies, personally, they improved my profitability and this is why I recommend researching more about them. I've been using it for crypto investing, let me know if it's worth for you on stock market!
If you have any questions or suggestions you can leave it in the comments!
Greetings!
Multiple Timeframe continuity with Crossover Alerts█ OVERVIEW
This Indicator calculates the EMA 9/20 and the RSI with its SMA on multiple timeframes and indicates their crossings. In addition this script alerts the user when crossings appear.
█ USAGE
Use the checkboxes to activate different timeframes. With the dropdown menu you can select the timeframe in minutes.
Furthermroie use the checkboxes to activate different crossovers. At the end of the settings you can find the same options for the RSI.
You can also let the script indicate only the overlapping of both indicator crossovers by using the combination option.
█ KNOWLEDGE
EMA: The ema function returns the exponentially weighted moving average. In ema weighting factors decrease exponentially. It calculates by using a formula: EMA = alpha * source + (1 - alpha) * EMA , where alpha = 2 / (length + 1).
SMA: The sma function returns the moving average, that is the sum of last y values of x, divided by y.
RSI: The RSI is classified as a momentum oscillator, measuring the velocity and magnitude of price movements. Momentum is the rate of the rise or fall in price. The RSI computes momentum as the ratio of higher closes to lower closes: stocks which have had more or stronger positive changes have a higher RSI than stocks which have had more or stronger negative changes.
RMA: Moving average used in RSI. It is the exponentially weighted moving average with alpha = 1 / length.
(Source: TradingView PineScript reference & en.wikipedia.org)
█ Credits
Thanks to @KhanPhelan with his EMA 9/20 trading idea
Credits to TradingView for their RSI function
█ Disclaimer
This is my first Script, any feedback is welcome.
XABCD Harmonic Pattern Custom Range Interactive█ OVERVIEW
This indicator was designed based on Harmonic Pattern Book written by Scott Carney. It was simplified to user who may always used tools such as XABCD Pattern and Long Position / Short Position, which consume a lot of time, recommended for both beginner and expert of Harmonic Pattern Traders. XABCD Pattern require tool usage of Magnet tool either Strong Magnet, Week Magnet or none, which cause error or human mistake especially daily practice.
Simplified Guideline by sequence for Harmonic Pattern if using manual tools :
Step 1 : Trade Identification - XABCD Pattern
Step 2 : Trade Execution - Any manual tools of your choice
Step 3 : Trade Management - Position / Short Position
█ INSPIRATION
Inspired by design, code and usage of CAGR. Basic usage of custom range / interactive, pretty much explained here . Credits to TradingView.
I use a lot of XABCD Pattern and Long Position / Short Position, require 5 to 10 minutes on average, upon determine the validity of harmonic pattern.
Upon creating this indicator, I believed that time can be reduced, gain more confidence, reduce error during drawing XABCD, which helps most of harmonic pattern users.
█ FEATURES
Table can positioned by any postion and font size can be resized.
Table can be display through optimized display or manual control.
Validility of harmonic pattern depends on BC ratio.
Harmonic pattern can be displayed fully or optimized while showing BC ratio validity.
Trade Execution at point D can be displayed on / off.
Stop Loss and Take Profit can be calculated automatically or manually.
Optimized table display based extend line setup and profit and loss setup.
Execution zone can be offset to Point C, by default using Point D.
Currency can be show or hide.
Profit and Loss can be displayed on axis once line is extended.
█ HOW TO USE
Step 1 : Trade Identification - Draw points from Point X to Point C. Dont worry about magnet, point will attached depends on High or Low of the candle.
Step 2 : Trade Execution - Check the validity of BC to determine the validity of harmonic pattern generated. Pattern only generate 1 pattern upon success. Otherwise, redraw to other points.
Step 3 : Trade Management - Determine the current candle either reach Point D or Potential Reversal Zone (PRZ). Check for Profit & Loss once reach PRZ.
█ USAGE LIMITATIONS
Harmonic Patterns only limits to patterns mentioned in Harmonic Trading Volume 3 due to other pattern may have other or different philosophy.
Only can be used for Daily timeframe and below due to bar_time is based on minutes by default.
Not recommended for Weekly and Monthly timeframe.
If Point X, A, B, C and D is next to each other, it is recommend to use lower timeframe.
Automated alert is not supported for this release. However, alert can be done manually. Alert will updated on the version.
█ PINE SCRIPT LIMITATIONS
Known bug for when calculate time in array, causing label may not appeared or offset.
Unable to convert to library due to usage of array.get(). I prefer usage for a combination of array.get(id, 0), array.get(id, 1), array.get(id, 2) into custom function, however I faced this issue during make arrays of label. Index can be simply refered as int, for id, i not sure, already try id refered as simple, nothing happens.
linefill.new() will appeared as diamond box if overused.
Text in box.new() unable to use ternary condition or switch to change color. Bgcolor also affected.
Label display is larger than XABCD tool. Hopefully in future, have function to resize label similar to XABCD tools.
█ IMPORTANTS
Trade Management (Profit & Loss) is calculated from Point A to D.
Take Profit is calculated based on ratio 0.382 and 0.618 of Point A to D.
Always check BC validity before proceed to Trade Management.
Length of XABCD is equal to XAB plus BCD, where XAB and BCD are one to one ratio. Length is measured in time.
Use other oscillator to countercheck. Normally use built-in Relative Strength Index (RSI) and Divergence Indicator to determine starting point of Point X and A.
█ HARMONIC PATTERNS SUPPORTED
// Credits to Scott M Carney, author of Harmonic Trading Volume 3: Reaction vs. Reversal
Alt Bat - Page 101
Bat - Page 98
Crab - Page 104
Gartley - Page 92
Butterfly - Page 113
Deep Crab - Page 107
Shark - Page 119 - 220
█ FAQ
Pattern such as 5-0, perfect XABCD and ABCD that not included, will updated on either next version or new release.
Point D time is for approximation only, not including holidays and extended session.
Basic explaination for Harmonic Trading System (Trade Identification, Trade Execution and Trade Management).
Harmonic Patterns values is pretty much summarized here including Stop Loss.
Basic explanation for Alt Bat, Bat, Crab, Gartley, Deep Crab and Butterfly.
█ USAGE / TIPS EXAMPLES (Description explained in each image)
Gann Angle Table Calculator PlotterThis indicator was build upon based on Gann Angle and Gann Multiplier as refered of The Tunnel Thru The Air Or Looking Back From 1940, written by WD Gann .
Credits to FaizHebat for calculation.
Indicator features :
1. Font size from tiny to huge.
2. Table position.
3. Color for cell table and plot can be input or reversed.
4. Plot can be show or hide and also price can be track.
5. Able show currency and decimal price.
6. Gann table and Calculation table can show or hide.
7. Each Gann point can be hide including plot and calculation.
8. For desktop display only, not for mobile.
Pros :
1. Can be used as single or more indicator. (Chart show 2 same indicator with different setup)
Cons :
1. Price must be input before selecting any Gann Point.
FAQ
1. Credits
WD Gann , The Tunnel Thru The Air Or Looking Back From 1940
FaizHebat
2. Code Usage
Free to use for personal usage.
Positive direction with Currency and Decimal
Positive direction with Currency and Decimal
