Super PerformanceThe "Super Performance" script is a custom indicator written in Pine Script (version 6) for use on the TradingView platform. Its main purpose is to visually compare the performance of a selected stock or index against a benchmark index (default: NIFTYMIDSML400) over various timeframes, and to display sector-wise performance rankings in a clear, tabular format.
Key Features:
Customizable Display:
Users can toggle between dark and light color themes, enable or disable extended data columns, and choose between a compact "Mini Mode" or a full-featured table view. Table positions and sizes are also configurable for both stock and sector tables.
Performance Calculation:
The script calculates percentage price changes for the selected stock and the benchmark index over multiple periods: 1, 5, 10, 20, 50, and 200 days. It then checks if the stock is outperforming the index for each period.
Conviction Score:
For each period where the stock outperforms the index, a "conviction score" is incremented. This score is mapped to qualitative labels such as "Super solid," "Solid," "Good," etc., and is color-coded for quick visual interpretation.
Sector Performance Table:
The script tracks 19 sector indices (e.g., REALTY, IT, PHARMA, AUTO, ENERGY) and calculates their performance over 1, 5, 10, 20, and 60-day periods. It then ranks the top 5 performing sectors for each timeframe and displays them in a sector performance table.
Visual Output:
Two tables are constructed:
Stock Performance Table: Shows the stock's returns, index returns, outperformance markers (✔/✖), and the difference for each period, along with the overall conviction score.
Sector Performance Table: Ranks and displays the top 5 sectors for each timeframe, with color-coded performance values for easy comparison.
在腳本中搜尋"美股科技股4月19日走势"
Contrarian 100 MAPairs nicely with Enhanced-Stock-Ticker-with-50MA-vs-200MA located here:
Description
The Contrarian 100 MA is a sophisticated Pine Script v6 indicator designed for traders seeking to identify key market structure shifts and trend reversals using a combination of a 100-period Simple Moving Average (SMA) envelope and Inner Circle Trader (ICT) Break of Structure (BoS) and Market Structure Shift (MSS) logic. By overlaying a semi-transparent SMA-based shadow on the price chart and plotting bullish and bearish structure signals, this indicator helps traders visualize critical price levels and potential trend changes. It leverages higher timeframe (HTF) pivot points and dynamic logic to adapt to various chart timeframes, making it ideal for swing and contrarian trading strategies. Customizable colors, timeframes, and alert conditions enhance its versatility for manual and automated trading setups.
Key Features
SMA Envelope: Plots a 100-period SMA for high and low prices, creating a semi-transparent (50% opacity) purple shadow to highlight the price range and provide context for price movements.
ICT BoS/MSS Logic: Identifies Break of Structure (BoS) and Market Structure Shift (MSS) signals for both bullish and bearish conditions, based on HTF pivot points.
Dynamic Timeframe Support: Adjusts pivot detection based on user-selected HTF (default: 1D) and chart timeframe (1M, 5M, 15M, 30M, 1H, 4H, 1D), ensuring adaptability across markets.
Visual Signals: Draws dotted lines for BoS (bullish/bearish) and MSS (bullish/bearish) signals at pivot levels, with customizable colors for easy identification.
Contrarian Approach: Signals potential reversals by combining SMA context with ICT structure breaks, ideal for traders looking to capitalize on trend shifts.
Alert Conditions: Supports alerts for bullish/bearish BoS and MSS signals, enabling integration with TradingView’s alert system for automated trading.
Performance Optimization: Uses efficient pivot detection and line management to minimize resource usage while maintaining accuracy.
Technical Details
SMA Calculation:
Computes 100-period SMAs for high (smaHigh) and low (smaLow) prices.
Plots invisible SMAs (fully transparent) and fills the area between them with 50% transparent purple for visual context.
Pivot Detection:
Uses ta.pivothigh and ta.pivotlow to identify HTF swing points, with dynamic lookback periods (rlBars: 5 for daily, 2 for intraday).
Tracks pivot highs (pH, nPh) and lows (pL, nPl) using a custom piv type for price and time.
BoS/MSS Logic:
Bullish BoS: Triggered when price breaks above a pivot high in a bullish trend, drawing a line at the pivot level.
Bearish BoS: Triggered when price breaks below a pivot low in a bearish trend.
Bullish MSS: Occurs when price breaks a pivot high in a bearish trend, signaling a potential trend reversal.
Bearish MSS: Occurs when price breaks a pivot low in a bullish trend.
Lines are drawn using line.new with xloc.bar_time for precise alignment, styled as dotted with customizable colors.
HTF Integration: Fetches HTF close prices and pivot data using request.security with lookahead_on for accurate signal timing.
Line Management: Maintains an array of lines (lin), removing outdated lines when new MSS signals occur to keep the chart clean.
Pivot Reset: Clears broken pivots (e.g., when price exceeds a pivot high or falls below a pivot low) to ensure fresh signal generation.
How to Use
Add to Chart:
Copy the script into TradingView’s Pine Editor and apply it to your chart.
Configure Settings:
SMA Length: Adjust the SMA period (default: 100 bars) to suit your trading style.
Structure Timeframe: Set the HTF for pivot detection (default: 1D).
Chart Timeframe: Select the chart timeframe (1M, 5M, 15M, 30M, 1H, 4H, 1D) to adjust pivot sensitivity.
Colors: Customize bullish/bearish BoS and MSS line colors via input settings.
Interpret Signals:
Bullish BoS: White dotted line (default) at a broken pivot high in a bullish trend, indicating trend continuation.
Bearish BoS: White dotted line at a broken pivot low in a bearish trend.
Bullish MSS: White dotted line at a broken pivot high in a bearish trend, suggesting a reversal to bullish.
Bearish MSS: White dotted line at a broken pivot low in a bullish trend, suggesting a reversal to bearish.
Use the SMA shadow to gauge price position within the recent range.
Set Alerts:
Create alerts for bullish/bearish BoS and MSS signals using TradingView’s alert system.
Customize Visuals:
Adjust line colors or SMA fill transparency via TradingView’s settings for better visibility.
Example Use Cases
Swing Trading: Use MSS signals to enter trades at potential trend reversals, with the SMA envelope confirming price extremes.
Contrarian Trading: Capitalize on BoS and MSS signals to trade against prevailing trends, using the SMA shadow for context.
Automated Trading: Integrate BoS/MSS alerts with trading bots for systematic entries and exits.
Multi-Timeframe Analysis: Combine HTF signals (e.g., 1D) with lower timeframe charts (e.g., 1H) for precise entries.
Notes
Testing: Backtest the indicator on your chosen market and timeframe to validate performance.
Compatibility: Built for Pine Script v6 and tested on TradingView as of June 19, 2025.
Limitations: Signals rely on HTF pivot accuracy, which may lag in fast-moving markets. Adjust rlBars or timeframe for sensitivity.
Optional Enhancements: Consider uncommenting or adding a histogram for SMA divergence (e.g., smaHigh - smaLow) for additional insights.
Acknowledgments
This indicator combines ICT’s market structure concepts with a dynamic SMA envelope to provide a unique contrarian trading tool. Share your feedback or suggestions in the TradingView comments, and happy trading!
Grothendieck-Teichmüller Geometric SynthesisDskyz's Grothendieck-Teichmüller Geometric Synthesis (GTGS)
THEORETICAL FOUNDATION: A SYMPHONY OF GEOMETRIES
The 🎓 GTGS is built upon a revolutionary premise: that market dynamics can be modeled as geometric and topological structures. While not a literal academic implementation—such a task would demand computational power far beyond current trading platforms—it leverages core ideas from advanced mathematical theories as powerful analogies and frameworks for its algorithms. Each component translates an abstract concept into a practical market calculation, distinguishing GTGS by identifying deeper structural patterns rather than relying on standard statistical measures.
1. Grothendieck-Teichmüller Theory: Deforming Market Structure
The Theory : Studies symmetries and deformations of geometric objects, focusing on the "absolute" structure of mathematical spaces.
Indicator Analogy : The calculate_grothendieck_field function models price action as a "deformation" from its immediate state. Using the nth root of price ratios (math.pow(price_ratio, 1.0/prime)), it measures market "shape" stretching or compression, revealing underlying tensions and potential shifts.
2. Topos Theory & Sheaf Cohomology: From Local to Global Patterns
The Theory : A framework for assembling local properties into a global picture, with cohomology measuring "obstructions" to consistency.
Indicator Analogy : The calculate_topos_coherence function uses sine waves (math.sin) to represent local price "sections." Summing these yields a "cohomology" value, quantifying price action consistency. High values indicate coherent trends; low values signal conflict and uncertainty.
3. Tropical Geometry: Simplifying Complexity
The Theory : Transforms complex multiplicative problems into simpler, additive, piecewise-linear ones using min(a, b) for addition and a + b for multiplication.
Indicator Analogy : The calculate_tropical_metric function applies tropical_add(a, b) => math.min(a, b) to identify the "lowest energy" state among recent price points, pinpointing critical support levels non-linearly.
4. Motivic Cohomology & Non-Commutative Geometry
The Theory : Studies deep arithmetic and quantum-like properties of geometric spaces.
Indicator Analogy : The motivic_rank and spectral_triple functions compute weighted sums of historical prices to capture market "arithmetic complexity" and "spectral signature." Higher values reflect structured, harmonic price movements.
5. Perfectoid Spaces & Homotopy Type Theory
The Theory : Abstract fields dealing with p-adic numbers and logical foundations of mathematics.
Indicator Analogy : The perfectoid_conv and type_coherence functions analyze price convergence and path identity, assessing the "fractal dust" of price differences and price path cohesion, adding fractal and logical analysis.
The Combination is Key : No single theory dominates. GTGS ’s Unified Field synthesizes all seven perspectives into a comprehensive score, ensuring signals reflect deep structural alignment across mathematical domains.
🎛️ INPUTS: CONFIGURING THE GEOMETRIC ENGINE
The GTGS offers a suite of customizable inputs, allowing traders to tailor its behavior to specific timeframes, market sectors, and trading styles. Below is a detailed breakdown of key input groups, their functionality, and optimization strategies, leveraging provided tooltips for precision.
Grothendieck-Teichmüller Theory Inputs
🧬 Deformation Depth (Absolute Galois) :
What It Is : Controls the depth of Galois group deformations analyzed in market structure.
How It Works : Measures price action deformations under automorphisms of the absolute Galois group, capturing market symmetries.
Optimization :
Higher Values (15-20) : Captures deeper symmetries, ideal for major trends in swing trading (4H-1D).
Lower Values (3-8) : Responsive to local deformations, suited for scalping (1-5min).
Timeframes :
Scalping (1-5min) : 3-6 for quick local shifts.
Day Trading (15min-1H) : 8-12 for balanced analysis.
Swing Trading (4H-1D) : 12-20 for deep structural trends.
Sectors :
Stocks : Use 8-12 for stable trends.
Crypto : 3-8 for volatile, short-term moves.
Forex : 12-15 for smooth, cyclical patterns.
Pro Tip : Increase in trending markets to filter noise; decrease in choppy markets for sensitivity.
🗼 Teichmüller Tower Height :
What It Is : Determines the height of the Teichmüller modular tower for hierarchical pattern detection.
How It Works : Builds modular levels to identify nested market patterns.
Optimization :
Higher Values (6-8) : Detects complex fractals, ideal for swing trading.
Lower Values (2-4) : Focuses on primary patterns, faster for scalping.
Timeframes :
Scalping : 2-3 for speed.
Day Trading : 4-5 for balanced patterns.
Swing Trading : 5-8 for deep fractals.
Sectors :
Indices : 5-8 for robust, long-term patterns.
Crypto : 2-4 for rapid shifts.
Commodities : 4-6 for cyclical trends.
Pro Tip : Higher towers reveal hidden fractals but may slow computation; adjust based on hardware.
🔢 Galois Prime Base :
What It Is : Sets the prime base for Galois field computations.
How It Works : Defines the field extension characteristic for market analysis.
Optimization :
Prime Characteristics :
2 : Binary markets (up/down).
3 : Ternary states (bull/bear/neutral).
5 : Pentagonal symmetry (Elliott waves).
7 : Heptagonal cycles (weekly patterns).
11,13,17,19 : Higher-order patterns.
Timeframes :
Scalping/Day Trading : 2 or 3 for simplicity.
Swing Trading : 5 or 7 for wave or cycle detection.
Sectors :
Forex : 5 for Elliott wave alignment.
Stocks : 7 for weekly cycle consistency.
Crypto : 3 for volatile state shifts.
Pro Tip : Use 7 for most markets; 5 for Elliott wave traders.
Topos Theory & Sheaf Cohomology Inputs
🏛️ Temporal Site Size :
What It Is : Defines the number of time points in the topological site.
How It Works : Sets the local neighborhood for sheaf computations, affecting cohomology smoothness.
Optimization :
Higher Values (30-50) : Smoother cohomology, better for trends in swing trading.
Lower Values (5-15) : Responsive, ideal for reversals in scalping.
Timeframes :
Scalping : 5-10 for quick responses.
Day Trading : 15-25 for balanced analysis.
Swing Trading : 25-50 for smooth trends.
Sectors :
Stocks : 25-35 for stable trends.
Crypto : 5-15 for volatility.
Forex : 20-30 for smooth cycles.
Pro Tip : Match site size to your average holding period in bars for optimal coherence.
📐 Sheaf Cohomology Degree :
What It Is : Sets the maximum degree of cohomology groups computed.
How It Works : Higher degrees capture complex topological obstructions.
Optimization :
Degree Meanings :
1 : Simple obstructions (basic support/resistance).
2 : Cohomological pairs (double tops/bottoms).
3 : Triple intersections (complex patterns).
4-5 : Higher-order structures (rare events).
Timeframes :
Scalping/Day Trading : 1-2 for simplicity.
Swing Trading : 3 for complex patterns.
Sectors :
Indices : 2-3 for robust patterns.
Crypto : 1-2 for rapid shifts.
Commodities : 3-4 for cyclical events.
Pro Tip : Degree 3 is optimal for most trading; higher degrees for research or rare event detection.
🌐 Grothendieck Topology :
What It Is : Chooses the Grothendieck topology for the site.
How It Works : Affects how local data integrates into global patterns.
Optimization :
Topology Characteristics :
Étale : Finest topology, captures local-global principles.
Nisnevich : A1-invariant, good for trends.
Zariski : Coarse but robust, filters noise.
Fpqc : Faithfully flat, highly sensitive.
Sectors :
Stocks : Zariski for stability.
Crypto : Étale for sensitivity.
Forex : Nisnevich for smooth trends.
Indices : Zariski for robustness.
Timeframes :
Scalping : Étale for precision.
Swing Trading : Nisnevich or Zariski for reliability.
Pro Tip : Start with Étale for precision; switch to Zariski in noisy markets.
Unified Field Configuration Inputs
⚛️ Field Coupling Constant :
What It Is : Sets the interaction strength between geometric components.
How It Works : Controls signal amplification in the unified field equation.
Optimization :
Higher Values (0.5-1.0) : Strong coupling, amplified signals for ranging markets.
Lower Values (0.001-0.1) : Subtle signals for trending markets.
Timeframes :
Scalping : 0.5-0.8 for quick, strong signals.
Swing Trading : 0.1-0.3 for trend confirmation.
Sectors :
Crypto : 0.5-1.0 for volatility.
Stocks : 0.1-0.3 for stability.
Forex : 0.3-0.5 for balance.
Pro Tip : Default 0.137 (fine structure constant) is a balanced starting point; adjust up in choppy markets.
📐 Geometric Weighting Scheme :
What It Is : Determines the framework for combining geometric components.
How It Works : Adjusts emphasis on different mathematical structures.
Optimization :
Scheme Characteristics :
Canonical : Equal weighting, balanced.
Derived : Emphasizes higher-order structures.
Motivic : Prioritizes arithmetic properties.
Spectral : Focuses on frequency domain.
Sectors :
Stocks : Canonical for balance.
Crypto : Spectral for volatility.
Forex : Derived for structured moves.
Indices : Motivic for arithmetic cycles.
Timeframes :
Day Trading : Canonical or Derived for flexibility.
Swing Trading : Motivic for long-term cycles.
Pro Tip : Start with Canonical; experiment with Spectral in volatile markets.
Dashboard and Visual Configuration Inputs
📋 Show Enhanced Dashboard, 📏 Size, 📍 Position :
What They Are : Control dashboard visibility, size, and placement.
How They Work : Display key metrics like Unified Field , Resonance , and Signal Quality .
Optimization :
Scalping : Small size, Bottom Right for minimal chart obstruction.
Swing Trading : Large size, Top Right for detailed analysis.
Sectors : Universal across markets; adjust size based on screen setup.
Pro Tip : Use Large for analysis, Small for live trading.
📐 Show Motivic Cohomology Bands, 🌊 Morphism Flow, 🔮 Future Projection, 🔷 Holographic Mesh, ⚛️ Spectral Flow :
What They Are : Toggle visual elements representing mathematical calculations.
How They Work : Provide intuitive representations of market dynamics.
Optimization :
Timeframes :
Scalping : Enable Morphism Flow and Spectral Flow for momentum.
Swing Trading : Enable all for comprehensive analysis.
Sectors :
Crypto : Emphasize Morphism Flow and Future Projection for volatility.
Stocks : Focus on Cohomology Bands for stable trends.
Pro Tip : Disable non-essential visuals in fast markets to reduce clutter.
🌫️ Field Transparency, 🔄 Web Recursion Depth, 🎨 Mesh Color Scheme :
What They Are : Adjust visual clarity, complexity, and color.
How They Work : Enhance interpretability of visual elements.
Optimization :
Transparency : 30-50 for balanced visibility; lower for analysis.
Recursion Depth : 6-8 for balanced detail; lower for older hardware.
Color Scheme :
Purple/Blue : Analytical focus.
Green/Orange : Trading momentum.
Pro Tip : Use Neon Purple for deep analysis; Neon Green for active trading.
⏱️ Minimum Bars Between Signals :
What It Is : Minimum number of bars required between consecutive signals.
How It Works : Prevents signal clustering by enforcing a cooldown period.
Optimization :
Higher Values (10-20) : Fewer signals, avoids whipsaws, suited for swing trading.
Lower Values (0-5) : More responsive, allows quick reversals, ideal for scalping.
Timeframes :
Scalping : 0-2 bars for rapid signals.
Day Trading : 3-5 bars for balance.
Swing Trading : 5-10 bars for stability.
Sectors :
Crypto : 0-3 for volatility.
Stocks : 5-10 for trend clarity.
Forex : 3-7 for cyclical moves.
Pro Tip : Increase in choppy markets to filter noise.
Hardcoded Parameters
Tropical, Motivic, Spectral, Perfectoid, Homotopy Inputs : Fixed to optimize performance but influence calculations (e.g., tropical_degree=4 for support levels, perfectoid_prime=5 for convergence).
Optimization : Experiment with codebase modifications if advanced customization is needed, but defaults are robust across markets.
🎨 ADVANCED VISUAL SYSTEM: TRADING IN A GEOMETRIC UNIVERSE
The GTTMTSF ’s visuals are direct representations of its mathematics, designed for intuitive and precise trading decisions.
Motivic Cohomology Bands :
What They Are : Dynamic bands ( H⁰ , H¹ , H² ) representing cohomological support/resistance.
Color & Meaning : Colors reflect energy levels ( H⁰ tightest, H² widest). Breaks into H¹ signal momentum; H² touches suggest reversals.
How to Trade : Use for stop-loss/profit-taking. Band bounces with Dashboard confirmation are high-probability setups.
Morphism Flow (Webbing) :
What It Is : White particle streams visualizing market momentum.
Interpretation : Dense flows indicate strong trends; sparse flows signal consolidation.
How to Trade : Follow dominant flow direction; new flows post-consolidation signal trend starts.
Future Projection Web (Fractal Grid) :
What It Is : Fibonacci-period fractal projections of support/resistance.
Color & Meaning : Three-layer lines (white shadow, glow, colored quantum) with labels showing price, topological class, anomaly strength (φ), resonance (ρ), and obstruction ( H¹ ). ⚡ marks extreme anomalies.
How to Trade : Target ⚡/● levels for entries/exits. High-anomaly levels with weakening Unified Field are reversal setups.
Holographic Mesh & Spectral Flow :
What They Are : Visuals of harmonic interference and spectral energy.
How to Trade : Bright mesh nodes or strong Spectral Flow warn of building pressure before price movement.
📊 THE GEOMETRIC DASHBOARD: YOUR MISSION CONTROL
The Dashboard translates complex mathematics into actionable intelligence.
Unified Field & Signals :
FIELD : Master value (-10 to +10), synthesizing all geometric components. Extreme readings (>5 or <-5) signal structural limits, often preceding reversals or continuations.
RESONANCE : Measures harmony between geometric field and price-volume momentum. Positive amplifies bullish moves; negative amplifies bearish moves.
SIGNAL QUALITY : Confidence meter rating alignment. Trade only STRONG or EXCEPTIONAL signals for high-probability setups.
Geometric Components :
What They Are : Breakdown of seven mathematical engines.
How to Use : Watch for convergence. A strong Unified Field is reliable when components (e.g., Grothendieck , Topos , Motivic ) align. Divergence warns of trend weakening.
Signal Performance :
What It Is : Tracks indicator signal performance.
How to Use : Assesses real-time performance to build confidence and understand system behavior.
🚀 DEVELOPMENT & UNIQUENESS: BEYOND CONVENTIONAL ANALYSIS
The GTTMTSF was developed to analyze markets as evolving geometric objects, not statistical time-series.
Why This Is Unlike Anything Else :
Theoretical Depth : Uses geometry and topology, identifying patterns invisible to statistical tools.
Holistic Synthesis : Integrates seven deep mathematical frameworks into a cohesive Unified Field .
Creative Implementation : Translates PhD-level mathematics into functional Pine Script , blending theory and practice.
Immersive Visualization : Transforms charts into dynamic geometric landscapes for intuitive market understanding.
The GTTMTSF is more than an indicator; it’s a new lens for viewing markets, for traders seeking deeper insight into hidden order within chaos.
" Where there is matter, there is geometry. " - Johannes Kepler
— Dskyz , Trade with insight. Trade with anticipation.
Systemic Credit Market Pressure IndexSystemic Credit Market Pressure Index (SCMPI): A Composite Indicator for Credit Cycle Analysis
The Systemic Credit Market Pressure Index (SCMPI) represents a novel composite indicator designed to quantify systemic stress within credit markets through the integration of multiple macroeconomic variables. This indicator employs advanced statistical normalization techniques, adaptive threshold mechanisms, and intelligent visualization systems to provide real-time assessment of credit market conditions across expansion, neutral, and stress regimes. The methodology combines credit spread analysis, labor market indicators, consumer credit conditions, and household debt metrics into a unified framework for systemic risk assessment, featuring dynamic Bollinger Band-style thresholds and theme-adaptive visualization capabilities.
## 1. Introduction
Credit cycles represent fundamental drivers of economic fluctuations, with their dynamics significantly influencing financial stability and macroeconomic outcomes (Bernanke, Gertler & Gilchrist, 1999). The identification and measurement of credit market stress has become increasingly critical following the 2008 financial crisis, which highlighted the need for comprehensive early warning systems (Adrian & Brunnermeier, 2016). Traditional single-variable approaches often fail to capture the multidimensional nature of credit market dynamics, necessitating the development of composite indicators that integrate multiple information sources.
The SCMPI addresses this gap by constructing a weighted composite index that synthesizes four key dimensions of credit market conditions: corporate credit spreads, labor market stress, consumer credit accessibility, and household leverage ratios. This approach aligns with the theoretical framework established by Minsky (1986) regarding financial instability hypothesis and builds upon empirical work by Gilchrist & Zakrajšek (2012) on credit market sentiment.
## 2. Theoretical Framework
### 2.1 Credit Cycle Theory
The theoretical foundation of the SCMPI rests on the credit cycle literature, which posits that credit availability fluctuates in predictable patterns that amplify business cycle dynamics (Kiyotaki & Moore, 1997). During expansion phases, credit becomes increasingly available as risk perceptions decline and collateral values rise. Conversely, stress phases are characterized by credit contraction, elevated risk premiums, and deteriorating borrower conditions.
The indicator incorporates Kindleberger's (1978) framework of financial crises, which identifies key stages in credit cycles: displacement, boom, euphoria, profit-taking, and panic. By monitoring multiple variables simultaneously, the SCMPI aims to capture transitions between these phases before they become apparent in individual metrics.
### 2.2 Systemic Risk Measurement
Systemic risk, defined as the risk of collapse of an entire financial system or entire market (Kaufman & Scott, 2003), requires measurement approaches that capture interconnectedness and spillover effects. The SCMPI follows the methodology established by Bisias et al. (2012) in constructing composite measures that aggregate individual risk indicators into system-wide assessments.
The index employs the concept of "financial stress" as defined by Illing & Liu (2006), encompassing increased uncertainty about fundamental asset values, increased uncertainty about other investors' behavior, increased flight to quality, and increased flight to liquidity.
## 3. Methodology
### 3.1 Component Variables
The SCMPI integrates four primary components, each representing distinct aspects of credit market conditions:
#### 3.1.1 Credit Spreads (BAA-10Y Treasury)
Corporate credit spreads serve as the primary indicator of credit market stress, reflecting risk premiums demanded by investors for corporate debt relative to risk-free government securities (Gilchrist & Zakrajšek, 2012). The BAA-10Y spread specifically captures investment-grade corporate credit conditions, providing insight into broad credit market sentiment.
#### 3.1.2 Unemployment Rate
Labor market conditions directly influence credit quality through their impact on borrower repayment capacity (Bernanke & Gertler, 1995). Rising unemployment typically precedes credit deterioration, making it a valuable leading indicator for credit stress.
#### 3.1.3 Consumer Credit Rates
Consumer credit accessibility reflects the transmission of monetary policy and credit market conditions to household borrowing (Mishkin, 1995). Elevated consumer credit rates indicate tightening credit conditions and reduced credit availability for households.
#### 3.1.4 Household Debt Service Ratio
Household leverage ratios capture the debt burden relative to income, providing insight into household financial stress and potential credit losses (Mian & Sufi, 2014). High debt service ratios indicate vulnerable household sectors that may contribute to credit market instability.
### 3.2 Statistical Methodology
#### 3.2.1 Z-Score Normalization
Each component variable undergoes robust z-score normalization to ensure comparability across different scales and units:
Z_i,t = (X_i,t - μ_i) / σ_i
Where X_i,t represents the value of variable i at time t, μ_i is the historical mean, and σ_i is the historical standard deviation. The normalization period employs a rolling 252-day window to capture annual cyclical patterns while maintaining sensitivity to regime changes.
#### 3.2.2 Adaptive Smoothing
To reduce noise while preserving signal quality, the indicator employs exponential moving average (EMA) smoothing with adaptive parameters:
EMA_t = α × Z_t + (1-α) × EMA_{t-1}
Where α = 2/(n+1) and n represents the smoothing period (default: 63 days).
#### 3.2.3 Weighted Aggregation
The composite index combines normalized components using theoretically motivated weights:
SCMPI_t = w_1×Z_spread,t + w_2×Z_unemployment,t + w_3×Z_consumer,t + w_4×Z_debt,t
Default weights reflect the relative importance of each component based on empirical literature: credit spreads (35%), unemployment (25%), consumer credit (25%), and household debt (15%).
### 3.3 Dynamic Threshold Mechanism
Unlike static threshold approaches, the SCMPI employs adaptive Bollinger Band-style thresholds that automatically adjust to changing market volatility and conditions (Bollinger, 2001):
Expansion Threshold = μ_SCMPI - k × σ_SCMPI
Stress Threshold = μ_SCMPI + k × σ_SCMPI
Neutral Line = μ_SCMPI
Where μ_SCMPI and σ_SCMPI represent the rolling mean and standard deviation of the composite index calculated over a configurable period (default: 126 days), and k is the threshold multiplier (default: 1.0). This approach ensures that thresholds remain relevant across different market regimes and volatility environments, providing more robust regime classification than fixed thresholds.
### 3.4 Visualization and User Interface
The SCMPI incorporates advanced visualization capabilities designed for professional trading environments:
#### 3.4.1 Adaptive Theme System
The indicator features an intelligent dual-theme system that automatically optimizes colors and transparency levels for both dark and bright chart backgrounds. This ensures optimal readability across different trading platforms and user preferences.
#### 3.4.2 Customizable Visual Elements
Users can customize all visual aspects including:
- Color Schemes: Automatic theme adaptation with optional custom color overrides
- Line Styles: Configurable widths for main index, trend lines, and threshold boundaries
- Transparency Optimization: Automatic adjustment based on selected theme for optimal contrast
- Dynamic Zones: Color-coded regime areas with adaptive transparency
#### 3.4.3 Professional Data Table
A comprehensive 13-row data table provides real-time component analysis including:
- Composite index value and regime classification
- Individual component z-scores with color-coded stress indicators
- Trend direction and signal strength assessment
- Dynamic threshold status and volatility metrics
- Component weight distribution for transparency
## 4. Regime Classification
The SCMPI classifies credit market conditions into three distinct regimes:
### 4.1 Expansion Regime (SCMPI < Expansion Threshold)
Characterized by favorable credit conditions, low risk premiums, and accommodative lending standards. This regime typically corresponds to economic expansion phases with low default rates and increasing credit availability.
### 4.2 Neutral Regime (Expansion Threshold ≤ SCMPI ≤ Stress Threshold)
Represents balanced credit market conditions with moderate risk premiums and stable lending standards. This regime indicates neither significant stress nor excessive exuberance in credit markets.
### 4.3 Stress Regime (SCMPI > Stress Threshold)
Indicates elevated credit market stress with high risk premiums, tightening lending standards, and deteriorating borrower conditions. This regime often precedes or coincides with economic contractions and financial market volatility.
## 5. Technical Implementation and Features
### 5.1 Alert System
The SCMPI includes a comprehensive alert framework with seven distinct conditions:
- Regime Transitions: Expansion, Neutral, and Stress phase entries
- Extreme Conditions: Values exceeding ±2.0 standard deviations
- Trend Reversals: Directional changes in the underlying trend component
### 5.2 Performance Optimization
The indicator employs several optimization techniques:
- Efficient Calculations: Pre-computed statistical measures to minimize computational overhead
- Memory Management: Optimized variable declarations for real-time performance
- Error Handling: Robust data validation and fallback mechanisms for missing data
## 6. Empirical Validation
### 6.1 Historical Performance
Backtesting analysis demonstrates the SCMPI's ability to identify major credit stress episodes, including:
- The 2008 Financial Crisis
- The 2020 COVID-19 pandemic market disruption
- Various regional banking crises
- European sovereign debt crisis (2010-2012)
### 6.2 Leading Indicator Properties
The composite nature and dynamic threshold system of the SCMPI provides enhanced leading indicator properties, typically signaling regime changes 1-3 months before they become apparent in individual components or market indices. The adaptive threshold mechanism reduces false signals during high-volatility periods while maintaining sensitivity during regime transitions.
## 7. Applications and Limitations
### 7.1 Applications
- Risk Management: Portfolio managers can use SCMPI signals to adjust credit exposure and risk positioning
- Academic Research: Researchers can employ the index for credit cycle analysis and systemic risk studies
- Trading Systems: The comprehensive alert system enables automated trading strategy implementation
- Financial Education: The transparent methodology and visual design facilitate understanding of credit market dynamics
### 7.2 Limitations
- Data Dependency: The indicator relies on timely and accurate macroeconomic data from FRED sources
- Regime Persistence: Dynamic thresholds may exhibit brief lag during extremely rapid regime transitions
- Model Risk: Component weights and parameters require periodic recalibration based on evolving market structures
- Computational Requirements: Real-time calculations may require adequate processing power for optimal performance
## References
Adrian, T. & Brunnermeier, M.K. (2016). CoVaR. *American Economic Review*, 106(7), 1705-1741.
Bernanke, B. & Gertler, M. (1995). Inside the black box: the credit channel of monetary policy transmission. *Journal of Economic Perspectives*, 9(4), 27-48.
Bernanke, B., Gertler, M. & Gilchrist, S. (1999). The financial accelerator in a quantitative business cycle framework. *Handbook of Macroeconomics*, 1, 1341-1393.
Bisias, D., Flood, M., Lo, A.W. & Valavanis, S. (2012). A survey of systemic risk analytics. *Annual Review of Financial Economics*, 4(1), 255-296.
Bollinger, J. (2001). *Bollinger on Bollinger Bands*. McGraw-Hill Education.
Gilchrist, S. & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. *American Economic Review*, 102(4), 1692-1720.
Illing, M. & Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. *Journal of Financial Stability*, 2(3), 243-265.
Kaufman, G.G. & Scott, K.E. (2003). What is systemic risk, and do bank regulators retard or contribute to it? *The Independent Review*, 7(3), 371-391.
Kindleberger, C.P. (1978). *Manias, Panics and Crashes: A History of Financial Crises*. Basic Books.
Kiyotaki, N. & Moore, J. (1997). Credit cycles. *Journal of Political Economy*, 105(2), 211-248.
Mian, A. & Sufi, A. (2014). What explains the 2007–2009 drop in employment? *Econometrica*, 82(6), 2197-2223.
Minsky, H.P. (1986). *Stabilizing an Unstable Economy*. Yale University Press.
Mishkin, F.S. (1995). Symposium on the monetary transmission mechanism. *Journal of Economic Perspectives*, 9(4), 3-10.
Engulfing DetectorThis script detects classic candlestick reversal patterns known as Engulfing formations:
Bullish Engulfing: A green candle fully engulfs the previous red candle.
Bearish Engulfing: A red candle fully engulfs the previous green candle.
🔎 Features:
Works on any time frame or instrument.
Optional filter to ignore overly large or irregular candles.
Visual signals on the chart (BE/SE labels).
Built-in alerts for automation or notification.
✅ Recommended usage:
For intraday trading, this indicator performs best on the 5-minute chart of the Nasdaq (NQ) between 9:45 AM and 1:00 PM ET (15:45–19:00 CET).
💡 Suggested trading approach:
Optimized for scalping with short-term trades and small take-profits around +0.10%.
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.
Recent Session High Low Marker - SimpleDescription:
This Pine Script marks the high and low points of the most recent trading sessions: Asian, London, and New York.
Asian Session: From 19:00 to 21:00 New York time (UTC-4).
London Session: From 02:00 to 05:00 New York time (UTC-4).
New York Session: From 07:00 to 10:00 New York time (UTC-4).
For each session, the script draws two lines:
One for the high of the session.
One for the low of the session.
The lines are drawn horizontally across the chart, making them easy to spot. Each session is marked by two lines with specific colors:
Orange for the Asian session.
Blue for the London session.
Purple for the New York session.
The lines are dynamically updated during the active session, and they will reset when the next session begins. The width of the lines is set to 2 to ensure they are visible without being too thick.
Credits:
Script created by Jdv.
Bloomberg Financial Conditions Index (Proxy)The Bloomberg Financial Conditions Index (BFCI): A Proxy Implementation
Financial conditions indices (FCIs) have become essential tools for economists, policymakers, and market participants seeking to quantify and monitor the overall state of financial markets. Among these measures, the Bloomberg Financial Conditions Index (BFCI) has emerged as a particularly influential metric. Originally developed by Bloomberg L.P., the BFCI provides a comprehensive assessment of stress or ease in financial markets by aggregating various market-based indicators into a single, standardized value (Hatzius et al., 2010).
The original Bloomberg Financial Conditions Index synthesizes approximately 50 different financial market variables, including money market indicators, bond market spreads, equity market valuations, and volatility measures. These variables are normalized using a Z-score methodology, weighted according to their relative importance to overall financial conditions, and then aggregated to produce a composite index (Carlson et al., 2014). The resulting measure is centered around zero, with positive values indicating accommodative financial conditions and negative values representing tighter conditions relative to historical norms.
As Angelopoulou et al. (2014) note, financial conditions indices like the BFCI serve as forward-looking indicators that can signal potential economic developments before they manifest in traditional macroeconomic data. Research by Adrian et al. (2019) demonstrates that deteriorating financial conditions, as measured by indices such as the BFCI, often precede economic downturns by several months, making these indices valuable tools for predicting changes in economic activity.
Proxy Implementation Approach
The implementation presented in this Pine Script indicator represents a proxy of the original Bloomberg Financial Conditions Index, attempting to capture its essential features while acknowledging several significant constraints. Most critically, while the original BFCI incorporates approximately 50 financial variables, this proxy version utilizes only six key market components due to data accessibility limitations within the TradingView platform.
These components include:
Equity market performance (using SPY as a proxy for S&P 500)
Bond market yields (using TLT as a proxy for 20+ year Treasury yields)
Credit spreads (using the ratio between LQD and HYG as a proxy for investment-grade to high-yield spreads)
Market volatility (using VIX directly)
Short-term liquidity conditions (using SHY relative to equity prices as a proxy)
Each component is transformed into a Z-score based on log returns, weighted according to approximated importance (with weights derived from literature on financial conditions indices by Brave and Butters, 2011), and aggregated into a composite measure.
Differences from the Original BFCI
The methodology employed in this proxy differs from the original BFCI in several important ways. First, the variable selection is necessarily limited compared to Bloomberg's comprehensive approach. Second, the proxy relies on ETFs and publicly available indices rather than direct market rates and spreads used in the original. Third, the weighting scheme, while informed by academic literature, is simplified compared to Bloomberg's proprietary methodology, which may employ more sophisticated statistical techniques such as principal component analysis (Kliesen et al., 2012).
These differences mean that while the proxy BFCI captures the general direction and magnitude of financial conditions, it may not perfectly replicate the precision or sensitivity of the original index. As Aramonte et al. (2013) suggest, simplified proxies of financial conditions indices typically capture broad movements in financial conditions but may miss nuanced shifts in specific market segments that more comprehensive indices detect.
Practical Applications and Limitations
Despite these limitations, research by Arregui et al. (2018) indicates that even simplified financial conditions indices constructed from a limited set of variables can provide valuable signals about market stress and future economic activity. The proxy BFCI implemented here still offers significant insight into the relative ease or tightness of financial conditions, particularly during periods of market stress when correlations among financial variables tend to increase (Rey, 2015).
In practical applications, users should interpret this proxy BFCI as a directional indicator rather than an exact replication of Bloomberg's proprietary index. When the index moves substantially into negative territory, it suggests deteriorating financial conditions that may precede economic weakness. Conversely, strongly positive readings indicate unusually accommodative financial conditions that might support economic expansion but potentially also signal excessive risk-taking behavior in markets (López-Salido et al., 2017).
The visual implementation employs a color gradient system that enhances interpretation, with blue representing neutral conditions, green indicating accommodative conditions, and red signaling tightening conditions—a design choice informed by research on optimal data visualization in financial contexts (Few, 2009).
References
Adrian, T., Boyarchenko, N. and Giannone, D. (2019) 'Vulnerable Growth', American Economic Review, 109(4), pp. 1263-1289.
Angelopoulou, E., Balfoussia, H. and Gibson, H. (2014) 'Building a financial conditions index for the euro area and selected euro area countries: what does it tell us about the crisis?', Economic Modelling, 38, pp. 392-403.
Aramonte, S., Rosen, S. and Schindler, J. (2013) 'Assessing and Combining Financial Conditions Indexes', Finance and Economics Discussion Series, Federal Reserve Board, Washington, D.C.
Arregui, N., Elekdag, S., Gelos, G., Lafarguette, R. and Seneviratne, D. (2018) 'Can Countries Manage Their Financial Conditions Amid Globalization?', IMF Working Paper No. 18/15.
Brave, S. and Butters, R. (2011) 'Monitoring financial stability: A financial conditions index approach', Economic Perspectives, Federal Reserve Bank of Chicago, 35(1), pp. 22-43.
Carlson, M., Lewis, K. and Nelson, W. (2014) 'Using policy intervention to identify financial stress', International Journal of Finance & Economics, 19(1), pp. 59-72.
Few, S. (2009) Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press, Oakland, CA.
Hatzius, J., Hooper, P., Mishkin, F., Schoenholtz, K. and Watson, M. (2010) 'Financial Conditions Indexes: A Fresh Look after the Financial Crisis', NBER Working Paper No. 16150.
Kliesen, K., Owyang, M. and Vermann, E. (2012) 'Disentangling Diverse Measures: A Survey of Financial Stress Indexes', Federal Reserve Bank of St. Louis Review, 94(5), pp. 369-397.
López-Salido, D., Stein, J. and Zakrajšek, E. (2017) 'Credit-Market Sentiment and the Business Cycle', The Quarterly Journal of Economics, 132(3), pp. 1373-1426.
Rey, H. (2015) 'Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence', NBER Working Paper No. 21162.
PORTFOLIO TABLE Full [Titans_Invest]PORTFOLIO TABLE Full
This is a complete table for monitoring your assets or cryptocurrencies in your SPOT wallet without needing to access your broker’s website or app.
⯁ HOW TO USE THIS TABLE❓
Simply select the asset and enter the amount you hold.
The table will display the value of each asset and the total value of your portfolio.
You can monitor up to 19 assets in real time.
⯁ CONVERT VALUES
You can also enable and select a currency for conversion.
For example, cryptocurrencies are calculated in US dollars by default, but you can choose euros as the conversion currency.
The values originally in dollars will then be displayed in euros.
⯁ TRACK THE DAILY VARIATION OF YOUR PORTFOLIO
You’ll be able to monitor your portfolio’s raw daily variation in real time.
🔶 Track your Portfolio in real time:
🔶 Add your local Currency to Convert Values:
🔶 Follow your Portfolio Live:
___________________________________________________________
📜 SCRIPT : PORTFOLIO TABLE Full
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
___________________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
Liquid Pulse Liquid Pulse by Dskyz (DAFE) Trading Systems
Liquid Pulse is a trading algo built by Dskyz (DAFE) Trading Systems for futures markets like NQ1!, designed to snag high-probability trades with tight risk control. it fuses a confluence system—VWAP, MACD, ADX, volume, and liquidity sweeps—with a trade scoring setup, daily limits, and VIX pauses to dodge wild volatility. visuals include simple signals, VWAP bands, and a dashboard with stats.
Core Components for Liquid Pulse
Volume Sensitivity (volumeSensitivity) controls how much volume spikes matter for entries. options: 'Low', 'Medium', 'High' default: 'High' (catches small spikes, good for active markets) tweak it: 'Low' for calm markets, 'High' for chaos.
MACD Speed (macdSpeed) sets the MACD’s pace for momentum. options: 'Fast', 'Medium', 'Slow' default: 'Medium' (solid balance) tweak it: 'Fast' for scalping, 'Slow' for swings.
Daily Trade Limit (dailyTradeLimit) caps trades per day to keep risk in check. range: 1 to 30 default: 20 tweak it: 5-10 for safety, 20-30 for action.
Number of Contracts (numContracts) sets position size. range: 1 to 20 default: 4 tweak it: up for big accounts, down for small.
VIX Pause Level (vixPauseLevel) stops trading if VIX gets too hot. range: 10 to 80 default: 39.0 tweak it: 30 to avoid volatility, 50 to ride it.
Min Confluence Conditions (minConditions) sets how many signals must align. range: 1 to 5 default: 2 tweak it: 3-4 for strict, 1-2 for more trades.
Min Trade Score (Longs/Shorts) (minTradeScoreLongs/minTradeScoreShorts) filters trade quality. longs range: 0 to 100 default: 73 shorts range: 0 to 100 default: 75 tweak it: 80-90 for quality, 60-70 for volume.
Liquidity Sweep Strength (sweepStrength) gauges breakouts. range: 0.1 to 1.0 default: 0.5 tweak it: 0.7-1.0 for strong moves, 0.3-0.5 for small.
ADX Trend Threshold (adxTrendThreshold) confirms trends. range: 10 to 100 default: 41 tweak it: 40-50 for trends, 30-35 for weak ones.
ADX Chop Threshold (adxChopThreshold) avoids chop. range: 5 to 50 default: 20 tweak it: 15-20 to dodge chop, 25-30 to loosen.
VWAP Timeframe (vwapTimeframe) sets VWAP period. options: '15', '30', '60', '240', 'D' default: '60' (1-hour) tweak it: 60 for day, 240 for swing, D for long.
Take Profit Ticks (Longs/Shorts) (takeProfitTicksLongs/takeProfitTicksShorts) sets profit targets. longs range: 5 to 100 default: 25.0 shorts range: 5 to 100 default: 20.0 tweak it: 30-50 for trends, 10-20 for chop.
Max Profit Ticks (maxProfitTicks) caps max gain. range: 10 to 200 default: 60.0 tweak it: 80-100 for big moves, 40-60 for tight.
Min Profit Ticks to Trail (minProfitTicksTrail) triggers trailing. range: 1 to 50 default: 7.0 tweak it: 10-15 for big gains, 5-7 for quick locks.
Trailing Stop Ticks (trailTicks) sets trail distance. range: 1 to 50 default: 5.0 tweak it: 8-10 for room, 3-5 for fast locks.
Trailing Offset Ticks (trailOffsetTicks) sets trail offset. range: 1 to 20 default: 2.0 tweak it: 1-2 for tight, 5-10 for loose.
ATR Period (atrPeriod) measures volatility. range: 5 to 50 default: 9 tweak it: 14-20 for smooth, 5-9 for reactive.
Hardcoded Settings volLookback: 30 ('Low'), 20 ('Medium'), 11 ('High') volThreshold: 1.5 ('Low'), 1.8 ('Medium'), 2 ('High') swingLen: 5
Execution Logic Overview trades trigger when confluence conditions align, entering long or short with set position sizes. exits use dynamic take-profits, trailing stops after a profit threshold, hard stops via ATR, and a time stop after 100 bars.
Features Multi-Signal Confluence: needs VWAP, MACD, volume, sweeps, and ADX to line up.
Risk Control: ATR-based stops (capped 15 ticks), take-profits (scaled by volatility), and trails.
Market Filters: VIX pause, ADX trend/chop checks, volatility gates. Dashboard: shows scores, VIX, ADX, P/L, win %, streak.
Visuals Simple signals (green up triangles for longs, red down for shorts) and VWAP bands with glow. info table (bottom right) with MACD momentum. dashboard (top right) with stats.
Chart and Backtest:
NQ1! futures, 5-minute chart. works best in trending, volatile conditions. tweak inputs for other markets—test thoroughly.
Backtesting: NQ1! Frame: Jan 19, 2025, 09:00 — May 02, 2025, 16:00 Slippage: 3 Commission: $4.60
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Disclaimer this is for education only. past results don’t predict future wins. trading’s risky—only use money you can lose. backtest and validate before going live. (expect moderators to nitpick some random chart symbol rule—i’ll fix and repost if they pull it.)
About the Author Dskyz (DAFE) Trading Systems crafts killer trading algos. Liquid Pulse is pure research and grit, built for smart, bold trading. Use it with discipline. Use it with clarity. Trade smarter. I’ll keep dropping badass strategies ‘til i build a brand or someone signs me up.
2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
[TTM] ICT Sessions & Ranges🌟 Overview 🌟
The ICT Sessions & Ranges Indicator helps traders identify key intraday price levels by marking custom session highs/lows and opening ranges.
It helps traders spot potential liquidity grabs, reversals, and breakout zones by tracking price behavior around these key areas
🌟 Session Highs & Lows – Liquidity Zones 🌟
Session highs and lows often attract price due to stop orders resting above or below them. These levels are frequently targeted during high-volatility moves.
🔹 Asia Session
- Usually ranges in low volatility.
- Highs/lows often get swept during early London.
- Price may raid these levels, then reverse.
🔹 London Session
- First major volatility of the day.
- Highs/lows often tested or swept in New York.
- Commonly forms the day’s true high or low.
🌟 Opening Range Concepts 🌟
The Opening Range is the first 15, 30, or 60 minutes of a session (e.g., New York).
The high (ORH) and low (ORL) define the market’s initial balance and key reaction levels.
🔹 Breakout Trade
- Price breaks ORH/ORL with momentum.
- Signals directional intent.
- Traders enter on the breakout, with stops inside the range.
🔹 Liquidity Raid
- Price briefly breaks ORH/ORL to trigger stops.
- Reverses after the sweep.
- Look for structure shift and entry near FVG or OB.
🌟 Customizable Settings 🌟
The indicator includes 3 configurable ranges , each with:
Start & End Time – Set any custom time window.
Display Type – Choose Box (highlight range) or Lines (mark high/low).
Color Settings – Set custom colors for boxes and lines.
🌟 Default Settings 🌟
Range 1 : 19:00–00:00 (Asia Session)
Range 2 : 01:45–05:15 (London Session)
Range 3 : 09:30–10:00 (NY Opening Range – 30m)
BTC Markup/Markdown Zones by Koenigsegg📈 BTC Markup/Markdown Zones
A handcrafted indicator designed to mark Bitcoin's most critical High Time Frame (HTF) structure shifts. This tool overlays true institutional-level Markup and Markdown Zones, selected manually after deep market review. Whether you're testing strategies or actively trading, this tool gives you the bigger picture at all times.
🔍 Key Features:
✅ HTF Markup & Markdown Zones
Every zone is manually selected — no indicators, no repainting. Just raw market history and real structure.
✅ Two Display Modes
• Background Zones — soft overlays with low opacity for visual context — with the option to increase opacity manually if desired.
• Start Candle Highlight — sharply highlighted candle marking the final pivot before a macro reversal.
✅ Custom Color Controls (Style Tab)
All visual styling lives in the Style tab, with clearly labeled fields:
• Markup Zone
• Markdown Zone
• Start Candle Highlight Markup
• Start Candle Highlight Markdown
✅ Minimal Input Section
Just one toggle: display mode. Everything else is kept clean and intuitive.
🧠 Purpose:
This script is made for any timeframe:
• Zoom into lower timeframes to know whether you're trading inside a Markup or Markdown
• Use it during strategy testing for true structural awareness
📅 Handpicked Macro Turning Points:
Each zone originates from a manually confirmed candle — the last meaningful candle before a shift in control between bulls and bears:
• FRI 19 AUG 2011 12PM – MARK DOWN
• THU 20 OCT 2011 12AM – MARK UP
• WED 10 APR 2013 12PM – MARK DOWN
• FRI 12 APR 2013 12PM – MARK UP
• SAT 30 NOV 2013 12AM – MARK DOWN
• WED 14 JAN 2015 12PM – MARK UP
• SUN 17 DEC 2017 12PM – MARK DOWN
• SAT 15 DEC 2018 12PM – MARK UP
• WED 14 APR 2021 4AM – MARK DOWN
• TUE 22 JUN 2021 12PM – MARK UP
• WED 10 NOV 2021 12PM – MARK DOWN
• MON 21 NOV 2022 8PM – MARK UP
• THU 14 MAR 2024 4AM – MARK DOWN
• MON 5 AUG 2024 12PM – MARK UP
• MON 20 JAN 2025 4AM – MARK DOWN
💡 Zones are manually updated by me after each new confirmed Markup or Markdown.
🧬 Fractal Structure for MTF Systems
Price is fractal — meaning the same principles of structure repeat across all timeframes. In Version 2, this tool evolves by introducing manually selected sub-zones inside each High Time Frame (HTF) Markup or Markdown. These sub-zones reflect Medium Timeframe (MTF) structure shifts, offering precision for traders who operate on both intraday and swing levels.
This makes the indicator ideal for low timeframe (LTF) Markup/Markdown awareness — whether you're managing 15m entries or building multi-timeframe confluence systems.
No auto-zones. No guesswork. Just clean, intentional structure division within the broader trend, handpicked for maximum clarity and edge.
💡 Pro Tip:
When price is inside a Markup Zone, shorting becomes riskier — you're trading against a macro bullish structure.
When inside a Markdown Zone, longing becomes riskier — you're fighting against confirmed bearish momentum.
Use this tool to stay aligned with the broader move, especially when zoomed into smaller timeframes or managing entries/exits during intraday setups.
📈 Markup Phase – Bullish Sentiment
Definition: A period where price makes higher highs and higher lows — the uptrend is in full force.
Why sentiment is bullish:
- Institutions and smart money are already positioned long.
- Public/institutional demand drives prices up.
- Momentum is supported by positive news, breakouts, and FOMO.
- Higher highs confirm buyers are in control.
📉 Markdown Phase – Bearish Sentiment
Definition: A period where price makes lower lows and lower highs — clear downtrend.
Why sentiment is bearish:
- Distribution has already occurred, and supply outweighs demand.
- Smart money is short or sidelined, waiting for deeper prices.
- Panic selling or trend-following traders add downside momentum.
- Lower lows confirm sellers are in control.
❌ Trading Against the Trend — Consequences:
-Reduced Probability of Success
-You’re fighting the dominant flow. Most participants are pushing in the opposite direction.
-Drawdowns & Stop-Outs
-Countertrend trades often get wicked or flushed before any meaningful move, especially without structure-based entries.
-Low Risk-Reward Ratio
-Trends offer sustained moves. Countertrend trades may have small take-profit zones or chop.
-Mental Drain & Doubt
-Fighting momentum causes anxiety, second-guessing, and emotional reactions.
-Missed Opportunities
-Focusing on fighting the trend makes you blind to the high-probability setups with the trend.
-Increased Transaction Costs
-More stop-outs and re-entries mean more fees, more friction.
-FOMO from Watching the Trend Run
-Entering countertrend means you might watch the trend explode without you.
-Confirmation Bias & Stubbornness
-Countertrend traders often look for reasons to justify staying in the wrong direction — leading to bigger losses.
🧠 Summary
In markup = bulls dominate → you swim with the current.
In markdown = bears dominate → going long is like pushing a rock uphill.
Trading with the trend is not just safer, it's smarter. The edge lives in momentum — not ego.
⚠️ Disclaimer
This indicator is for educational and analytical use only. It is not financial advice and should not be relied on for decision-making without personal analysis.
This is not a predictive tool. No indicator can forecast upcoming price movements.
What you see here is based purely on past market behavior — specifically, historical tops and bottoms that marked the start of confirmed reversals.
This script does not know where the next reversal begins, nor can it determine where a new Markup or Markdown starts or ends. It is designed to provide context, not prediction.
Always trade with responsibility and perform your own due diligence.
EXODUS EXODUS by (DAFE) Trading Systems
EXODUS is a sophisticated trading algorithm built by Dskyz (DAFE) Trading Systems for competitive and competition purposes, designed to identify high-probability trades with robust risk management. this strategy leverages a multi-signal voting system, combining three core components—SPR, VWMO, and VEI—alongside ADX, choppiness filters, and ATR-based volatility gates to ensure trades are taken only in favorable market conditions. the algo uses a take-profit to stop-loss ratio, dynamic position sizing, and a strict voting mechanism requiring all signals to align before entering a trade.
EXODUS was not overfitted for any specific symbol. instead, it uses a generic tuned setting, making it versatile across various markets. while it can trade futures, it’s not currently set up for it but has the potential to do more with further development. visuals are intentionally minimal due to its competition focus, prioritizing performance over aesthetics. a more visually stunning version may be released in the future with enhanced graphics.
The Unique Core Components Developed for EXODUS
SPR (Session Price Recalibration)
SPR measures momentum during regular trading hours (RTH, 0930-1600, America/New_York) to catch session-specific trends.
spr_lookback = input.int(15, "SPR Lookback") this sets how many bars back SPR looks to calculate momentum (default 15 bars). it compares the current session’s price-volume score to the score 15 bars ago to gauge momentum strength.
how it works: a longer lookback smooths out the signal, focusing on bigger trends. a shorter one makes SPR more sensitive to recent moves.
how to adjust: on a 1-hour chart, 15 bars is 15 hours (about 2 trading days). if you’re on a shorter timeframe like 5 minutes, 15 bars is just 75 minutes, so you might want to increase it to 50 or 100 to capture more meaningful trends. if you’re trading a choppy stock, a shorter lookback (like 5) can help catch quick moves, but it might give more false signals.
spr_threshold = input.float (0.7, "SPR Threshold")
this is the cutoff for SPR to vote for a trade (default 0.7). if SPR’s normalized value is above 0.7, it votes for a long; below -0.7, it votes for a short.
how it works: SPR normalizes its momentum score by ATR, so this threshold ensures only strong moves count. a higher threshold means fewer trades but higher conviction.
how to adjust: if you’re getting too few trades, lower it to 0.5 to let more signals through. if you’re seeing too many false entries, raise it to 1.0 for stricter filtering. test on your chart to find a balance.
spr_atr_length = input.int(21, "SPR ATR Length") this sets the ATR period (default 21 bars) used to normalize SPR’s momentum score. ATR measures volatility, so this makes SPR’s signal relative to market conditions.
how it works: a longer ATR period (like 21) smooths out volatility, making SPR less jumpy. a shorter one makes it more reactive.
how to adjust: if you’re trading a volatile stock like TSLA, a longer period (30 or 50) can help avoid noise. for a calmer stock, try 10 to make SPR more responsive. match this to your timeframe—shorter timeframes might need a shorter ATR.
rth_session = input.session("0930-1600","SPR: RTH Sess.") rth_timezone = "America/New_York" this defines the session SPR uses (0930-1600, New York time). SPR only calculates momentum during these hours to focus on RTH activity.
how it works: it ignores pre-market or after-hours noise, ensuring SPR captures the main market action.
how to adjust: if you trade a different session (like London hours, 0300-1200 EST), change the session to match. you can also adjust the timezone if you’re in a different region, like "Europe/London". just make sure your chart’s timezone aligns with this setting.
VWMO (Volume-Weighted Momentum Oscillator)
VWMO measures momentum weighted by volume to spot sustained, high-conviction moves.
vwmo_momlen = input.int(21, "VWMO Momentum Length") this sets how many bars back VWMO looks to calculate price momentum (default 21 bars). it takes the price change (close minus close 21 bars ago).
how it works: a longer period captures bigger trends, while a shorter one reacts to recent swings.
how to adjust: on a daily chart, 21 bars is about a month—good for trend trading. on a 5-minute chart, it’s just 105 minutes, so you might bump it to 50 or 100 for more meaningful moves. if you want faster signals, drop it to 10, but expect more noise.
vwmo_volback = input.int(30, "VWMO Volume Lookback") this sets the period for calculating average volume (default 30 bars). VWMO weights momentum by volume divided by this average.
how it works: it compares current volume to the average to see if a move has strong participation. a longer lookback smooths the average, while a shorter one makes it more sensitive.
how to adjust: for stocks with spiky volume (like NVDA on earnings), a longer lookback (50 or 100) avoids overreacting to one-off spikes. for steady volume stocks, try 20. match this to your timeframe—shorter timeframes might need a shorter lookback.
vwmo_smooth = input.int(9, "VWMO Smoothing")
this sets the SMA period to smooth VWMO’s raw momentum (default 9 bars).
how it works: smoothing reduces noise in the signal, making VWMO more reliable for voting. a longer smoothing period cuts more noise but adds lag.
how to adjust: if VWMO is too jumpy (lots of false votes), increase to 15. if it’s too slow and missing trades, drop to 5. test on your chart to see what keeps the signal clean but responsive.
vwmo_threshold = input.float(10, "VWMO Threshold") this is the cutoff for VWMO to vote for a trade (default 10). above 10, it votes for a long; below -10, a short.
how it works: it ensures only strong momentum signals count. a higher threshold means fewer but stronger trades.
how to adjust: if you want more trades, lower it to 5. if you’re getting too many weak signals, raise it to 15. this depends on your market—volatile stocks might need a higher threshold to filter noise.
VEI (Velocity Efficiency Index)
VEI measures market efficiency and velocity to filter out choppy moves and focus on strong trends.
vei_eflen = input.int(14, "VEI Efficiency Smoothing") this sets the EMA period for smoothing VEI’s efficiency calc (bar range / volume, default 14 bars).
how it works: efficiency is how much price moves per unit of volume. smoothing it with an EMA reduces noise, focusing on consistent efficiency. a longer period smooths more but adds lag.
how to adjust: for choppy markets, increase to 20 to filter out noise. for faster markets, drop to 10 for quicker signals. this should match your timeframe—shorter timeframes might need a shorter period.
vei_momlen = input.int(8, "VEI Momentum Length") this sets how many bars back VEI looks to calculate momentum in efficiency (default 8 bars).
how it works: it measures the change in smoothed efficiency over 8 bars, then adjusts for inertia (volume-to-range). a longer period captures bigger shifts, while a shorter one reacts faster.
how to adjust: if VEI is missing quick reversals, drop to 5. if it’s too noisy, raise to 12. test on your chart to see what catches the right moves without too many false signals.
vei_threshold = input.float(4.5, "VEI Threshold") this is the cutoff for VEI to vote for a trade (default 4.5). above 4.5, it votes for a long; below -4.5, a short.
how it works: it ensures only strong, efficient moves count. a higher threshold means fewer trades but higher quality.
how to adjust: if you’re not getting enough trades, lower to 3. if you’re seeing too many false entries, raise to 6. this depends on your market—fast stocks like NQ1 might need a lower threshold.
Features
Multi-Signal Voting: requires all three signals (SPR, VWMO, VEI) to align for a trade, ensuring high-probability setups.
Risk Management: uses ATR-based stops (2.1x) and take-profits (4.1x), with dynamic position sizing based on a risk percentage (default 0.4%).
Market Filters: ADX (default 27) ensures trending conditions, choppiness index (default 54.5) avoids sideways markets, and ATR expansion (default 1.12) confirms volatility.
Dashboard: provides real-time stats like SPR, VWMO, VEI values, net P/L, win rate, and streak, with a clean, functional design.
Visuals
EXODUS prioritizes performance over visuals, as it was built for competitive and competition purposes. entry/exit signals are marked with simple labels and shapes, and a basic heatmap highlights market regimes. a more visually stunning update may be released later, with enhanced graphics and overlays.
Usage
EXODUS is designed for stocks and ETFs but can be adapted for futures with adjustments. it performs best in trending markets with sufficient volatility, as confirmed by its generic tuning across symbols like TSLA, AMD, NVDA, and NQ1. adjust inputs like SPR threshold, VWMO smoothing, or VEI momentum length to suit specific assets or timeframes.
Setting I used: (Again, these are a generic setting, each security needs to be fine tuned)
SPR LB = 19 SPR TH = 0.5 SPR ATR L= 21 SPR RTH Sess: 9:30 – 16:00
VWMO L = 21 VWMO LB = 18 VWMO S = 6 VWMO T = 8
VEI ES = 14 VEI ML = 21 VEI T = 4
R % = 0.4
ATR L = 21 ATR M (S) =1.1 TP Multi = 2.1 ATR min mult = 0.8 ATR Expansion = 1.02
ADX L = 21 Min ADX = 25
Choppiness Index = 14 Chop. Max T = 55.5
Backtesting: TSLA
Frame: Jan 02, 2018, 08:00 — May 01, 2025, 09:00
Slippage: 3
Commission .01
Disclaimer
this strategy is for educational purposes. past performance is not indicative of future results. trading involves significant risk, and you should only trade with capital you can afford to lose. always backtest and validate any strategy before using it in live markets.
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
About the Author
Dskyz (DAFE) Trading Systems is dedicated to building high-performance trading algorithms. EXODUS is a product of rigorous research and development, aimed at delivering consistent, and data-driven trading solutions.
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
2025 Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Hippo Battlefield - Bulls VS Bears 20 bars## Hippo Battlefield – Bulls VS Bears (20 Bars)
**What it is**
A multi-dimensional momentum-and-sentiment oscillator that combines classic Bull/Bear Power with ATR- or peak-normalization, then layers on RSI and MACD-derived metrics into:
1. **A colored bar series** showing net Bull+Bear Power strength over the last 20 bars,
2. **A dynamic table** of each of those 20 BBP values (grouped into four 5-bar “quartals”), with symbols, per-bar change, and rolling averages, and
3. **A composite “Weighted BBP” histogram** blending normalized RSI, MACD, and BBP into a single view.
---
### Key Inputs
- **Length (EMA)** – look-back for the underlying EMA (default 60)
- **Normalization Length** – look-back window for peak-normalization (default 60)
- **Use ATR for Norm.** – toggle ATR-based normalization vs. highest-abs(BBP)
- **Show Tables** – toggle the bottom-right 21×11 grid of raw and average BBP values
---
### What You See
#### 1. Colored Bars (Overlay = false)
- Bars are colored by normalized BBP intensity:
- Extreme Bull (≥+10): deep blue
- Strong Bull (+5 to +10): green/yellow
- Weak Bull (+0 to +5): dark green
- Weak Bear (–0 to –5): dark red
- Strong Bear (–5 to –10): pink/red
- Extreme Bear (<–10): magenta
#### 2. Bottom-Right Table (20 Bars of Data)
- Divided into four columns (0–4, 5–9, 10–14, 15–19 bars ago) and one “average” row.
- Each cell shows:
1. Bar index (1–20),
2. Normalized BBP value (to four decimals),
3. Direction symbol (↑/↓/=),
4. Bar-to-bar change (± value),
5. A separator “|”.
- At the very bottom, each column’s 5-bar average is displayed as “Avg: X.XXXX” with a dot marker.
#### 3. Top-Center Mini-Table
- When ≥20 bars have elapsed, shows the date at 20 bars ago and the average BBP across the full 20-bar window.
#### 4. Normalized RSI Line
- Rescales the classic 14-period RSI into a –20…+20 band to align with BBP.
#### 5. MACD Lines (Hidden) & Composite Histogram
- MACD and signal lines are calculated but not plotted by default.
- A “Weighted BBP” histogram combines:
- 20% normalized RSI,
- 20% average of (MACD + signal + normalized BBP),
- 60% normalized BBP
- Plotted as columns, color-coded by strength using the same palette as the main bars.
#### 6. Middle Reference Line
- A horizontal zero line to anchor over/under-zero readings.
---
### How to Use It
- **Trend confirmation**: Strong blue/green bars alongside a rising histogram suggest bull conviction; strong reds/magentas signal bear dominance.
- **Divergence spotting**: Watch for price making new highs/lows while BBP or the histogram fails to follow.
- **Quartal analysis**: The 5-bar group averages can reveal whether recent momentum is accelerating or waning.
- **Cross-indicator weighting**: Because RSI, MACD, and raw BBP all feed into the final histogram, you get a smoothed, blended view of momentum shifts.
---
**Tip:** Tweak the EMA and normalization length to suit your preferred timeframe (e.g. shorter for intraday scalps, longer for swing trades). Enable/disable the table if you prefer a cleaner pane.
TCloud Future📘 Tcloud Future – Indicator Description & How to Use
Tcloud Future is a trend-based indicator that creates a forward-projected cloud between:
A customizable Exponential Moving Average (EMA)
A dynamic McGinley Moving Average
The cloud is shifted into the future (like the Ichimoku Cloud), giving traders a visual projection of potential trend direction.
🔧 Components:
EMA (default: 19-period) – fast-reacting average to short-term price action
McGinley Dynamic (default: 26-period) – smoother, adaptive average that reacts to volatility
Forward Projection (default: 26 candles) – pushes the cloud into the future to help anticipate trend continuation or reversal
Cloud Color
Green when EMA is above McGinley (bullish bias)
Red when EMA is below McGinley (bearish bias)
🟢 How to Trade with Tcloud Future
✅ Trend Confirmation
Use the cloud color and slope to confirm the current trend.
Green cloud sloping up → bullish momentum
Red cloud sloping down → bearish momentum
🟩 Entry Strategy (Trend-Following)
Go long when price is above the green cloud and the cloud is rising.
Go short when price is below the red cloud and the cloud is falling.
🔁 Cloud Crossovers (Trend Shift)
A color change in the projected cloud can signal a potential trend reversal.
Use this as a heads-up to prepare for position changes or tighten stops.
🛡️ Support/Resistance Zones
The cloud often acts as a dynamic support/resistance zone.
During an uptrend, pullbacks to the top or middle of the green cloud can be good entries.
During a downtrend, rallies into the red cloud can offer shorting opportunities.
🧠 Tips
Combine with RSI, MACD, or Volume for confirmation.
Avoid using it alone in sideways markets — it performs best in trending conditions.
Adjust projection and smoothing settings to fit the asset/timeframe you're trading.
RSI Trigger Count (30 Days) - Both SidesRSI Dual Trigger Counter (30 Days)
This indicator tracks both oversold ( crossunder ) and overbought ( crossover ) RSI events on a 30-minute chart, featuring:
Dual-Mode Selector:
Counts either RSI < 30 (oversold) or RSI > 70 (overbought) crossings
Toggle between modes via input menu
30-Day Rolling Count:
Displays total triggers in the last 30 days (e.g., "Times triggered (Oversold) ① 19")
Visual Alerts:
Red triangles ↓ for oversold crossunders
Green triangles ↑ for overbought crossovers
Customizable:
Adjustable RSI length (2-100) and thresholds (1-100)
Works on any timeframe (auto-scales calculations)
Purpose: Identifies frequent reversal signals for both buying dips (oversold) and selling rallies (overbought).
Buffett Indicator with Historical Bubbles (Clean)The Buffett Indicator is a trusted macroeconomic gauge that compares the total US stock market capitalization to the nation’s GDP. Popularized by Warren Buffett, this metric highlights periods of overvaluation and undervaluation in the market.
This tool offers a clean and accurate visualization of the Buffett Indicator, enhanced with historical bubble annotations for key market events:
Dot-com Bubble (2000)
Global Financial Crisis Peak (2007)
COVID-19 Pre-crash Peak (2020)
Post-COVID Bull Market Peak (2021)
Features:
Dynamic Buffett Ratio (%) calculation using Wilshire 5000 Index as the market cap proxy.
Customizable GDP input for accuracy (update quarterly).
Visual thresholds for fair value, undervaluation, and overvaluation zones.
Historical event markers for educational and analytical context.
Optimized to display clearly across all timeframes: Daily, Weekly, Monthly.
How to Use:
Manually update the GDP input as new data is released.
Use this indicator for macro-level market sentiment analysis and valuation tracking.
Combine with other tools and risk management strategies for comprehensive market insights.
Disclaimer:
This indicator is for educational purposes only. It does not constitute financial advice. Always perform your own research and analysis.
Version: 1.0
we ask Allah reconcile and repay
#BuffettIndicator #MarketValuation #MacroAnalysis #BubbleDetector #LongTermInvestor #USMarket #Wilshire5000 #TradingViewScript
BIN Based Support and Resistance [SS]This indicator presents a version of an alternative way to determine support and resistance, using a method called "Bins".
Bins provide for a flexible and interesting way to determine support and resistance levels.
First off, let's discuss BINS:
Bins are ranges or containers into which your data points can be sorted. For example, if you're grouping ages, you might have bins like 0–18, 19–35, 36–50, and 51+. Any data point within these intervals gets placed in the corresponding bin.
Binning simplifies complex data sets by grouping values into categories. This is useful for such things as
Visualizing data in histograms or bar charts.
Reducing noise and highlighting trends.
This indicator groups the price action into 10 separate bins. It determines the Support / Resistance level by averaging the values in the Bins to find an iteration of the "central tendency" or average reoccurring value.
Pros and Cons
Since this is a different approach to support and resistance, I think its important to highlight some of the pros and advantages, but also be open about the cons.
First off the PROS
Bin Based Support and Resistance Levels dynamically adjust to ranges as opposed to hard / fast peaks and valleys. This makes them better at analyzing price action vs simply drawing lines at random peaks and valleys.
Because Bins are analyzing ALL PA within a period's max and min range, Bin Support and Resistance can actually be used similar to Volume profile, where you are able to identify a pseudo-POC, or areas where price tends to consolidate. Take a look at this example on SPY:
You can see these 2 SR lines are close together. This represents that this general price range is an area where price likes to accumulate/consolidate. You can see the SPY ended up coming back to this range and consolidating there for a bit.
This is a strength of using a BIN based approach to calculating support and resistance, because as indicated before, it looks at price action vs peaks and valleys.
As a tip, these areas are areas you want to wait for a break in one direction or the other.
The indicator provides for backtest results of the support and resistance lines, to see how many times certain areas acted as resistance or support. Because this is analyzing and distributing PA evenly throughout the period's max and min, the indicator can tell you which areas tend to have higher rejection zones and which have higher support zones.
Now the CONS
Because bin based SR take an average approach, the SR lines can sometimes be slightly broken before the ticker finds rejection:
To combat this, make sure there is confirmed support. How the indicator actually backtests these lines is by waiting to see if the ticker has 3 consecutive closes above the support line or below the resistance line. So these are things to be mindful of.
It doesn't consider pivots. Most support and resistance indicators either identify max and min peaks and valleys or use pivot points. Pivot points are a great way to identify peaks and valleys and thus by extension support and resistance. However, this is also somewhat of a strength, as using BINS forces the indicator to consider ALL price action and not just the extremes (highs and lows).
Can be slightly skewed in highly volatile environments. Any time there is a massive drop or rally, it can skew the indicator to give extreme ranges to both ends. For example, the Tariff news collapse on ES1!:
Owning to limitations in lookback length, sometimes the min and max range can be exceeded and other traditional areas of support / resistance is where a ticker will find support.
Using the indicator
Here are some basic use/functionalities of the indicator:
Selecting display of backtest results: You can select to have the backtest results shown in a table:
Or directly on the lines:
Inversely, you can toggle them off completely:
You can modify the lookback length. The suggested lookback length is between 250 to 500 candles on smaller timeframes. I also suggest 252 on daily timeframes (which represents 1 trading year).
And that's the indicator!
It is very easy to use, so you should pick it up in no time!
Enjoy and as always, 🚀🚀 safe trades! 🚀🚀
First EMA Touch (Last N Bars)Okay, here's a description of the "First EMA Touch (Last N Bars)" TradingView indicator:
Indicator Name: First EMA Touch (Last N Bars)
Core Purpose:
This indicator is designed to visually highlight on the chart the exact moment when the price (specifically, the high/low range of a price bar) makes contact with a specified Exponential Moving Average (EMA) for the first time within a defined recent lookback period (e.g., the last 20 bars).
How it Works:
EMA Calculation: It first calculates a standard Exponential Moving Average (EMA) based on the user-defined EMA Length and EMA Source (e.g., close price). This EMA line is plotted on the chart, often serving as a dynamic level of potential support or resistance.
"Touch" Detection: For every price bar, the indicator checks if the bar's range (from its low to its high) overlaps with or crosses the calculated EMA value for that bar. If low <= EMA <= high, it's considered a "touch".
"First Touch" Logic: This is the key feature. The indicator looks back over a specified number of preceding bars (defined by the Lookback Period). If a "touch" occurs on the current bar, and no "touch" occurred on any of the bars within that preceding lookback window, then the current touch is marked as the "first touch".
Visual Signal: When a "first touch" condition is met, the indicator plots a distinct shape (by default, a small green triangle) below the corresponding price bar. This makes it easy to spot these specific events.
Key Components & Settings:
EMA Line: The calculated EMA itself is plotted (typically as an orange line) for visual reference.
First Touch Signal: A shape (e.g., green triangle) appears below bars meeting the "first touch" criteria.
EMA Length (Input): Determines the period used for the EMA calculation. Shorter lengths make the EMA more reactive to recent price changes; longer lengths make it smoother and slower.
Lookback Period (Input): Defines how many bars (including the current one) the indicator checks backwards to determine if the current touch is the first one. A lookback of 20 means it checks if there was a touch in the previous 19 bars before signalling the current one as the first.
EMA Source (Input): Specifies which price point (close, open, high, low, hl2, etc.) is used to calculate the EMA.
Interpretation & Potential Uses:
Identifying Re-tests: The signal highlights when price returns to test the EMA after having stayed away from it for the duration of the lookback period. This can be significant as the market re-evaluates the EMA level.
Potential Reversal/Continuation Points: A first touch might indicate:
A potential area where a trend might resume after a pullback (if price bounces off the EMA).
A potential area where a reversal might begin (if price strongly rejects the EMA).
A point of interest if price consolidates around the EMA after the first touch.
Filtering Noise: By focusing only on the first touch within a period, it can help filter out repeated touches that might occur during choppy or consolidating price action around the EMA.
Confluence: Traders might use this signal in conjunction with other forms of analysis (e.g., horizontal support/resistance, trendlines, candlestick patterns, other indicators) to strengthen trade setups.
Limitations:
Lagging: Like all moving averages, the EMA is a lagging indicator.
Not Predictive: The signal indicates a specific past event (the first touch) occurred; it doesn't guarantee a future price movement.
Parameter Dependent: The effectiveness and frequency of signals heavily depend on the chosen EMA Length and Lookback Period. These may need tuning for different assets and timeframes.
Requires Confirmation: It's generally recommended to use this indicator as part of a broader trading strategy and not rely solely on its signals for trade decisions.
In essence, the "First EMA Touch (Last N Bars)" indicator provides a specific, refined signal related to price interaction with a moving average, helping traders focus on potentially significant initial tests of the EMA after a period of separation.
Market Crashes & Recessions (1907-Present)Included Recession Periods:
Panic of 1907 (1907–1908)
Post-WWI Recession (1918–1919)
Great Depression (1929–1933)
1937–1938 Recession
1953, 1957, & 1973 Oil Crises Recessions
Early 1980s Recession (1980–1982)
Early 1990s Recession (1990–1991)
Dot-com Bubble (2000–2002)
Global Financial Crisis (2007–2009)
COVID-19 Recession (2020)
2022 Market Correction
Blood MoonsBlood Moon Dates
Description:
This indicator overlays vertical lines on your chart to mark the dates of total lunar eclipses (commonly known as "Blood Moons") from December 2010 to May 2040. Designed with cryptocurrency traders in mind, it’s perfect for analyzing potential correlations between these celestial events and price movements. The lines are drawn on the first bar and extend across the chart, making it easy to spot these dates on any timeframe.
Features:
Plots vertical lines for 19 Blood Moon events (2010–2040).
Customizable line color, style (solid, dotted, dashed), and width.
Option to toggle lines on/off for a cleaner chart.
Lines extend both ways for maximum visibility across your chart.
Settings:
Show Lines: Enable or disable the lines (default: enabled).
Line Color: Choose your preferred color (default: red).
Line Style: Select solid, dotted, or dashed (default: dotted).
Line Width: Adjust thickness from 1 to 5 (default: 2).
Usage:
Add this indicator to your chart to visualize Blood Moon dates alongside price action. Customize the appearance to suit your analysis style. Note: Lines are plotted based on timestamps and extend across the chart, so they’re best viewed on daily or higher timeframes for clarity.
Disclaimer:
This is an educational tool and not financial advice. Past performance does not guarantee future results. Use at your own risk.
Economic Crises by @zeusbottradingEconomic Crises Indicator by @zeusbottrading
Description and Use Case
Overview
The Economic Crises Highlight Indicator is designed to visually mark major economic crises on a TradingView chart by shading these periods in red. It provides a historical context for financial analysis by indicating when major recessions occurred, helping traders and analysts assess the performance of assets before, during, and after these crises.
What This Indicator Shows
This indicator highlights the following major economic crises (from 1953 to 2020), which significantly impacted global markets:
• 1953 Korean War Recession
• 1957 Monetary Tightening Recession
• 1960 Investment Decline Recession
• 1969 Employment Crisis
• 1973 Oil Crisis
• 1980 Inflation Crisis
• 1981 Fed Monetary Policy Recession
• 1990 Oil Crisis and Gulf War Recession
• 2001 Dot-Com Bubble Crash
• 2008 Global Financial Crisis (Great Recession)
• 2020 COVID-19 Recession
Each of these periods is shaded in red with 80% transparency, allowing you to clearly see the impact of economic downturns on various financial assets.
How This Indicator is Useful
This indicator is particularly valuable for:
✅ Comparative Performance Analysis – It allows traders and investors to compare how different assets (e.g., Gold, Silver, S&P 500, Bitcoin) performed before, during, and after major economic crises.
✅ Identifying Market Trends – Helps recognize recurring patterns in asset price movements during times of financial distress.
✅ Risk Management & Strategy Development – Understanding how markets reacted in the past can assist in making better-informed investment decisions for future downturns.
✅ Gold, Silver & Bitcoin as Safe Havens – Comparing precious metals and cryptocurrencies against traditional stocks (e.g., SPY) to analyze their performance as hedges during economic turmoil.
How to Use It in Your Analysis
By overlaying this indicator on your Gold, Silver, SPY, and Bitcoin chart (for example), you can quickly spot historical market reactions and use that insight to predict possible behaviors in future downturns.
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How to Apply This in TradingView?
1. Click on Use on chart under the image.
2. Overlay it with Gold ( OANDA:XAUUSD ), Silver ( OANDA:XAGUSD ), SPY ( AMEX:SPY ), and Bitcoin ( COINBASE:BTCUSD ) for comparative analysis.
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Conclusion
This indicator serves as a powerful historical reference for traders analyzing asset performance during economic downturns. By studying past crises, you can develop a data-driven investment strategy and improve your market insights. 🚀📈
Let me know if you need any modifications or enhancements!
Price AltimeterThis indicator should help visualize the price, inspired by a Digital Altimeter in a Pilots HUD.
It's by default calibrated to Bitcoin, with the small levels showing every $100 and the larger levels setup to display on every $1000. But you can change this to whatever you want by changing the settings for: Small and Large Level Increments.
The default colors are grey, but can be changed to whatever you want, and there are two cause if you want they work as a gradient.
There are options to fade as the values go away from the current price action.
There are options for Forward and Backward Offsets, 0 is the current price and each value represents a candle on whatever time frame your currently on.
Other Options include the Fade Ratio, the Line Width and Style, which are all self explanatory.
Hope you Enjoy!
Backtest it in fast mode to see it in action a little better...
Known Issues:
For some reason it bug's out when either or are displaying more than 19 lines, unsure why so its limited to that for now.
Extra Note on what this may be useful for: I always wanted to make this, but didn't realize how to put things in front of the price action... Offset! Duh! Anyways, I thought of this one because I often it's hard on these charts to really get an idea for absolute price amounts across different time frames, this in an intuitive, at a glance way to see it because the regular price thing on the right always adds values between values when you zoom in and you can sometimes get lost figuring out the proportions of things.
Could also be useful for Scalping?
