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Global Risk Terminal – Multi-Asset Macro Sentiment IndicatorDescription: The Global Risk Terminal is a sophisticated macro sentiment indicator that synthesizes signals from three key cross-asset relationships to produce a single, actionable risk appetite score. It is designed to help traders and investors identify whether global markets are in a risk-on (growth-seeking) or risk-off (defensive) regime. The indicator analyzes the behavior of commodities, equities, bonds, and currencies to generate a comprehensive view of market conditions. Indicator Output: The Global Risk Terminal produces a normalized risk score ranging from -1 to +1: Positive values indicate risk-on conditions (growth assets favored) Negative values indicate risk-off conditions (safe-haven assets favored) Core Components: Growth Pulse (Copper to Gold Ratio, HG/GC) Purpose: Measures investor preference for industrial growth versus safe-haven assets. Interpretation: Rising ratio → Copper outperforming gold → Risk-on environment Falling ratio → Gold outperforming copper → Risk-off environment Flat ratio → Transitional market phase Technical Implementation: Dual moving average slope method (fast MA default 20, slow MA default 40). Positive slope = +1, negative slope = -1, flat slope = 0 Equity Rotation (Russell 2000 to S&P 500 Ratio, RTY/ES) Purpose: Tracks rotation between small-cap and large-cap equities, revealing market risk appetite. Interpretation: Rising ratio → Small-caps outperforming → Strong risk-on Falling ratio → Large-caps outperforming → Defensive positioning Technical Implementation: Dual moving average slope method (same as Growth Pulse) Flow Gauge (10-Year Treasury to US Dollar Index, ZN/DXY) Purpose: Captures liquidity conditions and cross-asset capital flows. Interpretation: Rising ratio → Treasury prices rising or USD weakening → Liquidity expansion, risk-on environment Falling ratio → Treasury prices falling or USD strengthening → Liquidity contraction, risk-off environment Technical Implementation: Dual moving average slope method Composite Risk Score Calculation: Analyze each component for trend using dual moving averages Assign signal values: +1 (risk-on), -1 (risk-off), 0 (neutral) Average the three signals: Risk Score = (Growth Pulse + Equity Rotation + Flow Gauge) / 3 Optional smoothing with exponential moving average (default 3 periods) to reduce noise Interpreting the Risk Score: +0.66 to +1.0: Full risk-on – favor cyclical sectors, small-caps, growth strategies +0.33 to +0.66: Moderate risk-on – mostly bullish environment, watch for fading momentum -0.33 to +0.33: Neutral/transition – markets in flux, signals mixed, exercise caution -0.66 to -0.33: Cautious risk-off – favor defensive sectors, reduce high-beta exposure -1.0 to -0.66: Full risk-off – strong defensive positioning, prioritize safe-haven assets How to Use the Global Risk Terminal to Frame Trades: Aligning Trades with Market Regime Risk-On (+0.33 and above): Look for buying opportunities in cyclical stocks, high-beta equities, commodities, and emerging markets. Use long entries for swing trades or intraday positions, following confirmed price action. Risk-Off (-0.33 and below): Shift focus to defensive sectors, large-cap quality stocks, U.S. Treasuries, and safe-haven currencies. Prefer short entries or reduced exposure in risky assets. Entry and Exit Framing Use the risk score as a macro filter before executing trades: Example: The risk score is +0.7 (strong risk-on). Prefer long positions in equities or commodities that are showing bullish confirmation on your regular chart. Conversely, if the risk score is -0.7 (strong risk-off), avoid aggressive longs and consider short or defensive trades. Watch for threshold crossings (+/-0.33, +/-0.66) as potential inflection points for adjusting position size, stop-loss levels, or sector rotation. Confirming Trade Decisions Combine the Global Risk Terminal with price action, volume, and trend indicators: If equities rally but the risk score is declining, this may indicate a fragile rally driven by few leaders—trade cautiously. If equities fall but the risk score is rising, consider counter-trend entries or buying dips. Risk Management and Position Sizing Strong alignment across components → increase position size and hold with wider stops Mixed or neutral signals → reduce exposure, tighten stops, or avoid new trades Defensive regimes → rotate into stable, low-volatility assets and increase cash buffer Framing Trades Across Timeframes Use the indicator as a strategic guide rather than a precise timing tool. Even without the MTF table: Daily trend alignment → Guide swing trade bias Shorter timeframe price action → Refine entry points and stop placement Example: Daily chart shows +0.6 risk score → identify high-probability long setups using intraday technical patterns (breakouts, trend continuation). Sector and Asset Rotation Risk-On: Focus on cyclical sectors (financials, industrials, materials, energy), small-caps, high-beta instruments Risk-Off: Focus on defensive sectors (utilities, consumer staples, healthcare), large-caps, safe-haven instruments Alert Integration Set alerts on the risk score to notify you when markets move from neutral to risk-on or risk-off regimes. Use these alerts to plan entries, exits, or portfolio adjustments in advance. Customization Options: Moving Average Length (5–100): Adjust sensitivity of trend detection Score Smoothing (1–10): Reduce noise or see raw risk score Visual Themes: Six preset themes (Cyber, Ocean, Sunset, Monochrome, Matrix, Custom) Display Options: Show or hide component dashboards, main header, risk level lines, gradient fill, and component signals Label Size: Tiny, Small, Normal, Large Alert Conditions: Risk score crosses above +0.66 → Strong risk-on Risk score crosses below -0.66 → Strong risk-off Risk score crosses zero → Neutral line Risk score crosses above +0.33 → Moderate risk-on Risk score crosses below -0.33 → Moderate risk-off Data Sources: HG1! – Copper Futures (COMEX) GC1! – Gold Futures (COMEX) RTY1! – Russell 2000 E-mini Futures (CME) ES1! – S&P 500 E-mini Futures (CME) ZN1! – 10-Year U.S. Treasury Note Futures (CBOT) DXY – U.S. Dollar Index (ICE) Notes and Limitations: Works best during clear macro regimes and aligned trends Use with price action, volume, and other technical tools Not a standalone trading system; serves as a macro context filter Equal weighting assumes all three components are equally important, but market conditions may vary Past performance does not guarantee future results Conclusion: The Global Risk Terminal consolidates complex cross-asset signals into a simple, actionable score that informs market regime, portfolio positioning, sector rotation, and trading decisions. Its user-friendly layout and extensive customization options make it suitable for traders of all experience levels seeking macro-driven insights. By framing trades around risk score thresholds and combining macro context with tactical execution, traders can identify higher-probability opportunities and optimize position sizing, entries, and exits across a wide range of market conditions.
อินดิเคเตอร์ Pine Script®
โดย joeljohnrussell
CloudShiftCloudShift + Bollinger Bands This version of CloudShift now includes fully optimized Bollinger Bands with all three dynamic lines: Upper Band: Highlights expansion during volatility spikes. Lower Band: Identifies compression and accumulation zones. Centerline (Basis): A smooth reference of the moving average, providing better visual balance and directional context. The bands are drawn with thin, clean lime lines, designed to integrate perfectly with the cloud logic — keeping your chart minimalist yet powerful. This update enhances the CloudShift indicator by providing a clear visual framework of market volatility and structure without altering its original logic. Recommended for use on: NASDAQ, S&P 500, and other high-volatility futures. Recommended timeframe: 5–15 minutes.
อินดิเคเตอร์ Pine Script®
โดย THEGREATNAPLESTRADERX
BTC Price Spread Heatmap (Coinbase vs Binance (chart symbol))Observation was made about price spread varying among exchanges. Original event took place Oct 4-5th. BIN/BTC was about 1K lower than COIN/BTC. Implemented a visualization to capture specifically Binance and coinbase spread comparison. This may lend as a tool to help build a case if there is a correlation. At this time Binance holds 43.1% of the market share and coinbase a mear 5.5%...
อินดิเคเตอร์ Pine Script®
โดย HyptoCrypto_
MTF Trend Dashboard LITE (Daily & 4H)Overview The MTF Trend Dashboard LITE by G.C provides instant directional clarity between the Daily and 4-Hour timeframes using refined EMA (9 / 21) logic. It’s built for traders who want simple, accurate trend confirmation without switching between multiple charts. Why It Matters One of the biggest causes of trading errors is timeframe conflict — seeing a bullish setup on one chart and a bearish signal on another. This tool solves that problem by showing both the Daily and 4H structure side-by-side, using objective EMA-based logic. When both agree, bias is strong. When they conflict, it’s a signal to wait. Core Features • Two-timeframe dashboard ( Daily + 4H ) • Non-repainting EMA (9 / 21) logic • Color-coded states ( Bullish / Bearish / Neutral ) • Lightweight and easy to read • Adjustable panel position: Top Right / Top Left / Bottom Right / Bottom Left How to Use Add the indicator to any chart. Wait for both timeframes to align before entering trades. Mixed signals often indicate sideways or transitional markets. Combine with your existing trading strategy for confirmation. Trend Condition Glossary ▲ STRONG BULLISH – EMA(9) well above EMA(21) and both sloping upward. Strong upward momentum with price above both EMAs. BULLISH – EMA(9) above EMA(21) but weaker slope or price confirmation. Moderate uptrend or early recovery. NEUTRAL – EMAs crossing or flat. Market lacks structure — consolidation or indecision. ▼ BEARISH – EMA(9) below EMA(21) with limited slope. Mild correction or early reversal. ▼ STRONG BEARISH – EMA(9) below EMA(21), both sloping down, price below both. Strong downtrend, clear seller control. Who It’s For Traders who want a clean, no-noise confirmation tool to simplify directional bias decisions in swing and intraday setups. Disclaimer This indicator is for analytical and educational purposes only. Always trade with discipline, patience, and sound risk management.
อินดิเคเตอร์ Pine Script®
โดย Gurhan_c
2
r - g Oscillator | Norm + Sigma-BandsThe r–g Oscillator measures the macro-liquidity regime by tracking the gap between real interest rates (r) and nominal GDP growth (g). It approximates real rate pressure using the 10-Year Treasury yield minus the 5-Year/5-Year forward inflation expectation, and compares that to either Real or Nominal U.S. GDP YoY growth. Green (g > r): Expansionary backdrop — growth outpaces real yields; liquidity tailwinds. Red (r > g): Contractionary backdrop — real rates restrictive; liquidity headwinds. The σ-bands (standard-deviation envelopes) highlight statistically extreme expansions or contractions in the r–g spread. The “sweet-spot” shading marks moments when r–g breaks strongly above/below zero — early-cycle thrusts or late-cycle stress. Optional normalization rescales r–g between –1 and +1 to compare across cycles. Use: Track shifts in the macro tide rather than short-term waves. Sustained green phases typically align with bull-market environments; red phases often coincide with tightening cycles or recessions. Combine with faster liquidity or breadth measures (e.g., WRESBAL ROC) for tactical confirmation.
อินดิเคเตอร์ Pine Script®
โดย kWhDealer_
WRESBAL ROC Oscillator (Clean)This indicator tracks the rate of change in Federal Reserve reserve balances (WRESBAL) to visualize shifts in systemic liquidity. It measures how quickly reserves are expanding or contracting over a chosen lookback window (default 26 weeks), then smooths the result to highlight durable macro trends rather than short-term noise. Green = expanding reserves → liquidity easing → risk-asset support Red = contracting reserves → liquidity tightening → headwind for risk assets The oscillator is designed for macro context rather than short-term trading. It correlates strongly with major equity and credit cycles, often leading inflection points in the S&P 500 and Nasdaq by several weeks. Use it to identify transitions between QE (quantitative easing) and QT (quantitative tightening) regimes and to gauge the liquidity environment driving broad market behavior.
อินดิเคเตอร์ Pine Script®
โดย kWhDealer_
Gold Total Market Cap By Wave Trader Gold Total Market Cap (Updated 2025) Overview This indicator calculates and visualizes the total market capitalization of gold in real-time, based on the current XAUUSD spot price and the estimated above-ground gold supply. It transforms the raw gold price into a scaled "market cap" view (in trillions USD), helping traders contextualize gold's global value—often compared to stocks, crypto, or fiat reserves. As of October 2025, gold's cap hovers around $26–27T, underscoring its status as a premier safe-haven asset. How It Works Core Formula: Market Cap = Gold Price (USD/oz) × Total Supply (troy oz), scaled to trillions for chart readability. Supply Data: Defaults to the World Gold Council (WGC) mid-2025 estimate of ~218,000 metric tonnes (~7.01B troy oz), but customizable for scenarios like historical or projected figures. Plotted as a smooth yellow line below the price pane, mirroring gold's price movements but in cap terms. Key Features Dynamic Label: A real-time label on the yellow line displays the exact market cap (e.g., "26.8") for instant reference, styled like popular TradingView cap indicators. Reference Line: Horizontal dashed line at 25T USD to highlight key thresholds (e.g., surpassing Bitcoin's cap). Info Table: Top-right panel shows current gold price and full market cap (e.g., "26.8T USD") for quick stats. Overlay-Free: Designed for a separate pane to avoid cluttering your main XAUUSD chart. Data Sources & Customization Relies on live XAUUSD close prices from TradingView. Supply input: Switch between "WGC Mid-2025" (default) or custom values—ideal for sensitivity analysis (e.g., adding future mining output). No external API calls; fully self-contained for fast performance. Usage Tips For Gold Traders: Spot divergences between price momentum and cap growth to gauge overbought/oversold conditions. Portfolio Context: Compare to S&P 500 cap (~$50T) or BTC (~$2T) by adding multi-symbol alerts. Timeframes: Best on daily/weekly for long-term trends; works on 1H+ for intraday macro views. Alerts: Set notifications for cap milestones (e.g., "Gold Cap > 28T") via TradingView's alert system. Track gold's "infinite market cap" evolution—because unlike stocks, gold's supply grows slowly, amplifying price impact. Updated for 2025 data; feedback welcome! 🚀
อินดิเคเตอร์ Pine Script®
โดย Wave-Trader
Chart-prepFxxDanny Chart-Prep A practical multi-tool script for clean and structured chart preparation. ✨ Features Weekly Close Levels Automatically plots the previous week’s close and the week before that, with clear styling to distinguish current and past levels. Trading Sessions Colored session boxes for the three key market sessions: Asia (20:00–23:00 UTC-4) Europe (02:00–05:00 UTC-4) New York (08:00–11:00 UTC-4) Each session box automatically adapts to the session’s high/low range and only keeps the last 5 visible to avoid clutter. Previous Day’s High & Low Plots the prior day’s high and low with lines that extend into the current session. Up to 10 days are kept on the chart. Daily & Weekly Separators Vertical lines to visually separate days (dotted) and weeks (solid, colored). Anchored to a rolling price window so the Y-axis scaling stays clean and unaffected. ✅ Benefits Stay focused with key price levels and session ranges marked automatically. No need for manual drawing or constant adjustments. Optimized performance – old objects are automatically removed. No axis distortion from “infinite” lines or boxes.
อินดิเคเตอร์ Pine Script®
โดย FxxDanny
CoT Bias Tracker [DOSALGO]Unlock a powerful new dimension in your market analysis with the CoT Bias Tracker . This advanced tool goes beyond price charts to reveal the positioning of the market's largest players, allowing you to track the "smart money" and make more informed trading decisions. By harnessing the weekly Commitment of Traders (CoT) report, this indicator automatically fetches, processes, and displays the net positioning of Commercials (Hedgers), Non-Commercials (Large Speculators), and Retail traders. Its standout feature is the unique dual-asset analysis for Forex pairs, which automatically breaks down a pair like EURUSD into its Base (EUR) and Quote (USD) components, giving you a crystal-clear view of the capital flows driving the market. Stop guessing the trend and start tracking the institutional bias that truly matters. Key Features 📈 Complete CoT Data Analysis: Automatically fetches and displays the latest weekly net positions for three key market participants: Commercials, Non-Commercials, and Retail Traders. 🌍 Unique Forex Pair Analysis: The only tool you'll need for Forex. It intelligently separates pairs (e.g., AUDJPY) into their Base (AUD) and Quote (JPY) currencies and displays a full CoT analysis for each, revealing which currency is truly in demand. 📊 Advanced Bias Dashboard: A comprehensive and fully customizable dashboard provides an at-a-glance summary of the market's sentiment, including current positions, weekly changes, and both short-term and long-term bias readings. 🧠 Conviction Analysis: This indicator goes deeper than just net positions. By analyzing the relationship between positioning changes and Open Interest, it gauges the conviction behind a move, distinguishing between a "Strong Long" (new money entering) and a "Weak Long" (short covering). 🚀 POIV Metric: Includes the Position x Open Interest Volume (POIV) metric, an advanced tool for measuring the cumulative force behind positioning changes over time. 📉 Historical Data Plotting: Visualize the net positioning data and its moving average directly on your chart's indicator pane. This is perfect for identifying historical extremes, divergences, and long-term trends in positioning. ⚙️ Automatic Symbol Recognition: The indicator intelligently detects the asset on your chart—from Forex pairs to indices like the S&P 500 and commodities like Gold—and automatically fetches the correct CoT data. 🎨 Full Customization: Tailor the entire tool to your workspace. Control the dashboard's position, size, and colors. Toggle the visibility of any data row or plot to focus only on what matters to you. The Dashboard Explained The dashboard gives you a complete, multi-faceted view of the market's positioning. Participant Groups: Commercials: Often considered the "smart money." They use futures to hedge their business operations and typically fade trends, buying into lows and selling into highs. Non-Commercials: Large speculators like hedge funds and institutions. They are typically trend-followers, and their positioning is a powerful indicator of the current dominant trend. Retail Traders: Small, non-reportable speculators. They are often seen as a contrarian indicator. Net Positions & Change: See the raw net long or short positions from the current and previous week's report, along with the net change to understand the weekly capital flow. S-Term Bias (Short-Term): Based on the weekly net change, this tells you who was buying and who was selling since the last report. L-Term Bias (Long-Term): Compares the current net position to its moving average to define the dominant positioning trend. (Note: This reading is most effective on the Weekly chart timeframe.) Conviction (via Open Interest): Found in the "Open Interest" row under the L-Term Bias column, this powerful metric tells you how positions are changing: Strong Long: New buyers are entering the market with conviction. Weak Long: Existing shorts are covering their positions. Strong Short: New sellers are entering the market with conviction. Weak Short: Existing longs are closing their positions. Use Cases & Strategy Trend Confirmation: Use the positioning of Non-Commercials to confirm the strength and direction of a trend you've identified with technical analysis. Reversal Signals: Look for extreme net positioning levels or divergences between Commercial and Non-Commercial sentiment, which can often precede major market reversals. Forex Strength Analysis: When trading a pair like GBPJPY, use the dashboard to see if Non-Commercials are strongly bullish on GBP while being bearish on JPY. This "double confirmation" can highlight high-probability trade setups. Important Notes Understanding CoT Data: The Commitment of Traders report is released by the CFTC every Friday afternoon (~3:30 PM ET). Crucially, it reflects the positions that were held on the preceding Tuesday. It is a tool for gauging medium- to long-term sentiment, not for intraday signals. Disclaimer: This tool is for analytical and educational purposes only and should not be considered financial advice. All forms of trading involve risk. Always conduct your own research and apply robust risk management.
อินดิเคเตอร์ Pine Script®
โดย DOSALGO
9
MARA / mNAV=1 (x)What it does This script overlays two signals on the MARA chart: mNAV=1 fair-value line — the MARA price implied by Bitcoin NAV: mNAV1 = (BTC price × BTC holdings) / MARA shares Premium/Discount ratio — how far MARA trades vs. its NAV fair value: Ratio = Close / mNAV1 (1.00 = fair; >1 = premium; <1 = discount) Inputs Shares outstanding (default: 370,460,000) BTC holdings (official or estimated; you can roll forward +25 BTC/day if you want) BTC symbol used for pricing (e.g., BTCUSD, BTCUSDT, BTCUSDTPERP) How to use When Price < mNAV=1 and Ratio < 1.00 → MARA trades at a discount to BTC NAV (potential mean-reversion if BTC is stable). When Price > mNAV=1 and Ratio > 1.00 → premium (premium often compresses during BTC chop/weakness). Rule of thumb (with ~53k BTC and 370.46M shares): +$1,000 BTC ≈ +$0.14 on the mNAV=1 line. Visuals Blue line = mNAV=1 (fair value) plotted directly on the MARA chart. Purple line = Ratio (×) on a separate right-hand scale centered around 1.00. Optional shading: green when Ratio > 1.05 (+5% premium), red when Ratio < 0.95 (−5% discount). Alerts (suggested) Premium > +5%: Ratio > 1.05 Discount < −5%: Ratio < 0.95 Notes This is a proxy for NAV parity; it assumes your BTC holdings input is correct (official last report or your estimate). Choice of BTC symbol matters; use the feed that best matches your workflow (spot, perp, or index). The ratio is most informative when BTC is range-bound; during fast BTC moves MARA can overshoot temporarily.
อินดิเคเตอร์ Pine Script®
โดย jimmyface49
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork. Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret? The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously. This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation. Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio. The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally. Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional. The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply. The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach. 1. Introduction and Theoretical Background 1.1 The Limitations of Static Portfolio Allocation Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies. The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework. 1.2 Multi-Factor Approaches in Asset Allocation Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding. 2. Data Foundation and Data Quality 2.1 Data Sources Used The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics. First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures. Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model. Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator. Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data. 2.2 Handling Missing Data A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals. 3. Component 1: Market Regime Detection 3.1 The Concept of Market Regimes The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly. DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis. 3.2 Trend Analysis Through Moving Averages Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs. The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent. 3.3 Volatility Regimes Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986). DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices. The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989). 3.4 Drawdown Analysis A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric. The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%. 3.5 Regime Classification Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations. 4. Component 2: Risk-Based Allocation 4.1 Volatility Targeting as Risk Management Approach The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced. Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility. 4.2 Market Volatility Calculation Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure. Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice. The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation. 4.3 Drawdown-Based Position Size Adjustment In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979). The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25). 4.4 Dynamic Risk Adjustment An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure. Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability. 5. Component 3: Valuation Analysis 5.1 Theoretical Foundations of Fundamental Valuation DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets. Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets. 5.2 Equity Risk Premium as Central Valuation Metric The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation. DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs. The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003). The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone. The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth. The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this. The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10. 5.3 Quality Adjustments to Valuation Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure. Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points. Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies. Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010). 6. Component 4: Sentiment Analysis 6.1 The Role of Sentiment in Financial Markets Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions. Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops. 6.2 VIX-Based Sentiment Assessment DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods. A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities. 6.3 VIX Term Structure as Sentiment Signal The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D). Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines. 6.4 Safe-Haven Flows During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion. The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases. Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection. 7. Component 5: Macroeconomic Analysis 7.1 The Yield Curve as Economic Indicator The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity. DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points. The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006). 7.2 Credit Conditions and Spreads Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns. The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points. Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score. 7.3 Financial Stability at Corporate Level While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health. A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions. Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases. 8. Component 6: Crisis Detection 8.1 The Need for Systematic Crisis Detection Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components. Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators. 8.2 Multi-Signal Crisis Identification The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence. The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility. The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns. The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis. The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points. The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point. A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms. 8.3 Crisis-Based Allocation Override When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007). This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability. 9. Integration and Final Allocation Calculation 9.1 Component Weighting The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results. The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations. The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017). The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988). Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable. 9.2 Model Type Adjustments DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments. Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons. Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets. Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged. 9.3 Smoothing and Stability The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change. The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals. 10. Visualization and Interpretation 10.1 Main Output: Equity Allocation DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash. The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases. 10.2 Dashboard Metrics A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status. Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments. Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down. 10.3 Component Breakdown (Optional) Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning." This feature is disabled by default to keep the chart clean but can be activated for deeper analysis. 10.4 Confidence Bands The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations. This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources. 11. Alert System 11.1 Allocation Alerts DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning. These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts. 11.2 Crisis Alerts Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors). 11.3 Regime Change Alerts An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics. 11.4 Risk Breach Alerts A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions. 12. Practical Application and Limitations 12.1 Portfolio Implementation DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation. A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs. 12.2 Adaptation to Individual Preferences The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight. Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation. Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect. 12.3 Limitations and Disclaimers DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007). The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market. The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination. Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation. The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems. 12.4 Relationship to Other Strategies DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing. The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory. The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio. 13. Scientific Foundation and Evaluation 13.1 Theoretical Consistency DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998). This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990). 13.2 Empirical Validation While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements: In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation. Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance. Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters. 13.3 Comparison with Existing Literature DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources. Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. 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อินดิเคเตอร์ Pine Script®
โดย EdgeTools
16
AutoDay MA (Session-Normalized)📊 AutoDay MA (Session-Normalized Moving Average) ⚡ Daily power, intraday precision. AutoDay MA automatically converts any N-day moving average into the exact equivalent on your current intraday timeframe. 💡 Concept inspired by Brian Shannon (Alphatrends) – mapping daily MAs onto intraday charts by normalizing session minutes. 🛠 How it works Set Days (N) (e.g., 5, 10, 20). Define Session Minutes per Day (⏱ 390 = US RTH, 🌍 1440 = 24h). The indicator detects your chart’s timeframe and computes: Length = (Days × SessionMinutes) / BarMinutes Applies your chosen MA type (📐 SMA / EMA / RMA / WMA) with rounding (nearest, up, down). Displays all details in a clear corner info panel. ✅ Why use it Consistency 🔄: Same 5-day smoothing across all intraday charts. Session-aware 🕒: Works for equities, futures, FX, crypto. Transparency 🔍: Always shows the math & final MA length. Alerts built-in 🔔: Cross up/down vs. price. 📈 Examples 5-Day on 1m → 1950-period MA 5-Day on 15m → 130-period MA 5-Day on 65m → 30-period MA 10-Day on 24h/15m (crypto) → 960-period MA
อินดิเคเตอร์ Pine Script®
โดย TradeQUO
4
Enhanced Std Dev Oscillator (Z-Score)Enhanced Std Dev Oscillator (Z-Score) Overview The Enhanced Std Dev Oscillator (ESDO) is a refined Z-Score indicator that normalizes price deviations from a moving mean using standard deviation, smoothed for clarity and equipped with divergence detection. This oscillator shines in identifying extreme overbought/oversold conditions and potential reversals, making it ideal for mean-reversion strategies in stocks, forex, or crypto. By highlighting when prices stray too far from the norm, it helps traders avoid chasing trends and focus on high-probability pullbacks. Key Features Customisable Mean & Deviation: Choose SMA or EMA for the mean (default: SMA, length 14); opt for Population or Sample standard deviation for precise statistical accuracy. Smoothing for Clarity: Apply a simple moving average (default: 3) to the raw Z-Score, reducing noise without lagging signals excessively. Zone Highlighting: Background colours flag extreme zones—red tint above +2 (overbought), green below -2 (oversold)—for quick visual scans. Divergence Alerts: Automatically detects bullish (price lows lower, Z-Score higher) and bearish (price highs higher, Z-Score lower) divergences using pivot points (default length: 5), with labeled shapes for easy spotting. Built-in Alerts: Notifications for Z-Score crossovers into OB/OS zones and divergence events to keep you informed without constant monitoring. How It Works Core Calculation: Computes the mean (SMA/EMA) over the specified length, then standard deviation (Population or adjusted Sample formula for N>1). Z-Score = (Source - Mean) / Std Dev, handling edge cases like zero deviation. Smoothing: Averages the Z-Score with an SMA to create a cleaner plot oscillating around zero. Levels & Zones: Plots horizontal lines at ±1 (orange dotted) and ±2 (red dashed) for reference; backgrounds activate in extreme zones. Divergence Logic: Scans for pivot highs/lows in price and Z-Score; flags divergences when price extremes diverge from oscillator extremes (looking back 2 pivots for confirmation). Visualisation: Blue line for the smoothed Z-Score; green/red labels for bull/bear divergences. Usage Tips Buy Signal: Z-Score crosses below -2 (oversold) or bullish divergence forms—pair with volume spike for confirmation. Sell Signal: Z-Score crosses above +2 (overbought) or bearish divergence—watch for resistance alignment. Customisation: Use EMA mean for trendier assets; enable Sample std dev for smaller datasets. Increase pivot length (7-10) in volatile markets to filter false signals. Timeframes: Excels on daily/4H for swing trades; test smoothing on lower frames to avoid over-smoothing. Always combine with trend filters like a 200-period MA. This open-source script is licensed under Mozilla Public License 2.0. Backtest thoroughly—past performance isn't indicative of future results. Trade with discipline! 📈 © HighlanderOne
อินดิเคเตอร์ Pine Script®
โดย HighlanderOne
Liquidity Sweep ReversalThe Liquidity Sweep Reversal indicator is a sophisticated price-action-based tool designed for TradingView that identifies high-probability reversal setups by combining institutional liquidity concepts with session-based market structure. It detects potential reversals after price "sweeps" key support/resistance levels—such as prior day/week highs and lows or session extremes (Asian, London, New York)—followed by a rejection pattern. The core logic revolves around two main signal types: CISD (Close Inside, Sweep, Divergence) patterns that confirm liquidity grabs on higher timeframes. Engulfing candlestick reversals occurring shortly after a touch of a key level within a defined lookback window. To enhance relevance and reduce noise, the indicator optionally restricts signals to high-volatility “Killzone” sessions—including Asian, London, and New York AM/PM overlap periods—where institutional activity is typically concentrated. Users can fully customize: Timezone and higher timeframe (HTF) settings Which key levels to monitor (PDH, PDL, PWH, PWL, session highs/lows) Visual styling (line types, colors, labels) Signal sensitivity (max bars after touch, signal size) Display options (background highlights, level visibility, historical signal filtering) Additionally, the script draws vertical lines for today’s and tomorrow’s London (08:00 CET) and New York (09:30 EST) market opens to provide contextual reference. This tool is ideal for traders using auction market theory, order flow, or institutional footprint strategies who seek confluence between liquidity pools, session structure, and price rejection.
อินดิเคเตอร์ Pine Script®
โดย ewinacademy