MA Cross + KillzonesCombines oscarvs' BTC Killzones with four EMAs and the trader's 13/49 golden cross as well as the investor's 50/200 golden cross. Note that the killzones highlight the first two hours of market action in each of the three main crypto markets, so this time period will have to be adjusted for daylight savings time in April and in November.
Pesquisar nos scripts por "GOLD"
MA Cross 50/200 - Moving Averageindicates short-term 50-day moving average AND long-term 200-day moving average CROSS
@GateTicker
Multi-Timeframe Probability Zones [DW]This is an experimental study based on multi-timeframe price action and a simple average.
Use it to quickly identify MTF support and resistance, and high probability price levels.
NOTE: Because higher timeframe levels are not certain until the interval is closed, refresh your chart as new levels are drawn.
Line Break StrategyLine Break Strategy
Entry rule:
Long on a bullish line and short on a bearish line.
Backtest:
Profit factors are shown below for three-line break.
Daily time frame, FXCM broker.
EURUSD: 1.267, USDJPY: 1.039, GBPUSD: -0.816, AUDUSD: -0.959
S&P500: -0.783, Nikkei225: 1.099
CrudeOil: 1.03, Gold: 1.196
BTCUSD: -0.883
Reference:
Steve Nison, Beyond Candlesticks - New Japanese Charting Techniques Revealed
Note:
This strategy doesn't work properly on the linebreak chart.
A good example is shown below. The entry prices are not always correct.
If you have signal, but the next candle moves in the opposite direction, the entry price is drawn at the Open of the new candle instead of the Close of the previous candle.
The results of backtest are unreliable due to this reason.
Earnings MultiplesMultiplies Quarterly Earnings x 13, x 21, x 34, x 55, x 89, x 144, x 233.
Yes its a fibonacci sequence.
"Goldilocks zone" seems to be in the 55x - 89x area.
Also when companies become profitable, the indicator looks like a "starburst".
EMA & SMA with FRACTAL DEVIATION BANDS by @XeL_ArjonaEMA & SMA with FRACTAL DEVIATION BANDS
Ver. 1.0.25.08.2015
By Ricardo M Arjona @XeL_Arjona
DISCLAIMER:
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets. The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
WHAT IS THIS?
This is the adaptation of the FRACTAL DEVIATION BANDS to be used on Traditional Moving Averages (Simple & Exponential).
ALL NEW IDEAS OR MODIFICATIONS to these indicator(s) are Welcome in favor to deploy a better and more accurate readings. I will be very glad to be notified at Twitter or TradingVew accounts at: @XeL_Arjona
Any important addition to this work MUST REMAIN PUBLIC by means of CreativeCommons CC & TradingView.
2015
Acc/Dist. Cloud with Fractal Deviation Bands by @XeL_ArjonaACCUMULATION / DISTRIBUTION CLOUD with MORPHIC DEVIATION BANDS
Ver. 2.0.beta.23:08:2015
by Ricardo M. Arjona @XeL_Arjona
DISCLAIMER
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
Pine Script code MOD's and adaptations by @XeL_Arjona with special mention in regard of:
Buy (Bull) and Sell (Bear) "Power Balance Algorithm by Vadim Gimelfarb published at Stocks & Commodities V. 21:10 (68-72).
Custom Weighting Coefficient for Exponential Moving Average (nEMA) adaptation work by @XeL_Arjona with contribution help from @RicardoSantos at TradingView @pinescript chat room.
Morphic Numbers (PHI & Plastic) Pine Script adaptation from it's algebraic generation formulas by @XeL_Arjona
Fractal Deviation Bands idea by @XeL_Arjona
CHANGE LOG:
ACCUMULATION / DISTRIBUTION CLOUD: I decided to change it's name from the Buy to Sell Pressure. The code is essentially the same as older versions and they are the center core (VORTEX?) of all derived New stuff which are:
MORPHIC NUMBERS: The "Golden Ratio" expressed by the result of the constant "PHI" and the newer and same in characteristics "Plastic Number" expressed as "PN". For more information about this regard take a look at: HERE!
CUSTOM(K) EXPONENTIAL MOVING AVERAGE: Some code has cleaned from last version to include as custom function the nEMA , which use an additional input (K) to customise the way the "exponentially" is weighted from the custom array. For the purpose of this indicator, I implement a volatility algorithm using the Average True Range of last 9 periods multiplied by the morphic number used in the fractal study. (Golden Ratio as default) The result is very similar in response to classic EMA but tend to accelerate or decelerate much more responsive with wider bars presented in trending average.
FRACTAL DEVIATION BANDS: The main idea is based on the so useful Standard Deviation process to create Bands in favor of a multiplier (As John Bollinger used in it's own bands) from a custom array, in which for this case is the "Volume Pressure Moving Average" as the main Vortex for the "Fractallitly", so then apply as many "Child bands" using the older one as the new calculation array using the same morphic constant as multiplier (Like Fibonacci but with other approach rather than %ratios). Results are AWSOME! Market tend to accelerate or decelerate their Trend in favor of a Fractal approach. This bands try to catch them, so please experiment and feedback me your own observations.
EXTERNAL TICKER FOR VOLUME DATA: I Added a way to input volume data for this kind of study from external tickers. This is just a quicky-hack given that currently TradingView is not adding Volume to their Indexes so; maybe this is temporary by now. It seems that this part of the code is conflicting with intraday timeframes, so You are advised.
This CODE is versioned as BETA FOR TESTING PROPOSES. By now TradingView Admins are changing lot's of things internally, so maybe this could conflict with correct rendering of this study with special tickers or timeframes. I will try to code by itself just the core parts of this study in order to use them at discretion in other areas. ALL NEW IDEAS OR MODIFICATIONS to these indicator(s) are Welcome in favor to deploy a better and more accurate readings. I will be very glad to be notified at Twitter or TradingView accounts at: @XeL_Arjona
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. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
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Macro Momentum – 4-Theme, Vol Target, RebalanceMacro Momentum — 4-Theme, Vol Target, Rebalance
Purpose. A macro-aware strategy that blends four economic “themes”—Business Cycle, Trade/USD, Monetary Policy, and Risk Sentiment—into a single, smoothed Composite signal. It then:
gates entries/exits with hysteresis bands,
enforces optional regime filters (200-day bias), and
sizes the position via volatility targeting with caps for long/short exposure.
It’s designed to run on any chart (index, ETF, futures, single stocks) while reading external macro proxies on a chosen Signal Timeframe.
How it works (high level)
Build four theme signals from robust macro proxies:
Business Cycle: XLI/XLU and Copper/Gold momentum, confirmed by the chart’s price vs a long SMA (default 200D).
Trade / USD: DXY momentum (sign-flipped so a rising USD is bearish for risk assets).
Monetary Policy: 10Y–2Y curve slope momentum and 10Y yield trend (steepening & falling 10Y = risk-on; rising 10Y = risk-off).
Risk Sentiment: VIX momentum (bearish if higher) and HYG/IEF momentum (bullish if credit outperforms duration).
Normalize & de-noise.
Optional Winsorization (MAD or stdev) clamps outliers over a lookback window.
Optional Z-score → tanh mapping compresses to ~ for stable weighting.
Theme lines are SMA-smoothed; the final Composite is LSMA-smoothed (linreg).
Decide direction with hysteresis.
Enter/hold long when Composite ≥ Entry Band; enter/hold short when Composite ≤ −Entry Band.
Exit bands are tighter than entry bands to avoid whipsaws.
Apply regime & direction constraints.
Optional Long-only above 200MA (chart symbol) and/or Short-only below 200MA.
Global Direction control (Long / Short / Both) and Invert switch.
Size via volatility targeting.
Realized close-to-close vol is annualized (choose 9-5 or 24/7 market profile).
Target exposure = TargetVol / RealizedVol, capped by Max Long/Max Short multipliers.
Quantity is computed from equity; futures are rounded to whole contracts.
Rebalance cadence & execution.
Trades are placed on Weekly / Monthly / Quarterly rebalance bars or when the sign of exposure flips.
Optional ATR stop/TP for single-stock style risk management.
Inputs you’ll actually tweak
General
Signal Timeframe: Where macro is sampled (e.g., D/W).
Rebalance Frequency: Weekly / Monthly / Quarterly.
ROC & SMA lengths: Defaults for theme momentum and the 200D regime filter.
Normalization: Z-score (tanh) on/off.
Winsorization
Toggle, lookback, multiplier, MAD vs Stdev.
Risk / Sizing
Target Annualized Vol & Realized Vol Lookback.
Direction (Long/Short/Both) and Invert.
Max long/short exposure caps.
Advanced Thresholds
Theme/Composite smoothing lengths.
Entry/Exit bands (hysteresis).
Regime / Execution
Long-only above 200MA, Short-only below 200MA.
Stops/TP (optional)
ATR length and SL/TP multiples.
Theme Weights
Per-theme scalars so you can push/pull emphasis (e.g., overweight Policy during rate cycles).
Macro Proxies
Symbols for each theme (XLI, XLU, HG1!, GC1!, DXY, US10Y, US02Y, VIX, HYG, IEF). Swap to alternatives as needed (e.g., UUP for DXY).
Signals & logic (under the hood)
Business Cycle = ½ ROC(XLI/XLU) + ½ ROC(Copper/Gold), then confirmed by (price > 200SMA ? +1 : −1).
Trade / USD = −ROC(DXY).
Monetary Policy = 0.6·ROC(10Y–2Y) − 0.4·ROC(10Y).
Risk Sentiment = −0.6·ROC(VIX) + 0.4·ROC(HYG/IEF).
Each theme → (optional Winsor) → (robust z or scaled ROC) → tanh → SMA smoothing.
Composite = weighted average → LSMA smoothing → compare to bands → dir ∈ {−1,0,+1}.
Rebalance & flips. Orders fire on your chosen cadence or when the sign of exposure changes.
Position size. exposure = clamp(TargetVol / realizedVol, maxLong/Short) × dir.
Note: The script also exposes Gross Exposure (% equity) and Signed Exposure (× equity) as diagnostics. These can help you audit how vol-targeting and caps translate into sizing over time.
Visuals & alerts
Composite line + columns (color/intensity reflect direction & strength).
Entry/Exit bands with green/red fills for quick polarity reads.
Hidden plots for each Theme if you want to show them.
Optional rebalance labels (direction, gross & signed exposure, σ).
Background heatmap keyed to Composite.
Alerts
Enter/Inc LONG when Composite crosses up (and on rebalance bars).
Enter/Inc SHORT when Composite crosses down (and on rebalance bars).
Exit to FLAT when Composite returns toward neutral (and on rebalance bars).
Practical tips
Start higher timeframes. Daily signals with Monthly rebalance are a good baseline; weekly signals with quarterly rebalances are even cleaner.
Tune Entry/Exit bands before anything else. Wider bands = fewer trades and less noise.
Weights reflect regime. If policy dominates markets, raise Monetary Policy weight; if credit stress drives moves, raise Risk Sentiment.
Proxies are swappable. Use UUP for USD, or futures-continuous symbols that match your data plan.
Futures vs ETFs. Quantity auto-rounds for futures; ETFs accept fractional shares. Check contract multipliers when interpreting exposure.
Caveats
Macro proxies can repaint at the selected signal timeframe as higher-TF bars form; that’s intentional for macro sampling, but test live.
Vol targeting assumes reasonably stationary realized vol over the lookback; if markets regime-shift, revisit volLook and targetVol.
If you disable normalization/winsorization, themes can become spikier; expect more hysteresis band crossings.
What to change first (quick start)
Set Signal Timeframe = D, Rebalance = Monthly, Z-score on, Winsor on (MAD).
Entry/Exit bands: 0.25 / 0.12 (defaults), then nudge until trade count and turnover feel right.
TargetVol: try 10% for diversified indices; lower for single stocks, higher for vol-sell strategies.
Leave weights = 1.0 until you’ve inspected the four theme lines; then tilt deliberately.
8 SMA Bands (Points)The "8 SMA Bands (Points)" indicator creates a set of eight Simple Moving Average (SMA) bands with adjustable offsets, overlaid on a price chart.
Here’s a breakdown:
Purpose: It tracks price trends using multiple SMAs of varying lengths (default 25, 50, 100, 200, 400, 800, 1600 periods) and adds upper and lower bands around each SMA based on point offsets, helping identify potential support, resistance, and trend strength.
Key Components:
SMAs: Eight SMAs are calculated using closing prices with lengths ranging from 25 to 1600 periods. Each SMA is plotted with a distinct color and line thickness (e.g., MA 1 is blue, MA 8 is white with thicker lines).
Bands: For each SMA, upper and lower bands are created by adding or subtracting a point-based offset (suggestions are to use default Murray Math based numbers e.g., 0.305176 for MA 1, 39.062528 for MA 8) multiplied by a global multiplier (default 1.0). These offsets define the band width and are customizable.
Customization: Users can adjust SMA lengths, offset points, colors, and the global multiplier via input settings grouped by each MA.
Visuals: SMAs are plotted as solid lines with increasing thickness for longer periods (e.g., MA 6–8 use thicker lines or circles).
Bands are plotted as semi-transparent lines matching the SMA color, with longer-term bands (MA 6–7) using a different style for emphasis.
Usage: The indicator helps traders visualize trend direction (upward if price is above most SMAs, downward if below) and potential reversal zones where price interacts with band boundaries.
The flattening or crossing of bands can signal momentum shifts. The coming together of multiple envelope tops/bottoms can signal reversal zones of various degrees based on how many envelopes come together. More envelopes converging mean a more significant top or bottom.
This indicator is particularly useful for identifying multi-timeframe trends and volatility zones on assets like Gold Futures, with flexibility to fine-tune based on market conditions.
Multi Moving Averages8MA Cross
Overview
The "8MA Cross" indicator is a versatile tool that allows traders to plot up to eight fully customizable moving averages (MAs) on their chart. This indicator provides a comprehensive view of market trends across different timeframes by offering extensive configuration options for each individual moving average. Whether you are looking to identify short-term momentum or long-term trend direction, the 8MA Cross indicator offers the flexibility to tailor your analysis to your specific trading strategy.
Features
Eight Independent Moving Averages: Display from one to eight moving averages simultaneously.
Multiple MA Types: Each moving average can be individually set to one of eight different types:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
VWMA (Volume-Weighted Moving Average)
RMA (RMA Moving Average)
HMA (Hull Moving Average)
ALMA (Arnaud Legoux Moving Average)
LSMA (Least Squares Moving Average)
Full Customization: For each of the eight moving averages, you can adjust the length, line width, and color to fit your visual preferences and analytical needs.
Toggle Visibility: Easily show or hide any of the moving averages directly from the settings menu, allowing you to focus on the MAs that are most relevant at any given time.
How to Use
The primary purpose of this indicator is to provide a configurable set of moving averages for trend analysis and signal generation.
Trend Identification: Use a combination of short-term and long-term moving averages to gauge the direction and strength of the prevailing trend. When shorter-term MAs are above longer-term MAs, it typically indicates an uptrend, and vice-versa for a downtrend.
Crossover Signals: Look for crossover events as potential entry or exit signals. For example, a classic "Golden Cross" occurs when a shorter-period MA (e.g., 50) crosses above a longer-period MA (e.g., 200), often signaling the start of a bull market. A "Death Cross" is the opposite.
Dynamic Support and Resistance: Moving averages often act as dynamic levels of support in an uptrend and resistance in a downtrend. Traders can watch for price to pull back and test these MAs.
To configure the indicator, open the settings panel. You will find eight sections, labeled "MA1 Settings" through "MA8 Settings". Within each section, you can enable or disable the MA, select its type, define its length, and customize its appearance.
Disclaimer
This indicator is designed as a technical analysis tool and should be used in conjunction with other forms of analysis and proper risk management.
Past performance does not guarantee future results, and traders should thoroughly test any strategy before implementing it with real capital.
Mongoose Compass Ribbon — Regime Overlay & SizingWhat it does
Mongoose Compass Ribbon paints the price chart background by market regime and displays a suggested position size.
It mirrors the Compass panel’s 4-pillar score (0–4) and can lock calculations to Weekly while you view Daily or intraday charts.
Regimes
Expansion: score ≥ 3 (green)
Neutral: score = 2 (orange)
Contraction: score ≤ 1 (red)
Pillars (same as panel):
RS IWM/SPY (small-cap leadership)
Credit HYG/LQD (risk financing)
Growth Copper/Gold (cyclical vs safety)
Participation (first available): Breadth → CBOE:DSPX → RSP/SPY proxy
A floating label shows Score and Suggested size (default ramp: 0/30/60/90/100% for scores 0–4).
How to use
Anchor on Weekly. Keep Regime Timeframe = W so the ribbon shows the higher-timeframe state while you trade on Daily.
Act on flips:
Expansion (≥3): increase beta, reduce hedges.
Neutral (2): keep moderate beta; favor quality/mega vs small caps until RS or Cu/Au turns.
Contraction (≤1): de-risk, rotate defensive, add hedges.
Turn on the built-in alerts: Expansion Regime and Contraction Regime.
Methodology
Prices are fetched via request.security on the selected Regime Timeframe.
Each pillar uses ratio signals smoothed with an SMA (Smoothing Length), and binary rules:
RS / Credit / Growth: fast SMA(len) vs slow SMA(len*2)
Breadth: normalized > 60
DSPX: normalized < 40
RSP/SPY proxy: fast > slow
Score is the count of green pillars (0–4).
Suggested size is a fixed mapping from score (user-editable).
Settings
Sources
Defaults use liquid ETFs (BATS/AMEX). Copper/Gold can be switched to futures if your plan supports them.
Breadth (optional): paste a %>MA symbol if you have one. If blank, the script uses CBOE:DSPX; if DSPX isn’t available it falls back to RSP/SPY.
Calculation
Smoothing Length (20) – higher = steadier regime; lower = faster.
Normalization Length (60) – window for 0–100 scaling in pillar tests.
Regime Timeframe (W) – lock regime to Weekly while viewing lower timeframes.
Visual
Ribbon Opacity controls how strong the background shading is.
Recommended usage
Apply the Ribbon to SPY/ES (broad beta) or IWM/RTY (small-cap rotation).
Pair it with the Mongoose Compass v2 panel in a separate pane for the full dashboard.
Limitations & disclaimer
For information and education; not investment advice.
Data availability varies by plan (especially futures and DSPX). Fallbacks apply automatically.
Mongoose Compass v2 — Regime & Position SizingWhat it does
Mongoose Compass v2 is a regime‐detection dashboard and optional price-chart ribbon. It combines four market “pillars” into a 0–4 score and a suggested equity beta/position size. It is scale-independent and works on any host symbol.
Pillars (green = expansion supportive):
RS IWM/SPY – small-cap relative strength vs large caps
Credit HYG/LQD – high-yield vs investment-grade credit
Growth Cu/Au – copper vs gold (cyclical demand vs safety)
Participation – uses the first available of:
Breadth (% > 200-DMA) if you provide a symbol, else
Cboe S&P 500 Dispersion (DSPX), else
RSP/SPY equal-weight proxy
Score (0–4):
≥ 3 = Expansion
2 = Neutral
≤ 1 = Contraction
A panel shows each pillar’s normalized value (0–100), bias, total score, and a suggested size (default mapping: 0/30/60/90/100% for scores 0–4). The companion “Ribbon” script paints the price chart background by regime and displays the suggested size.
How to use
Timeframes
Weekly for regime calls (recommended anchor).
Daily for execution within the active regime (adds, trims, hedges).
Playbook
Expansion (score ≥ 3): increase risk/beta; favor cyclicals, small caps, EM; reduce hedges.
Neutral (score = 2): keep moderate beta; use relative value (e.g., quality/mega vs small caps) until RS or Cu/Au turns.
Contraction (score ≤ 1): de-risk; rotate to defensives/quality, gold/long duration; add hedges.
Alerts (included):
Expansion Regime (score ≥ 3) – risk-on trigger
Contraction Regime (score ≤ 1) – risk-off trigger
Methodology
Prices are pulled with request.security on the chosen timeframe.
Pillars are built from ratios then smoothed with an SMA (Smoothing Length, default 20).
For display/comparison, series are normalized to 0–100 within a rolling window (Normalization Length, default 60).
Bias rules:
RS / Credit / Growth: fast SMA( len ) vs slow SMA( len*2 ) of each ratio
Breadth: normalized value > 60
DSPX: normalized value < 40 (lower dispersion supports index coherence)
RSP/SPY proxy: fast > slow trend test
Score is the count of green pillars (0–4).
Suggested size is a deterministic mapping from score (editable in settings).
Notes:
Host chart scaling (log vs linear) does not affect calculations.
If a breadth series is unavailable, the script automatically falls back to DSPX, then to RSP/SPY.
Settings
Sources
Default inputs use liquid ETFs (BATS/AMEX). You may switch Copper/Gold to futures (e.g., COMEX_DL:HG1!, COMEX_DL:GC1!) if your data plan supports them.
Optional Breadth: paste a percent-above-MA series if you have one.
DSPX: uses CBOE:DSPX when breadth is blank.
If neither breadth nor DSPX resolve, the script uses RSP/SPY as a participation proxy.
Calculation
Smoothing Length (20) – higher = steadier regime, fewer flips; lower = faster reaction.
Normalization Length (60) – window for the 0–100 scaling; increase to reduce pinning at extremes.
Regime Timeframe (Ribbon only) – lock the ribbon to Weekly while viewing Daily charts.
Visual
Show/hide dashboard table, choose table position, dark/light theme, ribbon opacity.
Recommended usage
Anchor decisions on Weekly Compass; use Daily for timing.
For small-cap rotation, apply on IWM/RTY; for broad beta, use SPY/ES. Output is identical regardless of host symbol because inputs are fetched internally.
Limitations & disclaimer
This is a systematic information tool, not investment advice.
Signals can whipsaw in fast markets; confirm with your risk framework.
Data availability varies by plan (especially futures and DSPX). When a source is unavailable the scripted fallbacks apply automatically.
LA - Opening Price based Previous day Range PivotThis "LA - Opening Price based Previous day Range Pivot" indicator is a custom technical analysis tool designed for Trading View charts. It plots support and resistance levels (often referred to as pivots or ranges) based on the current opening price combined with the previous period's trading range. The "previous period" can be daily, weekly, or monthly, making it a multi-timeframe tool. These levels are projected using Fibonacci-inspired multipliers to create potential breakout or reversal zones.
The core idea is inspired by concepts like the Opening Range Breakout (ORB) strategy or Fibonacci pivots, but it's customized here to use a dynamic range calculation (the maximum of several absolute price differences) rather than a simple high-low range. This makes it more robust for volatile markets. Levels are symmetric above (resistance) and below (support) the opening price, helping traders identify potential entry/exit points, stop-losses, or targets. This will be useful when there is a gap-up/down as in Nifty/Sensex .
Purpose of the Indicator:
To visualize potential support/resistance zones for the current trading session based on the opening price and historical range data. This helps traders anticipate price movements, such as breakouts above resistance or bounces off support
Use Cases:
Intraday Trading: On lower timeframes (e.g., 5-min or 15-min charts), it shows daily levels for short-term trades.
Swing Trading: On higher timeframes (e.g., hourly or daily), it displays weekly/monthly levels for longer holds.
Range Identification: The filled bands highlight "zones" where price might consolidate or reverse.
Conditional Display: Levels only appear on appropriate timeframes (e.g., daily levels on intraday charts <60min), preventing clutter.
Theoretical Basis: It builds on pivot point theory, where the opening price acts as a central pivot. Multipliers (e.g., 0.618 for Fibonacci golden ratio) project levels, assuming price often respects these ratios due to market psychology.
How Calculations Work
Let's dive into the math with examples. Assume a stock with:
Current daily open (cdo) = $100
Previous daily high (pdh) = $105, low (pdl) = $95, close (pdc) = $102, close 2 days ago (pdc2) = $98
Step 1: Dynamic Range Calculation (var_d2):
This is the max of:
|pdh - pdc2| = |105 - 98| = 7
|pdl - pdc2| = |95 - 98| = 3
|pdh - pdl| = |105 - 95| = 10 (previous day range)
|pdh - cdo| = |105 - 100| = 5
|pdl - cdo| = |95 - 100| = 5
|pdc - cdo| = |102 - 100| = 2
|pdc2 - cdo| = |98 - 100| = 2
Max = 10 (so range = 10). This ensures the range accounts for gaps and extended moves, not just high-low.
Step 2: Level Projections:
Resistance (above open): Open + (Range * Multiplier)
dre6 = 100 + (10 * 1.5) = 115
dre5 = 100 + (10 * 1.27) ≈ 112.7
... down to dre0 = 100 + (10 * 0.1) = 101
dre50 = 100 + (10 * 0.5) = 105 (midpoint)
Support (below open): Open - (Range * Multiplier)
dsu0 = 100 - (10 * 0.1) = 99
... up to dsu6 = 100 - (10 * 1.5) = 85
Without Indicator
With Indicator
Pros and Cons
Pros:
Multi-Timeframe Flexibility: Seamlessly integrates daily, weekly, and monthly levels, useful for aligning short-term trades with longer trends (e.g., intraday breakout confirmed by weekly support).
Dynamic Range Calculation: Unlike standard pivots (just (H+L+C)/3), it uses max of multiple diffs, capturing gaps/volatility better—great for stocks with overnight moves.
Customizable via Inputs: Users can toggle levels, adjust multipliers, or change timeframes without editing code. Inline inputs keep the UI clean.
Visual Aids: Filled bands make zones obvious; conditional colors highlight "tight" vs. "wide" ranges (e.g., for volatility assessment).
Fibonacci Integration: Levels based on proven ratios, appealing to technical traders. Symmetric supports/resistances simplify strategy building (e.g., buy at support, sell at resistance).
No Repainting: Uses historical data with lookahead, so levels are fixed once calculated—reliable for back-testing.
Cons:
Chart Clutter: With all toggles on, 50+ plots/fills can overwhelm the chart, especially on mobile or small screens. Requires manual disabling.
Complexity for Beginners: Many inputs and calculations; without understanding fib ratios or range logic, it might confuse new users.
Performance Overhead: On low timeframes (e.g., 1-min), fetching higher TF data multiple times could lag, especially with many symbols or back-tests.
Assumes Volatility Persistence: Relies on previous range projecting future moves; in low-vol markets (e.g., sideways trends), levels may be irrelevant or too wide/narrow.
No Alerts or Signals: Purely visual; no built-in buy/sell alerts or crossover conditions—users must add separately.
Hardcoded Styles/Colors: Limited customization without code edits (e.g., can't change line styles via inputs).
Also, not optimized for non-stock assets (e.g., forex with 24/7 trading).
In summary, this is a versatile pivot tool for range-based trading based on Opening price, excelling in volatile markets but requiring some setup. If you're using it, start with defaults on a daily chart and toggle off unnecessary levels.
MAMA [DCAUT]█ MAMA (MESA Adaptive Moving Average)
📊 OVERVIEW
The MESA Adaptive Moving Average (MAMA) represents an advanced implementation of John F. Ehlers' adaptive moving average system using the Hilbert Transform Discriminator. This indicator automatically adjusts to market cycles, providing superior responsiveness compared to traditional fixed-period moving averages while maintaining smoothness.
MAMA dynamically calculates two lines: the fast-adapting MAMA line and the following FAMA (Following Adaptive Moving Average) line. The system's core strength lies in its ability to automatically detect and adapt to the dominant market cycle, reducing lag during trending periods while providing stability during consolidation phases.
🎯 CORE CONCEPTS
Signal Interpretation:
• MAMA above FAMA: Indicates bullish trend momentum with the fast line leading upward movement
• MAMA below FAMA: Suggests bearish trend momentum with the fast line leading downward movement
• Golden Cross: MAMA crossing above FAMA signals potential upward momentum shift
• Death Cross: MAMA crossing below FAMA indicates potential downward momentum shift
• Line Convergence: MAMA and FAMA approaching each other suggests trend consolidation or potential reversal
Primary Applications:
• Trend Following: Enhanced responsiveness to trend changes compared to traditional moving averages
• Crossover Signals: MAMA/FAMA crossovers for identifying potential entry and exit points
• Cycle Analysis: Automatic adaptation to market's dominant cycle characteristics
• Reduced Lag: Minimized delay in trend detection while maintaining signal smoothness
📐 MATHEMATICAL FOUNDATION
Hilbert Transform Discriminator Technology:
The MAMA system employs John F. Ehlers' Hilbert Transform Discriminator, a sophisticated signal processing technique borrowed from telecommunications engineering. The Hilbert Transform creates a complex representation of the price series by generating a 90-degree phase-shifted version of the original signal, enabling precise cycle measurement.
The discriminator analyzes the instantaneous phase relationships between the original price series and its Hilbert Transform counterpart. This mathematical relationship reveals the dominant cycle period present in the market data at each point in time, forming the foundation for adaptive smoothing.
Instantaneous Period Calculation:
The algorithm computes the instantaneous period using the arctangent of the ratio between the Hilbert Transform and the original price series. This calculation produces a real-time measurement of the market's dominant cycle, typically ranging from short-term noise cycles to longer-term trend cycles.
The instantaneous period measurement undergoes additional smoothing to prevent erratic behavior from single-bar anomalies. This smoothed period value becomes the basis for calculating the adaptive alpha coefficient that controls the moving average's responsiveness.
Dynamic Alpha Coefficient System:
The adaptive alpha calculation represents the core mathematical innovation of MAMA. The alpha coefficient is derived from the instantaneous period measurement and constrained within the user-defined fast and slow limits.
The mathematical relationship converts the measured cycle period into an appropriate smoothing factor: shorter detected cycles result in higher alpha values (increased responsiveness), while longer cycles produce lower alpha values (increased stability). This creates an automatic adaptation mechanism that responds to changing market conditions.
MAMA/FAMA Calculation Process:
The MAMA line applies the dynamically calculated alpha coefficient to an exponential moving average formula: MAMA = alpha × Price + (1 - alpha) × MAMA . The FAMA line then applies a secondary smoothing operation to the MAMA line, creating a following average that provides confirmation signals.
This dual-line approach ensures that the fast-adapting MAMA line captures trend changes quickly, while the FAMA line offers a smoother confirmation signal, reducing the likelihood of acting on temporary price fluctuations.
Cycle Detection Mechanism:
The underlying cycle detection employs quadrature components derived from the Hilbert Transform to measure both amplitude and phase characteristics of price movements. This allows the system to distinguish between genuine trend changes and temporary price noise, automatically adjusting the smoothing intensity accordingly.
The mathematical framework ensures that during strong trending periods with clear directional movement, the algorithm reduces smoothing to minimize lag. Conversely, during consolidation phases with mixed signals, increased smoothing helps filter out false breakouts and whipsaws.
📋 PARAMETER CONFIGURATION
Source Selection Strategy:
• HL2 (High+Low)/2 (Default): Recommended for cycle analysis as it represents the midpoint of each period's trading range, reducing impact of opening gaps and closing spikes
• Close Price: Traditional choice reflecting final market sentiment, suitable for end-of-day analysis
• HLC3 (High+Low+Close)/3: Balanced approach incorporating range information with closing emphasis
• OHLC4 (Open+High+Low+Close)/4: Most comprehensive price representation for complete market view
Fast Limit Configuration (Default 0.5):
Controls the maximum responsiveness of the adaptive system. Higher values increase sensitivity to recent price changes but may introduce more noise. This parameter sets the upper bound for the dynamic alpha calculation.
Slow Limit Configuration (Default 0.05):
Determines the minimum responsiveness, providing stability during uncertain market conditions. Lower values increase smoothing but may cause delayed signals. This parameter sets the lower bound for the dynamic alpha calculation.
Parameter Relationship Considerations:
The fast and slow limits work together to define the adaptive range. The wider the range between these limits, the more dramatic the adaptation between trending and consolidating market conditions. Different market characteristics may benefit from different parameter configurations, requiring individual testing and validation.
📊 COLOR CODING SYSTEM
Line Visualization:
• Green Line (MAMA): The fast-adapting moving average that responds quickly to price changes
• Red Line (FAMA): The following adaptive moving average that provides confirmation signals
The fixed color scheme provides consistent visual identification of each line, enabling clear differentiation between the fast-adapting MAMA and the following FAMA throughout all market conditions.
💡 CORE VALUE PROPOSITION
Advantages Over Traditional Moving Averages:
• Cycle Adaptation: Automatically adjusts to market's dominant cycle rather than using fixed periods
• Reduced Lag: Faster response to genuine trend changes while filtering market noise
• Mathematical Foundation: Based on advanced signal processing techniques from telecommunications engineering
• Dual-Line System: Provides both fast adaptation (MAMA) and confirmation (FAMA) in one indicator
Comparative Performance Characteristics:
Unlike fixed-period moving averages that apply the same smoothing regardless of market conditions, MAMA adapts its behavior based on current market cycle characteristics. This may help reduce whipsaws during consolidation periods while maintaining responsiveness during trending phases.
Usage Considerations:
This indicator is designed for technical analysis purposes. The adaptive nature means that parameter optimization should consider the specific characteristics of the asset and timeframe being analyzed. Like all technical indicators, MAMA should be used as part of a comprehensive analysis approach rather than as a standalone signal generator.
Alert Functionality:
The indicator includes alert conditions for MAMA/FAMA crossovers, enabling automated notification of potential momentum shifts. These alerts can assist in timing analysis but should be combined with other forms of market analysis for decision-making purposes.
Alpha - Multi-Asset Adaptive Trading Strategy# Alpha - Multi-Asset Adaptive Trading Strategy
Overview
Alpha is a comprehensive trading strategy that combines multiple technical analysis components with pre-optimized settings for over 70 different trading instruments across cryptocurrencies, forex, and stocks. The strategy employs an adaptive approach using modified trend detection algorithms, dynamic support/resistance zones, and multi-timeframe confirmation.
Key Features & Originality
1. Adaptive Trend Detection System
- Modified trend-following algorithm with amplitude-based channel deviation
- Dynamic channel width adjustment based on ATR (Average True Range)
- Dual-layer trend confirmation using both price action and momentum indicators
2. Pre-Configured Asset Optimization
The strategy includes carefully backtested parameter sets for:
- **Cryptocurrencies**: BTC, ETH, and 40+ altcoin pairs
- **Forex Pairs**: Major and minor currency pairs
- **Stocks**: TSLA, AAPL, GOOG
- **Commodities**: Gold, Silver, Platinum
- Each configuration is optimized for specific timeframes (5m, 15m, 30m, 45m, 1h)
3. Advanced Risk Management
- Multiple take profit levels (4 targets with customizable position sizing)
- Dynamic stop-loss options (ATR-based or percentage-based)
- Position size allocation across profit targets (default: 30%, 30%, 30%, 10%)
4. Multi-Timeframe Analysis Dashboard
- Real-time analysis across 4 configurable timeframes
- Comprehensive performance metrics display
- Visual representation of current market conditions
5. Market Condition Filtering
- RSI-based trend strength filtering
- ATR-based volatility filtering
- Sideways market detection to avoid choppy conditions
- Customizable filter combinations (ATR only, RSI only, both, or disabled)
How to Use
Initial Setup
1. **Select Asset Configuration**: Choose your trading pair from the "Strategies" dropdown menu
2. **Enable Strategy**: Enter "Alpha" in the code confirmation field
3. **Adjust Timeframe**: Match your chart timeframe to the selected strategy configuration
Parameter Customization
- **Trendline Settings**: Adjust amplitude and channel deviation for sensitivity
- **TP/SL Method**: Choose between ATR-based or percentage-based targets
- **Filtering Options**: Select appropriate market filters for your trading style
- **Backtest Period**: Set the number of days for strategy testing (max 60)
Signal Interpretation
- **BUY/SELL Labels**: Primary entry signals based on trend changes
- **Support/Resistance Zones**: Visual zones showing key price levels
- **Dashboard**: Real-time display of position status, targets, and performance metrics
Important Considerations
Limitations and Warnings
- **Backtesting Period**: Results shown are based on historical data from the specified backtest period
- **No Guarantee**: Past performance does not guarantee future results
- **Market Conditions**: Strategy performance varies with market volatility and trending conditions
- **Repainting**: Some signals may repaint if "Wait For Confirmed Bar" is disabled
Risk Warnings
- The pre-configured settings are starting points and may require adjustment for current market conditions
- Always use appropriate position sizing and risk management
- Test thoroughly on demo accounts before live trading
- Monitor and adjust parameters regularly as market dynamics change
Technical Components
Core Indicators Used
- Modified trend detection with amplitude-based channels
- RSI (Relative Strength Index) for momentum confirmation
- ATR (Average True Range) for volatility measurement
- Support/Resistance detection using pivot points
- Bollinger Band variant for trend confirmation
Alert Functionality
The strategy includes comprehensive alert options for:
- Entry signals (long and short)
- Take profit levels (TP1, TP2, TP3, TP4)
- Stop loss triggers
- Integration with trading bots via webhook messages
Recommended Usage
Best Practices
1. Start with the pre-configured settings for your chosen asset
2. Run backtests over different time periods to verify performance
3. Use the dashboard to monitor real-time strategy performance
4. Adjust filters based on current market conditions
5. Always use stop losses and proper risk management
Timeframe Recommendations
- **Short-Term**: Use 5m, 15m configurations for scalping
- **Mid-Term**: Use 30m, 45m configurations for day trading
- **Long-Term**: Use 1h configurations for swing trading
Updates and Support
The strategy parameters are regularly reviewed and optimized. Users should periodically check for updates to ensure they have the latest configurations.
Disclaimer
This strategy is for educational and informational purposes only. Trading involves substantial risk of loss. Users should conduct their own research and consider their financial situation before trading. The author is not responsible for any trading losses incurred using this strategy.
Global M2 Money SupplyThis indicator calculates and plots an aggregated estimate of the Global M2 money supply, expressed in U.S. dollar terms. It combines M2 data from major economies and regions—including the U.S., Eurozone, Canada, the U.K., Switzerland, China, Japan, India, Brazil, and others—and adjusts each by its respective FX rate to USD. By summing these series, the script provides a broad view of worldwide liquidity conditions in one line.
A user-defined offset in days allows you to shift the global M2 line forward or backward, making it easier to visually compare liquidity trends against asset prices such as Bitcoin, gold, or equities. This tool is designed for traders and macro observers who want to study how global money supply growth or contraction correlates with financial markets over time.
이 지표는 전 세계 주요 국가와 지역의 M2 통화량을 달러 기준으로 합산하여 글로벌 유동성 지표로 보여줍니다. 미국, 유로존, 캐나다, 영국, 스위스, 중국, 일본, 인도, 브라질 등 여러 지역의 M2 데이터를 각 통화의 환율(USD 환산)로 조정한 뒤 합산해 하나의 흐름으로 표현합니다. 이를 통해 글로벌 차원의 통화 공급 변화를 한눈에 파악할 수 있습니다.
또한 사용자가 지정한 일 단위 오프셋 기능을 통해 글로벌 M2 라인을 앞뒤로 이동시켜, 비트코인·금·주식 등 다양한 자산 가격과의 시차적 관계를 직관적으로 비교할 수 있습니다. 거시경제 환경과 자산시장 간의 상관성을 연구하거나 시장 유동성 추이를 모니터링하려는 투자자에게 유용한 도구입니다.
AI Trading Alerts v6 — SL/TP + Confidence + Panel (Fixed)Overview
This Pine Script is designed to identify high-probability trading opportunities in Forex, commodities, and crypto markets. It combines EMA trend filters, RSI, and Stochastic RSI, with automatic stop-loss (SL) & take-profit (TP) suggestions, and provides a confidence panel to quickly assess the trade setup strength.
It also includes TradingView alert conditions so you can set up notifications for Long/Short setups and EMA crosses.
⚙️ Features
EMA Trend Filter
Uses EMA 50, 100, 200 for trend confirmation.
Bull trend = EMA50 > EMA100 > EMA200
Bear trend = EMA50 < EMA100 < EMA200
RSI Filter
Bullish trades require RSI > 50
Bearish trades require RSI < 50
Stochastic RSI Filter
Prevents entries during overbought/oversold extremes.
Bullish entry only if %K and %D < 80
Bearish entry only if %K and %D > 20
EMA Proximity Check
Price must be near EMA50 (within ATR × adjustable multiplier).
Signals
Continuation Signals:
Long if all bullish conditions align.
Short if all bearish conditions align.
Cross Events:
Long Cross when price crosses above EMA50 in bull trend.
Short Cross when price crosses below EMA50 in bear trend.
Automatic SL/TP Suggestions
SL size adjusts depending on asset:
Gold/Silver (XAU/XAG): 5 pts
Bitcoin/Ethereum: 100 pts
FX pairs (default): 20 pts
TP = SL × Risk:Reward ratio (default 1:2).
Confidence Score (0–4)
Based on conditions met (trend, RSI, Stoch, EMA proximity).
Labels:
Strongest (4/4)
Strong (3/4)
Medium (2/4)
Low (1/4)
Visual Panel on Chart
Shows ✅/❌ for each condition (trend, RSI, Stoch, EMA proximity, signal now).
Confidence row with color-coded strength.
Alerts
Long Setup
Short Setup
Long Cross
Short Cross
🖥️ How to Use
1. Add the Script
Open TradingView → Pine Editor.
Paste the full script.
Click Add to chart.
Save as "AI Trading Alerts v6 — SL/TP + Confidence + Panel".
2. Configure Inputs
EMA Lengths: Default 50/100/200 (works well for swing trading).
RSI Length: 14 (standard).
Stochastic Length/K/D: Default 14/3/3.
Risk:Reward Ratio: Default 2.0 (can change to 1.5, 3.0, etc.).
EMA Proximity Threshold: Default 0.20 × ATR (adjust to be stricter/looser).
3. Read the Panel
Top-right of chart, you’ll see ✅ or ❌ for:
Trend → Are EMAs aligned?
RSI → Above 50 (bull) or below 50 (bear)?
Stoch OK → Not extreme?
Near EMA50 → Close enough to EMA50?
Above/Below OK → Price position vs. EMA50 matches trend?
Signal Now → Entry triggered?
Confidence row:
🟢 Green = Strongest
🟩 Light green = Strong
🟧 Orange = Medium
🟨 Yellow = Low
⬜ Gray = None
4. Alerts Setup
Go to TradingView Alerts (⏰ icon).
Choose the script under “Condition”.
Select alert type:
Long Setup
Short Setup
Long Cross
Short Cross
Set notification method (popup, sound, email, mobile).
Click Create.
Now TradingView will notify you automatically when signals appear.
5. Example Workflow
Wait for Confidence = Strong/Strongest.
Check if market session supports volatility (e.g., XAU in London/NY).
Review SL/TP suggestions:
Long → Entry: current price, SL: close - risk_pts, TP: close + risk_pts × RR.
Short → Entry: current price, SL: close + risk_pts, TP: close - risk_pts × RR.
Adjust based on your own price action analysis.
📊 Best Practices
Use on H1 + D1 combo → align higher timeframe bias with intraday entries.
Risk only 1–2% of account per trade (position sizing required).
Filter with market sessions (Asia, Europe, US).
Strongest signals work best with trending pairs (e.g., XAUUSD, USDJPY, BTCUSD).
Multi-Symbol Volatility Tracker with Range DetectionMulti-Symbol Volatility Tracker with Range Detection
🎯 Main Purpose:
This indicator is specifically designed for scalpers to quickly identify symbols with high volatility that are currently in ranging conditions . It helps you spot the perfect opportunities for buying at lows and selling at highs repeatedly within the same trading session.
📊 Table Data Explanation:
The indicator displays a comprehensive table with 5 columns for 4 major symbols (GOLD, SILVER, NASDAQ, SP500):
SYMBOL: The trading instrument being analyzed
VOLATILITY: Color-coded volatility levels (NORMAL/HIGH/EXTREME) based on ATR values
Last Candle %: The percentage range of the most recent 5-minute candle
Last 5 Candle Avg %: Average percentage range over the last 5 candles
RANGE: Shows "YES" (blue) or "NO" (gray) indicating if the symbol is currently ranging
🔍 How to Identify Trading Opportunities:
Look for symbols that combine these characteristics:
RANGE column shows "YES" (highlighted in blue) - This means the symbol is moving sideways, perfect for range trading
VOLATILITY shows "HIGH" or "EXTREME" - Ensures there's enough movement for profitable scalping
Higher candlestick percentages - Indicates larger candle ranges, meaning more profit potential per trade
⚡ Optimal Usage:
Best Timeframe: Works optimally on 5-minute charts where the ranging patterns are most reliable for scalping
Trading Strategy: When you find a symbol with "YES" in the RANGE column, switch to that symbol and look for opportunities to buy near the lows and sell near the highs of the ranging pattern
Risk Management: Higher volatility symbols offer more profit potential but require tighter risk management
⚙️ Settings:
ATR Length: Adjusts the Average True Range calculation period (default: 14)
Range Sensitivity: Fine-tune range detection sensitivity (0.1-2.0, lower = more sensitive)
💡 Pro Tips:
The indicator updates in real-time, so monitor for symbols switching from "NO" to "YES" in the RANGE column
Combine HIGH/EXTREME volatility with RANGE: YES for the most profitable scalping setups
Use the candlestick percentages to gauge potential profit per trade - higher percentages mean more movement
The algorithm uses advanced statistical analysis including standard deviation, linear regression slopes, and range efficiency to accurately detect ranging conditions
Perfect for day traders and scalpers who want to quickly identify which symbols offer the best ranging opportunities for consistent buy-low, sell-high strategies.
All Levels This script draws key price levels on your chart, including:
• Previous Day (PD): High, Low, Close
• Day Before Yesterday (DBY): High, Low, Close
• Pre-Market (PM): High and Low
• Today’s levels: High, Low, Open, Close
• Current bar levels: High, Low, Open, Close
Each level is displayed as a horizontal line with a label showing the level value.
It works on any timeframe, including 1-minute charts, and automatically updates as new bars form.
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2. Features
1. Custom Colors
Each type of level has its own color, declared as a const color. For example:
• Previous Day High = red
• Today’s Close = gold
• Pre-Market High = fuchsia
2. Right-Extending Lines
All horizontal levels extend to the right, so you always see them on the chart.
3. Persistent Labels
Every line has a label at the right side showing its name and price. For example:
• PDH 422
• TODL 415.5
4. Dynamic Updates
The script updates automatically whenever a new bar forms, so levels stay accurate.
5. Session-Based Pre-Market
You can define the pre-market session (default “04:00–09:30 EST”). The script calculates the high and low of this session only.
6. Checkbox Inputs
You can enable/disable entire groups of levels:
• Previous Day
• Day Before Yesterday
• Pre-Market
• Today
• Current bar
Pairs Trading Scanner [BackQuant]Pairs Trading Scanner
What it is
This scanner analyzes the relationship between your chart symbol and a chosen pair symbol in real time. It builds a normalized “spread” between them, tracks how tightly they move together (correlation), converts the spread into a Z-Score (how far from typical it is), and then prints clear LONG / SHORT / EXIT prompts plus an at-a-glance dashboard with the numbers that matter.
Why pairs at all?
Markets co-move. When two assets are statistically related, their relationship (the spread) tends to oscillate around a mean.
Pairs trading doesn’t require calling overall market direction you trade the relative mispricing between two instruments.
This scanner gives you a robust, visual way to find those dislocations, size their significance, and structure the trade.
How it works (plain English)
Step 1 Pick a partner: Select the Pair Symbol to compare against your chart symbol. The tool fetches synchronized prices for both.
Step 2 Build a spread: Choose a Spread Method that defines “relative value” (e.g., Log Spread, Price Ratio, Return Difference, Price Difference). Each lens highlights a different flavor of divergence.
Step 3 Validate relationship: A rolling Correlation checks if the pair is moving together enough to be tradable. If correlation is weak, the scanner stands down.
Step 4 Standardize & score: The spread is normalized (mean & variability over a lookback) to form a Z-Score . Large absolute Z means “stretched,” small means “near fair.”
Step 5 Signals: When the Z-Score crosses user-defined thresholds with sufficient correlation , entries print:
LONG = long chart symbol / short pair symbol,
SHORT = short chart symbol / long pair symbol,
EXIT = mean reversion into the exit zone or correlation failure.
Core concepts (the three pillars)
Spread Method Your definition of “distance” between the two series.
Guidance:
Log Spread: Focuses on proportional differences; robust when prices live on different scales.
Price Ratio: Classic relative value; good when you care about “X per Y.”
Return Difference: Emphasizes recent performance gaps; nimble for momentum-to-mean plays.
Price Difference: Straight subtraction; intuitive for similar-scale assets (e.g., two ETFs).
Correlation A rolling score of co-movement. The scanner requires it to be above your Min Correlation before acting, so you’re not trading random divergence.
Z-Score “How abnormal is today’s spread?” Positive = chart richer than pair; negative = cheaper. Thresholds define entries/exits with transparent, statistical context.
What you’ll see on the chart
Correlation plot (blue line) with a dashed Min Correlation guide. Above the line = green zone for signals; below = hands off.
Z-Score plot (white line) with colored, dashed Entry bands and dotted Exit bands. Zero line for mean.
Normalized spread (yellow) for a quick “shape read” of recent divergence swings.
Signal markers :
LONG (green label) when Z < –Entry and corr OK,
SHORT (red label) when Z > +Entry and corr OK,
EXIT (gray label) when Z returns inside the Exit band or correlation drops below the floor.
Background tint for active state (faint green for long-spread stance, faint red for short-spread stance).
The two built-in dashboards
Statistics Table (top-right)
Pair Symbol Your chosen partner.
Correlation Live value vs. your minimum.
Z-Score How stretched the spread is now.
Current / Pair Prices Real-time anchors.
Signal State NEUTRAL / LONG / SHORT.
Price Ratio Context for ratio-style setups.
Analysis Table (bottom-right)
Avg Correlation Typical co-movement level over your window.
Max |Z| The recent extremes of dislocation.
Spread Volatility How “lively” the spread has been.
Trade Signal A human-readable prompt (e.g., “LONG A / SHORT B” or “NO TRADE” / “LOW CORRELATION”).
Risk Level LOW / MEDIUM / HIGH based on current stretch (absolute Z).
Signals logic (plain English)
Entry (LONG): The spread is unusually negative (chart cheaper vs pair) and correlation is healthy. Expect mean reversion upward in the spread: long chart, short pair.
Entry (SHORT): The spread is unusually positive (chart richer vs pair) and correlation is healthy. Expect mean reversion downward in the spread: short chart, long pair.
Exit: The spread relaxes back toward normal (inside your exit band), or correlation deteriorates (relationship no longer trusted).
A quick, repeatable workflow
1) Choose your pair in context (same sector/theme or known macro link). Think: “Do these two plausibly co-move?”
2) Pick a spread lens that matches your narrative (ratio for relative value, returns for short-term performance gaps, etc.).
3) Confirm correlation is above your floor no corr, no trade.
4) Wait for a stretch (Z beyond Entry band) and a printed LONG / SHORT .
5) Manage to the mean (EXIT band) or correlation failure; let the scanners’ state/labels keep you honest.
Settings that matter (and why)
Spread Method Defines the “mispricing” you care about.
Correlation Period Longer = steadier regime read, shorter = snappier to regime change.
Z-Score Period The window that defines “normal” for the spread; it sets the yardstick.
Use Percentage Returns Normalizes series when using return-based logic; keep on for mixed-scale assets.
Entry / Exit Thresholds Set your stretch and your target reversion zone. Wider entries = rarer but stronger signals.
Minimum Correlation The gatekeeper. Raising it favors quality over quantity.
Choosing pairs (practical cheat sheet)
Same family: two index ETFs, two oil-linked names, two gold miners, two L1 tokens.
Hedge & proxy: stock vs. sector ETF, BTC vs. BTC index, WTI vs. energy ETF.
Cross-venue or cross-listing: instruments that are functionally the same exposure but price differently intraday.
Reading the cues like a pro
Divergence shape: The yellow normalized spread helps you see rhythm fast spike and snap-back versus slow grind.
Corr-first discipline: Don’t fight the “Min Correlation” line. Good pairs trading starts with a relationship you can trust.
Exit humility: When Z re-centers, let the EXIT do its job. The edge is the journey to the mean, not overstaying it.
Frequently asked (quick answers)
“Long/Short means what exactly?”
LONG = long the chart symbol and short the pair symbol.
SHORT = short the chart symbol and long the pair symbol.
“Do I need same price scales?” No. The spread methods normalize in different ways; choose the one that fits your use case (log/ratio are great for mixed scales).
“What if correlation falls mid-trade?” The scanner will neutralize the state and print EXIT . Relationship first; trade second.
Field notes & patterns
Snap-back days: After a one-sided session, return-difference spreads often flag cleaner intraday mean reversions.
Macro rotations: Ratio spreads shine during sector re-weights (e.g., value vs. growth ETFs); look for steady corr + elevated |Z|.
Event bleed-through: If one symbol reacts to news and its partner lags, Z often flags a high-quality, short-horizon re-centering.
Display controls at a glance
Show Statistics Table Live state & key numbers, top-right.
Show Analysis Table Context/risk read, bottom-right.
Show Correlation / Spread / Z-Score Toggle the sub-charts you want visible.
Show Entry/Exit Signals Turn markers on/off as needed.
Coloring Adjust Long/Short/Neutral and correlation line colors to match your theme.
Alerts (ready to route to your workflow)
Pairs Long Entry Z falls through the long threshold with correlation above minimum.
Pairs Short Entry Z rises through the short threshold with correlation above minimum.
Pairs Trade Exit Z returns to neutral or the relationship fails your correlation floor.
Correlation Breakdown Rolling correlation crosses your minimum; relationship caution.
Final notes
The scanner is designed to keep you systematic: require relationship (correlation), quantify dislocation (Z-Score), act when stretched, stand down when it normalizes or the relationship degrades. It’s a full, visual loop for relative-value trading that stays out of your way when it should and gets loud only when the numbers line up.
Apex Edge Sentinel - Stop Loss HUDApex Edge – ATR Sentinel Stop Loss HUD
The Apex Edge – ATR Sentinel is a complete stop-loss intelligence system built as a clean, always-on HUD.
It delivers institutional-level risk guidance by calculating and displaying live ATR-based stop levels for both long and short trades at multiple risk tolerances.
Forget cluttered charts and repainting lines — Sentinel gives you a clear stop-loss reference panel that updates dynamically with every bar.
✅ Features
• Triple ATR Multipliers
User-defined (e.g. x1.5 / x2.0 / x2.5). Compare tight, medium, and wide stops instantly.
• Dual-Side SL Levels
Both Long and Short safe stop prices displayed side by side. No more guessing trend
bias.
• ATR Transparency
HUD shows ATR(length) so you always know the calculation basis. Default = 14, adjustable
to your style.
• ATR Regime Meter
Detects volatility conditions (LOW / NORMAL / HIGH) by comparing ATR to its SMA. Helps
you avoid over-tight stops in high-volatility markets.
• Tick-Aware Rounding
Stop levels auto-rounded to the instrument’s tick size (Gold = 0.10, FX = 0.0001, indices =
whole points).
Custom HUD Design
• Location: Top/Bottom, Left/Right
• Sizes: Compact / Medium / Large (desktop or mobile)
• Opacity control (25% default Apex styling)
How to Use
1. Load Sentinel on your chart.
2. Check the HUD:
• ATR(14): 2.6 → base volatility measure.
• x1.5 / x2.0 / x2.5 → instant SL levels for both long & short trades.
3. Before entering a trade → decide which multiplier matches your style (tight scalper vs wider swing).
4. Manually place your SL at the level displayed in the HUD.
Sentinel works as both:
• A pre-trade check (is ATR stop too wide for my RR?).
• A live risk compass (updated stop levels every bar).
Why Apex Sentinel?
Most ATR stop indicators clutter charts with lagging lines or repainting trails. Sentinel strips it back to what matters:
• The numbers.
• The risk levels.
• The context.
It’s a pure stop-loss HUD, designed for serious traders who want clarity, discipline, and instant reference points across any market or timeframe.
Notes
• This is a HUD-only system (no automatic SL line). Traders manually apply the SL level
shown in the panel.
• Defaults: ATR(14), multipliers 1.5 / 2.0 / 2.5. Adjust to your trading style.
• Best used on intraday pairs like XAUUSD, EURUSD, indices, but works universally.
Apex Edge Philosophy: Clean. Smart. Institutional.
No clutter. No gimmicks. Just precision tools for modern markets.
Futures Playbook: VWAP + OR + Cross-Asset TellsFutures Playbook: VWAP + OR + Cross-Asset Tells (with Trade Messages + Coach Panel)
This all-in-one futures trading toolkit combines Opening Range (OR) levels, VWAP, and cross-asset signals to help traders quickly read intraday structure, manage execution, and filter noise.
Core Features
• Opening Range (OR):
• Customizable OR window with High/Low and Midpoint.
• Automatic shading of the OR zone.
• VWAP & Bands:
• Built-in or session-anchored VWAP.
• Optional standard deviation bands for context.
• Cross-Asset Tells:
• Live reads on US 10Y yield, DXY, Crude, and Gold.
• Regime detection: rates risk, USD strength, energy softness, and real-rate easing.
• Confirmations:
• Volume vs. moving average filter.
• Cumulative delta with smoothing.
• ATR-based chop filter to avoid low-quality trends.
Trade Messages + Coach Panel
• Trade Messages (labels): Automatic on-chart prompts for OR completion, VWAP reclaim/loss, long/short setups, and EU close flows.
• Coach Panel (table): Real-time dashboard with regime context, directional bias, execution notes, risk reminders, and key levels (ORH, ORL, VWAP).
Alerts
• OR breakout (long/short with confirmations).
• VWAP reclaim or loss.
• 10Y yield crossing risk threshold.
Use Case
Designed for futures traders and scalpers who rely on VWAP + OR dynamics and need cross-asset confirmation before committing to trades. Great for structuring entries, managing risk, and filtering market noise throughout the session.
