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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).
References
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Multi SMA + Golden/Death + Heatmap + BB**Multi SMA (50/100/200) + Golden/Death + Candle Heatmap + BB**
A practical trend toolkit that blends classic 50/100/200 SMAs with clear crossover labels, special 🚀 Golden / 💀 Death Cross markers, and a readable candle heatmap based on a dynamic regression midline and volatility bands. Optional Bollinger Bands are included for context.
* See trend direction at a glance with SMAs.
* Get minimal, de-cluttered labels on important crosses (50↔100, 50↔200, 100↔200).
* Highlight big regime shifts with special Golden/Death tags.
* Read momentum and volatility with the candle heatmap.
* Add Bollinger Bands if you want classic mean-reversion context.
Designed to be lightweight, non-repainting on confirmed bars, and flexible across timeframes.
# What This Indicator Does (plain English)
* **Tracks trend** using **SMA 50/100/200** and lets you optionally compute each SMA on a higher or different timeframe (HTF-safe, no lookahead).
* **Prints labels** when SMAs cross each other (up or down). You can force signals only after bar close to avoid repaint.
* **Marks Golden/Death Crosses** (50 over/under 200) with special labels so major regime changes stand out.
* **Colors candles** with a **heatmap** built from a regression midline and volatility bands—greenish above, reddish below, with a smooth gradient.
* **Optionally shows Bollinger Bands** (basis SMA + stdev bands) and fills the area between them.
* **Includes alert conditions** for Golden and Death Cross so you can automate notifications.
---
# Settings — Simple Explanations
## Source
* **Source**: Price source used to calculate SMAs and Bollinger basis. Default: `close`.
## SMA 50
* **Show 50**: Turn the SMA(50) line on/off.
* **Length 50**: How many bars to average. Lower = faster but noisier.
* **Color 50** / **Width 50**: Visual style.
* **Timeframe 50**: Optional alternate timeframe for SMA(50). Leave empty to use the chart timeframe.
## SMA 100
* **Show 100**: Turn the SMA(100) line on/off.
* **Length 100**: Bars used for the mid-term trend.
* **Color 100** / **Width 100**: Visual style.
* **Timeframe 100**: Optional alternate timeframe for SMA(100).
## SMA 200
* **Show 200**: Turn the SMA(200) line on/off.
* **Length 200**: Bars used for the long-term trend.
* **Color 200** / **Width 200**: Visual style.
* **Timeframe 200**: Optional alternate timeframe for SMA(200).
## Signals (crossover labels)
* **Show crossover signals**: Prints triangle labels on SMA crosses (50↔100, 50↔200, 100↔200).
* **Wait for bar close (confirmed)**: If ON, signals only appear after the candle closes (reduces repaint).
* **Min bars between same-pair signals**: Minimum spacing to avoid duplicate labels from the same SMA pair too often.
* **Trend filter (buy: 50>100>200, sell: 50<100<200)**: Only show bullish labels when SMAs are stacked bullish (50 above 100 above 200), and only show bearish labels when stacked bearish.
### Label Offset
* **Offset mode**: Choose how to push labels away from price:
* **Percent**: Offset is a % of price.
* **ATR x**: Offset is ATR(14) × multiplier.
* **Percent of price (%)**: Used when mode = Percent.
* **ATR multiplier (for ‘ATR x’)**: Used when mode = ATR x.
### Label Colors
* **Bull color** / **Bear color**: Background of triangle labels.
* **Bull label text color** / **Bear label text color**: Text color inside the triangles.
## Golden / Death Cross
* **Show 🚀 Golden Cross (50↑200)**: Show a special “Golden” label when SMA50 crosses above SMA200.
* **Golden label color** / **Golden text color**: Styling for Golden label.
* **Show 💀 Death Cross (50↓200)**: Show a special “Death” label when SMA50 crosses below SMA200.
* **Death label color** / **Death text color**: Styling for Death label.
## Candle Heatmap
* **Enable heatmap candle colors**: Turns the heatmap on/off.
* **Length**: Lookback for the regression midline and volatility measure.
* **Deviation Multiplier**: Band width around the midline (bigger = wider).
* **Volatility basis**:
* **RMA Range** (smoothed high-low range)
* **Stdev** (standard deviation of close)
* **Upper/Middle/Lower color**: Gradient colors for the heatmap.
* **Heatmap transparency (0..100)**: 0 = solid, 100 = invisible.
* **Force override base candles**: Repaint base candles so heatmap stays visible even if your chart has custom coloring.
## Bollinger Bands (optional)
* **Show Bollinger Bands**: Toggle the overlay on/off.
* **Length**: Basis SMA length.
* **StdDev Multiplier**: Distance of bands from the basis in standard deviations.
* **Basis color** / **Band color**: Line colors for basis and bands.
* **Bands fill transparency**: Opacity of the fill between upper/lower bands.
---
# Features & How It Works
## 1) HTF-Safe SMAs
Each SMA can be calculated on the chart timeframe or a higher/different timeframe you choose. The script pulls HTF values **without lookahead** (non-repainting on confirmed bars).
## 2) Crossover Labels (Three Pairs)
* **50↔100**, **50↔200**, **100↔200**:
* **Triangle Up** label when the first SMA crosses **above** the second.
* **Triangle Down** label when it crosses **below**.
* Optional **Trend Filter** ensures only signals aligned with the overall stack (50>100>200 for bullish, 50<100<200 for bearish).
* **Debounce** spacing avoids repeated labels for the same pair too close together.
## 3) Golden / Death Cross Highlights
* **🚀 Golden Cross**: SMA50 crosses **above** SMA200 (often a longer-term bullish regime shift).
* **💀 Death Cross**: SMA50 crosses **below** SMA200 (often a longer-term bearish regime shift).
* Separate styling so they stand out from regular cross labels.
## 4) Candle Heatmap
* Builds a **regression midline** with **volatility bands**; colors candles by their position inside that channel.
* Smooth gradient: lower side → reddish, mid → yellowish, upper side → greenish.
* Helps you see momentum and “where price sits” relative to a dynamic channel.
## 5) Bollinger Bands (Optional)
* Classic **basis SMA** ± **StdDev** bands.
* Light visual context for mean-reversion and volatility expansion.
## 6) Alerts
* **Golden Cross**: `🚀 GOLDEN CROSS: SMA 50 crossed ABOVE SMA 200`
* **Death Cross**: `💀 DEATH CROSS: SMA 50 crossed BELOW SMA 200`
Add these to your alerts to get notified automatically.
---
# Tips & Notes
* For fewer false positives, keep **“Wait for bar close”** ON, especially on lower timeframes.
* Use the **Trend Filter** to align signals with the broader stack and cut noise.
* For HTF context, set **Timeframe 50/100/200** to higher frames (e.g., H1/H4/D) while you trade on a lower frame.
* Heatmap “Length” and “Deviation Multiplier” control smoothness and channel width—tune for your asset’s volatility.
Breadth Indicators NYSE Percent Above Moving AverageBreadth Indicators NYSE - transmits the processed data from the Barchart provider
NYSE - Breadth Indicators
S&P 500 - Breadth Indicators
DOW - Breadth Indicators
RUSSEL 1000 - Breadth Indicators
RUSSEL 2000 - Breadth Indicators
RUSSEL 3000 - Breadth Indicators
Moving Average - 5, 20, 50, 100, 150, 200
The "Percentage above 50-day SMA" indicator measures the percentage of stocks in the index trading above their 50-day moving average. It is a useful tool for assessing the general state of the market and identifying overbought and oversold conditions.
One way to use the "Percentage above 50-day SMA" indicator in a trading strategy is to combine it with a long-term moving average to determine whether the trend is bullish or bearish. Another way to use it is to combine it with a short-term moving average to identify pullbacks and rebounds within the overall trend.
The purpose of using the "Percentage above 50-day SMA" indicator is to participate in a larger trend with a better risk-reward ratio. By using this indicator to identify pullbacks and bounces, you can reduce the risk of entering trades at the wrong time.
Bull Signal Recap:
150-day EMA of $SPXA50R crosses above 52.5 and remains above 47.50 to set the bullish tone.
5-day EMA of $SPXA50R moves below 40 to signal a pullback
5-day EMA of $SPXA50R moves above 50 to signal an upturn
Bear Signal Recap:
150-day EMA of $SPXA50R crosses below 47.50 and remains below 52.50 to set the bearish tone.
5-day EMA of $SPXA50R moves above 60 to signal a bounce
5-day EMA of $SPXA50R moves below 50 to signal a downturn
Tweaking
There are numerous ways to tweak a trading system, but chartists should avoid over-optimizing the indicator settings. In other words, don't attempt to find the perfect moving average period or crossover level. Perfection is unattainable when developing a system or trading the markets. It is important to keep the system logical and focus tweaks on other aspects, such as the actual price chart of the underlying security.
What do levels above and below 50% signify in the long-term moving average?
A move above 52.5% is deemed bullish, and below 47.5% is deemed bearish. These levels help to reduce whipsaws by using buffers for bullish and bearish thresholds.
How does the short-term moving average work to identify pullbacks or bounces?
When using a 5-day EMA, a move below 40 signals a pullback, and a move above 60 signals a bounce.
How is the reversal of pullback or bounce identified?
A move back above 50 after a pullback or below 50 after a bounce signals that the respective trend may be resuming.
How can you ensure that the uptrend has resumed?
It’s important to wait for the surge above 50 to ensure the uptrend has resumed, signaling improved breadth.
Can the system be tweaked to optimize indicator settings?
While there are various ways to tweak the system, seeking perfection through over-optimizing settings is advised against. It's crucial to keep the system logical and focus tweaks on the price chart of the underlying security.
RUSSIAN \ Русская версия.
Индикатор "Процент выше 50-дневной скользящей средней" измеряет процент акций, торгующихся в индексе выше их 50-дневной скользящей средней. Это полезный инструмент для оценки общего состояния рынка и выявления условий перекупленности и перепроданности.
Один из способов использования индикатора "Процент выше 50-дневной скользящей средней" в торговой стратегии - это объединить его с долгосрочной скользящей средней, чтобы определить, является ли тренд бычьим или медвежьим. Другой способ использовать его - объединить с краткосрочной скользящей средней, чтобы выявить откаты и отскоки в рамках общего тренда.
Цель использования индикатора "Процент выше 50-дневной скользящей средней" - участвовать в более широком тренде с лучшим соотношением риска и прибыли. Используя этот индикатор для выявления откатов и отскоков, вы можете снизить риск входа в сделки в неподходящее время.
Краткое описание бычьего сигнала:
150-дневная ЕМА на уровне $SPXA50R пересекает отметку 52,5 и остается выше 47,50, что задает бычий настрой.
5-дневная ЕМА на уровне $SPXA50R опускается ниже 40, сигнализируя об откате
5-дневная ЕМА на уровне $SPXA50R поднимается выше 50, сигнализируя о росте
Обзор медвежьих сигналов:
150-дневная ЕМА на уровне $SPXA50R пересекает уровень ниже 47,50 и остается ниже 52,50, что указывает на медвежий настрой.
5-дневная ЕМА на уровне $SPXA50R поднимается выше 60, сигнализируя о отскоке
5-дневная ЕМА на уровне $SPXA50 опускается ниже 50, что сигнализирует о спаде
Корректировка
Существует множество способов настроить торговую систему, но графологам следует избегать чрезмерной оптимизации настроек индикатора. Другими словами, не пытайтесь найти идеальный период скользящей средней или уровень пересечения. Совершенство недостижимо при разработке системы или торговле на рынках. Важно поддерживать логику системы и уделять особое внимание другим аспектам, таким как график фактической цены базовой ценной бумаги.
Что означают уровни выше и ниже 50% в долгосрочной скользящей средней?
Движение выше 52,5% считается бычьим, а ниже 47,5% - медвежьим. Эти уровни помогают снизить риски, используя буферы для бычьих и медвежьих порогов.
Как краткосрочная скользящая средняя помогает идентифицировать откаты или отскоки?
При использовании 5-дневной ЕМА движение ниже 40 указывает на откат, а движение выше 60 указывает на отскок.
Как определяется разворот отката или отскока?
Движение выше 50 после отката или ниже 50 после отскока сигнализирует о возможном возобновлении соответствующего тренда.
Как вы можете гарантировать, что восходящий тренд возобновился?
Важно дождаться скачка выше 50, чтобы убедиться в возобновлении восходящего тренда, сигнализирующего о расширении диапазона.
Можно ли настроить систему для оптимизации настроек индикатора?
Хотя существуют различные способы настройки системы, не рекомендуется стремиться к совершенству с помощью чрезмерной оптимизации настроек. Крайне важно сохранить логичность системы и сфокусировать изменения на ценовом графике базовой ценной бумаги.
Parameter Free RSI [InvestorUnknown]The Parameter Free RSI (PF-RSI) is an innovative adaptation of the traditional Relative Strength Index (RSI), a widely used momentum oscillator that measures the speed and change of price movements. Unlike the standard RSI, which relies on a fixed lookback period (typically 14), the PF-RSI dynamically adjusts its calculation length based on real-time market conditions. By incorporating volatility and the RSI's deviation from its midpoint (50), this indicator aims to provide a more responsive and adaptable tool for identifying overbought/oversold conditions, trend shifts, and momentum changes. This adaptability makes it particularly valuable for traders navigating diverse market environments, from trending to ranging conditions.
PF-RSI offers a suite of customizable features, including dynamic length variants, smoothing options, visualization tools, and alert conditions.
Key Features
1. Dynamic RSI Length Calculation
The cornerstone of the PF-RSI is its ability to adjust the RSI calculation period dynamically, eliminating the need for a static parameter. The length is computed using two primary factors:
Volatility: Measured via the standard deviation of past RSI values.
Distance from Midpoint: The absolute deviation of the RSI from 50, reflecting the strength of bullish or bearish momentum.
The indicator offers three variants for calculating this dynamic length, allowing users to tailor its responsiveness:
Variant I (Aggressive): Increases the length dramatically based on volatility and a nonlinear scaling of the distance from 50. Ideal for traders seeking highly sensitive signals in fast-moving markets.
Variant II (Moderate): Combines volatility with a scaled distance from 50, using a less aggressive adjustment. Strikes a balance between responsiveness and stability, suitable for most trading scenarios.
Variant III (Conservative): Applies a linear combination of volatility and raw distance from 50. Offers a stable, less reactive length adjustment for traders prioritizing consistency.
// Function that returns a dynamic RSI length based on past RSI values
// The idea is to make the RSI length adaptive using volatility (stdev) and distance from the RSI midpoint (50)
// Different "variant" options control how aggressively the length changes
parameter_free_length(free_rsi, variant) =>
len = switch variant
// Variant I: Most aggressive adaptation
// Uses standard deviation scaled by a nonlinear factor of distance from 50
// Also adds another distance-based term to increase length more dramatically
"I" => math.ceil(
ta.stdev(free_rsi, math.ceil(free_rsi)) *
math.pow(1 + (math.ceil(math.abs(50 - (free_rsi - 50))) / 100), 2)
) +
(
math.ceil(math.abs(free_rsi - 50)) *
(1 + (math.ceil(math.abs(50 - (free_rsi - 50))) / 100))
)
// Variant II: Moderate adaptation
// Adds the standard deviation and a distance-based scaling term (less nonlinear)
"II" => math.ceil(
ta.stdev(free_rsi, math.ceil(free_rsi)) +
(
math.ceil(math.abs(free_rsi - 50)) *
(1 + (math.ceil(math.abs(50 - (free_rsi - 50))) / 100))
)
)
// Variant III: Least aggressive adaptation
// Simply adds standard deviation and raw distance from 50 (linear scaling)
"III" => math.ceil(
ta.stdev(free_rsi, math.ceil(free_rsi)) +
math.ceil(math.abs(free_rsi - 50))
)
2. Smoothing Options
To refine the dynamic RSI and reduce noise, the PF-RSI provides smoothing capabilities:
Smoothing Toggle: Enable or disable smoothing of the dynamic length used for RSI.
Smoothing MA Type for RSI MA: Choose between SMA and EMA
Smoothing Length Options for RSI MA:
Full: Uses the entire calculated dynamic length.
Half: Applies half of the dynamic length for smoother output.
SQRT: Uses the square root of the dynamic length, offering a compromise between responsiveness and smoothness.
The smoothed RSI is complemented by a separate moving average (MA) of the RSI itself, further enhancing signal clarity.
3. Visualization Tools
The PF-RSI includes visualization options to help traders interpret market conditions at a glance.
Plots:
Dynamic RSI: Displayed as a white line, showing the adaptive RSI value.
RSI Moving Average: Plotted in yellow, providing a smoothed reference for trend and momentum analysis.
Dynamic Length: A secondary plot (in faint white) showing how the calculation period evolves over time.
Histogram: Represents the RSI’s position relative to 50, with color gradients.
Fill Area: The space between the RSI and its MA is filled with a gradient (green for RSI > MA, red for RSI < MA), highlighting momentum shifts.
Customizable bar colors on the price chart reflect trend and momentum:
Trend (Raw RSI): Green (RSI > 50), Red (RSI < 50).
Trend (RSI MA): Green (MA > 50), Red (MA < 50).
Trend (Raw RSI) + Momentum: Adds momentum shading (lighter green/red when RSI and MA diverge).
Trend (RSI MA) + Momentum: Similar, but based on the MA’s trend.
Momentum: Green (RSI > MA), Red (RSI < MA).
Off: Disables bar coloring.
Intrabar Updating: Optional real-time updates within each bar for enhanced responsiveness.
4. Alerts
The PF-RSI supports customizable alerts to keep traders informed of key events.
Trend Alerts:
Raw RSI: Triggers when the RSI crosses above (uptrend) or below (downtrend) 50.
RSI MA: Triggers when the moving average crosses 50.
Off: Disables trend alerts.
Momentum Alerts:
Triggers when the RSI crosses its moving average, indicating rising (RSI > MA) or declining (RSI < MA) momentum.
Alerts are fired once per bar close, with descriptive messages including the ticker symbol (e.g., " Uptrend on: AAPL").
How It Works
The PF-RSI operates in a multi-step process:
Initialization
On the first run, it calculates a standard RSI with a 14-period length to seed the dynamic calculation.
Dynamic Length Computation
Once seeded, the indicator switches to a dynamic length based on the selected variant, factoring in volatility and distance from 50.
If smoothing is enabled, the length is further refined using an SMA.
RSI Calculation
The adaptive RSI is computed using the dynamic length, ensuring it reflects current market conditions.
Moving Average
A separate MA (SMA or EMA) is applied to the RSI, with a length derived from the dynamic length (Full, Half, or SQRT).
Visualization and Alerts
The results are plotted, and alerts are triggered based on user settings.
This adaptive approach minimizes lag in fast markets and reduces false signals in choppy conditions, offering a significant edge over fixed-period RSI implementations.
Why Use PF-RSI?
The Parameter Free RSI stands out by eliminating the guesswork of selecting an RSI period. Its dynamic length adjusts to market volatility and momentum, providing timely signals without manual tweaking.
Support and resistance levels (Day, Week, Month) + EMAs + SMAs(ENG): This Pine 5 script provides various tools for configuring and displaying different support and resistance levels, as well as moving averages (EMA and SMA) on charts. Using these tools is an essential strategy for determining entry and exit points in trades.
Support and Resistance Levels
Daily, weekly, and monthly support and resistance levels play a key role in analyzing price movements:
Daily levels: Represent prices where a cryptocurrency has tended to bounce within the current trading day.
Weekly levels: Reflect strong prices that hold throughout the week.
Monthly levels: Indicate the most significant levels that can influence price movement over the month.
When trading cryptocurrencies, traders use these levels to make decisions about entering or exiting positions. For example, if a cryptocurrency approaches a weekly resistance level and fails to break through it, this may signal a sell opportunity. If the price reaches a daily support level and starts to bounce up, it may indicate a potential long position.
Market context and trading volumes are also important when analyzing support and resistance levels. High volume near a level can confirm its significance and the likelihood of subsequent price movement. Traders often combine analysis across different time frames to get a more complete picture and improve the accuracy of their trading decisions.
Moving Averages
Moving averages (EMA and SMA) are another important tool in the technical analysis of cryptocurrencies:
EMA (Exponential Moving Average): Gives more weight to recent prices, allowing it to respond more quickly to price changes.
SMA (Simple Moving Average): Equally considers all prices over a given period.
Key types of moving averages used by traders:
EMA 50 and 200: Often used to identify trends. The crossing of the 50-day EMA with the 200-day EMA is called a "golden cross" (buy signal) or a "death cross" (sell signal).
SMA 50, 100, 150, and 200: These periods are often used to determine long-term trends and support/resistance levels. Similar to the EMA, the crossings of these averages can signal potential trend changes.
Settings Groups:
EMA Golden Cross & Death Cross: A setting to display the "golden cross" and "death cross" for the EMA.
EMA 50 & 200: A setting to display the 50-day and 200-day EMA.
Support and Resistance Levels: Includes settings for daily, weekly, and monthly levels.
SMA 50, 100, 150, 200: A setting to display the 50, 100, 150, and 200-day SMA.
SMA Golden Cross & Death Cross: A setting to display the "golden cross" and "death cross" for the SMA.
Components:
Enable/disable the display of support and resistance levels.
Show level labels.
Parameters for adjusting offset, display of EMA and SMA, and their time intervals.
Parameters for configuring EMA and SMA Golden Cross & Death Cross.
EMA Parameters:
Enable/disable the display of 50 and 200-day EMA.
Color and style settings for EMA.
Options to use bar gaps and the "LookAhead" function.
SMA Parameters:
Enable/disable the display of 50, 100, 150, and 200-day SMA.
Color and style settings for SMA.
Options to use bar gaps and the "LookAhead" function.
Effective use of support and resistance levels, as well as moving averages, requires an understanding of technical analysis, discipline, and the ability to adapt the strategy according to changing market conditions.
(RUS) Данный Pine 5 скрипт предоставляет разнообразные инструменты для настройки и отображения различных уровней поддержки и сопротивления, а также скользящих средних (EMA и SMA) на графиках. Использование этих инструментов является важной стратегией для определения точек входа и выхода из сделок.
Уровни поддержки и сопротивления
Дневные, недельные и месячные уровни поддержки и сопротивления играют ключевую роль в анализе движения цен:
Дневные уровни: Представляют собой цены, на которых криптовалюта имела тенденцию отскакивать в течение текущего торгового дня.
Недельные уровни: Отражают сильные цены, которые сохраняются в течение недели.
Месячные уровни: Указывают на наиболее значимые уровни, которые могут влиять на движение цены в течение месяца.
При торговле криптовалютами трейдеры используют эти уровни для принятия решений о входе в позицию или закрытии сделки. Например, если криптовалюта приближается к недельному уровню сопротивления и не удается его преодолеть, это может стать сигналом для продажи. Если цена достигает дневного уровня поддержки и начинает отскакивать вверх, это может указывать на возможность открытия длинной позиции.
Контекст рынка и объемы торговли также важны при анализе уровней поддержки и сопротивления. Высокий объем при приближении к уровню может подтвердить его значимость и вероятность последующего движения цены. Трейдеры часто комбинируют анализ различных временных рамок для получения более полной картины и улучшения точности своих торговых решений.
Скользящие средние
Скользящие средние (EMA и SMA) являются еще одним важным инструментом в техническом анализе криптовалют:
EMA (Exponential Moving Average): Экспоненциальная скользящая средняя, которая придает большее значение последним ценам. Это позволяет более быстро реагировать на изменения в ценах.
SMA (Simple Moving Average): Простая скользящая средняя, которая равномерно учитывает все цены в заданном периоде.
Основные виды скользящих средних, которые используются трейдерами:
EMA 50 и 200: Часто используются для выявления трендов. Пересечение 50-дневной EMA с 200-дневной EMA называется "золотым крестом" (сигнал на покупку) или "крестом смерти" (сигнал на продажу).
SMA 50, 100, 150 и 200: Эти периоды часто используются для определения долгосрочных трендов и уровней поддержки/сопротивления. Аналогично EMA, пересечения этих средних могут сигнализировать о возможных изменениях тренда.
Группы настроек:
EMA Golden Cross & Death Cross: Настройка для отображения "золотого креста" и "креста смерти" для EMA.
EMA 50 & 200: Настройка для отображения 50-дневной и 200-дневной EMA.
Уровни поддержки и сопротивления: Включает настройки для дневных, недельных и месячных уровней.
SMA 50, 100, 150, 200: Настройка для отображения 50, 100, 150 и 200-дневных SMA.
SMA Golden Cross & Death Cross: Настройка для отображения "золотого креста" и "креста смерти" для SMA.
Компоненты:
Включение/отключение отображения уровней поддержки и сопротивления.
Показ ярлыков уровней.
Параметры для настройки смещения, отображения EMA и SMA, а также их временных интервалов.
Параметры для настройки EMA и SMA Golden Cross & Death Cross.
Параметры EMA:
Включение/отключение отображения 50 и 200-дневных EMA.
Настройки цвета и стиля для EMA.
Опции для использования разрыва баров и функции "LookAhead".
Параметры SMA:
Включение/отключение отображения 50, 100, 150 и 200-дневных SMA.
Настройки цвета и стиля для SMA.
Опции для использования разрыва баров и функции "LookAhead".
Эффективное использование уровней поддержки и сопротивления, а также скользящих средних, требует понимания технического анализа, дисциплины и умения адаптировать стратегию в зависимости от изменяющихся условий рынка.
ICT Killzones and Sessions W/ Silver Bullet + MacrosForex and Equity Session Tracker with Killzones, Silver Bullet, and Macro Times
This Pine Script indicator is a comprehensive timekeeping tool designed specifically for ICT traders using any time-based strategy. It helps you visualize and keep track of forex and equity session times, kill zones, macro times, and silver bullet hours.
Features:
Session and Killzone Lines:
Green: London Open (LO)
White: New York (NY)
Orange: Australian (AU)
Purple: Asian (AS)
Includes AM and PM session markers.
Dotted/Striped Lines indicate overlapping kill zones within the session timeline.
Customization Options:
Display sessions and killzones in collapsed or full view.
Hide specific sessions or killzones based on your preferences.
Customize colors, texts, and sizes.
Option to hide drawings older than the current day.
Automatic Updates:
The indicator draws all lines and boxes at the start of a new day.
Automatically adjusts time-based boxes according to the New York timezone.
Killzone Time Windows (for indices):
London KZ: 02:00 - 05:00
New York AM KZ: 07:00 - 10:00
New York PM KZ: 13:30 - 16:00
Silver Bullet Times:
03:00 - 04:00
10:00 - 11:00
14:00 - 15:00
Macro Times:
02:33 - 03:00
04:03 - 04:30
08:50 - 09:10
09:50 - 10:10
10:50 - 11:10
11:50 - 12:50
Latest Update:
January 15:
Added option to automatically change text coloring based on the chart.
Included additional optional macro times per user request:
12:50 - 13:10
13:50 - 14:15
14:50 - 15:10
15:50 - 16:15
Usage:
To maximize your experience, minimize the pane where the script is drawn. This minimizes distractions while keeping the essential time markers visible. The script is designed to help traders by clearly annotating key trading periods without overwhelming their charts.
Originality and Justification:
This indicator uniquely integrates various time-based strategies essential for ICT traders. Unlike other indicators, it consolidates session times, kill zones, macro times, and silver bullet hours into one comprehensive tool. This allows traders to have a clear and organized view of critical trading periods, facilitating better decision-making.
Credits:
This script incorporates open-source elements with significant improvements to enhance functionality and user experience.
Forex and Equity Session Tracker with Killzones, Silver Bullet, and Macro Times
This Pine Script indicator is a comprehensive timekeeping tool designed specifically for ICT traders using any time-based strategy. It helps you visualize and keep track of forex and equity session times, kill zones, macro times, and silver bullet hours.
Features:
Session and Killzone Lines:
Green: London Open (LO)
White: New York (NY)
Orange: Australian (AU)
Purple: Asian (AS)
Includes AM and PM session markers.
Dotted/Striped Lines indicate overlapping kill zones within the session timeline.
Customization Options:
Display sessions and killzones in collapsed or full view.
Hide specific sessions or killzones based on your preferences.
Customize colors, texts, and sizes.
Option to hide drawings older than the current day.
Automatic Updates:
The indicator draws all lines and boxes at the start of a new day.
Automatically adjusts time-based boxes according to the New York timezone.
Killzone Time Windows (for indices):
London KZ: 02:00 - 05:00
New York AM KZ: 07:00 - 10:00
New York PM KZ: 13:30 - 16:00
Silver Bullet Times:
03:00 - 04:00
10:00 - 11:00
14:00 - 15:00
Macro Times:
02:33 - 03:00
04:03 - 04:30
08:50 - 09:10
09:50 - 10:10
10:50 - 11:10
11:50 - 12:50
Latest Update:
January 15:
Added option to automatically change text coloring based on the chart.
Included additional optional macro times per user request:
12:50 - 13:10
13:50 - 14:15
14:50 - 15:10
15:50 - 16:15
ICT Sessions and Kill Zones
What They Are:
ICT Sessions: These are specific times during the trading day when market activity is expected to be higher, such as the London Open, New York Open, and the Asian session.
Kill Zones: These are specific time windows within these sessions where the probability of significant price movements is higher. For example, the New York AM Kill Zone is typically from 8:30 AM to 11:00 AM EST.
How to Use Them:
Identify the Session: Determine which trading session you are in (London, New York, or Asian).
Focus on Kill Zones: Within that session, focus on the kill zones for potential trade setups. For instance, during the New York session, look for setups between 8:30 AM and 11:00 AM EST.
Silver Bullets
What They Are:
Silver Bullets: These are specific, high-probability trade setups that occur within the kill zones. They are designed to be "one shot, one kill" trades, meaning they aim for precise and effective entries and exits.
How to Use Them:
Time-Based Setup: Look for these setups within the designated kill zones. For example, between 10:00 AM and 11:00 AM for the New York AM session .
Chart Analysis: Start with higher time frames like the 15-minute chart and then refine down to 5-minute and 1-minute charts to identify imbalances or specific patterns .
Macros
What They Are:
Macros: These are broader market conditions and trends that influence your trading decisions. They include understanding the overall market direction, seasonal tendencies, and the Commitment of Traders (COT) reports.
How to Use Them:
Understand Market Conditions: Be aware of the macroeconomic factors and market conditions that could affect price movements.
Seasonal Tendencies: Know the seasonal patterns that might influence the market direction.
COT Reports: Use the Commitment of Traders reports to understand the positioning of large traders and commercial hedgers .
Putting It All Together
Preparation: Understand the macro conditions and review the COT reports.
Session and Kill Zone: Identify the trading session and focus on the kill zones.
Silver Bullet Setup: Look for high-probability setups within the kill zones using refined chart analysis.
Execution: Execute the trade with precision, aiming for a "one shot, one kill" outcome.
By following these steps, you can effectively use ICT sessions, kill zones, silver bullets, and macros to enhance your trading strategy.
Usage:
To maximize your experience, shrink the pane where the script is drawn. This minimizes distractions while keeping the essential time markers visible. The script is designed to help traders by clearly annotating key trading periods without overwhelming their charts.
Originality and Justification:
This indicator uniquely integrates various time-based strategies essential for ICT traders. Unlike other indicators, it consolidates session times, kill zones, macro times, and silver bullet hours into one comprehensive tool. This allows traders to have a clear and organized view of critical trading periods, facilitating better decision-making.
Credits:
This script incorporates open-source elements with significant improvements to enhance functionality and user experience. All credit goes to itradesize for the SB + Macro boxes
Pinescript - Common Label & Line Array Functions Library by RRBPinescript - Common Label & Line Array Functions Library by RagingRocketBull 2021
Version 1.0
This script provides a library of common array functions for arrays of label and line objects with live testing of all functions.
Using this library you can easily create, update, delete, join label/line object arrays, and get/set properties of individual label/line object array items.
You can find the full list of supported label/line array functions below.
There are several libraries:
- Common String Functions Library
- Standard Array Functions Library
- Common Fixed Type Array Functions Library
- Common Label & Line Array Functions Library
- Common Variable Type Array Functions Library
Features:
- 30 array functions in categories create/update/delete/join/get/set with support for both label/line objects (45+ including all implementations)
- Create, Update label/line object arrays from list/array params
- GET/SET properties of individual label/line array items by index
- Join label/line objects/arrays into a single string for output
- Supports User Input of x,y coords of 5 different types: abs/rel/rel%/inc/inc% list/array, auto transforms x,y input into list/array based on type, base and xloc, translates rel into abs bar indexes
- Supports User Input of lists with shortened names of string properties, auto expands all standard string properties to their full names for use in functions
- Live Output for all/selected functions based on User Input. Test any function for possible errors you may encounter before using in script.
- Output filters: hide all excluded and show only allowed functions using a list of function names
- Output Panel customization options: set custom style, color, text size, and line spacing
Usage:
- select create function - create label/line arrays from lists or arrays (optional). Doesn't affect the update functions. The only change in output should be function name regardless of the selected implementation.
- specify num_objects for both label/line arrays (default is 7)
- specify common anchor point settings x,y base/type for both label/line arrays and GET/SET items in Common Settings
- fill lists with items to use as inputs for create label/line array functions in Create Label/Line Arrays section
- specify label/line array item index and properties to SET in corresponding sections
- select label/line SET function to see the changes applied live
Code Structure:
- translate x,y depending on x,y type, base and xloc as specified in UI (required for all functions)
- expand all shortened standard property names to full names (required for create/update* from arrays and set* functions, not needed for create/update* from lists) to prevent errors in label.new and line.new
- create param arrays from string lists (required for create/update* from arrays and set* functions, not needed for create/update* from lists)
- create label/line array from string lists (property names are auto expanded) or param arrays (requires already expanded properties)
- update entire label/line array or
- get/set label/line array item properties by index
Transforming/Expanding Input values:
- for this script to work on any chart regardless of price/scale, all x*,y* are specified as % increase relative to x0,y0 base levels by default, but user can enter abs x,price values specific for that chart if necessary.
- all lists can be empty, contain 1 or several items, have the same/different lengths. Array Length = min(min(len(list*)), mum_objects) is used to create label/line objects. Missing list items are replaced with default property values.
- when a list contains only 1 item it is duplicated (label name/tooltip is also auto incremented) to match the calculated Array Length
- since this script processes user input, all x,y values must be translated to abs bar indexes before passing them to functions. Your script may provide all data internally and doesn't require this step.
- at first int x, float y arrays are created from user string lists, transformed as described below and returned as x,y arrays.
- translated x,y arrays can then be passed to create from arrays function or can be converted back to x,y string lists for the create from lists function if necessary.
- all translation logic is separated from create/update/set functions for the following reasons:
- to avoid redundant code/dependency on ext functions/reduce local scopes and to be able to translate everything only once in one place - should be faster
- to simplify internal logic of all functions
- because your script may provide all data internally without user input and won't need the translation step
- there are 5 types available for both x,y: abs, rel, rel%, inc, inc%. In addition to that, x can be: bar index or time, y is always price.
- abs - absolute bar index/time from start bar0 (x) or price (y) from 0, is >= 0
- rel - relative bar index/time from cur bar n (x) or price from y0 base level, is >= 0
- rel% - relative % increase of bar index/time (x) or price (y) from corresponding base level (x0 or y0), can be <=> 0
- inc - relative increment (step) for each new level of bar index/time (x) or price (y) from corresponding base level (x0 or y0), can be <=> 0
- inc% - relative % increment (% step) for each new level of bar index/time (x) or price (y) from corresponding base level (x0 or y0), can be <=> 0
- x base level >= 0
- y base level can be 0 (empty) or open, close, high, low of cur bar
- single item x1_list = "50" translates into:
- for x type abs: "50, 50, 50 ..." num_objects times regardless of xloc => x = 50
- for x type rel: "50, 50, 50 ... " num_objects times => x = x_base + 50
- for x type rel%: "50%, 50%, 50% ... " num_objects times => x_base * (1 + 0.5)
- for x type inc: "0, 50, 100 ... " num_objects times => x_base + 50 * i
- for x type inc%: "0%, 50%, 100% ... " num_objects times => x_base * (1 + 0.5 * i)
- when xloc = xloc.bar_index each rel*/inc* value in the above list is then subtracted from n: n - x to convert rel to abs bar index, values of abs type are not affected
- x1_list = "0, 50, 100, ..." of type rel is the same as "50" of type inc
- x1_list = "50, 50, 50, ..." of type abs/rel/rel% produces a sequence of the same values and can be shortened to just "50"
- single item y1_list = "2" translates into (ragardless of yloc):
- for y type abs: "2, 2, 2 ..." num_objects times => y = 2
- for y type rel: "2, 2, 2 ... " num_objects times => y = y_base + 2
- for y type rel%: "2%, 2%, 2% ... " num_objects times => y = y_base * (1 + 0.02)
- for y type inc: "0, 2, 4 ... " num_objects times => y = y_base + 2 * i
- for y type inc%: "0%, 2%, 4% ... " num_objects times => y = y_base * (1 + 0.02 * i)
- when yloc != yloc.price all calculated values above are simply ignored
- y1_list = "0, 2, 4" of type rel% is the same as "2" with type inc%
- y1_list = "2, 2, 2" of type abs/rel/rel% produces a sequence of the same values and can be shortened to just "2"
- you can enter shortened property names in lists. To lookup supported shortened names use corresponding dropdowns in Set Label/Line Array Item Properties sections
- all shortened standard property names must be expanded to full names (required for create/update* from arrays and set* functions, not needed for create/update* from lists) to prevent errors in label.new and line.new
- examples of shortened property names that can be used in lists: bar_index, large, solid, label_right, white, left, left, price
- expanded to their corresponding full names: xloc.bar_index, size.large, line.style_solid, label.style_label_right, color.white, text.align_left, extend.left, yloc.price
- all expanding logic is separated from create/update* from arrays and set* functions for the same reasons as above, and because param arrays already have different types, implying the use of final values.
- all expanding logic is included in the create/update* from lists functions because it seemed more natural to process string lists from user input directly inside the function, since they are already strings.
Creating Label/Line Objects:
- use study max_lines_count and max_labels_count params to increase the max number of label/line objects to 500 (+3) if necessary. Default number of label/line objects is 50 (+3)
- all functions use standard param sequence from methods in reference, except style always comes before colors.
- standard label/line.get* functions only return a few properties, you can't read style, color, width etc.
- label.new(na, na, "") will still create a label with x = n-301, y = NaN, text = "" because max default scope for a var is 300 bars back.
- there are 2 types of color na, label color requires color(na) instead of color_na to prevent error. text_color and line_color can be color_na
- for line to be visible both x1, x2 ends must be visible on screen, also when y1 == y2 => abs(x1 - x2) >= 2 bars => line is visible
- xloc.bar_index line uses abs x1, x2 indexes and can only be within 0 and n ends, where n <= 5000 bars (free accounts) or 10000 bars (paid accounts) limit, can't be plotted into the future
- xloc.bar_time line uses abs x1, x2 times, can't go past bar0 time but can continue past cur bar time into the future, doesn't have a length limit in bars.
- xloc.bar_time line with length = exact number of bars can be plotted only within bar0 and cur bar, can't be plotted into the future reliably because of future gaps due to sessions on some charts
- xloc.bar_index line can't be created on bar 0 with fixed length value because there's only 1 bar of horiz length
- it can be created on cur bar using fixed length x < n <= 5000 or
- created on bar0 using na and then assigned final x* values on cur bar using set_x*
- created on bar0 using n - fixed_length x and then updated on cur bar using set_x*, where n <= 5000
- default orientation of lines (for style_arrow* and extend) is from left to right (from bar 50 to bar 0), it reverses when x1 and x2 are swapped
- price is a function, not a line object property
Variable Type Arrays:
- you can't create an if/function that returns var type value/array - compiler uses strict types and doesn't allow that
- however you can assign array of any type to another array of any type creating an arr pointer of invalid type that must be reassigned to a matching array type before used in any expression to prevent error
- create_any_array2 uses this loophole to return an int_arr pointer of a var type array
- this works for all array types defined with/without var keyword and doesn't work for string arrays defined with var keyword for some reason
- you can't do this with var type vars, only var type arrays because arrays are pointers passed by reference, while vars are actual values passed by value.
- you can only pass a var type value/array param to a function if all functions inside support every type - otherwise error
- alternatively values of every type must be passed simultaneously and processed separately by corresponding if branches/functions supporting these particular types returning a common single type result
- get_var_types solves this problem by generating a list of dummy values of every possible type including the source type, tricking the compiler into allowing a single valid branch to execute without error, while ignoring all dummy results
Notes:
- uses Pinescript v3 Compatibility Framework
- uses Common String Functions Library, Common Fixed Type Array Functions Library, Common Variable Type Array Functions Library
- has to be a separate script to reduce the number of local scopes/compiled file size, can't be merged with another library.
- lets you live test all label/line array functions for errors. If you see an error - change params in UI
- if you see "Loop too long" error - hide/unhide or reattach the script
- if you see "Chart references too many candles" error - change x type or value between abs/rel*. This can happen on charts with 5000+ bars when a rel bar index x is passed to label.new or line.new instead of abs bar index n - x
- create/update_label/line_array* use string lists, while create/update_label/line_array_from_arrays* use array params to create label/line arrays. "from_lists" is dropped to shorten the names of the most commonly used functions.
- create_label/line_array2,4 are preferable, 5,6 are listed for pure demonstration purposes only - don't use them, they don't improve anything but dramatically increase local scopes/compiled file size
- for this reason you would mainly be using create/update_label/line_array2,4 for list params or create/update_label/line_array_from_arrays2 for array params
- all update functions are executed after each create as proof of work and can be disabled. Only create functions are required. Use update functions when necessary - when list/array params are changed by your script.
- both lists and array item properties use the same x,y_type, x,y_base from common settings
- doesn't use pagination, a single str contains all output
- why is this so complicated? What are all these functions for?
- this script merges standard label/line object methods with standard array functions to create a powerful set of label/line object array functions to simplify manipulation of these arrays.
- this library also extends the functionality of Common Variable Type Array Functions Library providing support for label/line types in var type array functions (any_to_str6, join_any_array5)
- creating arrays from either lists or arrays adds a level of flexibility that comes with complexity. It's very likely that in your script you'd have to deal with both string lists as input, and arrays internally, once everything is converted.
- processing user input, allowing customization and targeting for any chart adds a whole new layer of complexity, all inputs must be translated and expanded before used in functions.
- different function implementations can increase/reduce local scopes and compiled file size. Select a version that best suits your needs. Creating complex scripts often requires rewriting your code multiple times to fit the limits, every line matters.
P.S. Don't rely too much on labels, for too often they are fables.
List of functions*:
* - functions from other libraries are not listed
1. Join Functions
Labels
- join_label_object(label_, d1, d2)
- join_label_array(arr, d1, d2)
- join_label_array2(arr, d1, d2, d3)
Lines
- join_line_object(line_, d1, d2)
- join_line_array(arr, d1, d2)
- join_line_array2(arr, d1, d2, d3)
Any Type
- any_to_str6(arr, index, type)
- join_any_array4(arr, d1, d2, type)
- join_any_array5(arr, d, type)
2. GET/SET Functions
Labels
- label_array_get_text(arr, index)
- label_array_get_xy(arr, index)
- label_array_get_fields(arr, index)
- label_array_set_text(arr, index, str)
- label_array_set_xy(arr, index, x, y)
- label_array_set_fields(arr, index, x, y, str)
- label_array_set_all_fields(arr, index, x, y, str, xloc, yloc, label_style, label_color, text_color, text_size, text_align, tooltip)
- label_array_set_all_fields2(arr, index, x, y, str, xloc, yloc, label_style, label_color, text_color, text_size, text_align, tooltip)
Lines
- line_array_get_price(arr, index, bar)
- line_array_get_xy(arr, index)
- line_array_get_fields(arr, index)
- line_array_set_text(arr, index, width)
- line_array_set_xy(arr, index, x1, y1, x2, y2)
- line_array_set_fields(arr, index, x1, y1, x2, y2, width)
- line_array_set_all_fields(arr, index, x1, y1, x2, y2, xloc, extend, line_style, line_color, width)
- line_array_set_all_fields2(arr, index, x1, y1, x2, y2, xloc, extend, line_style, line_color, width)
3. Create/Update/Delete Functions
Labels
- delete_label_array(label_arr)
- create_label_array(list1, list2, list3, list4, list5, d)
- create_label_array2(x_list, y_list, str_list, xloc_list, yloc_list, style_list, color1_list, color2_list, size_list, align_list, tooltip_list, d)
- create_label_array3(x_list, y_list, str_list, xloc_list, yloc_list, style_list, color1_list, color2_list, size_list, align_list, tooltip_list, d)
- create_label_array4(x_list, y_list, str_list, xloc_list, yloc_list, style_list, color1_list, color2_list, size_list, align_list, tooltip_list, d)
- create_label_array5(x_list, y_list, str_list, xloc_list, yloc_list, style_list, color1_list, color2_list, size_list, align_list, tooltip_list, d)
- create_label_array6(x_list, y_list, str_list, xloc_list, yloc_list, style_list, color1_list, color2_list, size_list, align_list, tooltip_list, d)
- update_label_array2(label_arr, x_list, y_list, str_list, xloc_list, yloc_list, style_list, color1_list, color2_list, size_list, align_list, tooltip_list, d)
- update_label_array4(label_arr, x_list, y_list, str_list, xloc_list, yloc_list, style_list, color1_list, color2_list, size_list, align_list, tooltip_list, d)
- create_label_array_from_arrays2(x_arr, y_arr, str_arr, xloc_arr, yloc_arr, style_arr, color1_arr, color2_arr, size_arr, align_arr, tooltip_arr, d)
- create_label_array_from_arrays4(x_arr, y_arr, str_arr, xloc_arr, yloc_arr, style_arr, color1_arr, color2_arr, size_arr, align_arr, tooltip_arr, d)
- update_label_array_from_arrays2(label_arr, x_arr, y_arr, str_arr, xloc_arr, yloc_arr, style_arr, color1_arr, color2_arr, size_arr, align_arr, tooltip_arr, d)
Lines
- delete_line_array(line_arr)
- create_line_array(list1, list2, list3, list4, list5, list6, d)
- create_line_array2(x1_list, y1_list, x2_list, y2_list, xloc_list, extend_list, style_list, color_list, width_list, d)
- create_line_array3(x1_list, y1_list, x2_list, y2_list, xloc_list, extend_list, style_list, color_list, width_list, d)
- create_line_array4(x1_list, y1_list, x2_list, y2_list, xloc_list, extend_list, style_list, color_list, width_list, d)
- create_line_array5(x1_list, y1_list, x2_list, y2_list, xloc_list, extend_list, style_list, color_list, width_list, d)
- create_line_array6(x1_list, y1_list, x2_list, y2_list, xloc_list, extend_list, style_list, color_list, width_list, d)
- update_line_array2(line_arr, x1_list, y1_list, x2_list, y2_list, xloc_list, extend_list, style_list, color_list, width_list, d)
- update_line_array4(line_arr, x1_list, y1_list, x2_list, y2_list, xloc_list, extend_list, style_list, color_list, width_list, d)
- create_line_array_from_arrays2(x1_arr, y1_arr, x2_arr, y2_arr, xloc_arr, extend_arr, style_arr, color_arr, width_arr, d)
- update_line_array_from_arrays2(line_arr, x1_arr, y1_arr, x2_arr, y2_arr, xloc_arr, extend_arr, style_arr, color_arr, width_arr, d)
Impulse Range Compression & Expansion (IRCE)📌 Impulse Range Compression & Expansion (IRCE) – Visualizing Price Traps Before Breakouts
📖 Overview
The IRCE Indicator is a precision breakout detection tool designed to identify consolidation traps and price coil zones before expansion moves occur. Unlike traditional volatility indicators that rely solely on statistical thresholds (e.g., Bollinger Bands or ATR), IRCE focuses on behavioral price compression, detecting tight-range candle clusters and validating breakouts through body expansion and/or volume surges.
This makes it ideal for traders looking to:
• Catch breakouts from range traps
• Avoid choppy and premature signals
• Spot early-stage momentum moves based on clean price behavior
⸻
⚙️ How It Works
1. Impulse Range Compression Detection
• Measures the high-low range of each candle
• Compares it to a user-defined average range (default 7 bars)
• Flags candles where the range is significantly smaller (e.g., <60% of average)
• Groups these into tight clusters, indicating compression zones or potential “trap ranges”
2. Cluster Box Construction
• When a valid cluster (e.g., 3 or more tight candles) is detected, the indicator:
• Marks the high and low of the cluster
• Draws a shaded box over this “trap zone”
• This helps visually track where price has coiled before a breakout
3. Breakout Confirmation Logic
A breakout from the trap zone is only validated when:
• Price closes above the cluster high (bullish) or below the cluster low (bearish)
• One or both of the following confirm strength:
• Body Expansion: Current candle body is 120%+ of recent average
• Volume Expansion: Volume exceeds recent volume average
4. Optional Trend Filter
• An optional EMA filter (default: 50 EMA) ensures breakout signals align with trend direction
• Helps filter out countertrend noise in ranging markets
5. Signal Cooldown
• Prevents repeated signals by enforcing a cooldown period (e.g., 10 bars) between entries
⸻
🖥️ Visual Elements
• 📦 Yellow compression boxes represent tight price traps
• 🟢 Buy labels appear when price breaks above the trap with confirmation
• 🔴 Sell labels appear when price breaks below with confirmation
• All visuals are non-repainting and updated in real-time
🧠 How to Use
1. Wait for a yellow trap box to appear
2. Watch for a confirmed breakout from the trap zone
3. Take the trade in the direction of the breakout:
• Only if it satisfies body or volume confirmation
• And if trend alignment is enabled, it must match EMA direction
4. Place stops just outside the opposite end of the trap zone
5. Use risk/reward ratios or structure levels for exits
This logic works great on:
• Lower timeframes (scalping breakouts)
• Higher timeframes (detecting price coiling before major moves)
• Any market: Stocks, Crypto, FX, Commodities
⸻
🔒 Technical Notes
• ✅ No repainting
• ✅ No future-looking logic
• ✅ Suitable for both discretionary and systematic traders
• ✅ Built in Pine Script v6
EMA 20+50 + MACD Strateji ( omerprıme)EASY BUY-SELL basitçe al -sat yapabileceğiniz macd indikatörü ve ema kullanılmış bir indikatördür unutmayın ki ne kadar basit o kadar verimli.
Moving Averages) to generate trading signals and trend confirmation.
Trend Identification with EMA
Two EMAs are used to determine the overall market trend (commonly a short-term EMA and a long-term EMA).
When the short EMA crosses above the long EMA, it indicates an uptrend.
When the short EMA crosses below the long EMA, it signals a downtrend.
Signal Confirmation with MACD
The MACD line and Signal line are analyzed to detect momentum shifts.
A bullish signal occurs when the MACD line crosses above the Signal line, especially if the EMAs confirm an uptrend.
A bearish signal occurs when the MACD line crosses below the Signal line, especially if the EMAs confirm a downtrend.
Trading Logic
Buy signals appear only when both the EMA trend is bullish and the MACD confirms momentum to the upside.
Sell signals appear only when both the EMA trend is bearish and the MACD confirms momentum to the downside.
Volume Weighted RSI (VW RSI)The Volume Weighted RSI (VW RSI) is a momentum oscillator designed for TradingView, implemented in Pine Script v6, that enhances the traditional Relative Strength Index (RSI) by incorporating trading volume into its calculation. Unlike the standard RSI, which measures the speed and change of price movements based solely on price data, the VW RSI weights its analysis by volume, emphasizing price movements backed by significant trading activity. This makes the VW RSI particularly effective for identifying bullish or bearish momentum, overbought/oversold conditions, and potential trend reversals in markets where volume plays a critical role, such as stocks, forex, and cryptocurrencies.
Key Features
Volume-Weighted Momentum Calculation:
The VW RSI calculates momentum by comparing the volume associated with upward price movements (up-volume) to the volume associated with downward price movements (down-volume).
Up-volume is the volume on bars where the closing price is higher than the previous close, while down-volume is the volume on bars where the closing price is lower than the previous close.
These volumes are smoothed over a user-defined period (default: 14 bars) using a Running Moving Average (RMA), and the VW RSI is computed using the formula:
\text{VW RSI} = 100 - \frac{100}{1 + \text{VoRS}}
where
\text{VoRS} = \frac{\text{Average Up-Volume}}{\text{Average Down-Volume}}
.
Oscillator Range and Interpretation:
The VW RSI oscillates between 0 and 100, with a centerline at 50.
Above 50: Indicates bullish volume momentum, suggesting that volume on up bars dominates, which may signal buying pressure and a potential uptrend.
Below 50: Indicates bearish volume momentum, suggesting that volume on down bars dominates, which may signal selling pressure and a potential downtrend.
Overbought/Oversold Levels: User-defined thresholds (default: 70 for overbought, 30 for oversold) help identify potential reversal points:
VW RSI > 70: Overbought, indicating a possible pullback or reversal.
VW RSI < 30: Oversold, indicating a possible bounce or reversal.
Visual Elements:
VW RSI Line: Plotted in a separate pane below the price chart, colored dynamically based on its value:
Green when above 50 (bullish momentum).
Red when below 50 (bearish momentum).
Gray when at 50 (neutral).
Centerline: A dashed line at 50, optionally displayed, serving as the neutral threshold between bullish and bearish momentum.
Overbought/Oversold Lines: Dashed lines at the user-defined overbought (default: 70) and oversold (default: 30) levels, optionally displayed, to highlight extreme conditions.
Background Coloring: The background of the VW RSI pane is shaded red when the indicator is in overbought territory and green when in oversold territory, providing a quick visual cue of potential reversal zones.
Alerts:
Built-in alerts for key events:
Bullish Momentum: Triggered when the VW RSI crosses above 50, indicating a shift to bullish volume momentum.
Bearish Momentum: Triggered when the VW RSI crosses below 50, indicating a shift to bearish volume momentum.
Overbought Condition: Triggered when the VW RSI crosses above the overbought threshold (default: 70), signaling a potential pullback.
Oversold Condition: Triggered when the VW RSI crosses below the oversold threshold (default: 30), signaling a potential bounce.
Input Parameters
VW RSI Length (default: 14): The period over which the up-volume and down-volume are smoothed to calculate the VW RSI. A longer period results in smoother signals, while a shorter period increases sensitivity.
Overbought Level (default: 70): The threshold above which the VW RSI is considered overbought, indicating a potential reversal or pullback.
Oversold Level (default: 30): The threshold below which the VW RSI is considered oversold, indicating a potential reversal or bounce.
Show Centerline (default: true): Toggles the display of the 50 centerline, which separates bullish and bearish momentum zones.
Show Overbought/Oversold Lines (default: true): Toggles the display of the overbought and oversold threshold lines.
How It Works
Volume Classification:
For each bar, the indicator determines whether the price movement is upward or downward:
If the current close is higher than the previous close, the bar’s volume is classified as up-volume.
If the current close is lower than the previous close, the bar’s volume is classified as down-volume.
If the close is unchanged, both up-volume and down-volume are set to 0 for that bar.
Smoothing:
The up-volume and down-volume are smoothed using a Running Moving Average (RMA) over the specified period (default: 14 bars) to reduce noise and provide a more stable measure of volume momentum.
VW RSI Calculation:
The Volume Relative Strength (VoRS) is calculated as the ratio of smoothed up-volume to smoothed down-volume.
The VW RSI is then computed using the standard RSI formula, but with volume data instead of price changes, resulting in a value between 0 and 100.
Visualization and Alerts:
The VW RSI is plotted with dynamic coloring to reflect its momentum direction, and optional lines are drawn for the centerline and overbought/oversold levels.
Background coloring highlights overbought and oversold conditions, and alerts notify the trader of significant crossings.
Usage
Timeframe: The VW RSI can be used on any timeframe, but it is particularly effective on intraday charts (e.g., 1-hour, 4-hour) or daily charts where volume data is reliable. Shorter timeframes may require a shorter length for increased sensitivity, while longer timeframes may benefit from a longer length for smoother signals.
Markets: Best suited for markets with significant and reliable volume data, such as stocks, forex, and cryptocurrencies. It may be less effective in markets with low or inconsistent volume, such as certain futures contracts.
Trading Strategies:
Trend Confirmation:
Use the VW RSI to confirm the direction of a trend. For example, in an uptrend, look for the VW RSI to remain above 50, indicating sustained bullish volume momentum, and consider buying on pullbacks when the VW RSI dips but stays above 50.
In a downtrend, look for the VW RSI to remain below 50, indicating sustained bearish volume momentum, and consider selling on rallies when the VW RSI rises but stays below 50.
Overbought/Oversold Conditions:
When the VW RSI crosses above 70, the market may be overbought, suggesting a potential pullback or reversal. Consider taking profits on long positions or preparing for a short entry, but confirm with price action or other indicators.
When the VW RSI crosses below 30, the market may be oversold, suggesting a potential bounce or reversal. Consider entering long positions or covering shorts, but confirm with additional signals.
Divergences:
Look for divergences between the VW RSI and price to spot potential reversals. For example, if the price makes a higher high but the VW RSI makes a lower high, this bearish divergence may signal an impending downtrend.
Conversely, if the price makes a lower low but the VW RSI makes a higher low, this bullish divergence may signal an impending uptrend.
Momentum Shifts:
A crossover above 50 can signal the start of bullish momentum, making it a potential entry point for long trades.
A crossunder below 50 can signal the start of bearish momentum, making it a potential entry point for short trades or an exit for long positions.
Example
On a 4-hour SOLUSDT chart:
During an uptrend, the VW RSI might rise above 50 and stay there, confirming bullish volume momentum. If it approaches 70, it may indicate overbought conditions, as seen near a price peak of 145.08, suggesting a potential pullback.
During a downtrend, the VW RSI might fall below 50, confirming bearish volume momentum. If it drops below 30 near a price low of 141.82, it may indicate oversold conditions, suggesting a potential bounce, as seen in a slight recovery afterward.
A bullish divergence might occur if the price makes a lower low during the downtrend, but the VW RSI makes a higher low, signaling a potential reversal.
Limitations
Lagging Nature: Like the traditional RSI, the VW RSI is a lagging indicator because it relies on smoothed data (RMA). It may not react quickly to sudden price reversals, potentially missing the start of new trends.
False Signals in Ranging Markets: In choppy or ranging markets, the VW RSI may oscillate around 50, generating frequent crossovers that lead to false signals. Combining it with a trend filter (e.g., ADX) can help mitigate this.
Volume Data Dependency: The VW RSI relies on accurate volume data, which may be inconsistent or unavailable in some markets (e.g., certain forex pairs or futures contracts). In such cases, the indicator’s effectiveness may be reduced.
Overbought/Oversold in Strong Trends: During strong trends, the VW RSI can remain in overbought or oversold territory for extended periods, leading to premature exit signals. Use additional confirmation to avoid exiting too early.
Potential Improvements
Smoothing Options: Add options to use different smoothing methods (e.g., EMA, SMA) instead of RMA for the up/down volume calculations, allowing users to adjust the indicator’s responsiveness.
Divergence Detection: Include logic to detect and plot bullish/bearish divergences between the VW RSI and price, providing visual cues for potential reversals.
Customizable Colors: Allow users to customize the colors of the VW RSI line, centerline, overbought/oversold lines, and background shading.
Trend Filter: Integrate a trend strength filter (e.g., ADX > 25) to ensure signals are generated only during strong trends, reducing false signals in ranging markets.
The Volume Weighted RSI (VW RSI) is a powerful tool for traders seeking to incorporate volume into their momentum analysis, offering a unique perspective on market dynamics by emphasizing price movements backed by significant trading activity. It is best used in conjunction with other indicators and price action analysis to confirm signals and improve trading decisions.
Extreme Pressure Zones Indicator (EPZ) [BullByte]Extreme Pressure Zones Indicator(EPZ)
The Extreme Pressure Zones (EPZ) Indicator is a proprietary market analysis tool designed to highlight potential overbought and oversold "pressure zones" in any financial chart. It does this by combining several unique measurements of price action and volume into a single, bounded oscillator (0–100). Unlike simple momentum or volatility indicators, EPZ captures multiple facets of market pressure: price rejection, trend momentum, supply/demand imbalance, and institutional (smart money) flow. This is not a random mashup of generic indicators; each component was chosen and weighted to reveal extreme market conditions that often precede reversals or strong continuations.
What it is?
EPZ estimates buying/selling pressure and highlights potential extreme zones with a single, bounded 0–100 oscillator built from four normalized components. Context-aware weighting adapts to volatility, trendiness, and relative volume. Visual tools include adaptive thresholds, confirmed-on-close extremes, divergence, an MTF dashboard, and optional gradient candles.
Purpose and originality (not a mashup)
Purpose: Identify when pressure is building or reaching potential extremes while filtering noise across regimes and symbols.
Originality: EPZ integrates price rejection, momentum cascade, pressure distribution, and smart money flow into one bounded scale with context-aware weighting. It is not a cosmetic mashup of public indicators.
Why a trader might use EPZ
EPZ provides a multi-dimensional gauge of market extremes that standalone indicators may miss. Traders might use it to:
Spot Reversals: When EPZ enters an "Extreme High" zone (high red), it implies selling pressure might soon dominate. This can hint at a topside reversal or at least a pause in rallies. Conversely, "Extreme Low" (green) can highlight bottom-fish opportunities. The indicator's divergence module (optional) also finds hidden bullish/bearish divergences between price and EPZ, a clue that price momentum is weakening.
Measure Momentum Shifts: Because EPZ blends momentum and volume, it reacts faster than many single metrics. A rising MPO indicates building bullish pressure, while a falling MPO shows increasing bearish pressure. Traders can use this like a refined RSI: above 50 means bullish bias, below 50 means bearish bias, but with context provided by the thresholds.
Filter Trades: In trend-following systems, one could require EPZ to be in the bullish (green) zone before taking longs, or avoid new trades when EPZ is extreme. In mean-reversion systems, one might specifically look to fade extremes flagged by EPZ.
Multi-Timeframe Confirmation: The dashboard can fetch a higher timeframe EPZ value. For example, you might trade a 15-minute chart only when the 60-minute EPZ agrees on pressure direction.
Components and how they're combined
Rejection (PRV) – Captures price rejection based on candle wicks and volume (see Price Rejection Volume).
Momentum Cascade (MCD) – Blends multiple momentum periods (3,5,8,13) into a normalized momentum score.
Pressure Distribution (PDI) – Measures net buy/sell pressure by comparing volume on up vs down candles.
Smart Money Flow (SMF) – An adaptation of money flow index that emphasizes unusual volume spikes.
Each of these components produces a 0–100 value (higher means more bullish pressure). They are then weighted and averaged into the final Market Pressure Oscillator (MPO), which is smoothed and scaled. By combining these four views, EPZ stands out as a comprehensive pressure gauge – the whole is greater than the sum of parts
Context-aware weighting:
Higher volatility → more PRV weight
Trendiness up (RSI of ATR > 25) → more MCD weight
Relative volume > 1.2x → more PDI weight
SMF holds a stable weight
The weighted average is smoothed and scaled into MPO ∈ with 50 as the neutral midline.
What makes EPZ stand out
Four orthogonal inputs (price action, momentum, pressure, flow) unified in a single bounded oscillator with consistent thresholds.
Adaptive thresholds (optional) plus robust extreme detection that also triggers on crossovers, so static thresholds work reliably too.
Confirm Extremes on Bar Close (default ON): dots/arrows/labels/alerts print on closed bars to avoid repaint confusion.
Clean dashboard, divergence tools, pre-alerts, and optional on-price gradients. Visual 3D layering uses offsets for depth only,no lookahead.
Recommended markets and timeframes
Best: liquid symbols (index futures, large-cap equities, major FX, BTC/ETH).
Timeframes: 5–15m (more signals; consider higher thresholds), 1H–4H (balanced), 1D (clear regimes).
Use caution on illiquid or very low TFs where wick/volume geometry is erratic.
Logic and thresholds
MPO ∈ ; 50 = neutral. Above 50 = bullish pressure; below 50 = bearish.
Static thresholds (defaults): thrHigh = 70, thrLow = 30; warning bands 5 pts inside extremes (65/35).
Adaptive thresholds (optional):
thrHigh = min(BaseHigh + 5, mean(MPO,100) + stdev(MPO,100) × ExtremeSensitivity)
thrLow = max(BaseLow − 5, mean(MPO,100) − stdev(MPO,100) × ExtremeSensitivity)
Extreme detection
High: MPO ≥ thrHigh with peak/slope or crossover filter.
Low: MPO ≤ thrLow with trough/slope or crossover filter.
Cooldown: 5 bars (default). A new extreme will not print until the cooldown elapses, even if MPO re-enters the zone.
Confirmation
"Confirm Extremes on Bar Close" (default ON) gates extreme markers, pre-alerts, and alerts to closed bars (non-repainting).
Divergences
Pivot-based bullish/bearish divergence; tags appear only after left/right bars elapse (lookbackPivot).
MTF
HTF MPO retrieved with lookahead_off; values can update intrabar and finalize at HTF close. This is disclosed and expected.
Inputs and defaults (key ones)
Core: Sensitivity=1.0; Analysis Period=14; Smoothing=3; Adaptive Thresholds=OFF.
Extremes: Base High=70, Base Low=30; Extreme Sensitivity=1.5; Confirm Extremes on Bar Close=ON; Cooldown=5; Dot size Small/Tiny.
Visuals: Heatmap ON; 3D depth optional; Strength bars ON; Pre-alerts OFF; Divergences ON with tags ON; Gradient candles OFF; Glow ON.
Dashboard: ON; Position=Top Right; Size=Normal; MTF ON; HTF=60m; compact overlay table on price chart.
Advanced caps: Max Oscillator Labels=80; Max Extreme Guide Lines=80; Divergence objects=60.
Dashboard: what each element means
Header: EPZ ANALYSIS.
Large readout: Current MPO; color reflects state (extreme, approaching, or neutral).
Status badge: "Extreme High/Low", "Approaching High/Low", "Bullish/Neutral/Bearish".
HTF cell (when MTF ON): Higher-timeframe MPO, color-coded vs extremes; updates intrabar, settles at HTF close.
Predicted (when MTF OFF): Simple MPO extrapolation using momentum/acceleration—illustrative only.
Thresholds: Current thrHigh/thrLow (static or adaptive).
Components: ASCII bars + values for PRV, MCD, PDI, SMF.
Market metrics: Volume Ratio (x) and ATR% of price.
Strength: Bar indicator of |MPO − 50| × 2.
Confidence: Heuristic gauge (100 in extremes, 70 in warnings, 50 with divergence, else |MPO − 50|). Convenience only, not probability.
How to read the oscillator
MPO Value (0–100): A reading of 50 is neutral. Values above ~55 are increasingly bullish (green), while below ~45 are increasingly bearish (red). Think of these as "market pressure".
Extreme Zones: When MPO climbs into the bright orange/red area (above the base-high line, default 70), the chart will display a dot and downward arrow marking that extreme. Traders often treat this as a sign to tighten stops or look for shorts. Similarly, a bright green dot/up-arrow appears when MPO falls below the base-low (30), hinting at a bullish setup.
Heatmap/Candles: If "Pressure Heatmap" is enabled, the background of the oscillator pane will fade green or red depending on MPO. Users can optionally color the price candles by MPO value (gradient candles) to see these extremes on the main chart.
Prediction Zone(optional): A dashed projection line extends the MPO forward by a small number of bars (prediction_bars) using current MPO momentum and acceleration. This is a heuristic extrapolation best used for short horizons (1–5 bars) to anticipate whether MPO may touch a warning or extreme zone. It is provisional and becomes less reliable with longer projection lengths — always confirm predicted moves with bar-close MPO and HTF context before acting.
Divergences: When price makes a higher high but EPZ makes a lower high (bearish divergence), the indicator can draw dotted lines and a "Bear Div" tag. The opposite (lower low price, higher EPZ) gives "Bull Div". These signals confirm waning momentum at extremes.
Zones: Warning bands near extremes; Extreme zones beyond thresholds.
Crossovers: MPO rising through 35 suggests easing downside pressure; falling through 65 suggests waning upside pressure.
Dots/arrows: Extreme markers appear on closed bars when confirmation is ON and respect the 5-bar cooldown.
Pre-alert dots (optional): Proximity cues in warning zones; also gated to bar close when confirmation is ON.
Histogram: Distance from neutral (50); highlights strengthening or weakening pressure.
Divergence tags: "Bear Div" = higher price high with lower MPO high; "Bull Div" = lower price low with higher MPO low.
Pressure Heatmap : Layered gradient background that visually highlights pressure strength across the MPO scale; adjustable intensity and optional zone overlays (warning / extreme) for quick visual scanning.
A typical reading: If the oscillator is rising from neutral towards the high zone (green→orange→red), the chart may see strong buying culminating in a stall. If it then turns down from the extreme, that peak EPZ dot signals sell pressure.
Alerts
EPZ: Extreme Context — fires on confirmed extremes (respects cooldown).
EPZ: Approaching Threshold — fires in warning zones if no extreme.
EPZ: Divergence — fires on confirmed pivot divergences.
Tip: Set alerts to "Once per bar close" to align with confirmation and avoid intrabar repaint.
Practical usage ideas
Trend continuation: In positive regimes (MPO > 50 and rising), pullbacks holding above 50 often precede continuation; mirror for bearish regimes.
Exhaustion caution: E High/E Low can mark exhaustion risk; many wait for MPO rollover or divergence to time fades or partial exits.
Adaptive thresholds: Useful on assets with shifting volatility regimes to maintain meaningful "extreme" levels.
MTF alignment: Prefer setups that agree with the HTF MPO to reduce countertrend noise.
Examples
Screenshots captured in TradingView Replay to freeze the bar at close so values don't fluctuate intrabar. These examples use default settings and are reproducible on the same bars; they are for illustration, not cherry-picking or performance claims.
Example 1 — BTCUSDT, 1h — E Low
MPO closed at 26.6 (below the 30 extreme), printing a confirmed E Low. HTF MPO is 26.6, so higher-timeframe pressure remains bearish. Components are subdued (Momentum/Pressure/Smart$ ≈ 29–37), with Vol Ratio ≈ 1.19x and ATR% ≈ 0.37%. A prior Bear Div flagged weakening impulse into the drop. With cooldown set to 5 bars, new extremes are rate-limited. Many traders wait for MPO to curl up and reclaim 35 or for a fresh Bull Div before considering countertrend ideas; if MPO cannot reclaim 35 and HTF stays weak, treat bounces cautiously. Educational illustration only.
Example 2 — ETHUSD, 30m — E High
A strong impulse pushed MPO into the extreme zone (≥ 70), printing a confirmed E High on close. Shortly after, MPO cooled to ~61.5 while a Bear Div appeared, showing momentum lag as price pushed a higher high. Volume and volatility were elevated (≈ 1.79x / 1.25%). With a 5-bar cooldown, additional extremes won't print immediately. Some treat E High as exhaustion risk—either waiting for MPO rollover under 65/50 to fade, or for a pullback that holds above 50 to re-join the trend if higher-timeframe pressure remains constructive. Educational illustration only.
Known limitations and caveats
The MPO line itself can change intrabar; extreme markers/alerts do not repaint when "Confirm Extremes on Bar Close" is ON.
HTF values settle at the close of the HTF bar.
Illiquid symbols or very low TFs can be noisy; consider higher thresholds or longer smoothing.
Prediction line (when enabled) is a visual extrapolation only.
For coders
Pine v6. MTF via request.security with lookahead_off.
Extremes include crossover triggers so static thresholds also yield E High/E Low.
Extreme markers and pre-alerts are gated by barstate.isconfirmed when confirmation is ON.
Arrays prune oldest objects to respect resource limits; defaults (80/80/60) are conservative for low TFs.
3D layering uses negative offsets purely for drawing depth (no lookahead).
Screenshot methodology:
To make labels legible and to demonstrate non-repainting behavior, the examples were captured in TradingView Replay with "Confirm Extremes on Bar Close" enabled. Replay is used only to freeze the bar at close so plots don't change intrabar. The examples use default settings, include both Extreme Low and Extreme High cases, and can be reproduced by scrolling to the same bars outside Replay. This is an educational illustration, not a performance claim.
Disclaimer
This script is for educational purposes only and does not constitute financial advice. Markets involve risk; past behavior does not guarantee future results. You are responsible for your own testing, risk management, and decisions.
QTrade Golden, Bronze & Death, Bubonic Cross AlertsThis indicator highlights key EMA regime shifts with simple, color-coded triangles:
- Golden / Death Cross — 50 EMA crossing above/below the 200 EMA.
- Bronze / Bubonic Cross — 50 EMA crossing above/below the 100 EMA.
- Early-Warning Proxy — tiny triangles for the 4 EMA vs. 200 EMA (4↑200 and 4↓200). These often fire before the 50/100 and 50/200 crosses.
No text clutter on the chart—just triangles. Colors: gold (50↑200), red (50↓200), darker-yellow bronze (50↑100), burgundy (50↓100), turquoise (4↑200), purple (4↓200).
What it tells you (in order of warning → confirmation)
- First warning: 4 EMA crosses the 200 EMA (proxy for price shifting around the 200 line).
- Second warning: 50 EMA crosses the 100 EMA (Bronze/Bubonic).
- Confirmation: 50 EMA crosses the 200 EMA (Golden/Death).
Alerts included
- Golden Cross (50↑200) and Death Cross (50↓200)
- Bronze Cross (50↑100) and Bubonic Cross (50↓100)
- 4 EMA vs. 200 EMA crosses (up & down) — early-warning proxy
- Price–100 EMA events (touch/cross, if enabled in settings)
ScalpSwing Pro SetupScript Overview
This script is a multi-tool setup designed for both scalping (1m–5m) and swing trading (1H–4H–Daily). It combines the power of trend-following , momentum , and mean-reversion tools:
What’s Included in the Script
1. EMA Indicators (20, 50, 200)
- EMA 20 (blue) : Short-term trend
- EMA 50 (orange) : Medium-term trend
- EMA 200 (red) : Long-term trend
- Use:
- EMA 20 crossing above 50 → bullish trend
- EMA 20 crossing below 50 → bearish trend
- Price above 200 EMA = uptrend bias
2. VWAP (Volume Weighted Average Price)
- Shows the average price weighted by volume
- Best used in intraday (1m to 15m timeframes)
- Use:
- Price bouncing from VWAP = reversion trade
- Price far from VWAP = likely pullback incoming
3. RSI (14) + Key Levels
- Shows momentum and overbought/oversold zones
- Levels:
- 70 = Overbought (potential sell)
- 30 = Oversold (potential buy)
- 50 = Trend confirmation
- Use:
- RSI 30–50 in uptrend = dip buying zone
- RSI 70–50 in downtrend = pullback selling zone
4. MACD Crossovers
- Standard MACD with histogram & cross alerts
- Shows trend momentum shifts
- Green triangle = Bullish MACD crossover
- Red triangle = Bearish MACD crossover
- Use:
- Confirm swing trades with MACD crossover
- Combine with RSI divergence
5. Buy & Sell Signal Logic
BUY SIGNAL triggers when:
- EMA 20 crosses above EMA 50
- RSI is between 50 and 70 (momentum bullish, not overbought)
SELL SIGNAL triggers when:
- EMA 20 crosses below EMA 50
- RSI is between 30 and 50 (bearish momentum, not oversold)
These signals appear as:
- BUY : Green label below the candle
- SELL : Red label above the candle
How to Trade with It
For Scalping (1m–5m) :
- Focus on EMA crosses near VWAP
- Confirm with RSI between 50–70 (buy) or 50–30 (sell)
- Use MACD triangle as added confluence
For Swing (1H–4H–Daily) :
- Look for EMA 20–50 cross + price above EMA 200
- Confirm trend with MACD and RSI
- Trade breakout or pullback depending on structure
ATM Option Selling StrategyATM Option Selling Strategy – Explained
This strategy is designed for intraday option selling based on the 9/15 EMA crossover, 50/80 MA trend filter, and RSI 50 level. It ensures that all trades are exited before market close (3:24 PM IST).
. Indicators Used:
9 EMA & 15 EMA → For short-term trend identification.
50 MA & 80 MA → To determine the overall trend.
RSI (14) → To confirm momentum (above or below 50 level).
2. Entry Conditions:
🔴 Sell ATM Call (CE) when:
Price is below 50 & 80 MA (Bearish trend).
9 EMA crosses below 15 EMA (Short-term trend turns bearish).
RSI is below 50 (Momentum confirms weakness).
🟢 Sell ATM Put (PE) when:
Price is above 50 & 80 MA (Bullish trend).
9 EMA crosses above 15 EMA (Short-term trend turns bullish).
RSI is above 50 (Momentum confirms strength).
3. Position Sizing & Risk Management:
Sell 375 quantity per trade (Lot size).
50-Point Stop Loss → If option premium moves against us by 50 points, exit.
50-Point Take Profit → If option premium moves in our favor by 50 points, book profit.
Exit all trades at 3:24 PM IST → No overnight positions.
4. Exit Conditions:
✅ Stop Loss or Take Profit Hits → Automatically exits based on a 50-point move.
✅ Time-Based Exit at 3:24 PM → Ensures no open positions at market close.
Why This Works?
✔ Trend Confirmation → 50/80 MA ensures we only sell options in the direction of the market trend.
✔ Momentum Confirmation → RSI prevents entering weak trades.
✔ Controlled Risk → SL and TP protect against large losses.
✔ No Overnight Risk → All trades close before market close.
Donchian Quest Research// =================================
Trend following strategy.
// =================================
Strategy uses two channels. One channel - for opening trades. Second channel - for closing.
Channel is similar to Donchian channel, but uses Close prices (not High/Low). That helps don't react to wicks of volatile candles (“stop hunting”). In most cases openings occur earlier than in Donchian channel. Closings occur only for real breakout.
// =================================
Strategy waits for beginning of trend - when price breakout of channel. Default length of both channels = 50 candles.
Conditions of trading:
- Open Long: If last Close = max Close for 50 closes.
- Close Long: If last Close = min Close for 50 closes.
- Open Short: If last Close = min Close for 50 closes.
- Close Short: If last Close = max Close for 50 closes.
// =================================
Color of lines:
- black - channel for opening trade.
- red - channel for closing trade.
- yellow - entry price.
- fuchsia - stoploss and breakeven.
- vertical green - go Long.
- vertical red - go Short.
- vertical gray - close in end, don't trade anymore.
// =================================
Order size calculated with ATR and volatility.
You can't trade 1 contract in BTC and 1 contract in XRP - for example. They have different price and volatility, so 1 contract BTC not equal 1 contract XRP.
Script uses universal calculation for every market. It is based on:
- Risk - USD sum you ready to loss in one trade. It calculated as percent of Equity.
- ATR indicator - measurement of volatility.
With default setting your stoploss = 0.5 percent of equity:
- If initial capital is 1000 USD and used parameter "Permit stop" - loss will be 5 USD (0.5 % of equity).
- If your Equity rises to 2000 USD and used parameter "Permit stop"- loss will be 10 USD (0.5 % of Equity).
// =================================
This Risk works only if you enable “Permit stop” parameter in Settings.
If this parameter disabled - strategy works as reversal strategy:
⁃ If close Long - channel border works as stoploss and momentarily go Short.
⁃ If close Short - channel border works as stoploss and momentarily go Long.
Channel borders changed dynamically. So sometime your loss will be greater than ‘Risk %’. Sometime - less than ‘Risk %’.
If this parameter enabled - maximum loss always equal to 'Risk %'. This parameter also include breakeven: if profit % = Risk %, then move stoploss to entry price.
// =================================
Like all trend following strategies - it works only in trend conditions. If no trend - slowly bleeding. There is no special additional indicator to filter trend/notrend. You need to trade every signal of strategy.
Strategy gives many losses:
⁃ 30 % of trades will close with profit.
⁃ 70 % of trades will close with loss.
⁃ But profit from 30% will be much greater than loss from 70 %.
Your task - patiently wait for it and don't use risky setting for position sizing.
// =================================
Recommended timeframe - Daily.
// =================================
Trend can vary in lengths. Selecting length of channels determine which trend you will be hunting:
⁃ 20/10 - from several days to several weeks.
⁃ 20/20 or 50/20 - from several weeks to several months.
⁃ 50/50 or 100/50 or 100/100 - from several months to several years.
// =================================
Inputs (Settings):
- Length: length of channel for trade opening/closing. You can choose 20/10, 20/20, 50/20, 50/50, 100/50, 100/100. Default value: 50/50.
- Permit Long / Permit short: Longs are most profitable for this strategy. You can disable Shorts and enable Longs only. Default value: permit all directions.
- Risk % of Equity: for position sizing used Equity percent. Don't use values greater than 5 % - it's risky. Default value: 0.5%.
⁃ ATR multiplier: this multiplier moves stoploss up or down. Big multiplier = small size of order, small profit, stoploss far from entry, low chance of stoploss. Small multiplier = big size of order, big profit, stop near entry, high chance of stoploss. Default value: 2.
- ATR length: number of candles to calculate ATR indicator. It used for order size and stoploss. Default value: 20.
- Close in end - to close active trade in the end (and don't trade anymore) or leave it open. You can see difference in Strategy Tester. Default value: don’t close.
- Permit stop: use stop or go reversal. Default value: without stop, reversal strategy.
// =================================
Properties (Settings):
- Initial capital - 1000 USD.
- Script don't uses 'Order size' - you need to change 'Risk %' in Inputs instead.
- Script don't uses 'Pyramiding'.
- 'Commission' 0.055 % and 'Slippage' 0 - this parameters are for crypto exchanges with perpetual contracts (for example Bybit). If use on other markets - set it accordingly to your exchange parameters.
// =================================
Big dataset used for chart - 'BITCOIN ALL TIME HISTORY INDEX'. It gives enough trades to understand logic of script. It have several good trends.
// =================================
Rollover LTEThis indicator shows where price needs to be and when in order to cause the 20-sma and 50-sma moving averages to change directions. A change in direction requires the slope of a moving average to change from negative to positive or from positive to negative. When a moving average changes direction, it can be said that it has “rolled over” or “rolled up,” with the latter only applying if slope went from negative to positive.
Theory:
In order to solve for the price of the current bar that will cause the moving average to roll up, the slope from the previous bar’s average to the current bar’s average must be set equal to zero which is to say that the averages must be the same.
For the 20-sma, the equation simply stated in words is as follows:
Current MA as a function of current price and previous 19 values = previous MA which is fixed based on previous 20 values
The denominators which are both 20 cancel and the previous 19 values cancel. What’s left is current price on the left side and the value from 20 bars ago on the right.
Current price = value from 20 bars ago
and since the equation was set up for solving for the price of the current bar that will cause the MA to roll over
Rollover price = value from 20 bars ago
This makes plotting rollover price, both current and forecasted, fairly simple, as it’s merely the closing price plotted with an offset to the right the same distance as the moving average length.
Application:
The 20-sma and 50-sma rollover prices are plotted because they are considered to be the two most important moving averages for rollover analysis. Moving average lengths can be modified in the indicator settings. The 20-sma and 20-sma rollover price are both plotted in white and the 50-sma and 50-sma rollover price are both plotted in blue. There are two rollover prices because the 20-sma rollover price is the price that will cause the 20-sma to roll over and the 50-sma rollover price is the price that will cause the 50-sma to roll over. The one that's vertically furthest away from the current price is the one that will cause both to rollover, as should become clearer upon reading the explanation below.
The distance between the current price and the 20-sma rollover price is referred to as the “rollover strength” of the price relative to the 20-sma. A large disparity between the current price and the rollover price suggests bearishness (negative rollover strength) if the rollover price is overhead because price would need to travel all that distance in order to cause the moving average to roll up. If the rollover price and price are converging, as is often the case, a change in moving average and price direction becomes more plausible. The rollover strengths of the 20-sma and 50-sma are added together to calculate the Rollover Strength and if a negative number is the result then the background color of the plot cloud turns red. If the result is positive, it turns green. Rollover Strength is plotted below price as a separate indicator in this publication for reference only and it's not part of this indicator. It does not look much different from momentum indicators. The code is below if anybody wants to try to use it. The important thing is that the distances between the rollover prices and the price action are kept in mind as having shrinking, growing, or neutral bearish and bullish effects on current and forecasted price direction. Trades should not be entered based on cloud colorization changes alone.
If you are about to crash into a wall of the 20-sma rollover price, as is indicated on the chart by the green arrow, you might consider going long so long as the rollover strength, both current and forecasted, of the 50-sma isn’t questionably bearish. This is subject to analysis and interpretation. There was a 20-sma rollover wall as indicated with yellow arrow, but the bearish rollover strength of the 50-sma was growing and forecasted to remain strong for a while at that time so a long entry would have not been suggested by both rollover prices. If you are about to crash into both the 20-sma and 50-sma rollover prices at the same time (not shown on this chart), that’s a good time to place a trade in anticipation of both slopes changing direction. You may, in the case of this chart, see that a 20-sma rollover wall precedes a 50-sma rollover convergence with price and anticipate a cascade which turned out to be the case with this recent NQ rally.
Price exiting the cloud entirely to either the upside or downside has strong implications. When exiting to the downside, the 20-sma and 50-sma have both rolled over and price is below both of them. The same is true for upside exits. Re-entering the cloud after a rally may indicate a reversal is near, especially if the forecasted rollover prices, particularly the 50-sma, agree.
This indicator should be used in conjunction with other technical analysis tools.
Additional Notes:
The original version of this script which will not be published was much heavier, cluttered, and is not as useful. This is the light version, hence the “LTE” suffix.
LTE stands for “long-term evolution” in telecommunications, not “light.”
Bar colorization (red, yellow, and green bars) was added using the MACD Hybrid BSH script which is another script I’ve published.
If you’re not sure what a bar is, it’s the same thing as a candle or a data point on a line chart. Every vertical line showing price action on the chart above is a bar and it is a bar chart.
sma = simple moving average
Rollover Strength Script:
// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © Skipper86
//@version=5
indicator(title="Rollover Strength", shorttitle="Rollover Strength", overlay=false)
source = input.source(close)
length1 = input.int(20, "Length 1", minval=1)
length2 = input.int(50, "Length 2", minval=1)
RolloverPrice1 = source
RolloverPrice2 = source
RolloverStrength1 = source-RolloverPrice1
RolloverStrength2 = source-RolloverPrice2
RolloverStrength = RolloverStrength1 + RolloverStrength2
Color1 = color.rgb(155, 155, 155, 0)
Color2 = color.rgb(0, 0, 200, 0)
Color3 = color.rgb(0, 200, 0, 0)
plot(RolloverStrength, title="Rollover Strength", color=Color3)
hline(0, "Middle Band", color=Color1)
//End of Rollover Strength Script
FOTSI - Open sourceI WOULD LIKE TO SPECIFY TWO THINGS:
- The indicator was absolutely not designed by me, I do not take any credit and much less I want them, I am just making this fantastic indicator open source and accessible to all
- The script code was not recycled from other indicators, but was created from 0 following the theory behind it to the letter, thus avoiding copyright infringement
- Advices and improvements are accepted, as having very little programming experience in Pine Script I consider this work still rough and slow
WHAT IS THE FOTSI?
The FOTSI is an oscillator that measures the relative strength of the individual currencies that make up the 28 major Forex exchanges.
By identifying the currencies that are in the overbought (+50) and oversold (-50) areas, it is possible to anticipate the correction of a currency pair following a strong trend.
THE THEORY BEHIND
1) At the base of everything is the 1-period momentum (close-open) of the single currency pairs that contain a certain currency. For example, the momentum of the USD currency is composed of all the exchange rates that contain the US dollar inside it: mom_usd = - mom_eurusd - mom_gbpusd + mom_usdchf + mom_usdjpy - mom_audusd + mom_usdcad - mom_nzdusd. Where the base currency is in second position, the momentum is subtracted instead of adding it.
2) The IST formula is applied to the momentum of the individual currencies obtained. In this way we get an oscillator that oscillates between 0 and its overbought and oversold areas. The area between +25 and -25 is an area in which we can consider the movements of individual currencies to be neutral.
3) The TSI is nothing more than a double smoothing on the momentum of individual currencies. This particularity makes the indicator very reactive, minimizing the delays of the trend reversal.
HOW TO USE
1) A currency is identified that is in the overbought (+50) or oversold (-50) area. Example GBP = 50
2) The second currency is identified as the one most opposite to the first. Example USD = -25
3) The currency pair consisting of the two currencies opens. So GBP / USD
4) Considering that GBP is oversold, we anticipate its future devaluation. So in this case we are short on GBP / SUD. Otherwise if GBP had been oversold (-50) we expect its future valuation and therefore we enter long.
5) It is used on the H1, H4 and D1 timeframes
6) Closing conditions: the position on the 50-period exponential moving average is split / the position at target on the 100-period exponential moving average is closed
7) Stoploss: it is recommended not to use it, if you want to use it it is equivalent to 5 times the ATR on the reference timeframe
8) Position sizing: go very slow! Being a counter-trend strategy, it is very risky to position yourself heavily. Use common sense in everything!
9) To insert the alerts that warn you of an overbought and oversold condition, it is necessary to enter the signals called "Overbought Signal" and "Oversold Signal" for each chart used, in the specific Trading View window. like me using multiple charts in the same window.
I hope you enjoy my work. For any questions write in the comments.
Thanks <3
//--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
TENGO A PRECISARE DUE COSE:
- L'indicatore non è stato assolutamente ideato da me, non mi assumo nessun merito e tanto meno li voglio, io sto solo rendendo questo fantastico indicatore open source ed accessibile a tutti
- Il codice dello script non è stato riciclato da altri indicatori, ma è stato creato da 0 seguendo alla lettere la teoria che sta alla sua base, evitando così di violare il copyright
- Si accettano consigli e migliorie, visto che avendo pochissima esperienza di programmazione in Pine Script considero questo lavoro ancora grezzo e lento
COS'È IL FOTSI?
Il FOTSI è un oscillatore che misura la forza relativa delle singole valute che compongono i 28 cambi major del Forex.
Individuando le valute che si trovano nelle aree di ipercomprato (+50) ed ipervenduto (-50) , è possibile anticipare la correzione di una coppia valutaria al seguito di un forte trend.
LA TEORIA ALLA BASE
1) Alla base di tutto c'è il momentum ad 1 periodo (close-open) delle singole coppie valutarie che contengono una determinata valuta. Ad esempio il momentum della valuta USD è composto da tutti i cambi che contengono il dollaro americano al suo interno: mom_usd = - mom_eurusd - mom_gbpusd + mom_usdchf + mom_usdjpy - mom_audusd + mom_usdcad - mom_nzdusd . Ove la valuta base si trova in seconda posizione si sottrae il momentum al posto che sommarlo.
2) Si applica la formula del TSI ai momentum delle singole valute ottenute. In questo modo otteniamo un oscillatore che oscilla tra lo 0 e le sue aree di ipercomprato ed ipervenduto. L'area compresa tra +25 e -25 è un area in cui possiamo considerare neutri i movimenti delle singole valute.
3) Il TSI non è altro che un doppio smoothing sul momentum delle singole valute. Questa particolarità rende l'indicatore molto reattivo, minimizzando i ritardi dell'inversione del trend.
COME SI USA
1) Si individua una valuta che si trova nell'area di ipercomprato (+50) o ipervenduto (-50) . Esempio GBP = 50
2) Si individua come seconda valuta quella più opposta alla prima. Esempio USD = -25
3) Si apre la coppia di valuta composta dalle due valute. Quindi GBP/USD
4) Considerando che GBP è in fase di ipervenduto prevediamo una sua futura svalutazione. Quindi in questo caso entriamo short su GBP/SUD. Diversamente se GBP fosse stato in fase di ipervenduto (-50) ci aspettiamo una sua futura valutazione e quindi entriamo long.
5) Si usa sui timeframe H1, H4 e D1
6) Condizioni di chiusura: si smezza la posizione sulla media mobile esponenziale a 50 periodi / si chiude la posizione a target sulla media mobile esponenziale a 100 periodi
7) Stoploss: è consigliato non usarlo, nel caso lo si voglia utilizzare esso equivale a 5 volte l'ATR sul timeframe di riferimento
8) Position sizing: andateci molto piano! Essendo una strategia contro trend è molto rischioso posizionarsi in modo pesante. Usate il buonsenso in tutto!
9) Per inserire gli allert che ti avvertono di una condizione di ipercomprato ed ipervenduto, è necessario inserire dall'apposita finestra di Trading View i segnali denominati "Segnale di ipercomprato" ed "Segnale di ipervenduto" per ogni grafico che si usa, nel caso come me che si utilizzano più grafici nella stessa finestra.
Spero che possiate apprezzare il mio lavoro. Per qualsiasi domanda scrivete nei commenti.
Grazie<3
ATR Future Movement Range Projection
The "ATR Future Movement Range Projection" is a custom TradingView Pine Script indicator designed to forecast potential price ranges for a stock (or any asset) over short-term (1-month) and medium-term (3-month) horizons. It leverages the Average True Range (ATR) as a measure of volatility to estimate how far the price might move, while incorporating recent momentum bias based on the proportion of bullish (green) vs. bearish (red) candles. This creates asymmetric projections: in bullish periods, the upside range is larger than the downside, and vice versa.
The indicator is overlaid on the chart, plotting horizontal lines for the projected high and low prices for both timeframes. Additionally, it displays a small table in the top-right corner summarizing the projected prices and the percentage change required from the current close to reach them. This makes it useful for traders assessing potential targets, risk-reward ratios, or option strategies, as it combines volatility forecasting with directional sentiment.
Key features:
- **Volatility Basis**: Uses weekly ATR to derive a stable daily volatility estimate, avoiding noise from shorter timeframes.
- **Momentum Adjustment**: Analyzes recent candle colors to tilt projections toward the prevailing trend (e.g., more upside if more green candles).
- **Time Horizons**: Fixed at 1 month (21 trading days) and 3 months (63 trading days), assuming ~21 trading days per month (excluding weekends/holidays).
- **User Adjustable**: The ATR length/lookback (default 50) can be tweaked via inputs.
- **Visuals**: Green/lime lines for highs, red/orange for lows; a semi-transparent table for quick reference.
- **Limitations**: This is a probabilistic projection based on historical volatility and momentum—it doesn't predict direction with certainty and assumes volatility persists. It ignores external factors like news, earnings, or market regimes. Best used on daily charts for stocks/ETFs.
The indicator doesn't generate buy/sell signals but helps visualize "expected" ranges, similar to how implied volatility informs option pricing.
### How It Works Step-by-Step
The script executes on each bar update (typically daily timeframe) and follows this logic:
1. **Input Configuration**:
- ATR Length (Lookback): Default 50 bars. This controls both the ATR calculation period and the candle count window. You can adjust it in the indicator settings.
2. **Calculate Weekly ATR**:
- Fetches the ATR from the weekly timeframe using `request.security` with a length of 50 weeks.
- ATR measures average price range (high-low, adjusted for gaps), representing volatility.
3. **Derive Daily ATR**:
- Divides the weekly ATR by 5 (approximating 5 trading days per week) to get an equivalent daily volatility estimate.
- Example: If weekly ATR is $5, daily ATR ≈ $1.
4. **Define Projection Periods**:
- 1 Month: 21 trading days.
- 3 Months: 63 trading days (21 × 3).
- These are hardcoded but based on standard trading calendar assumptions.
5. **Compute Base Projections**:
- Base projection = Daily ATR × Days in period.
- This gives the total expected movement (range) without direction: e.g., for 3 months, $1 daily ATR × 63 = $63 total range.
6. **Analyze Candle Momentum (Win Rate)**:
- Counts green candles (close > open) and red candles (close < open) over the last 50 bars (ignores dojis where close == open).
- Total colored candles = green + red.
- Win rate = green / total colored (as a fraction, e.g., 0.7 for 70%). Defaults to 0.5 if no colored candles.
- This acts as a simple momentum proxy: higher win rate implies bullish bias.
7. **Adjust Projections Asymmetrically**:
- Upside projection = Base projection × Win rate.
- Downside projection = Base projection × (1 - Win rate).
- This skews the range: e.g., 70% win rate means 70% of the total range allocated to upside, 30% to downside.
8. **Calculate Projected Prices**:
- High = Current close + Upside projection.
- Low = Current close - Downside projection.
- Done separately for 1M and 3M.
9. **Plot Lines**:
- 3M High: Solid green line.
- 3M Low: Solid red line.
- 1M High: Dashed lime line.
- 1M Low: Dashed orange line.
- Lines extend horizontally from the current bar onward.
10. **Display Table**:
- A 3-column table (Projection, Price, % Change) in the top-right.
- Rows for 1M High/Low and 3M High/Low, color-coded.
- % Change = ((Projected price - Close) / Close) × 100.
- Updates dynamically with new data.
The entire process repeats on each new bar, so projections evolve as volatility and momentum change.
### Examples
Here are two hypothetical examples using the indicator on a daily chart. Assume it's applied to a stock like AAPL, but with made-up data for illustration. (In TradingView, you'd add the script to see real outputs.)
#### Example 1: Bullish Scenario (High Win Rate)
- Current Close: $150.
- Weekly ATR (50 periods): $10 → Daily ATR: $10 / 5 = $2.
- Last 50 Candles: 35 green, 15 red → Total colored: 50 → Win Rate: 35/50 = 0.7 (70%).
- Base Projections:
- 1M: $2 × 21 = $42.
- 3M: $2 × 63 = $126.
- Adjusted Projections:
- 1M Upside: $42 × 0.7 = $29.4 → High: $150 + $29.4 = $179.4 (+19.6%).
- 1M Downside: $42 × 0.3 = $12.6 → Low: $150 - $12.6 = $137.4 (-8.4%).
- 3M Upside: $126 × 0.7 = $88.2 → High: $150 + $88.2 = $238.2 (+58.8%).
- 3M Downside: $126 × 0.3 = $37.8 → Low: $150 - $37.8 = $112.2 (-25.2%).
- On the Chart: Green/lime lines skewed higher; table shows bullish % changes (e.g., +58.8% for 3M high).
- Interpretation: Suggests stronger potential upside due to recent bullish momentum; useful for call options or long positions.
#### Example 2: Bearish Scenario (Low Win Rate)
- Current Close: $50.
- Weekly ATR (50 periods): $3 → Daily ATR: $3 / 5 = $0.6.
- Last 50 Candles: 20 green, 30 red → Total colored: 50 → Win Rate: 20/50 = 0.4 (40%).
- Base Projections:
- 1M: $0.6 × 21 = $12.6.
- 3M: $0.6 × 63 = $37.8.
- Adjusted Projections:
- 1M Upside: $12.6 × 0.4 = $5.04 → High: $50 + $5.04 = $55.04 (+10.1%).
- 1M Downside: $12.6 × 0.6 = $7.56 → Low: $50 - $7.56 = $42.44 (-15.1%).
- 3M Upside: $37.8 × 0.4 = $15.12 → High: $50 + $15.12 = $65.12 (+30.2%).
- 3M Downside: $37.8 × 0.6 = $22.68 → Low: $50 - $22.68 = $27.32 (-45.4%).
- On the Chart: Red/orange lines skewed lower; table highlights larger downside % (e.g., -45.4% for 3M low).
- Interpretation: Indicates bearish risk; might prompt protective puts or short strategies.
#### Example 3: Neutral Scenario (Balanced Win Rate)
- Current Close: $100.
- Weekly ATR: $5 → Daily ATR: $1.
- Last 50 Candles: 25 green, 25 red → Win Rate: 0.5 (50%).
- Projections become symmetric:
- 1M: Base $21 → Upside/Downside $10.5 each → High $110.5 (+10.5%), Low $89.5 (-10.5%).
- 3M: Base $63 → Upside/Downside $31.5 each → High $131.5 (+31.5%), Low $68.5 (-31.5%).
- Interpretation: Pure volatility-based range, no directional bias—ideal for straddle options or range trading.
In real use, test on historical data: e.g., if past projections captured actual moves ~68% of the time (1 standard deviation for ATR), it validates the volatility assumption. Adjust the lookback for different assets (shorter for volatile cryptos, longer for stable blue-chips).
Constance Brown RSI with Composite IndexConstance Brown RSI with Composite Index
Overview
This indicator combines Constance Brown's RSI interpretation methodology with a Composite Index and ATR Distance to VWAP measurement to provide a comprehensive trading tool. It helps identify trends, momentum shifts, overbought/oversold conditions, and potential reversal points.
Key Features
Color-coded RSI zones for immediate trend identification
Composite Index for momentum analysis and divergence detection
ATR Distance to VWAP for identifying extreme price deviations
Automatic divergence detection for early reversal warnings
Pre-configured alerts for key trading signals
How to Use This Indicator
Trend Identification
The RSI line changes color based on its position:
Blue zone (RSI > 50): Bullish trend - look for buying opportunities
Purple zone (RSI < 50): Bearish trend - look for selling opportunities
Gray zone (RSI 40-60): Neutral/transitional market - prepare for potential breakout
The 40-50 area (light blue fill) acts as support during uptrends, while the 50-60 area (light purple fill) acts as resistance during downtrends.
// From the code:
upTrendZone = rsiValue > 50 and rsiValue <= 90
downTrendZone = rsiValue < 50 and rsiValue >= 10
neutralZone = rsiValue > 40 and rsiValue < 60
rsiColor = neutralZone ? neutralRSI : upTrendZone ? upTrendRSI : downTrendRSI
Momentum Analysis
The Composite Index (fuchsia line) provides momentum confirmation:
Values above 50 indicate positive momentum
Values below 40 indicate negative momentum
Crossing above/below these thresholds signals potential momentum shifts
// From the code:
compositeIndexRaw = rsiChange / ta.stdev(rsiValue, rsiLength)
compositeIndex = ta.sma(compositeIndexRaw, compositeSmoothing)
compositeScaled = compositeIndex * 10 + 50 // Scaled to fit 0-100 range
Overbought/Oversold Detection
The ATR Distance to VWAP table in the top-right corner shows how far price has moved from VWAP in terms of ATR units:
Extreme positive values (orange/red): Potentially overbought
Extreme negative values (purple/red): Potentially oversold
Near zero (gray): Price near average value
// From the code:
priceDistance = (close - vwapValue) / ta.atr(atrPeriod)
// Color coding based on distance value
Divergence Trading
The indicator automatically detects divergences between the Composite Index and price:
Bullish divergence: Price makes lower low but Composite Index makes higher low
Bearish divergence: Price makes higher high but Composite Index makes lower high
// From the code:
divergenceBullish = ta.lowest(compositeIndex, rsiLength) > ta.lowest(close, rsiLength)
divergenceBearish = ta.highest(compositeIndex, rsiLength) < ta.highest(close, rsiLength)
Trading Strategies
Trend Following
1. Identify the trend using RSI color:
Blue = Uptrend, Purple = Downtrend
2. Wait for pullbacks to support/resistance zones:
In uptrends: Buy when RSI pulls back to 40-50 zone and bounces
In downtrends: Sell when RSI rallies to 50-60 zone and rejects
3. Confirm with Composite Index:
Uptrends: Composite Index stays above 50 or quickly returns above it
Downtrends: Composite Index stays below 50 or quickly returns below it
4. Manage risk using ATR Distance:
Take profits when ATR Distance reaches extreme values
Place stops beyond recent swing points
Reversal Trading
1. Look for divergences
Bullish: Price makes lower low but Composite Index makes higher low
Bearish: Price makes higher high but Composite Index makes lower high
2. Confirm with ATR Distance:
Extreme readings suggest potential reversals
3. Wait for RSI zone transition:
Bullish: RSI crosses above 40 (purple to neutral/blue)
Bearish: RSI crosses below 60 (blue to neutral/purple)
4. Enter after confirmation:
Use candlestick patterns for precise entry
Place stops beyond the divergence point
Four pre-configured alerts are available:
Momentum High: Composite Index above 50
Momentum Low: Composite Index below 40
Bullish Divergence: Composite Index higher low
Bearish Divergence: Composite Index lower high
Customization
Adjust these parameters to optimize for your trading style:
RSI Length: Default 14, lower for more sensitivity, higher for fewer signals
Composite Index Smoothing: Default 10, lower for quicker signals, higher for less noise
ATR Period: Default 14, affects the ATR Distance to VWAP calculation
This indicator works well across various markets and timeframes, though the default settings are optimized for daily charts. Adjust parameters for shorter or longer timeframes as needed.
Happy trading!
CHAKRA RISS ENGULFING CANDLESTICK STRATEGYChakra RISS Engulfing Candlestick Strategy
Type: Technical Indicator & Strategy
Platform: TradingView
Script Version: Pine Script v6
Overview:
The Chakra RISS Engulfing Candlestick Strategy combines a momentum-based approach using the Relative Strength Index (RSI) with Engulfing Candlestick Patterns to generate buy and sell signals. The strategy filters trades based on price movement relative to a 50-period Simple Moving Average (SMA), making it a trend-following strategy.
The indicator uses color-coded bars to visually represent market conditions, helping traders easily identify bullish and bearish trends. The strategy is designed to be dynamic, adapting to changing market conditions and filtering out noise using key technical indicators.
How It Works:
RSI-Based Color Conditions:
Green Bars: When the RSI crosses above a specified UpLevel (default: 50), indicating a bullish momentum and signaling potential buy conditions.
Red Bars: When the RSI crosses below a specified DownLevel (default: 50), indicating a bearish momentum and signaling potential sell conditions.
Buy Signal:
Triggered when the following conditions are met:
RSI crosses from below the UpLevel (default: 50) to above it, signaling increasing bullish momentum.
The close price is above the 50-period Simple Moving Average (SMA), confirming an uptrend.
The Buy Signal is plotted below the bar with a green arrow and a "BUY" label.
Sell Signal:
Triggered when the following conditions are met:
RSI crosses from above the DownLevel (default: 50) to below it, signaling increasing bearish momentum.
The close price is below the 50-period Simple Moving Average (SMA), confirming a downtrend.
The Sell Signal is plotted above the bar with a red arrow and a "SELL" label.
Stop Loss and Take Profit:
For long trades (buy signals), the stop loss is placed below the previous bar's low, and the take profit is set at 3% above the entry price.
For short trades (sell signals), the stop loss is placed above the previous bar's high, and the take profit is set at 3% below the entry price.
Dynamic Bar Coloring:
The bar colors change dynamically based on RSI levels:
Green Bars: Indicating a potential uptrend (bullish).
Red Bars: Indicating a potential downtrend (bearish).
These visual cues help traders quickly identify market trends and potential reversals.
Trend Filtering:
The 50-period Simple Moving Average (SMA) is used to filter trades based on the overall market trend:
Buy signals are only considered when the price is above the moving average, indicating an uptrend.
Sell signals are only considered when the price is below the moving average, indicating a downtrend.
Alerting System:
Alerts can be set for both buy and sell signals. These alerts notify traders in real-time when potential trades are generated, allowing them to act promptly.
Alerts can be configured to send notifications through email, SMS, or a webhook for integration with other services like IFTTT or Zapier.
Key Features:
RSI and Moving Average-Based Signals: Combines RSI with a moving average for more accurate trade signals.
Stop Loss and Take Profit: Dynamic risk management with custom stop loss and take profit levels based on previous high and low prices.
Buy and Sell Alerts: Provides real-time alerts when a buy or sell signal is triggered.
Trend Confirmation: Uses the 50-period Simple Moving Average to filter signals and confirm the direction of the trend.
Visual Bar Color Changes: Makes it easy to identify bullish or bearish trends with color-coded bars.
Usage:
This strategy is suitable for traders who prefer a trend-following approach and want to combine momentum indicators (RSI) with price action (Engulfing Candlestick patterns). It is particularly useful in volatile markets where quick identification of trend changes can lead to profitable trades.
Best Used For: Day trading, swing trading, and trend-following strategies.
Timeframes: Works well on various timeframes, from 1-minute charts for scalping to daily charts for swing trading.
Markets: Can be applied to any market with sufficient liquidity (stocks, forex, crypto, etc.).
Settings:
UpLevel: The RSI level above which the market is considered bullish (default: 50).
DownLevel: The RSI level below which the market is considered bearish (default: 50).
SMA Length: The period of the Simple Moving Average used to filter trades (default: 50).
Risk Management: Customizable stop loss and take profit settings based on price action (default: 3% above/below the entry price).
Austin MTF EMA Entry PointsAustin MTF EMA Entry Points
Overview
The Austin MTF EMA Entry Points is a custom TradingView indicator designed to assist traders in identifying high-probability entry points by combining multiple time frame (MTF) analysis. It leverages exponential moving averages (EMAs) from the daily, 1-hour, and 15-minute charts to generate buy and sell signals that align with the overall trend.
This indicator is ideal for traders who:
Want to trade in the direction of the broader daily trend.
Seek precise entry points on lower time frames (1H and 15M).
Prefer using EMAs as their main trend-following tool.
How It Works
Daily Trend Filter:
The indicator calculates the 50 EMA on the daily chart.
The daily EMA acts as the primary trend filter:
If the current price is above the daily 50 EMA, the trend is bullish.
If the current price is below the daily 50 EMA, the trend is bearish.
Lower Time Frame Entry Points:
The indicator calculates the 20 EMA on both the 1-hour (1H) and 15-minute (15M) time frames.
Buy and sell signals are generated when the price aligns with the trend on all three time frames:
Buy Signal: Price is above the daily 50 EMA and also above the 20 EMA on both the 1H and 15M charts.
Sell Signal: Price is below the daily 50 EMA and also below the 20 EMA on both the 1H and 15M charts.
Visual and Alert Features:
Plot Lines:
The daily 50 EMA is plotted in yellow for easy identification of the main trend.
The 20 EMA from the 1H chart is plotted in blue, and the 15M chart's EMA is in purple for comparison.
Buy/Sell Markers:
Green "Up" arrows appear for buy signals.
Red "Down" arrows appear for sell signals.
Alerts:
Alerts notify users when a buy or sell signal is triggered, making it easier to act on trading opportunities in real-time.
How to Use the Indicator
Identify the Main Trend:
Check the relationship between the price and the daily 50 EMA (yellow line):
Only look for buy signals if the price is above the daily 50 EMA.
Only look for sell signals if the price is below the daily 50 EMA.
Wait for Lower Time Frame Alignment:
For a valid signal, ensure that the price is also above or below the 20 EMA (blue and purple lines) on both the 1H and 15M time frames:
This alignment confirms short-term momentum in the same direction as the daily trend.
Act on Signals:
Use the arrows as visual cues for entry points:
Enter long trades on green "Up" arrows.
Enter short trades on red "Down" arrows.
The alerts will notify you of these signals, so you don’t have to monitor the chart constantly.
Exit Strategy:
Use your preferred stop-loss, take-profit, or trailing stop strategy.
You can also exit trades if the price crosses back below/above the daily 50 EMA, signaling a potential reversal.
Use Cases
Swing Traders: Use the daily trend filter to trade in the direction of the dominant trend, while using 1H and 15M signals to fine-tune entries.
Day Traders: Leverage the 1H and 15M time frames to capitalize on short-term momentum while respecting the broader daily trend.
Position Traders: Monitor the indicator to determine potential reversals or significant alignment across time frames.
Customizable Inputs
The indicator includes the following inputs:
Daily EMA Length: Default is 50. Adjust this to change the length of the trend filter EMA.
Lower Time Frame EMA Length: Default is 20. Adjust this to change the short-term EMA for the 1H and 15M charts.
Time Frames: Hardcoded to "D", "60", and "15", but you can modify the script for different time frames if needed.
Example Scenarios
Buy Signal:
Price is above the daily 50 EMA.
Price crosses above the 20 EMA on both the 1H and 15M time frames.
A green "Up" arrow is displayed, and an alert is triggered.
Sell Signal:
Price is below the daily 50 EMA.
Price crosses below the 20 EMA on both the 1H and 15M time frames.
A red "Down" arrow is displayed, and an alert is triggered.
Strengths and Limitations
Strengths:
Aligns trades with the higher time frame trend for increased probability.
Uses multiple time frame analysis to identify precise entry points.
Visual signals and alerts make it easy to use in real-time.
Limitations:
May produce fewer signals in choppy or ranging markets.
Requires discipline to avoid overtrading when conditions are unclear.
Lag in EMAs could result in late entries in fast-moving markets.
Final Notes
The Austin MTF EMA Entry Points indicator is a powerful tool for traders who value multiple time frame alignment and trend-following strategies. While it simplifies decision-making, it is always recommended to backtest and practice proper risk management before using it in live markets.
Try it out and make smarter, trend-aligned trades today! 🚀
RSI Signals Multi-Layer RSI System with Classical Divergence**DrFX RSI Signals Fixed** is an advanced RSI-based trading system that combines duration-filtered extreme conditions with classical divergence detection and momentum confirmation. This enhanced version addresses common RSI false signals through multi-layer filtering while adding proper divergence analysis for identifying high-probability reversal points.
**Core Innovation & Originality**
This indicator uniquely integrates five analytical layers:
1. **Duration-Validated Extreme Zones** - Confirms RSI has remained overbought/oversold for minimum bars within lookback period
2. **Classical Divergence Detection** - Proper implementation comparing swing highs/lows in both price and RSI
3. **Momentum Confirmation Signals** - RSI crossing 50-line after extreme conditions for trend confirmation
4. **Multi-Signal Classification** - Four distinct signal types (Buy, Sell, Strong Buy, Strong Sell, Momentum)
5. **Visual Zone Highlighting** - Background coloring for instant extreme zone identification
**Technical Implementation & Improvements**
**Enhanced Duration Filter:**
Unlike the previous version, this system uses a refined approach:
```
for i = 0 to lookback_bars - 1
if rsi > overbought
barsInOverbought := barsInOverbought + 1
```
This counts actual bars within the lookback period (default 20 bars) where RSI was extreme, requiring minimum duration (default 4 bars) for signal validation.
**Classical Divergence Detection:**
The system implements proper divergence analysis, a significant improvement over simple delta comparison:
**Bullish Divergence Logic:**
- Price makes lower low: `low < prevPriceLow`
- RSI makes higher low: `rsi > prevRsiLow`
- Indicates weakening downward momentum despite lower prices
**Bearish Divergence Logic:**
- Price makes higher high: `high > prevPriceHigh`
- RSI makes lower high: `rsi < prevRsiHigh`
- Indicates weakening upward momentum despite higher prices
**Signal Generation Framework:**
**Primary Signals:**
- **Buy Signal**: RSI crosses above oversold (30) after meeting duration requirements
- **Sell Signal**: RSI crosses below overbought (70) after meeting duration requirements
**Strong Signals:**
- **Strong Buy**: Buy signal + bullish divergence confirmation
- **Strong Sell**: Sell signal + bearish divergence confirmation
**Momentum Signals:**
- **Momentum Buy (M+)**: RSI crosses above 50 after recent oversold conditions
- **Momentum Sell (M-)**: RSI crosses below 50 after recent overbought conditions
**What Makes This Version Superior**
**Compared to Standard RSI:**
1. **Duration Requirement**: Prevents signals on brief RSI spikes
2. **Lookback Validation**: Ensures extreme conditions actually occurred recently
3. **Proper Divergence**: Uses swing high/low comparison, not just bar-to-bar deltas
4. **Momentum Layer**: Adds trend confirmation via 50-line crosses
**Compared to Previous Version:**
1. **Pine Script v5**: Modern syntax with improved performance
2. **Configurable Parameters**: All values adjustable via inputs
3. **Better Divergence**: Classical divergence logic replaces simplified delta method
4. **Additional Signals**: Momentum confirmations for trend following
5. **Visual Enhancements**: Background coloring and improved signal differentiation
6. **Alert System**: Built-in alert conditions for all signal types
**Parameter Configuration**
**Customizable Inputs:**
- **Overbought Level** (70): Upper threshold, range 50-90
- **Oversold Level** (30): Lower threshold, range 10-50
- **RSI Period** (14): Calculation period, range 2-50
- **Minimum Duration** (4): Required bars in extreme zone, range 1-20
- **Lookback Bars** (20): Period to check for extreme conditions, range 5-100
- **Divergence Lookback** (5): Period for divergence swing comparison, range 2-20
**Optimization Guidelines:**
- **Shorter Duration** (2-3): More frequent signals, higher noise
- **Longer Duration** (5-7): Fewer signals, better quality
- **Smaller Lookback** (10-15): Faster response, may miss context
- **Larger Lookback** (30-50): More context, potentially delayed signals
**Signal Interpretation Guide**
**Visual Signal Hierarchy:**
**Light Green Triangle (Buy):**
- RSI recovered from oversold
- Duration requirements met
- Entry on reversal from oversold territory
**Light Red Triangle (Sell):**
- RSI declined from overbought
- Duration requirements met
- Entry on reversal from overbought territory
**Blue Triangle (Strong Buy):**
- Buy signal with bullish divergence
- Highest probability long setup
- Price made lower low, RSI made higher low
**Magenta Triangle (Strong Sell):**
- Sell signal with bearish divergence
- Highest probability short setup
- Price made higher high, RSI made lower high
**Tiny Green Circle (M+):**
- RSI crossed above 50 after oversold
- Momentum confirmation for uptrend
- Secondary entry or trend confirmation
**Tiny Red Circle (M-):**
- RSI crossed below 50 after overbought
- Momentum confirmation for downtrend
- Secondary entry or trend confirmation
**Background Coloring:**
- **Light Red Background**: RSI > 70 (overbought zone)
- **Light Green Background**: RSI < 30 (oversold zone)
**Trading Strategy Application**
**Conservative Approach (Strong Signals Only):**
1. Wait for blue/magenta triangles (divergence confirmed)
2. Enter on signal bar close or next bar open
3. Stop loss beyond recent swing high/low
4. Target minimum 2:1 risk/reward ratio
**Aggressive Approach (All Signals):**
1. Take light green/red triangles for earlier entries
2. Use momentum circles as confirmation
3. Tighter stops with partial profit taking
4. Scale positions based on signal strength
**Momentum Trading:**
1. Use momentum signals (M+/M-) as trend filters
2. Take primary signals aligned with momentum direction
3. Avoid counter-momentum signals in strong trends
4. Exit when opposing momentum signal appears
**Multi-Timeframe Strategy:**
1. Check higher timeframe for strong signals
2. Execute on lower timeframe primary signals
3. Use momentum signals for position management
4. Align all timeframe signals for best probability
**Optimal Market Conditions**
**Best Performance:**
- Mean-reverting markets with clear RSI extremes
- Range-bound or consolidating conditions
- Markets respecting support/resistance levels
- Timeframes: 15min to 4H for active trading
**Strong Signal Advantages:**
- Divergence signals often mark major turning points
- Work well at market structure levels
- Effective in both trending and ranging markets
- Higher success rate justifies waiting for setup
**Momentum Signal Benefits:**
- Confirms trend direction after extreme readings
- Useful for adding to positions
- Helps avoid counter-trend trades
- Works well in trending markets where reversals fail
**Technical Advantages**
**Divergence Accuracy:**
The improved divergence detection uses proper swing analysis rather than simple bar-to-bar comparison. This identifies genuine momentum shifts where price action diverges from oscillator movement over a meaningful period.
**Duration Logic:**
The for-loop counting method ensures the system checks actual RSI values within the lookback period, not just whether RSI touched levels. This distinguishes between sustained conditions and brief spikes.
**Momentum Filter:**
The 50-line crosses after extreme conditions provide an additional confirmation layer, helping traders distinguish between failed reversals (no momentum follow-through) and sustained moves (momentum confirmation).
**Risk Management Integration**
**Signal Priority:**
1. **Highest**: Strong signals with divergence (blue/magenta triangles)
2. **Medium**: Primary signals without divergence (light green/red triangles)
3. **Confirmation**: Momentum signals (tiny circles)
**Position Sizing:**
- Larger positions on strong signals (divergence present)
- Standard positions on primary signals
- Smaller positions or adds on momentum signals
**Stop Placement:**
- Beyond recent swing structure
- Below/above divergence swing low/high for strong signals
- Trail stops when momentum signals align with position
**Alert System**
Built-in alert conditions for:
- Buy Signal: RSI buy without divergence
- Sell Signal: RSI sell without divergence
- Strong Buy Alert: Buy with bullish divergence
- Strong Sell Alert: Sell with bearish divergence
Configure alerts via TradingView's alert system to receive notifications for chosen signal types.
**Important Considerations**
**Strengths:**
- Multiple confirmation layers reduce false signals
- Classical divergence improves reversal detection
- Momentum signals add trend-following capability
- Highly customizable for different trading styles
- No repainting - all signals fixed at bar close
**Limitations:**
- Duration requirements may cause missed quick reversals
- Divergence lookback period affects sensitivity
- Not suitable as standalone system
- Requires understanding of RSI principles and divergence concepts
**Best Practices:**
- Combine with price action and support/resistance
- Use higher timeframe context for directional bias
- Respect overall market trend and structure
- Implement proper position sizing based on signal type
- Test parameters on your specific instrument and timeframe
**Comparison Summary**
This enhanced version represents a significant upgrade:
- Upgraded to Pine Script v5 modern standards
- Proper classical divergence detection (not simplified)
- Added momentum confirmation signals
- Fully customizable parameters via inputs
- Visual background zone highlighting
- Comprehensive alert system
- Better signal differentiation through color coding
The system transforms basic RSI analysis into a multi-dimensional trading tool suitable for various market conditions and trading styles.
**Disclaimer**: This indicator is designed for educational and analytical purposes. While the multi-layer filtering and classical divergence detection improve upon standard RSI implementations, no indicator guarantees profitable trades. The duration filtering reduces false signals but may delay entries. Divergence signals, while statistically favorable, can fail in strong trending conditions. Always use proper risk management, position sizing, and stop-loss orders. Consider multiple confirmation methods and market context before making trading decisions. Past performance does not guarantee future results.
