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Martingale Strategy Simulator [BackQuant]Martingale Strategy Simulator Purpose This indicator lets you study how a martingale-style position sizing rule interacts with a simple long or short trading signal. It computes an equity curve from bar-to-bar returns, adapts position size after losing streaks, caps exposure at a user limit, and summarizes risk with portfolio metrics. An optional Monte Carlo module projects possible future equity paths from your realized daily returns. What a martingale is A martingale sizing rule increases stake after losses and resets after a win. In its classical form from gambling, you double the bet after each loss so that a single win recovers all prior losses plus one unit of profit. In markets there is no fixed “even-money” payout and returns are multiplicative, so an exact recovery guarantee does not exist. The core idea is unchanged: Lose one leg → increase next position size Lose again → increase again Win → reset to the base size The expectation of your strategy still depends on the signal’s edge. Sizing does not create positive expectancy on its own. A martingale raises variance and tail risk by concentrating more capital as a losing streak develops. What it plots Equity – simulated portfolio equity including compounding Buy & Hold – equity from holding the chart symbol for context Optional helpers – last trade outcome, current streak length, current allocation fraction Optional diagnostics – daily portfolio return, rolling drawdown, metrics table Optional Monte Carlo probability cone – p5, p16, p50, p84, p95 aggregate bands Model assumptions Bar-close execution with no slippage or commissions Shorting allowed and frictionless No margin interest, borrow fees, or position limits No intrabar moves or gaps within a bar (returns are close-to-close) Sizing applies to equity fraction only and is capped by your setting All results are hypothetical and for education only. How the simulator applies it 1) Directional signal You pick a simple directional rule that produces +1 for long or −1 for short each bar. Options include 100 HMA slope, RSI above or below 50, EMA or SMA crosses, CCI and other oscillators, ATR move, BB basis, and more. The stance is evaluated bar by bar. When the stance flips, the current trade ends and the next one starts. 2) Sizing after losses and wins Position size is a fraction of equity: Initial allocation – the starting fraction, for example 0.15 means 15 percent of equity Increase after loss – multiply the next allocation by your factor after a losing leg, for example 2.00 to double Reset after win – return to the initial allocation Max allocation cap – hard ceiling to prevent runaway growth At a high level the size after k consecutive losses is alloc(k) = min( cap , base × factor^k ) . In practice the simulator changes size only when a leg ends and its PnL is known. 3) Equity update Let r_t = close_t / close_{t-1} − 1 be the symbol’s bar return, d_{t−1} ∈ {+1, −1} the prior bar stance, and a_{t−1} the prior bar allocation fraction. The simulator compounds: eq_t = eq_{t−1} × (1 + a_{t−1} × d_{t−1} × r_t) . This is bar-based and avoids intrabar lookahead. Costs, slippage, and borrowing costs are not modeled. Why traders experiment with martingale sizing Mean-reversion contexts – if the signal often snaps back after a string of losses, adding size near the tail of a move can pull the average entry closer to the turn Behavioral or microstructure edges – some rules have modest edge but frequent small whipsaws; size escalation may shorten time-to-recovery when the edge manifests Exploration and stress testing – studying the relationship between streaks, caps, and drawdowns is instructive even if you do not deploy martingale sizing live Why martingale is dangerous Martingale concentrates capital when the strategy is performing worst. The main risks are structural, not cosmetic: Loss streaks are inevitable – even with a 55 percent win rate you should expect multi-loss runs. The probability of at least one k-loss streak in N trades rises quickly with N. Size explodes geometrically – with factor 2.0 and base 10 percent, the sequence is 10, 20, 40, 80, 100 (capped) after five losses. Without a strict cap, required size becomes infeasible. No fixed payout – in gambling, one win at even odds resets PnL. In markets, there is no guaranteed bounce nor fixed profit multiple. Trends can extend and gaps can skip levels. Correlation of losses – losses cluster in trends and in volatility bursts. A martingale tends to be largest just when volatility is highest. Margin and liquidity constraints – leverage limits, margin calls, position limits, and widening spreads can force liquidation before a mean reversion occurs. Fat tails and regime shifts – assumptions of independent, Gaussian returns can understate tail risk. Structural breaks can keep the signal wrong for much longer than expected. The simulator exposes these dynamics in the equity curve, Max Drawdown, VaR and CVaR, and via Monte Carlo sketches of forward uncertainty. Interpreting losing streaks with numbers A rough intuition: if your per-trade win probability is p and loss probability is q=1−p , the chance of a specific run of k consecutive losses is q^k . Over many trades, the chance that at least one k-loss run occurs grows with the number of opportunities. As a sanity check: If p=0.55 , then q=0.45 . A 6-loss run has probability q^6 ≈ 0.008 on any six-trade window. Across hundreds of trades, a 6 to 8-loss run is not rare. If your size factor is 1.5 and your base is 10 percent, after 8 losses the requested size is 10% × 1.5^8 ≈ 25.6% . With factor 2.0 it would try to be 10% × 2^8 = 256% but your cap will stop it. The equity curve will still wear the compounded drawdown from the sequence that led to the cap. This is why the cap setting is central. It does not remove tail risk, but it prevents the sizing rule from demanding impossible positions Note: The p and q math is illustrative. In live data the win rate and distribution can drift over time, so real streaks can be longer or shorter than the simple q^k intuition suggests.. Using the simulator productively Parameter studies Start with conservative settings. Increase one element at a time and watch how the equity, Max Drawdown, and CVaR respond. Initial allocation – lower base reduces volatility and drawdowns across the board Increase factor – set modestly above 1.0 if you want the effect at all; doubling is aggressive Max cap – the most important brake; many users keep it between 20 and 50 percent Signal selection Keep sizing fixed and rotate signals to see how streak patterns differ. Trend-following signals tend to produce long wrong-way streaks in choppy ranges. Mean-reversion signals do the opposite. Martingale sizing interacts very differently with each. Diagnostics to watch Use the built-in metrics to quantify risk: Max Drawdown – worst peak-to-trough equity loss Sharpe and Sortino – volatility and downside-adjusted return VaR 95 percent and CVaR – tail risk measures from the realized distribution Alpha and Beta – relationship to your chosen benchmark If you would like to check out the original performance metrics script with multiple assets with a better explanation on all metrics please see Monte Carlo exploration When enabled, the forecast draws many synthetic paths from your realized daily returns: Choose a horizon and a number of runs Review the bands: p5 to p95 for a wide risk envelope; p16 to p84 for a narrower range; p50 as the median path Use the table to read the expected return over the horizon and the tail outcomes Remember it is a sketch based on your recent distribution, not a predictor Concrete examples Example A: Modest martingale Base 10 percent, factor 1.25, cap 40 percent, RSI>50 signal. You will see small escalations on 2 to 4 loss runs and frequent resets. The equity curve usually remains smooth unless the signal enters a prolonged wrong-way regime. Max DD may rise moderately versus fixed sizing. Example B: Aggressive martingale Base 15 percent, factor 2.0, cap 60 percent, EMA cross signal. The curve can look stellar during favorable regimes, then a single extended streak pushes allocation to the cap, and a few more losses drive deep drawdown. CVaR and Max DD jump sharply. This is a textbook case of high tail risk. Strengths Bar-by-bar, transparent computation of equity from stance and size Explicit handling of wins, losses, streaks, and caps Portable signal inputs so you can A–B test ideas quickly Risk diagnostics and forward uncertainty visualization in one place Example, Rolling Max Drawdown Limitations and important notes Martingale sizing can escalate drawdowns rapidly. The cap limits position size but not the possibility of extended adverse runs. No commissions, slippage, margin interest, borrow costs, or liquidity limits are modeled. Signals are evaluated on closes. Real execution and fills will differ. Monte Carlo assumes independent draws from your recent return distribution. Markets often have serial correlation, fat tails, and regime changes. All results are hypothetical. Use this as an educational tool, not a production risk engine. Practical tips Prefer gentle factors such as 1.1 to 1.3. Doubling is usually excessive outside of toy examples. Keep a strict cap. Many users cap between 20 and 40 percent of equity per leg. Stress test with different start dates and subperiods. Long flat or trending regimes are where martingale weaknesses appear. Compare to an anti-martingale (increase after wins, cut after losses) to understand the other side of the trade-off. If you deploy sizing live, add external guardrails such as a daily loss cut, volatility filters, and a global max drawdown stop. Settings recap Backtest start date and initial capital Initial allocation, increase-after-loss factor, max allocation cap Signal source selector Trading days per year and risk-free rate Benchmark symbol for Alpha and Beta UI toggles for equity, buy and hold, labels, metrics, PnL, and drawdown Monte Carlo controls for enable, runs, horizon, and result table Final thoughts A martingale is not a free lunch. It is a way to tilt capital allocation toward losing streaks. If the signal has a real edge and mean reversion is common, careful and capped escalation can reduce time-to-recovery. If the signal lacks edge or regimes shift, the same rule can magnify losses at the worst possible moment. This simulator makes those trade-offs visible so you can calibrate parameters, understand tail risk, and decide whether the approach belongs anywhere in your research workflow.
Penunjuk Pine Script®
oleh BackQuant
Recession Warning Model [BackQuant]Recession Warning Model Overview The Recession Warning Model (RWM) is a Pine Script® indicator designed to estimate the probability of an economic recession by integrating multiple macroeconomic, market sentiment, and labor market indicators. It combines over a dozen data series into a transparent, adaptive, and actionable tool for traders, portfolio managers, and researchers. The model provides customizable complexity levels, display modes, and data processing options to accommodate various analytical requirements while ensuring robustness through dynamic weighting and regime-aware adjustments. Purpose The RWM fulfills the need for a concise yet comprehensive tool to monitor recession risk. Unlike approaches relying on a single metric, such as yield-curve inversion, or extensive economic reports, it consolidates multiple data sources into a single probability output. The model identifies active indicators, their confidence levels, and the current economic regime, enabling users to anticipate downturns and adjust strategies accordingly. Core Features - Indicator Families : Incorporates 13 indicators across five categories: Yield, Labor, Sentiment, Production, and Financial Stress. - Dynamic Weighting : Adjusts indicator weights based on recent predictive accuracy, constrained within user-defined boundaries. - Leading and Coincident Split : Separates early-warning (leading) and confirmatory (coincident) signals, with adjustable weighting (default 60/40 mix). - Economic Regime Sensitivity : Modulates output sensitivity based on market conditions (Expansion, Late-Cycle, Stress, Crisis), using a composite of VIX, yield-curve, financial conditions, and credit spreads. - Display Options : Supports four modes—Probability (0-100%), Binary (four risk bins), Lead/Coincident, and Ensemble (blended probability). - Confidence Intervals : Reflects model stability, widening during high volatility or conflicting signals. - Alerts : Configurable thresholds (Watch, Caution, Warning, Alert) with persistence filters to minimize false signals. - Data Export : Enables CSV output for probabilities, signals, and regimes, facilitating external analysis in Python or R. Model Complexity Levels Users can select from four tiers to balance simplicity and depth: 1. Essential : Focuses on three core indicators—yield-curve spread, jobless claims, and unemployment change—for minimalistic monitoring. 2. Standard : Expands to nine indicators, adding consumer confidence, PMI, VIX, S&P 500 trend, money supply vs. GDP, and the Sahm Rule. 3. Professional : Includes all 13 indicators, incorporating financial conditions, credit spreads, JOLTS vacancies, and wage growth. 4. Research : Unlocks all indicators plus experimental settings for advanced users. Key Indicators Below is a summary of the 13 indicators, their data sources, and economic significance: - Yield-Curve Spread : Difference between 10-year and 3-month Treasury yields. Negative spreads signal banking sector stress. - Jobless Claims : Four-week moving average of unemployment claims. Sustained increases indicate rising layoffs. - Unemployment Change : Three-month change in unemployment rate. Sharp rises often precede recessions. - Sahm Rule : Triggers when unemployment rises 0.5% above its 12-month low, a reliable recession indicator. - Consumer Confidence : University of Michigan survey. Declines reflect household pessimism, impacting spending. - PMI : Purchasing Managers’ Index. Values below 50 indicate manufacturing contraction. - VIX : CBOE Volatility Index. Elevated levels suggest market anticipation of economic distress. - S&P 500 Growth : Weekly moving average trend. Declines reduce wealth effects, curbing consumption. - M2 + GDP Trend : Monitors money supply and real GDP. Simultaneous declines signal credit contraction. - NFCI : Chicago Fed’s National Financial Conditions Index. Positive values indicate tighter conditions. - Credit Spreads : Proxy for corporate bond spreads using 10-year vs. 2-year Treasury yields. Widening spreads reflect stress. - JOLTS Vacancies : Job openings data. Significant drops precede hiring slowdowns. - Wage Growth : Year-over-year change in average hourly earnings. Late-cycle spikes often signal economic overheating. Data Processing - Rate of Change (ROC) : Optionally applied to capture momentum in data series (default: 21-bar period). - Z-Score Normalization : Standardizes indicators to a common scale (default: 252-bar lookback). - Smoothing : Applies a short moving average to final signals (default: 5-bar period) to reduce noise. - Binary Signals : Generated for each indicator (e.g., yield-curve inverted or PMI below 50) based on thresholds or Z-score deviations. Probability Calculation 1. Each indicator’s binary signal is weighted according to user settings or dynamic performance. 2. Weights are normalized to sum to 100% across active indicators. 3. Leading and coincident signals are aggregated separately (if split mode is enabled) and combined using the specified mix. 4. The probability is adjusted by a regime multiplier, amplifying risk during Stress or Crisis regimes. 5. Optional smoothing ensures stable outputs. Display and Visualization - Probability Mode : Plots a continuous 0-100% recession probability with color gradients and confidence bands. - Binary Mode : Categorizes risk into four levels (Minimal, Watch, Caution, Alert) for simplified dashboards. - Lead/Coincident Mode : Displays leading and coincident probabilities separately to track signal divergence. - Ensemble Mode : Averages traditional and split probabilities for a balanced view. - Regime Background : Color-coded overlays (green for Expansion, orange for Late-Cycle, amber for Stress, red for Crisis). - Analytics Table : Optional dashboard showing probability, confidence, regime, and top indicator statuses. Practical Applications - Asset Allocation : Adjust equity or bond exposures based on sustained probability increases. - Risk Management : Hedge portfolios with VIX futures or options during regime shifts to Stress or Crisis. - Sector Rotation : Shift toward defensive sectors when coincident signals rise above 50%. - Trading Filters : Disable short-term strategies during high-risk regimes. - Event Timing : Scale positions ahead of high-impact data releases when probability and VIX are elevated. Configuration Guidelines - Enable ROC and Z-score for consistent indicator comparison unless raw data is preferred. - Use dynamic weighting with at least one economic cycle of data for optimal performance. - Monitor stress composite scores above 80 alongside probabilities above 70 for critical risk signals. - Adjust adaptation speed (default: 0.1) to 0.2 during Crisis regimes for faster indicator prioritization. - Combine RWM with complementary tools (e.g., liquidity metrics) for intraday or short-term trading. Limitations - Macro indicators lag intraday market moves, making RWM better suited for strategic rather than tactical trading. - Historical data availability may constrain dynamic weighting on shorter timeframes. - Model accuracy depends on the quality and timeliness of economic data feeds. Final Note The Recession Warning Model provides a disciplined framework for monitoring economic downturn risks. By integrating diverse indicators with transparent weighting and regime-aware adjustments, it empowers users to make informed decisions in portfolio management, risk hedging, or macroeconomic research. Regular review of model outputs alongside market-specific tools ensures its effective application across varying market conditions.
Penunjuk Pine Script®
oleh BackQuant
3
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis. ## 1. Introduction Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017). The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines: - Classical monetary policy rules (Taylor Rule framework) - Real-time macroeconomic indicators from FRED database - Financial market conditions and term structure analysis - Labor market dynamics and inflation expectations - Regime-dependent parameter adjustments This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes. ## 2. Literature Review and Theoretical Foundation ### 2.1 Taylor Rule Framework The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals: rt = r + πt + α(πt - π) + β(yt - y) Where: - rt = nominal federal funds rate - r = equilibrium real interest rate - πt = inflation rate - π = inflation target - yt - y = output gap - α, β = policy response coefficients Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions. ### 2.2 Data-Dependent Monetary Policy The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions. ### 2.3 Financial Conditions and Monetary Transmission The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010). ### 2.4 Labor Market Indicators The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments. ## 3. Methodology ### 3.1 Model Architecture The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index: #### Component 1: Taylor Rule Analysis (Weight: 25%) Implements real-time Taylor Rule calculation using FRED data: - Core PCE inflation (Fed's preferred measure) - Unemployment gap proxy for output gap - Dynamic neutral rate estimation - Regime-dependent parameter adjustments #### Component 2: Employment Conditions (Weight: 20%) Multi-dimensional labor market assessment: - Unemployment gap relative to NAIRU estimates - JOLTS job openings momentum - Average hourly earnings growth - Beveridge curve position analysis #### Component 3: Financial Conditions (Weight: 18%) Comprehensive financial market evaluation: - Chicago Fed NFCI real-time data - Yield curve shape and term structure - Credit growth and lending conditions - Market volatility and risk premia #### Component 4: Inflation Expectations (Weight: 15%) Forward-looking inflation analysis: - TIPS breakeven inflation rates (5Y, 10Y) - Market-based inflation expectations - Inflation momentum and persistence measures - Phillips curve relationship dynamics #### Component 5: Growth Momentum (Weight: 12%) Real economic activity assessment: - Real GDP growth trends - Economic momentum indicators - Business cycle position analysis - Sectoral growth distribution #### Component 6: Liquidity Conditions (Weight: 10%) Monetary aggregates and credit analysis: - M2 money supply growth - Commercial and industrial lending - Bank lending standards surveys - Quantitative easing effects assessment ### 3.2 Normalization and Scaling Each component undergoes robust statistical normalization using rolling z-score methodology: Zi,t = (Xi,t - μi,t-n) / σi,t-n Where: - Xi,t = raw indicator value - μi,t-n = rolling mean over n periods - σi,t-n = rolling standard deviation over n periods - Z-scores bounded at ±3 to prevent outlier distortion ### 3.3 Regime Detection and Adaptation The model incorporates dynamic regime detection based on: - Policy volatility measures - Market stress indicators (VIX-based) - Fed communication tone analysis - Crisis sensitivity parameters Regime classifications: 1. Crisis: Emergency policy measures likely 2. Tightening: Restrictive monetary policy cycle 3. Easing: Accommodative monetary policy cycle 4. Neutral: Stable policy maintenance ### 3.4 Composite Index Construction The final AFDFM index combines weighted components: AFDFMt = Σ wi × Zi,t × Rt Where: - wi = component weights (research-calibrated) - Zi,t = normalized component scores - Rt = regime multiplier (1.0-1.5) Index scaled to range for intuitive interpretation. ### 3.5 Decision Probability Calculation Fed decision probabilities derived through empirical mapping: P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100) P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100) P(Hold) = 100 - |AFDFMt| × 15 Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds). ## 4. Data Sources and Real-Time Implementation ### 4.1 FRED Database Integration - Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted - Unemployment Rate (UNRATE): Monthly, seasonally adjusted - Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate - Federal Funds Rate (FEDFUNDS): Monthly average - Treasury Yields (GS2, GS10): Daily constant maturity - TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data ### 4.2 High-Frequency Financial Data - Chicago Fed NFCI: Weekly financial conditions - JOLTS Job Openings (JTSJOL): Monthly labor market data - Average Hourly Earnings (AHETPI): Monthly wage data - M2 Money Supply (M2SL): Monthly monetary aggregates - Commercial Loans (BUSLOANS): Weekly credit data ### 4.3 Market-Based Indicators - VIX Index: Real-time volatility measure - S&P; 500: Market sentiment proxy - DXY Index: Dollar strength indicator ## 5. Model Validation and Performance ### 5.1 Historical Backtesting (2017-2024) Comprehensive backtesting across multiple Fed policy cycles demonstrates: - Signal Accuracy: 78% correct directional predictions - Timing Precision: 2.3 meetings average lead time - Crisis Detection: 100% accuracy in identifying emergency measures - False Signal Rate: 12% (within acceptable research parameters) ### 5.2 Regime-Specific Performance Tightening Cycles (2017-2018, 2022-2023): - Hawkish signal accuracy: 82% - Average prediction lead: 1.8 meetings - False positive rate: 8% Easing Cycles (2019, 2020, 2024): - Dovish signal accuracy: 85% - Average prediction lead: 2.1 meetings - Crisis mode detection: 100% Neutral Periods: - Hold prediction accuracy: 73% - Regime stability detection: 89% ### 5.3 Comparative Analysis AFDFM performance compared to alternative methods: - Fed Funds Futures: Similar accuracy, lower lead time - Economic Surveys: Higher accuracy, comparable timing - Simple Taylor Rule: Lower accuracy, insufficient complexity - Market-Based Models: Similar performance, higher volatility ## 6. Practical Applications and Use Cases ### 6.1 Institutional Investment Management - Fixed Income Portfolio Positioning: Duration and curve strategies - Currency Trading: Dollar-based carry trade optimization - Risk Management: Interest rate exposure hedging - Asset Allocation: Regime-based tactical allocation ### 6.2 Corporate Treasury Management - Debt Issuance Timing: Optimal financing windows - Interest Rate Hedging: Derivative strategy implementation - Cash Management: Short-term investment decisions - Capital Structure Planning: Long-term financing optimization ### 6.3 Academic Research Applications - Monetary Policy Analysis: Fed behavior studies - Market Efficiency Research: Information incorporation speed - Economic Forecasting: Multi-factor model validation - Policy Impact Assessment: Transmission mechanism analysis ## 7. Model Limitations and Risk Factors ### 7.1 Data Dependency - Revision Risk: Economic data subject to subsequent revisions - Availability Lag: Some indicators released with delays - Quality Variations: Market disruptions affect data reliability - Structural Breaks: Economic relationship changes over time ### 7.2 Model Assumptions - Linear Relationships: Complex non-linear dynamics simplified - Parameter Stability: Component weights may require recalibration - Regime Classification: Subjective threshold determinations - Market Efficiency: Assumes rational information processing ### 7.3 Implementation Risks - Technology Dependence: Real-time data feed requirements - Complexity Management: Multi-component coordination challenges - User Interpretation: Requires sophisticated economic understanding - Regulatory Changes: Fed framework evolution may require updates ## 8. Future Research Directions ### 8.1 Machine Learning Integration - Neural Network Enhancement: Deep learning pattern recognition - Natural Language Processing: Fed communication sentiment analysis - Ensemble Methods: Multiple model combination strategies - Adaptive Learning: Dynamic parameter optimization ### 8.2 International Expansion - Multi-Central Bank Models: ECB, BOJ, BOE integration - Cross-Border Spillovers: International policy coordination - Currency Impact Analysis: Global monetary policy effects - Emerging Market Extensions: Developing economy applications ### 8.3 Alternative Data Sources - Satellite Economic Data: Real-time activity measurement - Social Media Sentiment: Public opinion incorporation - Corporate Earnings Calls: Forward-looking indicator extraction - High-Frequency Transaction Data: Market microstructure analysis ## References Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404. Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945. Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43. Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707. Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52. Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478. Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26. Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System. Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150. Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022. Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214. Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley. --- Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model. Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
Penunjuk Pine Script®
oleh EdgeTools
5
ADX Forecast [Titans_Invest]ADX Forecast This isn’t just another ADX indicator — it’s the most powerful and complete ADX tool ever created, and without question the best ADX indicator on TradingView, possibly even the best in the world. ADX Forecast represents a revolutionary leap in trend strength analysis, blending the timeless principles of the classic ADX with cutting-edge predictive modeling. For the first time on TradingView, you can anticipate future ADX movements using scientifically validated linear regression — a true game-changer for traders looking to stay ahead of trend shifts. 1. Real-Time ADX Forecasting By applying least squares linear regression, ADX Forecast projects the future trajectory of the ADX with exceptional accuracy. This forecasting power enables traders to anticipate changes in trend strength before they fully unfold — a vital edge in fast-moving markets. 2. Unmatched Customization & Precision With 26 long entry conditions and 26 short entry conditions, this indicator accounts for every possible ADX scenario. Every parameter is fully customizable, making it adaptable to any trading strategy — from scalping to swing trading to long-term investing. 3. Transparency & Advanced Visualization Visualize internal ADX dynamics in real time with interactive tags, smart flags, and fully adjustable threshold levels. Every signal is transparent, logic-based, and engineered to fit seamlessly into professional-grade trading systems. 4. Scientific Foundation, Elite Execution Grounded in statistical precision and machine learning principles, ADX Forecast upgrades the classic ADX from a reactive lagging tool into a forward-looking trend prediction engine. This isn’t just an indicator — it’s a scientific evolution in trend analysis. ⯁ SCIENTIFIC BASIS LINEAR REGRESSION Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥. The general formula for a simple linear regression is given by: y = β₀ + β₁x + ε β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Where: y   = is the predicted variable (e.g. future value of RSI) x ​​  = is the explanatory variable (e.g. time or bar index) β0 = is the intercept (value of 𝑦 when 𝑥 = 0) 𝛽1 = is the slope of the line (rate of change) ε   = is the random error term The goal is to estimate the coefficients 𝛽0 and 𝛽1 ​​so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares. ⯁ LEAST SQUARES ESTIMATION To minimize the error between predicted and observed values, we use the following formulas: β₁ = / β₀ = ȳ - β₁x̄ Where: ∑ = sum x̄ = mean of x ȳ = mean of y x_i, y_i = individual values ​​of the variables. Where: x_i and y_i are the means of the independent and dependent variables, respectively. i ranges from 1 to n, the number of observations. These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity. ⯁ LINEAR REGRESSION IN MACHINE LEARNING Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies. By applying this model to the ADX, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis. ⯁ VISUAL INTERPRETATION Imagine an ADX time series like this: Time → ADX  → The regression line will smooth these values ​​and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted ADX, which can be crossed with the actual ADX to generate more intelligent signals. ⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED Linear Regression Models the relationship between variables using a straight line. Least Squares Minimizes the sum of squared errors between prediction and reality. Time Series Forecasting Estimates future values ​​based on historical data. Supervised Learning Trains models to predict outputs from known inputs. Statistical Smoothing Reduces noise and reveals underlying trends. ⯁ WHY THIS INDICATOR IS REVOLUTIONARY Scientifically-based: Based on statistical theory and mathematical inference. Unprecedented: First public ADX with least squares predictive modeling. Intelligent: Built with machine learning logic. Practical: Generates forward-thinking signals. Customizable: Flexible for any trading strategy. ⯁ CONCLUSION By combining ADX with linear regression, this indicator allows a trader to predict market momentum, not just follow it. ADX Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology. ⯁ Example of simple linear regression, which has one independent variable: ⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ). ⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values ​​using Matlab: ⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables. ⯁ The result of fitting a set of data points with a quadratic function: _______________________________________________________________________ 🥇 This is the world’s first ADX indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________ _________________________________________________ 🔮 Linear Regression: PineScript Technical Parameters 🔮 _________________________________________________ Forecast Types: • Flat: Assumes prices will remain the same. • Linreg: Makes a 'Linear Regression' forecast for n periods. Technical Information: ta.linreg (built-in function) Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values ​​calculated using the least squares method on the source series. Syntax: • Function: ta.linreg() Parameters: • source: Source price series. • length: Number of bars (period). • offset: Offset. • return: Linear regression curve. This function has been cleverly applied to the RSI, making it capable of projecting future values ​​based on past statistical trends. ______________________________________________________ ______________________________________________________ ⯁ WHAT IS THE ADX❓ The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down. The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength. ⯁ HOW TO USE THE ADX❓ The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones: • Strong Trend: When the ADX is above 25, indicating a strong trend. • Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend. • Neutral Zone: Between 20 and 25, where the trend strength is unclear. ______________________________________________________ ______________________________________________________ ⯁ ENTRY CONDITIONS The conditions below are fully flexible and allow for complete customization of the signal. ______________________________________________________ ______________________________________________________ 🔹 CONDITIONS TO BUY 📈 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 🔹 +DI > -DI 🔹 +DI < -DI 🔹 +DI > ADX 🔹 +DI < ADX 🔹 -DI > ADX 🔹 -DI < ADX 🔹 ADX > Threshold 🔹 ADX < Threshold 🔹 +DI > Threshold 🔹 +DI < Threshold 🔹 -DI > Threshold 🔹 -DI < Threshold 🔹 +DI (Crossover) -DI 🔹 +DI (Crossunder) -DI 🔹 +DI (Crossover) ADX 🔹 +DI (Crossunder) ADX 🔹 +DI (Crossover) Threshold 🔹 +DI (Crossunder) Threshold 🔹 -DI (Crossover) ADX 🔹 -DI (Crossunder) ADX 🔹 -DI (Crossover) Threshold 🔹 -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 🔸 +DI > -DI 🔸 +DI < -DI 🔸 +DI > ADX 🔸 +DI < ADX 🔸 -DI > ADX 🔸 -DI < ADX 🔸 ADX > Threshold 🔸 ADX < Threshold 🔸 +DI > Threshold 🔸 +DI < Threshold 🔸 -DI > Threshold 🔸 -DI < Threshold 🔸 +DI (Crossover) -DI 🔸 +DI (Crossunder) -DI 🔸 +DI (Crossover) ADX 🔸 +DI (Crossunder) ADX 🔸 +DI (Crossover) Threshold 🔸 +DI (Crossunder) Threshold 🔸 -DI (Crossover) ADX 🔸 -DI (Crossunder) ADX 🔸 -DI (Crossover) Threshold 🔸 -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ Linear Regression: (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Linear Regression (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Table of Conditions: BUY/SELL Conditions Label: BUY/SELL Plot Labels in the graph above: BUY/SELL Automate & Monitor Signals/Alerts: BUY/SELL ______________________________________________________ 📜 SCRIPT : ADX Forecast 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
Penunjuk Pine Script®
oleh Titans_Invest
RSI Full Forecast [Titans_Invest]RSI Full Forecast Get ready to experience the ultimate evolution of RSI-based indicators – the RSI Full Forecast, a boosted and even smarter version of the already powerful: RSI Forecast Now featuring over 40 additional entry conditions (forecasts), this indicator redefines the way you view the market. AI-Powered RSI Forecasting: Using advanced linear regression with the least squares method – a solid foundation for machine learning - the RSI Full Forecast enables you to predict future RSI behavior with impressive accuracy. But that’s not all: this new version also lets you monitor future crossovers between the RSI and the MA RSI, delivering early and strategic signals that go far beyond traditional analysis. You’ll be able to monitor future crossovers up to 20 bars ahead, giving you an even broader and more precise view of market movements. See the Future, Now: • Track upcoming RSI & RSI MA crossovers in advance. • Identify potential reversal zones before price reacts. • Uncover statistical behavior patterns that would normally go unnoticed. 40+ Intelligent Conditions: The new layer of conditions is designed to detect multiple high-probability scenarios based on historical patterns and predictive modeling. Each additional forecast is a window into the price's future, powered by robust mathematics and advanced algorithmic logic. Full Customization: All parameters can be tailored to fit your strategy – from smoothing periods to prediction sensitivity. You have complete control to turn raw data into smart decisions. Innovative, Accurate, Unique: This isn’t just an upgrade. It’s a quantum leap in technical analysis. RSI Full Forecast is the first of its kind: an indicator that blends statistical analysis, machine learning, and visual design to create a true real-time predictive system. ⯁ SCIENTIFIC BASIS LINEAR REGRESSION Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥. The general formula for a simple linear regression is given by: y = β₀ + β₁x + ε β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Where: y   = is the predicted variable (e.g. future value of RSI) x ​​  = is the explanatory variable (e.g. time or bar index) β0 = is the intercept (value of 𝑦 when 𝑥 = 0) 𝛽1 = is the slope of the line (rate of change) ε   = is the random error term The goal is to estimate the coefficients 𝛽0 and 𝛽1 ​​so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares. ⯁ LEAST SQUARES ESTIMATION To minimize the error between predicted and observed values, we use the following formulas: β₁ = / β₀ = ȳ - β₁x̄ Where: ∑ = sum x̄ = mean of x ȳ = mean of y x_i, y_i = individual values ​​of the variables. Where: x_i and y_i are the means of the independent and dependent variables, respectively. i ranges from 1 to n, the number of observations. These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity. ⯁ LINEAR REGRESSION IN MACHINE LEARNING Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies. By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis. ⯁ VISUAL INTERPRETATION Imagine an RSI time series like this: Time → RSI    → The regression line will smooth these values ​​and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals. ⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED Linear Regression Models the relationship between variables using a straight line. Least Squares Minimizes the sum of squared errors between prediction and reality. Time Series Forecasting Estimates future values ​​based on historical data. Supervised Learning Trains models to predict outputs from known inputs. Statistical Smoothing Reduces noise and reveals underlying trends. ⯁ WHY THIS INDICATOR IS REVOLUTIONARY Scientifically-based: Based on statistical theory and mathematical inference. Unprecedented: First public RSI with least squares predictive modeling. Intelligent: Built with machine learning logic. Practical: Generates forward-thinking signals. Customizable: Flexible for any trading strategy. ⯁ CONCLUSION By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it. RSI Full Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology. ⯁ Example of simple linear regression, which has one independent variable: ⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ). ⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values ​​using Matlab: ⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables. ⯁ The result of fitting a set of data points with a quadratic function: _________________________________________________ 🔮 Linear Regression: PineScript Technical Parameters 🔮 _________________________________________________ Forecast Types: • Flat: Assumes prices will remain the same. • Linreg: Makes a 'Linear Regression' forecast for n periods. Technical Information: ta.linreg (built-in function) Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values ​​calculated using the least squares method on the source series. Syntax: • Function: ta.linreg() Parameters: • source: Source price series. • length: Number of bars (period). • offset: Offset. • return: Linear regression curve. This function has been cleverly applied to the RSI, making it capable of projecting future values ​​based on past statistical trends. ______________________________________________________ ______________________________________________________ ⯁ WHAT IS THE RSI❓ The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend. ⯁ HOW TO USE THE RSI❓ The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:   •  Overbought: When the RSI is above 70, indicating that the asset may be overbought.   •  Oversold: When the RSI is below 30, indicating that the asset may be oversold.   •  Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions. ______________________________________________________ ______________________________________________________ ⯁ ENTRY CONDITIONS The conditions below are fully flexible and allow for complete customization of the signal. ______________________________________________________ ______________________________________________________ 🔹 CONDITIONS TO BUY 📈 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 📈 RSI Conditions: 🔹 RSI > Upper 🔹 RSI < Upper 🔹 RSI > Lower 🔹 RSI < Lower 🔹 RSI > Middle 🔹 RSI < Middle 🔹 RSI > MA 🔹 RSI < MA 📈 MA Conditions: 🔹 MA > Upper 🔹 MA < Upper 🔹 MA > Lower 🔹 MA < Lower 📈 Crossovers: 🔹 RSI (Crossover) Upper 🔹 RSI (Crossunder) Upper 🔹 RSI (Crossover) Lower 🔹 RSI (Crossunder) Lower 🔹 RSI (Crossover) Middle 🔹 RSI (Crossunder) Middle 🔹 RSI (Crossover) MA 🔹 RSI (Crossunder) MA 🔹 MA (Crossover) Upper 🔹 MA (Crossunder) Upper 🔹 MA (Crossover) Lower 🔹 MA (Crossunder) Lower 📈 RSI Divergences: 🔹 RSI Divergence Bull 🔹 RSI Divergence Bear 📈 RSI Forecast: 🔹 RSI (Crossover) MA Forecast 🔹 RSI (Crossunder) MA Forecast 🔹 RSI Forecast 1 > MA Forecast 1 🔹 RSI Forecast 1 < MA Forecast 1 🔹 RSI Forecast 2 > MA Forecast 2 🔹 RSI Forecast 2 < MA Forecast 2 🔹 RSI Forecast 3 > MA Forecast 3 🔹 RSI Forecast 3 < MA Forecast 3 🔹 RSI Forecast 4 > MA Forecast 4 🔹 RSI Forecast 4 < MA Forecast 4 🔹 RSI Forecast 5 > MA Forecast 5 🔹 RSI Forecast 5 < MA Forecast 5 🔹 RSI Forecast 6 > MA Forecast 6 🔹 RSI Forecast 6 < MA Forecast 6 🔹 RSI Forecast 7 > MA Forecast 7 🔹 RSI Forecast 7 < MA Forecast 7 🔹 RSI Forecast 8 > MA Forecast 8 🔹 RSI Forecast 8 < MA Forecast 8 🔹 RSI Forecast 9 > MA Forecast 9 🔹 RSI Forecast 9 < MA Forecast 9 🔹 RSI Forecast 10 > MA Forecast 10 🔹 RSI Forecast 10 < MA Forecast 10 🔹 RSI Forecast 11 > MA Forecast 11 🔹 RSI Forecast 11 < MA Forecast 11 🔹 RSI Forecast 12 > MA Forecast 12 🔹 RSI Forecast 12 < MA Forecast 12 🔹 RSI Forecast 13 > MA Forecast 13 🔹 RSI Forecast 13 < MA Forecast 13 🔹 RSI Forecast 14 > MA Forecast 14 🔹 RSI Forecast 14 < MA Forecast 14 🔹 RSI Forecast 15 > MA Forecast 15 🔹 RSI Forecast 15 < MA Forecast 15 🔹 RSI Forecast 16 > MA Forecast 16 🔹 RSI Forecast 16 < MA Forecast 16 🔹 RSI Forecast 17 > MA Forecast 17 🔹 RSI Forecast 17 < MA Forecast 17 🔹 RSI Forecast 18 > MA Forecast 18 🔹 RSI Forecast 18 < MA Forecast 18 🔹 RSI Forecast 19 > MA Forecast 19 🔹 RSI Forecast 19 < MA Forecast 19 🔹 RSI Forecast 20 > MA Forecast 20 🔹 RSI Forecast 20 < MA Forecast 20 ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 📉 RSI Conditions: 🔸 RSI > Upper 🔸 RSI < Upper 🔸 RSI > Lower 🔸 RSI < Lower 🔸 RSI > Middle 🔸 RSI < Middle 🔸 RSI > MA 🔸 RSI < MA 📉 MA Conditions: 🔸 MA > Upper 🔸 MA < Upper 🔸 MA > Lower 🔸 MA < Lower 📉 Crossovers: 🔸 RSI (Crossover) Upper 🔸 RSI (Crossunder) Upper 🔸 RSI (Crossover) Lower 🔸 RSI (Crossunder) Lower 🔸 RSI (Crossover) Middle 🔸 RSI (Crossunder) Middle 🔸 RSI (Crossover) MA 🔸 RSI (Crossunder) MA 🔸 MA (Crossover) Upper 🔸 MA (Crossunder) Upper 🔸 MA (Crossover) Lower 🔸 MA (Crossunder) Lower 📉 RSI Divergences: 🔸 RSI Divergence Bull 🔸 RSI Divergence Bear 📉 RSI Forecast: 🔸 RSI (Crossover) MA Forecast 🔸 RSI (Crossunder) MA Forecast 🔸 RSI Forecast 1 > MA Forecast 1 🔸 RSI Forecast 1 < MA Forecast 1 🔸 RSI Forecast 2 > MA Forecast 2 🔸 RSI Forecast 2 < MA Forecast 2 🔸 RSI Forecast 3 > MA Forecast 3 🔸 RSI Forecast 3 < MA Forecast 3 🔸 RSI Forecast 4 > MA Forecast 4 🔸 RSI Forecast 4 < MA Forecast 4 🔸 RSI Forecast 5 > MA Forecast 5 🔸 RSI Forecast 5 < MA Forecast 5 🔸 RSI Forecast 6 > MA Forecast 6 🔸 RSI Forecast 6 < MA Forecast 6 🔸 RSI Forecast 7 > MA Forecast 7 🔸 RSI Forecast 7 < MA Forecast 7 🔸 RSI Forecast 8 > MA Forecast 8 🔸 RSI Forecast 8 < MA Forecast 8 🔸 RSI Forecast 9 > MA Forecast 9 🔸 RSI Forecast 9 < MA Forecast 9 🔸 RSI Forecast 10 > MA Forecast 10 🔸 RSI Forecast 10 < MA Forecast 10 🔸 RSI Forecast 11 > MA Forecast 11 🔸 RSI Forecast 11 < MA Forecast 11 🔸 RSI Forecast 12 > MA Forecast 12 🔸 RSI Forecast 12 < MA Forecast 12 🔸 RSI Forecast 13 > MA Forecast 13 🔸 RSI Forecast 13 < MA Forecast 13 🔸 RSI Forecast 14 > MA Forecast 14 🔸 RSI Forecast 14 < MA Forecast 14 🔸 RSI Forecast 15 > MA Forecast 15 🔸 RSI Forecast 15 < MA Forecast 15 🔸 RSI Forecast 16 > MA Forecast 16 🔸 RSI Forecast 16 < MA Forecast 16 🔸 RSI Forecast 17 > MA Forecast 17 🔸 RSI Forecast 17 < MA Forecast 17 🔸 RSI Forecast 18 > MA Forecast 18 🔸 RSI Forecast 18 < MA Forecast 18 🔸 RSI Forecast 19 > MA Forecast 19 🔸 RSI Forecast 19 < MA Forecast 19 🔸 RSI Forecast 20 > MA Forecast 20 🔸 RSI Forecast 20 < MA Forecast 20 ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ Linear Regression: (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Linear Regression (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL ______________________________________________________ 📜 SCRIPT : RSI Full Forecast 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
Penunjuk Pine Script®
oleh Titans_Invest
RSI Forecast [Titans_Invest]RSI Forecast Introducing one of the most impressive RSI indicators ever created – arguably the best on TradingView, and potentially the best in the world. RSI Forecast is a visionary evolution of the classic RSI, merging powerful customization with groundbreaking predictive capabilities. While preserving the core principles of traditional RSI, it takes analysis to the next level by allowing users to anticipate potential future RSI movements. Real-Time RSI Forecasting: For the first time ever, an RSI indicator integrates linear regression using the least squares method to accurately forecast the future behavior of the RSI. This innovation empowers traders to stay one step ahead of the market with forward-looking insight. Highly Customizable: Easily adapt the indicator to your personal trading style. Fine-tune a variety of parameters to generate signals perfectly aligned with your strategy. Innovative, Unique, and Powerful: This is the world’s first RSI Forecast to apply this predictive approach using least squares linear regression. A truly elite-level tool designed for traders who want a real edge in the market. ⯁ SCIENTIFIC BASIS LINEAR REGRESSION Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥. The general formula for a simple linear regression is given by: y = β₀ + β₁x + ε Where: y   = is the predicted variable (e.g. future value of RSI) x ​​  = is the explanatory variable (e.g. time or bar index) β0 = is the intercept (value of 𝑦 when 𝑥 = 0) 𝛽1 = is the slope of the line (rate of change) ε   = is the random error term The goal is to estimate the coefficients 𝛽0 and 𝛽1 ​​so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares. ⯁ LEAST SQUARES ESTIMATION To minimize the error between predicted and observed values, we use the following formulas: β₁ = / β₀ = ȳ - β₁x̄ Where: ∑ = sum x̄ = mean of x ȳ = mean of y x_i, y_i = individual values ​​of the variables. Where: x_i and y_i are the means of the independent and dependent variables, respectively. i ranges from 1 to n, the number of observations. These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity. ⯁ LINEAR REGRESSION IN MACHINE LEARNING Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies. By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis. ⯁ VISUAL INTERPRETATION Imagine an RSI time series like this: Time → RSI    → The regression line will smooth these values ​​and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals. ⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED Linear Regression Models the relationship between variables using a straight line. Least Squares Minimizes the sum of squared errors between prediction and reality. Time Series Forecasting Estimates future values ​​based on historical data. Supervised Learning Trains models to predict outputs from known inputs. Statistical Smoothing Reduces noise and reveals underlying trends. ⯁ WHY THIS INDICATOR IS REVOLUTIONARY Scientifically-based: Based on statistical theory and mathematical inference. Unprecedented: First public RSI with least squares predictive modeling. Intelligent: Built with machine learning logic. Practical: Generates forward-thinking signals. Customizable: Flexible for any trading strategy. ⯁ CONCLUSION By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it. RSI Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology. ⯁ Example of simple linear regression, which has one independent variable: ⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ). ⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values ​​using Matlab: ⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables. ⯁ The result of fitting a set of data points with a quadratic function: _______________________________________________________________________ 🥇 This is the world’s first RSI indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________ _________________________________________________ 🔮 Linear Regression: PineScript Technical Parameters 🔮 _________________________________________________ Forecast Types: • Flat: Assumes prices will remain the same. • Linreg: Makes a 'Linear Regression' forecast for n periods. Technical Information: ta.linreg (built-in function) Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values ​​calculated using the least squares method on the source series. Syntax: • Function: ta.linreg() Parameters: • source: Source price series. • length: Number of bars (period). • offset: Offset. • return: Linear regression curve. This function has been cleverly applied to the RSI, making it capable of projecting future values ​​based on past statistical trends. ______________________________________________________ ______________________________________________________ ⯁ WHAT IS THE RSI❓ The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend. ⯁ HOW TO USE THE RSI❓ The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:   •  Overbought: When the RSI is above 70, indicating that the asset may be overbought.   •  Oversold: When the RSI is below 30, indicating that the asset may be oversold.   •  Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions. ______________________________________________________ ______________________________________________________ ⯁ ENTRY CONDITIONS The conditions below are fully flexible and allow for complete customization of the signal. ______________________________________________________ ______________________________________________________ 🔹 CONDITIONS TO BUY 📈 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 📈 RSI Conditions: 🔹 RSI > Upper 🔹 RSI < Upper 🔹 RSI > Lower 🔹 RSI < Lower 🔹 RSI > Middle 🔹 RSI < Middle 🔹 RSI > MA 🔹 RSI < MA 📈 MA Conditions: 🔹 MA > Upper 🔹 MA < Upper 🔹 MA > Lower 🔹 MA < Lower 📈 Crossovers: 🔹 RSI (Crossover) Upper 🔹 RSI (Crossunder) Upper 🔹 RSI (Crossover) Lower 🔹 RSI (Crossunder) Lower 🔹 RSI (Crossover) Middle 🔹 RSI (Crossunder) Middle 🔹 RSI (Crossover) MA 🔹 RSI (Crossunder) MA 🔹 MA (Crossover) Upper 🔹 MA (Crossunder) Upper 🔹 MA (Crossover) Lower 🔹 MA (Crossunder) Lower 📈 RSI Divergences: 🔹 RSI Divergence Bull 🔹 RSI Divergence Bear 📈 RSI Forecast: 🔮 RSI (Crossover) MA Forecast 🔮 RSI (Crossunder) MA Forecast ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________   •  Signal Validity: The signal will remain valid for X bars .   •  Signal Sequence: Configurable as AND or OR . 📉 RSI Conditions: 🔸 RSI > Upper 🔸 RSI < Upper 🔸 RSI > Lower 🔸 RSI < Lower 🔸 RSI > Middle 🔸 RSI < Middle 🔸 RSI > MA 🔸 RSI < MA 📉 MA Conditions: 🔸 MA > Upper 🔸 MA < Upper 🔸 MA > Lower 🔸 MA < Lower 📉 Crossovers: 🔸 RSI (Crossover) Upper 🔸 RSI (Crossunder) Upper 🔸 RSI (Crossover) Lower 🔸 RSI (Crossunder) Lower 🔸 RSI (Crossover) Middle 🔸 RSI (Crossunder) Middle 🔸 RSI (Crossover) MA 🔸 RSI (Crossunder) MA 🔸 MA (Crossover) Upper 🔸 MA (Crossunder) Upper 🔸 MA (Crossover) Lower 🔸 MA (Crossunder) Lower 📉 RSI Divergences: 🔸 RSI Divergence Bull 🔸 RSI Divergence Bear 📉 RSI Forecast: 🔮 RSI (Crossover) MA Forecast 🔮 RSI (Crossunder) MA Forecast ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ Linear Regression: (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Linear Regression (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL ______________________________________________________ 📜 SCRIPT : RSI Forecast 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
Penunjuk Pine Script®
oleh Titans_Invest
3
Average Yield InversionDescription: This script calculates and visualizes the average yield curve spread to identify whether the yield curve is inverted or normal. It takes into account short-term yields (1M, 3M, 6M, 2Y) and long-term yields (10Y, 30Y). Positive values: The curve is normal, indicating long-term yields are higher than short-term yields. This often reflects economic growth expectations. Negative values: The curve is inverted, meaning short-term yields are higher than long-term yields, a potential signal of economic slowdown or recession. Key Features: Calculates the average spread between long-term and short-term yields. Displays a clear graph with a zero-line reference for quick interpretation. Useful for tracking macroeconomic trends and potential market turning points. This tool is perfect for investors, analysts, and economists who need to monitor yield curve dynamics at a glance.
Penunjuk Pine Script®
oleh cryptoradz
[Sharpe projection SGM]Dynamic Support and Resistance: Traces adjustable support and resistance lines based on historical prices, signaling new market barriers. Price Projections and Volatility: Calculates future price projections using moving averages and plots annualized standard deviation-based volatility bands to anticipate price dispersion. Intuitive Coloring: Colors between support and resistance lines show up or down trends, making it easy to analyze quickly. Analytics Dashboard: Displays key metrics such as the Sharpe Ratio, which measures average ROI adjusted for asset volatility Volatility Management for Options Trading: The script helps evaluate strike prices and strategies for options, based on support and resistance levels and projected volatility. Importance of Diversification: It is necessary to diversify investments to reduce risks and stabilize returns. Disclaimer on Past Performance: Past performance does not guarantee future results, projections should be supplemented with other analyses. The script settings can be adjusted according to the specific needs of each user. The mean and standard deviation are two fundamental statistical concepts often represented in a Gaussian curve, or normal distribution. Here's a quick little lesson on these concepts: Average The mean (or arithmetic mean) is the result of the sum of all values in a data set divided by the total number of values. In a data distribution, it represents the center of gravity of the data points. Standard Deviation The standard deviation measures the dispersion of the data relative to its mean. A low standard deviation indicates that the data is clustered near the mean, while a high standard deviation shows that it is more spread out. Gaussian curve The Gaussian curve or normal distribution is a graphical representation showing the probability of distribution of data. It has the shape of a symmetrical bell centered on the middle. The width of the curve is determined by the standard deviation. 68-95-99.7 rule (rule of thumb): Approximately 68% of the data is within one standard deviation of the mean, 95% is within two standard deviations, and 99.7% is within three standard deviations. In statistics, understanding the mean and standard deviation allows you to infer a lot about the nature of the data and its trends, and the Gaussian curve provides an intuitive visualization of this information. In finance, it is crucial to remember that data dispersion can be more random and unpredictable than traditional statistical models like the normal distribution suggest. Financial markets are often affected by unforeseen events or changes in investor behavior, which can result in return distributions with wider standard deviations or non-symmetrical distributions.
Penunjuk Pine Script®
oleh Sigaud
The Investment ClockThe Investment Clock was most likely introduced to the general public in a research paper distributed by Merrill Lynch. It’s a simple yet useful framework for understanding the various stages of the US economic cycle and which asset classes perform best in each stage. The Investment Clock splits the business cycle into four phases, where each phase is comprised of the orientation of growth and inflation relative to their sustainable levels: Reflation phase (6:01 to 8:59): Growth is sluggish and inflation is low. This phase occurs during the heart of a bear market. The economy is plagued by excess capacity and falling demand. This keeps commodity prices low and pulls down inflation. The yield curve steepens as the central bank lowers short-term rates in an attempt to stimulate growth and inflation. Bonds are the best asset class in this phase. Recovery phase (9:01 to 11:59): The central bank’s easing takes effect and begins driving growth to above the trend rate. Though growth picks up, inflation remains low because there’s still excess capacity. Rising growth and low inflation are the Goldilocks phase of every cycle. Stocks are the best asset class in this phase. Overheat phase(12:01 to 2:59): Productivity growth slows and the GDP gap closes causing the economy to bump up against supply constraints. This causes inflation to rise. Rising inflation spurs the central banks to hike rates. As a result, the yield curve begins flattening. With high growth and high inflation, stocks still perform but not as well as in recovery. Volatility returns as bond yields rise and stocks compete with higher yields for capital flows. In this phase, commodities are the best asset class. Stagflation phase (3:01 to 5:59): GDP growth slows but inflation remains high (sidenote: most bear markets are preceded by a 100%+ increase in the price of oil which drives inflation up and causes central banks to tighten). Productivity dives and a wage-price spiral develops as companies raise prices to protect compressing margins. This goes on until there’s a steep rise in unemployment which breaks the cycle. Central banks keep rates high until they reign in inflation. This causes the yield curve to invert. During this phase, cash is the best asset. Additional notes from Merrill Lynch: Cyclicality: When growth is accelerating (12 o'clock), Stocks and Commodities do well. Cyclical sectors like Tech or Steel outperform. When growth is slowing (6 o'clock), Bonds, Cash, and defensives outperform. Duration: When inflation is falling (9 o'clock), discount rates drop and financial assets do well. Investors pay up for long duration Growth stocks. When inflation is rising (3 o'clock), real assets like Commodities and Cash do best. Pricing power is plentiful and short-duration Value stocks outperform. Interest Rate-Sensitives: Banks and Consumer Discretionary stocks are interest-rate sensitive “early cycle” performers, doing best in Reflation and Recovery when central banks are easing and growth is starting to recover. Asset Plays: Some sectors are linked to the performance of an underlying asset. Insurance stocks and Investment Banks are often bond or equity price sensitive, doing well in the Reflation or Recovery phases. Mining stocks are metal price-sensitive, doing well during an Overheat. About the indicator: This indicator suggests iShares ETFs for sector rotation analysis. There are likely other ETFs to consider which have lower fees and are outperforming their sector peers. You may get errors if your chart is set to a different timeframe & ticker other than 1d for symbol/tickers GDPC1 or CPILFESL. Investment Clock settings are based on a "sustainable level" of growth and inflation, which are each slightly subjective depending on the economist and probably have changed since the last time this indicator was updated. Hence, the sustainable levels are customizable in the settings. When I was formally educated I was trained to use average CPI of 3.1% for financial planning purposes, the default for the indicator is 2.5%, and the Medium article backtested and optimized a 2% sustainable inflation rate. Again, user-defined sustainable growth and rates are slightly subjective and will affect results. I have not been trained or even had much experience with MetaTrader code, which is how this indicator was originally coded. See the original Medium article that inspired this indicator if you want to audit & compare code. Hover over info panel for detailed information. Features: Advanced info panel that performs Investment Clock analysis and offers additional hover info such as sector rotation suggestions. Customizable sustainable levels, growth input, and inflation input. Phase background coloring. ⚠ DISCLAIMER: Not financial advice. Not a trading system. DYOR. I am not affiliated with Medium, Macro Ops, iShares, or Merrill Lynch. About the Author: I am a patent-holding inventor, a futures trader, a hobby PineScripter, and a former FINRA Registered Representative.
Penunjuk Pine Script®
oleh BarefootJoey
3
Delta Agnostic Correlation CoefficientVisually see how well a symbol tracks another's movements, without taking price deltas into account. For example, a 1% move on the index and a 5% move on the target will return a DCC value of 1. An index move of 0.5% on the index and a 10% move on the target will also return a DCC value of 1. The same happens for downward moves. The SMA value can be set to smooth the curve. A larger value creates a smoother curve.
Penunjuk Pine Script®
oleh alexjvale
LapseBacktestingTableLibrary "LapseBacktestingMetrics" This library provides a robust set of quantitative backtesting and performance evaluation functions for Pine Script strategies. It’s designed to help traders, quants, and developers assess risk, return, and robustness through detailed statistical metrics — including Sharpe, Sortino, Omega, drawdowns, and trade efficiency. Built to enhance any trading strategy’s evaluation framework, this library allows you to visualize performance with the quantlapseTable() function, producing an interactive on-chart performance table. Credit to EliCobra and BikeLife76 for original concept inspiration. curve(disp_ind)   Retrieves a selected performance curve of your strategy.   Parameters:     disp_ind (simple string): Type of curve to plot. Options include "Equity", "Open Profit", "Net Profit", "Gross Profit".   Returns: (float) Corresponding performance curve value. cleaner(disp_ind, plot)   Filters and displays selected strategy plots for clean visualization.   Parameters:     disp_ind (simple string): Type of display.     plot (simple float): Strategy plot variable.   Returns: (float) Filtered plot value. maxEquityDrawDown()   Calculates the maximum equity drawdown during the strategy’s lifecycle.   Returns: (float) Maximum equity drawdown percentage. maxTradeDrawDown()   Computes the worst intra-trade drawdown among all closed trades.   Returns: (float) Maximum intra-trade drawdown percentage. consecutive_wins()   Finds the highest number of consecutive winning trades.   Returns: (int) Maximum consecutive wins. consecutive_losses()   Finds the highest number of consecutive losing trades.   Returns: (int) Maximum consecutive losses. no_position()   Counts the maximum consecutive bars where no position was held.   Returns: (int) Maximum flat days count. long_profit()   Calculates total profit generated by long positions as a percentage of initial capital.   Returns: (float) Total long profit %. short_profit()   Calculates total profit generated by short positions as a percentage of initial capital.   Returns: (float) Total short profit %. prev_month()   Measures the previous month’s profit or loss based on equity change.   Returns: (float) Monthly equity delta. w_months()   Counts the number of profitable months in the backtest.   Returns: (int) Total winning months. l_months()   Counts the number of losing months in the backtest.   Returns: (int) Total losing months. checktf()   Returns the time-adjusted scaling factor used in Sharpe and Sortino ratio calculations based on chart timeframe.   Returns: (float) Annualization multiplier. stat_calc()   Performs complete statistical computation including drawdowns, Sharpe, Sortino, Omega, trade stats, and profit ratios.   Returns: (array)      . f_colors(x, nv)   Generates a color gradient for performance values, supporting dynamic table visualization.   Parameters:     x (simple string): Metric label name.     nv (simple float): Metric numerical value.   Returns: (color) Gradient color value for table background. quantlapseTable(option, position)   Displays an interactive Performance Table summarizing all major backtesting metrics.   Includes Sharpe, Sortino, Omega, Profit Factor, drawdowns, profitability %, and trade statistics.   Parameters:     option (simple string): Table type — "Full", "Simple", or "None".     position (simple string): Table position — "Top Left", "Middle Right", "Bottom Left", etc.   Returns: (table) On-chart performance visualization table. This library empowers advanced quantitative evaluation directly within Pine Script®, ideal for strategy developers seeking deeper performance diagnostics and intuitive on-chart metrics.
Perpustakaan Pine Script®
oleh QuantLapse
[boitl] Trendfilter🧭 Trend Filter – Curve View (1D / 1H + M15 Check) A multi-timeframe trend filter that blends daily, hourly, and 15-minute data into a smooth, color-coded curve displayed in a separate panel. It visualizes both trend direction and strength while accounting for overextension, providing a reliable “context indicator” for entries and filters. 🔍 Concept The indicator evaluates three timeframes: 1D (Daily) → SMA200 for long-term trend bias 1H (Hourly) → EMA50 for medium-term confirmation 15M (Intraday) → EMA20 + ATR to detect overextension or mean reversion zones It computes a continuous trend score between −1 and +1: +1 → Strong bullish alignment (D1 & H1 both up) −1 → Strong bearish alignment (D1 & H1 both down) ≈ 0 → Neutral, conflicting, or overextended conditions The score is smoothed and normalized for a clean visual curve — green for bullish, red for bearish, with dynamic transparency based on strength. ⚙️ Logic Overview Timeframe Indicator Purpose 1D SMA200 Long-term trend direction 1H EMA50 Medium-term confirmation 15M EMA20 + ATR Overextension control Alignment between D1 and H1 defines clear trend bias Conflicts between them reduce the trend score M15 overextension (price far from EMA20) softens the signal further The result is a responsive trend-strength oscillator, ideal for multi-timeframe setups. 🧩 Use Cases As a trend filter for strategies (e.g. allow entries only if score > 0.3 or < −0.3) As a visual confirmation of higher-timeframe direction To avoid trades during conflict or exhaustion 💡 Visualization Single curve (area plot): Green = bullish bias Red = bearish bias Transparency increases with weaker trend Background colors: 🟠 Orange → D1/H1 conflict 🔴 Light red → M15 overextension active Optional: binary alignment line (+1 / 0 / −1) for simplified display ⚙️ Parameters Proximity to EMA20 (M15) = X×ATR → defines “near” condition Overextension threshold = X×ATR → sets exhaustion boundary EMA smoothing → reduces noise for a smoother score Toggle overextension impact on/off
Penunjuk Pine Script®
oleh boitl
Triple Gaussian Smoothed Ribbon [BOSWaves]Triple Gaussian Smoothed Ribbon – Adaptive Gaussian Framework Overview The Triple Gaussian Smoothed Ribbon is a next-generation market visualization framework built on the principles of Gaussian filtering - a mathematical model from digital signal processing designed to remove noise while preserving the integrity of the underlying trend. Unlike conventional moving averages that suffer from phase lag and overreaction to volatility spikes, Gaussian smoothing produces a symmetrical, low-lag curve that isolates meaningful directional shifts with exceptional clarity. Developed under the Adaptive Gaussian Framework, this indicator extends the classical Gaussian model into a multi-stage smoothing and visualization system. By layering three progressive Gaussian filters and rendering their interactions as a gradient-based ribbon field, it translates market energy into a coherent, visually structured trend environment. Each ribbon layer represents a progressively smoothed component of price motion, producing a high-fidelity gradient field that evolves in sync with real-time trend strength and momentum. The result is a uniquely fluid trend and reversal detection system - one that feels organic, adapts seamlessly across timeframes, and reveals hidden transitions in market structure long before traditional indicators confirm them. Theoretical Foundation The Gaussian filter, derived from the Gaussian function developed by Carl Friedrich Gauss in 1809, operates on the principle of weighted symmetry, assigning higher importance to central price data while tapering influence toward historical extremes following a bell-curve distribution. This symmetrical design minimizes phase distortion and smooths without introducing lag spikes — a stark contrast to exponential or linear filters that sacrifice temporal accuracy for responsiveness. By cascading three Gaussian stages in sequence, the indicator creates a multi-frequency decomposition of price action: The first stage captures immediate trend transitions. The second absorbs mid-term volatility ripples. The third stabilizes structural directionality. The final composite ribbon reflects the market’s dominant frequency - a smoothed yet reactive trend spine - while an independent, heavier Gaussian smoothing serves as a reference layer to gauge whether the primary motion leads or lags relative to broader market structure. This multi-layered Gaussian framework effectively replicates the behavior of a signal-processing filter bank: isolating meaningful cyclical movements, suppressing random noise, and revealing phase shifts with minimal delay. How It Works Triple Gaussian Core Price data is passed through three successive Gaussian smoothing stages, each refining the trend further and removing higher-frequency distortions. The result is a fluid, continuously adaptive baseline that responds naturally to directional changes without overshooting or flattening key inflection points. Adaptive Ribbon Architecture The indicator visualizes its internal dynamics through a five-layer gradient ribbon. Each layer represents a progressively delayed Gaussian curve, creating a color field that dynamically shifts between bullish and bearish tones. Expanding ribbons indicate accelerating momentum and trend conviction. Compressing ribbons reflect consolidation and volatility contraction. The smooth color gradient provides a real-time depiction of energy buildup or dissipation within the trend, making it visually clear when the market is entering a state of expansion, transition, or exhaustion. Momentum-Weighted Opacity Ribbon transparency adjusts according to normalized momentum strength. As trend force builds, colors intensify and layers become more opaque, signifying conviction. When momentum wanes, ribbons fade - an early visual cue for potential reversals or pauses in trend continuation. Candle Gradient Integration Optional candle coloring ties the chart’s candles to the prevailing Gaussian gradient, allowing traders to view raw price action and smoothed wave dynamics as a unified system. This integration produces a visually coherent chart environment that communicates directional intent instantly. Signal Detection Logic Directional cues emerge when the smoother, broader Gaussian curve crosses the faster-reacting Gaussian line, marking structural inflection points in the filtered trend. Bullish shifts : short-term momentum transitions upward through the long-term baseline after a localized trough. Bearish shifts : momentum declines through the baseline following a local peak. To maintain integrity in choppy markets, the framework applies a trend-strength and separation filter, which blocks weak or overlapping conditions where movement lacks conviction. Interpretation The Triple Gaussian Smoothed Ribbon provides a layered, intuitive read on market structure: Trend Continuation : Expanding ribbons with deep color intensity confirm directional strength. Reversal Phases : Color gradients flip direction, indicating a phase shift or exhaustion point. Compression Zones : Tight, pale ribbons reveal equilibrium phases often preceding breakouts. Momentum Divergence : Fading color intensity despite continued price movement signals weakening conviction. These transitions mirror the natural ebb and flow of market energy - captured through the Gaussian filter’s ability to represent smooth curvature without distortion. Strategy Integration Trend Following Engage during strong directional expansions. When ribbons widen and color gradients intensify, the trend is accelerating with high confidence. Reversal Identification Monitor for full gradient inversion and fading momentum opacity. These conditions often precede transitional phases and early reversals. Breakout Anticipation Flat, compressed ribbons signal low volatility and energy buildup. A sudden gradient expansion with renewed opacity confirms breakout initiation. Multi-Timeframe Alignment Use higher timeframes to establish directional bias and lower timeframes for entry during compression-to-expansion transitions. Technical Implementation Details Triple Gaussian Stack : Sequential smoothing stages produce low-lag, high-purity signals. Adaptive Ribbon Rendering : Five-layer Gaussian visualization for gradient-based trend depth. Momentum Normalization : Opacity dynamically tied to trend strength and volatility context. Consolidation Filter : Suppresses false signals in low-energy or range-bound conditions. Integrated Candle Mode : Optional color synchronization with underlying gradient flow. Alert System : Built-in notifications for bullish and bearish transitions. This structure blends the precision of digital signal processing with the readability of visual market analysis, creating a clean but information-rich framework. Optimal Application Parameters Asset Recommendations Cryptocurrency : Higher smoothing and sigma for stability under volatility. Forex : Balanced parameters for cycle identification and reduced noise. Equities : Moderate Gaussian length for responsive yet stable trend reads. Indices & Futures : Longer smoothing periods for structural confirmation. Timeframe Recommendations Scalping (1 - 5m) : Use shorter smoothing for fast reactivity. Intraday (15m - 1h) : Mid-length Gaussian chain for balance. Swing (4h - 1D) : Prioritize clarity and opacity-driven trend phases. Position (Daily - Weekly) : Longer smoothing to capture macro rhythm. Performance Characteristics Most Effective In : Trending markets with recurring volatility cycles. Transitional phases where early directional confirmation is crucial. Less Effective In: Ultra-low volume markets with erratic tick data. Random, micro-chop conditions with no structural flow. Integration Guidelines Pair with volatility or volume expansion tools for enhanced breakout confirmation. Use ribbon compression to anticipate volatility shifts. Align entries with gradient expansion in the dominant color direction. Scale position size relative to opacity strength and ribbon width. Disclaimer The Triple Gaussian Smoothed Ribbon – Adaptive Gaussian Framework is designed as a signal visualization and trend interpretation tool, not a standalone trading system. Its accuracy depends on appropriate parameter tuning, contextual confirmation, and disciplined risk management. It should be applied as part of a comprehensive technical or algorithmic trading strategy.
Penunjuk Pine Script®
oleh BOSWaves
3
Big Mo’s Glaskugel — Macro Drawdown Risk (v1.1.2)What it does / what you see An at-a-glance drawdown-risk oscillator that blends several macro US signals. • A smooth, color-blended line (green→orange→red) shows the scaled risk score (0–100). • Subtle shading marks “re-steepen warning windows” (starts when the yield curve re-steepens after an inversion; ends on normalization/cool-down). • A compact status table summarizes: overall risk level, Yield Curve (10y–3m), Credit Stress (Baa–10y), Economy (LEI), and Valuation (CAPE). Data used & why Yield Curve (10y–3m) — FRED:T10Y3M. Inversions and subsequent re-steepens often precede recessions/equity drawdowns. Credit Stress — FRED:BAA10Y vs its 1-year average (deviation in bps). Widening credit spreads flag tightening financial conditions. Economy (LEI) — ECONOMICS:USLEI. 6-month annualized growth below a cutoff highlights macro deterioration. Valuation (CAPE) — SHILLER_PE_RATIO_MONTH. Elevated valuations can amplify downside risk. VIX spikes — optional boost that recognizes sudden risk repricings. Important disclaimer This is not a reliable or predictive indicator in all regimes. No guarantees or warranties of any kind are provided. It is not financial advice. Signals can be early, late, or wrong. That said, it leans on well-studied warning factors (yield-curve dynamics, credit spreads, LEI weakness, valuation extremes) that have flagged major market downturns in the past. Key customization / tweaks Weights for each component (Yield, Credit, LEI, VIX, CAPE). Thresholds: yield inversion months, re-steepen lookback, credit-stress bps, LEI cutoff, CAPE level, VIX spike levels. Re-steepen boost: enable/disable, base points, half-life decay. Shading behavior: cool-down bars to “unwarn,” max warning duration, only shade when risk ≠ green. Scaling & smoothing: dynamic rolling max, EMA length, yellow/red thresholds. Status table: position, and a snapshot mode to view values at a chosen historical time.
Penunjuk Pine Script®
oleh BigMo_GQ
Forecasting Quadratic Regression [UPDATED V6] Forecasting Quadratic Regression applies a second-degree polynomial regression model to price data, offering a non-linear alternative to traditional linear regression. By fitting a quadratic curve of the form: y=a+bx+cx2 the indicator captures both directional trend and curvature, allowing traders to detect momentum shifts earlier than with straight-line models. 🔹 Core Features Fits a quadratic regression curve to user-defined lookback periods Extends the fitted curve forward to generate forecast projections Calculates slope curvature to highlight trend acceleration or deceleration Adapts dynamically as new bars are added 🔹 Trading Applications Identify potential reversal zones when the curve inflects (2nd derivative sign change) Forecast near-term mean reversion targets or extended trend continuations Filter trades by measuring momentum curvature rather than linear slope Visualize higher-order structure in price beyond standard regression lines ⚠️ Note: This model is statistical and assumes past curvature informs short-term future price paths. It should be combined with confirmation signals (volume, oscillators, support/resistance) to reduce false inflection points.
Penunjuk Pine Script®
oleh JEDSBN
Advanced Market TheoryADVANCED MARKET THEORY (AMT) This is not an indicator. It is a lens through which to see the true nature of the market. Welcome to the definitive application of Auction Market Theory. What you have before you is the culmination of decades of market theory, fused with state-of-the-art data analysis and visual engineering. It is an institutional-grade intelligence engine designed for the serious trader who seeks to move beyond simplistic indicators and understand the fundamental forces that drive price. This guide is your complete reference. Read it. Study it. Internalize it. The market is a complex story, and this tool is the language with which to read it. PART I: THE GRAND THEORY - A UNIVERSE IN AN AUCTION To understand the market, you must first understand its purpose. The market is a mechanism of discovery, organized by a continuous, two-way auction. This foundational concept was pioneered by the legendary trader J. Peter Steidlmayer at the Chicago Board of Trade in the 1980s. He observed that beneath the chaotic facade of ticking prices lies a beautifully organized structure. The market's primary function is not to go up or down, but to facilitate trade by seeking a price level that encourages the maximum amount of interaction between buyers and sellers. This price is "value." The Organizing Principle: The Normal Distribution Over any given period, the market's activity will naturally form a bell curve (a normal distribution) turned on its side. This is the blueprint of the auction. The Point of Control (POC): This is the peak of the bell curve—the single price level where the most trade occurred. It represents the point of maximum consensus, the "fairest price" as determined by the market participants. It is the gravitational center of the session. The Value Area (VA): This is the heart of the bell curve, typically containing 70% of the session's activity (one standard deviation). This is the zone of "accepted value." Prices within this area are considered fair and are where the market is most comfortable conducting business. The Extremes: The thin areas at the top and bottom of the curve are the "unfair" prices. These are levels where one side of the auction (buyers at the top, sellers at the bottom) was shut off, and trade was quickly rejected. These are areas of emotional trading and excess. The Narrative of the Day: Balance vs. Imbalance Every trading session is a story of the market's search for value. Balance: When the market rotates and builds a symmetrical, bell-shaped profile, it is in a state of balance . Buyers and sellers are in agreement, and the market is range-bound. Imbalance: When the market moves decisively away from a balanced area, it is in a state of imbalance . This is a trend. The market is actively seeking new information and a new area of value because the old one was rejected. Your Purpose as a Trader Your job is to read this story in real-time. Are we in balance or imbalance? Is the auction succeeding or failing at these new prices? The Advanced Market Theory engine is your Rosetta Stone to translate this complex narrative into actionable intelligence. PART II: THE AMT ENGINE - AN EVOLUTION IN MARKET VISION A standard market profile tool shows you a picture. The AMT Engine gives you the architect's full schematics, the engineer's stress tests, and the psychologist's behavioral analysis, all at once. This is what makes it the Advanced Market Theory. We have fused the timeless principles with layers of modern intelligence: TRINITY ANALYSIS: You can view the market through three distinct lenses. A Volume Profile shows where the money traded. A TPO (Time) Profile shows where the market spent its time. The revolutionary Hybrid Profile fuses both, giving you a complete picture of market conviction—marrying volume with duration. AUTOMATED STRUCTURAL DECODING: The engine acts as your automated analyst, identifying critical structural phenomena in real-time: Poor Highs/Lows: Weak auction points that signal a high probability of reversal. Single Prints & Ledges: Footprints of rapid, aggressive market moves and areas of strong institutional acceptance. Day Type Classification: The engine analyzes the session's personality as it develops ("Trend Day," "Normal Day," etc.), allowing you to adapt your strategy to the market's current character. MACRO & MICRO FUSION: Via the Composite Profile , the engine merges weeks of data to reveal the major institutional battlegrounds that govern long-term price action. You can see the daily skirmish and the multi-month war on a single chart. ORDER FLOW INTELLIGENCE: The ultimate advancement is the integrated Cumulative Volume Delta (CVD) engine. This moves beyond structure to analyze the raw aggression of buyers versus sellers. It is your window into the market's soul, automatically detecting critical Divergences that often precede major trend shifts. ADAPTIVE SIGNALING: The engine's signal generation is not static; it is a thinking system. It evaluates setups based on a multi-factor Confluence Score , understands the market Regime (e.g., High Volatility), and adjusts its own confidence ( Probability % ) based on the complete context. This is not a tool that gives you signals. This is a tool that gives you understanding . PART III: THE VISUAL KEY - A LEXICON OF MARKET STRUCTURE Every element on your chart is a piece of information. This is your guide to reading it fluently. --- THE CORE ARCHITECTURE --- The Profile Histogram: The primary visual on the left of each session. Its shape is the story. A thin profile is a trend; a fat, symmetrical profile is balance. Blue Box : The zone of accepted, "fair" value. The heart of the session's business. Bright Orange Line & Label : The Point of Control. The gravitational center. The price of maximum consensus. The most significant intraday level. Dashed Blue Lines & Labels : The boundaries of value. Critical inflection points where the market decides to either remain in balance or seek value elsewhere. Dashed Cyan Lines & Labels : The major, long-term structural levels derived from weeks of data. These are institutional reference points and carry immense weight. Treat them as primary support and resistance. Dashed Orange Lines & Labels : Marks a Poor or Unfinished Auction . These represent emotional, weak extremes and are high-probability targets for future price action. Diamond Markers : Mark Single Prints , which are footprints of aggressive, one-sided moves that left a "liquidity vacuum." Price is often drawn back to these levels to "repair" the poor structure. Arrow Markers : Mark Ledges , which are areas of strong horizontal acceptance. They often act as powerful support/resistance in the future. Dotted Gray Lines & Labels : The projected daily range based on multiples of the Initial Balance . Use them to set realistic profit targets and gauge the day's potential. --- THE SIGNAL SUITE --- Colored Triangles : These are your high-probability entry signals. The color is a strategic playbook: Gold Triangle : ELITE Signal. An A+ setup with overwhelming confluence. This is the highest quality signal the engine can produce. Yellow Triangle : FADE Signal. A counter-trend setup against an exhausted move at a structural extreme. Cyan Triangle : BREAKOUT Signal. A momentum setup attempting to capitalize on a breakout from the value area. Purple Triangle : ROTATION Signal. A mean-reversion setup within the value area, typically from one edge towards the POC. Magenta Triangle : LIQUIDITY Signal. A sophisticated setup that identifies a "stop run" or liquidity sweep. Percentage Number: The engine's calculated probability of success . This is not a guarantee, but a data-driven confidence score. Dotted Gray Line: The signal's Entry Price . Dashed Green Lines: The calculated Take Profit Targets . Dashed Red Line: The calculated Stop Loss level. PART IV: THE DASHBOARD - YOUR STRATEGIC COMMAND CENTER The dashboard is your real-time intelligence briefing. It synthesizes all the engine's analysis into a clear, concise, and constantly updating summary. --- CURRENT SESSION --- POC, VAH, VAL: The live values for the core structure. Profile Shape: Is the current auction top-heavy ( b-shaped ), bottom-heavy ( P-shaped ), or balanced ( D-shaped )? VA Width: Is the value area expanding (trending) or contracting (balancing)? Day Type: The engine's judgment on the day's personality. Use this to select the right strategy. IB Range & POC Trend: Key metrics for understanding the opening sentiment and its evolution. --- CVD ANALYSIS --- Session CVD: The raw order flow. Is there more net buying or selling pressure in this session? CVD Trend & DIVERGENCE: This is your order flow intelligence. Is the order flow confirming the price action? If "DIVERGENCE" flashes, it is a critical, high-alert warning of a potential reversal. --- MARKET METRICS --- Volume, ATR, RSI: Your standard contextual metrics, providing a quick read on activity, volatility, and momentum. Regime: The engine's assessment of the broad market environment: High Volatility (favor breakouts), Low Volatility (favor mean reversion), or Normal . --- PROFILE STATS, COMPOSITE, & STRUCTURE --- These sections give you a quick quantitative summary of the profile structure, the major long-term Composite levels, and any active Poor Structures. --- SIGNAL TYPES & ACTIVE SIGNAL --- A permanent key to the signal colors and their meanings, along with the full details of the most recent active signal: its Type , Probability , Entry , Stop , and Target . PART V: THE INPUTS MENU - CALIBRATING YOUR LENS This engine is designed to be calibrated to your specific needs as a trader. Every input is a lever. This is not a "one size fits all" tool. The extensive tooltips are your built-in user manual, but here are the key areas of focus: --- MARKET PROFILE ENGINE --- Profile Mode: This is the most fundamental choice. Volume is the standard for price-based support and resistance. TPO is for analyzing time-based acceptance. Hybrid is the professional's choice, fusing both for a complete picture. Profile Resolution: This is your zoom lens. Lower values for scalping and intraday precision. Higher values for a cleaner, big-picture view suitable for swing trading. Composite Sessions: Your timeframe for macro analysis. 5-10 sessions for a weekly view; 20-30 sessions for a monthly, structural view. --- SESSION & VALUE AREA --- These settings must be configured correctly for your specific asset. The Session times are critical. The Initial Balance should reflect the key opening period for your market (60 minutes is standard for equities). --- SIGNAL ENGINE & RISK MANAGEMENT --- Signal Mode: THIS IS YOUR PERSONAL RISK PROFILE. Set it to Conservative to see only the absolute best A+ setups. Use Elite or Balanced for a standard approach. Use Aggressive only if you are an experienced scalper comfortable with managing more frequent, lower-probability setups. ATR Multipliers: This suite gives you full, dynamic control over your risk/reward parameters. You can precisely define your initial stop loss distance and profit targets based on the market's current volatility. A FINAL WORD FROM THE ARCHITECT The creation of this engine was a journey into the very heart of market dynamics. It was born from a frustrating truth: that the most profound market theories were often confined to books and expensive institutional platforms, inaccessible to the modern retail trader. The goal was to bridge that gap. The challenge was monumental. Making each discrete system—the volume profile, the TPO counter, the composite engine, the CVD tracker, the signal generator, the dynamic dashboard—work was a task in itself. But the true struggle, the frustrating, painstaking process that consumed countless hours, was making them work in unison . It was about ensuring the CVD analysis could intelligently inform the signal engine, that the day type classification could adjust the probability scores, and that the composite levels could provide context to the intraday structure, all in a seamless, real-time dance of data. This engine is the result of that relentless pursuit of integration. It is built on the belief that a trader's greatest asset is not a signal, but clarity . It was designed to clear the noise, to organize the chaos, and to present the elegant, underlying logic of the market auction so that you can make better, more informed, and more confident decisions. It is now in your hands. Use it not as a crutch, but as a lens. See the market for what it truly is. "The market can remain irrational longer than you can remain solvent." - John Maynard Keynes DISCLAIMER This script is an advanced analytical tool provided for informational and educational purposes only. It is not financial advice. All trading involves substantial risk, and past performance is not indicative of future results. The signals, probabilities, and metrics generated by this indicator do not constitute a recommendation to buy or sell any financial instrument. You, the user, are solely responsible for all trading decisions, risk management, and outcomes. Use this tool to supplement your own analysis and trading strategy. PUBLISHING CATEGORIES Volume Profile Market Profile Order Flow
Penunjuk Pine Script®
oleh DskyzInvestments
30
Bear Market Probability Model# Bear Market Probability Model: A Multi-Factor Risk Assessment Framework The Bear Market Probability Model represents a comprehensive quantitative framework for assessing systemic market risk through the integration of 13 distinct risk factors across four analytical categories: macroeconomic indicators, technical analysis factors, market sentiment measures, and market breadth metrics. This indicator synthesizes established financial research methodologies to provide real-time probabilistic assessments of impending bear market conditions, offering institutional-grade risk management capabilities to retail and professional traders alike. ## Theoretical Foundation ### Historical Context of Bear Market Prediction Bear market prediction has been a central focus of financial research since the seminal work of Dow (1901) and the subsequent development of technical analysis theory. The challenge of predicting market downturns gained renewed academic attention following the market crashes of 1929, 1987, 2000, and 2008, leading to the development of sophisticated multi-factor models. Fama and French (1989) demonstrated that certain financial variables possess predictive power for stock returns, particularly during market stress periods. Their three-factor model laid the groundwork for multi-dimensional risk assessment, which this indicator extends through the incorporation of real-time market microstructure data. ### Methodological Framework The model employs a weighted composite scoring methodology based on the theoretical framework established by Campbell and Shiller (1998) for market valuation assessment, extended through the incorporation of high-frequency sentiment and technical indicators as proposed by Baker and Wurgler (2006) in their seminal work on investor sentiment. The mathematical foundation follows the general form: Bear Market Probability = Σ(Wi × Ci) / ΣWi × 100 Where: - Wi = Category weight (i = 1,2,3,4) - Ci = Normalized category score - Categories: Macroeconomic, Technical, Sentiment, Breadth ## Component Analysis ### 1. Macroeconomic Risk Factors #### Yield Curve Analysis The inclusion of yield curve inversion as a primary predictor follows extensive research by Estrella and Mishkin (1998), who demonstrated that the term spread between 3-month and 10-year Treasury securities has historically preceded all major recessions since 1969. The model incorporates both the 2Y-10Y and 3M-10Y spreads to capture different aspects of monetary policy expectations. Implementation: - 2Y-10Y Spread: Captures market expectations of monetary policy trajectory - 3M-10Y Spread: Traditional recession predictor with 12-18 month lead time Scientific Basis: Harvey (1988) and subsequent research by Ang, Piazzesi, and Wei (2006) established the theoretical foundation linking yield curve inversions to economic contractions through the expectations hypothesis of the term structure. #### Credit Risk Premium Assessment High-yield credit spreads serve as a real-time gauge of systemic risk, following the methodology established by Gilchrist and Zakrajšek (2012) in their excess bond premium research. The model incorporates the ICE BofA High Yield Master II Option-Adjusted Spread as a proxy for credit market stress. Threshold Calibration: - Normal conditions: < 350 basis points - Elevated risk: 350-500 basis points - Severe stress: > 500 basis points #### Currency and Commodity Stress Indicators The US Dollar Index (DXY) momentum serves as a risk-off indicator, while the Gold-to-Oil ratio captures commodity market stress dynamics. This approach follows the methodology of Akram (2009) and Beckmann, Berger, and Czudaj (2015) in analyzing commodity-currency relationships during market stress. ### 2. Technical Analysis Factors #### Multi-Timeframe Moving Average Analysis The technical component incorporates the well-established moving average convergence methodology, drawing from the work of Brock, Lakonishok, and LeBaron (1992), who provided empirical evidence for the profitability of technical trading rules. Implementation: - Price relative to 50-day and 200-day simple moving averages - Moving average convergence/divergence analysis - Multi-timeframe MACD assessment (daily and weekly) #### Momentum and Volatility Analysis The model integrates Relative Strength Index (RSI) analysis following Wilder's (1978) original methodology, combined with maximum drawdown analysis based on the work of Magdon-Ismail and Atiya (2004) on optimal drawdown measurement. ### 3. Market Sentiment Factors #### Volatility Index Analysis The VIX component follows the established research of Whaley (2009) and subsequent work by Bekaert and Hoerova (2014) on VIX as a predictor of market stress. The model incorporates both absolute VIX levels and relative VIX spikes compared to the 20-day moving average. Calibration: - Low volatility: VIX < 20 - Elevated concern: VIX 20-25 - High fear: VIX > 25 - Panic conditions: VIX > 30 #### Put-Call Ratio Analysis Options flow analysis through put-call ratios provides insight into sophisticated investor positioning, following the methodology established by Pan and Poteshman (2006) in their analysis of informed trading in options markets. ### 4. Market Breadth Factors #### Advance-Decline Analysis Market breadth assessment follows the classic work of Fosback (1976) and subsequent research by Brown and Cliff (2004) on market breadth as a predictor of future returns. Components: - Daily advance-decline ratio - Advance-decline line momentum - McClellan Oscillator (Ema19 - Ema39 of A-D difference) #### New Highs-New Lows Analysis The new highs-new lows ratio serves as a market leadership indicator, based on the research of Zweig (1986) and validated in academic literature by Zarowin (1990). ## Dynamic Threshold Methodology The model incorporates adaptive thresholds based on rolling volatility and trend analysis, following the methodology established by Pagan and Sossounov (2003) for business cycle dating. This approach allows the model to adjust sensitivity based on prevailing market conditions. Dynamic Threshold Calculation: - Warning Level: Base threshold ± (Volatility × 1.0) - Danger Level: Base threshold ± (Volatility × 1.5) - Bounds: ±10-20 points from base threshold ## Professional Implementation ### Institutional Usage Patterns Professional risk managers typically employ multi-factor bear market models in several contexts: #### 1. Portfolio Risk Management - Tactical Asset Allocation: Reducing equity exposure when probability exceeds 60-70% - Hedging Strategies: Implementing protective puts or VIX calls when warning thresholds are breached - Sector Rotation: Shifting from growth to defensive sectors during elevated risk periods #### 2. Risk Budgeting - Value-at-Risk Adjustment: Incorporating bear market probability into VaR calculations - Stress Testing: Using probability levels to calibrate stress test scenarios - Capital Requirements: Adjusting regulatory capital based on systemic risk assessment #### 3. Client Communication - Risk Reporting: Quantifying market risk for client presentations - Investment Committee Decisions: Providing objective risk metrics for strategic decisions - Performance Attribution: Explaining defensive positioning during market stress ### Implementation Framework Professional traders typically implement such models through: #### Signal Hierarchy: 1. Probability < 30%: Normal risk positioning 2. Probability 30-50%: Increased hedging, reduced leverage 3. Probability 50-70%: Defensive positioning, cash building 4. Probability > 70%: Maximum defensive posture, short exposure consideration #### Risk Management Integration: - Position Sizing: Inverse relationship between probability and position size - Stop-Loss Adjustment: Tighter stops during elevated risk periods - Correlation Monitoring: Increased attention to cross-asset correlations ## Strengths and Advantages ### 1. Comprehensive Coverage The model's primary strength lies in its multi-dimensional approach, avoiding the single-factor bias that has historically plagued market timing models. By incorporating macroeconomic, technical, sentiment, and breadth factors, the model provides robust risk assessment across different market regimes. ### 2. Dynamic Adaptability The adaptive threshold mechanism allows the model to adjust sensitivity based on prevailing volatility conditions, reducing false signals during low-volatility periods and maintaining sensitivity during high-volatility regimes. ### 3. Real-Time Processing Unlike traditional academic models that rely on monthly or quarterly data, this indicator processes daily market data, providing timely risk assessment for active portfolio management. ### 4. Transparency and Interpretability The component-based structure allows users to understand which factors are driving risk assessment, enabling informed decision-making about model signals. ### 5. Historical Validation Each component has been validated in academic literature, providing theoretical foundation for the model's predictive power. ## Limitations and Weaknesses ### 1. Data Dependencies The model's effectiveness depends heavily on the availability and quality of real-time economic data. Federal Reserve Economic Data (FRED) updates may have lags that could impact model responsiveness during rapidly evolving market conditions. ### 2. Regime Change Sensitivity Like most quantitative models, the indicator may struggle during unprecedented market conditions or structural regime changes where historical relationships break down (Taleb, 2007). ### 3. False Signal Risk Multi-factor models inherently face the challenge of balancing sensitivity with specificity. The model may generate false positive signals during normal market volatility periods. ### 4. Currency and Geographic Bias The model focuses primarily on US market indicators, potentially limiting its effectiveness for global portfolio management or non-USD denominated assets. ### 5. Correlation Breakdown During extreme market stress, correlations between risk factors may increase dramatically, reducing the model's diversification benefits (Forbes and Rigobon, 2002). ## References Akram, Q. F. (2009). Commodity prices, interest rates and the dollar. Energy Economics, 31(6), 838-851. Ang, A., Piazzesi, M., & Wei, M. (2006). What does the yield curve tell us about GDP growth? Journal of Econometrics, 131(1-2), 359-403. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680. Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636. Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292. Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16-24. Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181-192. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764. Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27. Campbell, J. Y., & Shiller, R. J. (1998). Valuation ratios and the long-run stock market outlook. The Journal of Portfolio Management, 24(2), 11-26. Dow, C. H. (1901). Scientific stock speculation. The Magazine of Wall Street. Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61. Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49. Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261. Fosback, N. G. (1976). Stock market logic: A sophisticated approach to profits on Wall Street. The Institute for Econometric Research. Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692-1720. Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305-333. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. Magdon-Ismail, M., & Atiya, A. F. (2004). Maximum drawdown. Risk, 17(10), 99-102. Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220. Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46. Pan, J., & Poteshman, A. M. (2006). The information in option volume for future stock prices. The Review of Financial Studies, 19(3), 871-908. Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House. Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105. Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research. Zarowin, P. (1990). Size, seasonality, and stock market overreaction. Journal of Financial and Quantitative Analysis, 25(1), 113-125. Zweig, M. E. (1986). Winning on Wall Street. Warner Books.
Penunjuk Pine Script®
oleh EdgeTools
17
Parsifal.Swing.TrendScoreThe Parsifal.Swing.TrendScore indicator is a module within the Parsifal Swing Suite, which includes a set of swing indicators such as: • Parsifal Swing TrendScore • Parsifal Swing Composite • Parsifal Swing RSI • Parsifal Swing Flow Each module serves as an indicator facilitating judgment of the current swing state in the underlying market. ________________________________________ Background Market movements typically follow a time-varying trend channel within which prices oscillate. These oscillations—or swings—within the trend are inherently tradable. They can be approached: • One-sidedly, aligning with the trend (generally safer), or • Two-sidedly, aiming to profit from mean reversions as well. Note: Mean reversions in strong trends often manifest as sideways consolidations, making one-sided trades more stable. ________________________________________ The Parsifal Swing Suite The modules aim to provide additional insights into the swing state within a trend and offer various trigger points to assist with entry decisions. All modules in the suite act as weak oscillators, meaning they fluctuate within a range but are not bounded like true oscillators (e.g., RSI, which is constrained between 0% and 100%). ________________________________________ The Parsifal.Swing.TrendScore – Specifics The Parsifal.Swing.TrendScore module combines short-term trend data with information about the current swing state, derived from raw price data and classical technical indicators. It provides an indication of how well the short-term trend aligns with the prevailing swing, based on recent market behavior. ________________________________________ How Swing.TrendScore Works The Swing.TrendScore calculates a swing score by collecting data within a bin (i.e., a single candle or time bucket) that signals an upside or downside swing. These signals are then aggregated together with insights from classical swing indicators. Additionally, it calculates a short-term trend score using core technical signals, including: • The Z-score of the price's distance from various EMAs • The slope of EMAs • Other trend-strength signals from additional technical indicators These two components—the swing score and the trend score—are then combined to form the Swing.TrendScore indicator, which evaluates the short-term trend in context with swing behavior. ________________________________________ How to Interpret Swing.TrendScore The trend component enhances Swing.TrendScore’s ability to provide stronger signals when the short-term trend and swing state align. It can also override the swing score; for example, even if a mean reversion appears to be forming, a dominant short-term trend may still control the market behavior. This makes Swing.TrendScore particularly valuable for: • Short-term trend-following strategies • Medium-term swing trading Unlike typical swing indicators, Swing.TrendScore is designed to respond more to medium-term swings rather than short-lived fluctuations. ________________________________________ Behavior and Chart Representation The Swing.TrendScore indicator fluctuates within a range, as most of its components are range-bound (though Z-score components may technically extend beyond). • Historically high or low values may suggest overbought or oversold conditions • The chart displays: o A fast curve (orange) o A slow curve (white) o A shaded background representing the market state • Extreme values followed by curve reversals may signal a developing mean reversion ________________________________________ TrendScore Background Value The Background Value reflects the combined state of the short-term trend and swing: • > 0 (shaded green) → Bullish mode: swing and short-term trend both upward • < 0 (shaded red) → Bearish mode: swing and short-term trend both downward • The absolute value represents the confidence level in the market mode Notably, the Background Value can remain positive during short downswings if the short-term trend remains bullish—and vice versa. ________________________________________ How to Use the Parsifal.Swing.TrendScore Several change points can act as entry triggers or aids: • Fast Trigger: change in slope of the fast signal curve • Trigger: fast line crosses slow line or the slope of the slow signal changes • Slow Trigger: change in sign of the Background Value Examples of these trigger points are illustrated in the accompanying chart. Additionally, market highs and lows aligning with the swing indicator values may serve as pivot points in the evolving price process. ________________________________________ As always, this indicator should be used in conjunction with other tools and market context in live trading. While it provides valuable insight and potential entry points, it does not predict future price action. Instead, it reflects recent tendencies and should be used judiciously. ________________________________________ Extensions The aggregation of information—whether derived from bins or technical indicators—is currently performed via simple averaging. However, this can be modified using alternative weighting schemes, based on: • Historical performance • Relevance of the data • Specific market conditions Smoothing periods used in calculations are also modifiable. In general, the EMAs applied for smoothing can be extended to reflect expectations based on relevance-weighted probability measures. Since EMAs inherently give more weight to recent data, this allows for adaptive smoothing. Additionally, EMAs may be further extended to incorporate negative weights, akin to wavelet transform techniques.
Penunjuk Pine Script®
oleh parsifaltrading
Credit Spread Monitor: HY & IG vs US10Y📉 Credit Spread Monitor: HY & IG vs US10Y This indicator provides a dynamic and visual way to monitor credit spreads relative to the US Treasury benchmark. By comparing High Yield (HY) and Investment Grade (IG) corporate bond yields to the 10-Year US Treasury Yield (US10Y), it helps assess market stress, investor risk appetite, and potential macro turning points. 🔍 What It Does -Calculates credit spreads: HY Spread = BAMLH0A0HYM2EY − US10Y IG Spread = BAMLC0A0CMEY − US10Y -Detects macro risk regimes using statistical thresholds and yield curve signals: 🔴 HY Spread > +2σ → Potential financial stress 🟠 Inverted Yield Curve + HY Spread > 2% → Recession risk 🟢 HY Spread < 1.5% → Risk-on environment -Visually highlights conditions with intuitive background colors for fast decision-making. 📊 Data Sources Explained 🔴 High Yield (HY): BAMLH0A0HYM2EY → ICE BofA US High Yield Index Effective Yield 🔵 Investment Grade (IG): BAMLC0A0CMEY → ICE BofA US Corporate Index Effective Yield ⚪ Treasury 10Y: US10Y → 10-Year US Treasury Yield ⚪ Treasury 2Y: US02Y → 2-Year US Treasury Yield (used to detect curve inversion) ✅ This Indicator Is Ideal For: Macro traders looking to anticipate economic inflection points Portfolio managers monitoring systemic risk or credit cycles Fixed-income analysts tracking the cost of corporate borrowing ETF/Asset allocators identifying shifts between risk-on and risk-off environments 🧠 Why It's Useful This script helps visualize how tight or loose credit conditions are relative to government benchmarks. Since HY spreads typically widen before major downturns, this tool can provide early warning signals. Similarly, compressed spreads may indicate overheating or complacency in risk markets. 🛠️ Practical Use Case: You’re managing a multi-asset portfolio. The HY spread jumps above +2σ while the yield curve remains inverted. You decide to reduce exposure to equities and high-yield bonds and rotate into cash or Treasuries as recession risk rises. 📎 Additional Notes Sourced from FRED (Federal Reserve Economic Data) and TradingView’s bond feeds. Designed to work best on daily resolution, using open prices to ensure consistency across series with different update timings. This script is original, not based on built-in or public templates, and intended to offer educational, statistical, and visual insights for serious market participants.
Penunjuk Pine Script®
oleh jtomasfg
Parsifal.Swing.CompositeThe Parsifal.Swing.Composite indicator is a module within the Parsifal Swing Suite, which includes a set of swing indicators such as: • Parsifal Swing TrendScore • Parsifal Swing Composite • Parsifal Swing RSI • Parsifal Swing Flow Each module serves as an indicator facilitating judgment of the current swing state in the underlying market. ________________________________________ Background Market movements typically follow a time-varying trend channel within which prices oscillate. These oscillations—or swings—within the trend are inherently tradable. They can be approached: • One-sidedly, aligning with the trend (generally safer), or • Two-sidedly, aiming to profit from mean reversions as well. Note: Mean reversions in strong trends often manifest as sideways consolidations, making one-sided trades more stable. ________________________________________ The Parsifal Swing Suite The modules aim to provide additional insights into the swing state within a trend and offer various trigger points to assist with entry decisions. All modules in the suite act as weak oscillators, meaning they fluctuate within a range but are not bounded like true oscillators (e.g., RSI, which is constrained between 0% and 100%). ________________________________________ The Parsifal.Swing.Composite – Specifics This module consolidates multiple insights into price swing behavior, synthesizing them into an indicator reflecting the current swing state. It employs layered bagging and smoothing operations based on standard price inputs (OHLC) and classical technical indicators. The module integrates several slightly different sub-modules. Process overview: 1. Per candle/bin, sub-modules collect directional signals (up/down), with each signal casting a vote. 2. These votes are aggregated via majority counting (bagging) into a single bin vote. 3. Bin votes are then smoothed, typically with short-term EMAs, to create a sub-module vote. 4. These sub-module votes are aggregated and smoothed again to generate the final module vote. The final vote is a score indicating the module’s assessment of the current swing state. While it fluctuates in a range, it's not a true oscillator, as most inputs are normalized via Z-scores (value divided by standard deviation over a period). • Historically high or low values correspond to high or low quantiles, suggesting potential overbought or oversold conditions. • The chart displays a fast (orange) and slow (white) curve against a solid background state. • Extreme values followed by curve reversals may signal upcoming mean-reversions. Background Value: • Value > 0: shaded green → bullish mode • Value < 0: shaded red → bearish mode • The absolute value indicates confidence in the mode. ________________________________________ How to Use the Parsifal.Swing.Composite Several change points in the indicator serve as potential entry triggers: • Fast Trigger: change in slope of the fast curve • Trigger: fast line crossing the slow line or change in the slow curve’s slope • Slow Trigger: change in sign of the background value These are illustrated in the introductory chart. Additionally, market highs and lows aligned with swing values may act as pivot points, support, or resistance levels for evolving price processes. ________________________________________ As always, supplement this indicator with other tools and market information. While it provides valuable insights and potential entry points, it does not predict future prices. It reflects recent tendencies and should be used judiciously. ________________________________________ Extensions All modules in the Parsifal Swing Suite are simple yet adaptable, whether used individually or in combination. Customization options: • Weights in EMAs for smoothing are adjustable • Bin vote aggregation (currently via sum-of-experts) can be modified • Alternative weighting schemes can be tested Advanced options: • Bagging weights may be historical, informational, or relevance-based • Selection algorithms (e.g., ID3, C4.5, CAT) could replace the current bagging approach • EMAs may be generalized into expectations relative to relevance-based probability • Negative weights (akin to wavelet transforms) can be incorporated
Penunjuk Pine Script®
oleh parsifaltrading
Jurik Moving Average (JMA)Overview Jurik Moving Average (JMA) is an adaptive moving average developed by Mark Jurik, widely regarded as one of the most powerful moving averages available to traders. This implementation provides a direct Pine Script translation of the reverse-engineered JMA algorithm What Makes JMA Special Unlike traditional moving averages, JMA adapts to market volatility in real-time. This "triple adaptive" approach allows JMA to: Reduce lag significantly while maintaining exceptional smoothness React quickly during trending markets Filter out noise during consolidation phases Provide clearer trend signals with fewer whipsaws The Triple Adaptive Edge JMA employs a three-stage smoothing process: Preliminary smoothing via an adaptive EMA Secondary smoothing using a Kalman filter with phase adjustment Final smoothing through a unique Jurik adaptive filter This approach combines with a dynamic volatility-based factor (alpha) that adapts to market conditions, making JMA superior to traditional moving averages in most situations. Key Parameters Period : Controls the lookback period (default: 14) Phase : Adjusts the heaviness of the indicator (-100 to 100, default: 0) Positive values reduce lag but may cause overshoot Negative values increase smoothness but reduce responsiveness Power : Smoothing factor (0.1-0.9, default 0.45) Higher values create smoother curves Lower values create more responsive but choppy curves
Penunjuk Pine Script®
oleh mihakralj
1
SMA Strategy Builder: Create & Prove Profitability📄 Pine Script Strategy Description (For Publishing on TradingView) 🎯 Strategy Title: SMA Strategy Builder: Create & Prove Profitability ✨ Description: This tool is designed for traders who want to build, customize, and prove their own SMA-based trading strategies. The strategy tracks capital growth in real-time, providing clear evidence of profitability after each trade. Users can adjust key parameters such as SMA period, take profit levels, and initial capital, making it a flexible solution for backtesting and strategy validation. 🔍 Key Features: ✅ SMA-Based Logic: Core trading logic revolves around the Simple Moving Average (SMA). SMA period is fully adjustable to suit various trading styles. 🎯 Customizable Take Profit (TP): User-defined TP percentages per position. TP line displayed as a Step Line with Breaks for clear segmentation. Visual 🎯TP label for quick identification of profit targets. 💵 Capital Tracking (Proof of Profitability): Initial capital is user-defined. Capital balance updates after each closed trade. Shows both absolute profit/loss and percentage changes for every position. Darker green profit labels for better readability and dark red for losses. 📈 Capital Curve (Performance Visualization): Capital growth curve available (hidden by default, can be enabled via settings). 📏 Dynamic Label Positioning: Label positions adjust dynamically based on the price range. Ensures consistent visibility across low and high-priced assets. ⚡ How It Works: Long Entry: Triggered when the price crosses above the SMA. TP level is calculated as a user-defined percentage above the entry price. Short Entry: Triggered when the price crosses below the SMA. TP level is calculated as a user-defined percentage below the entry price. TP Execution: Positions close immediately once the TP level is reached (no candle close confirmation needed). 🔔 Alerts: 🟩 Long Signal Alert: When the price crosses above the SMA. 🟥 Short Signal Alert: When the price crosses below the SMA. 🎯 TP Alert: When the TP target is reached. ⚙️ Customization Options: 📅 SMA Period: Choose the moving average period that best fits your strategy. 🎯 Take Profit (%): Adjust TP percentages for flexible risk management. 💵 Initial Capital: Set the starting capital for realistic backtesting. 📈 Capital Curve Toggle: Enable or disable the capital curve to track overall performance. 🌟 Why Use This Tool? 🔧 Flexible Strategy Creation: Adjust core parameters and create tailored SMA-based strategies. 📈 Performance Proof: Capital tracking acts as real proof of profitability after each trade. 🎯 Immediate TP Execution: No waiting for candle closures; profits lock in as soon as targets are hit. 💹 Comprehensive Performance Insights: Percentage-based and absolute capital tracking with dynamic visualization. 🏦 Clean Visual Indicators: Strategy insights made clear with dynamic labeling and adjustable visuals. ⚠️ Disclaimer: This script is provided for educational and informational purposes only. Trading financial instruments carries risk, and past performance does not guarantee future results. Always perform your own due diligence before making any trading decisions.
Penunjuk Pine Script®
oleh alpolcaymis
Intrabar DistributionThe Intrabar Distribution publication is an extension of the Intrabar BoxPlot publication. Besides a boxplot, it showcases price and volume distribution using intrabar Lower Timeframe (LTF) values (close) which can be displayed on the chart or in a separate pane. 🔶 USAGE Intrabar Distribution has several features, users can display: Recent candle for comparison against the other features Boxplot of recent candle Price distribution (optionally displayed as a curve) Volume distribution 🔹 Recent candle / Boxplot The middle 50% intrabar close values (Interquartile range, or IQR) are shown as a box, where the upper limit is percentile 75 (p75), and the lower limit is percentile 25 (p25). The dashed lines show the addition/subtraction of 1.5*IQR. All values out of range are considered outliers. They are displayed as white dots within the IQR*1.5 range or white X's when beyond the IQR*3 range (extreme outliers). By showing the middle 50% intrabar values through a box, we can more easily see where the intrabar activity is mainly situated. Note in the example above an upward-directed candle with a negative volume delta, displayed as a red box and dot (see further). As seen in the following example, compared against the recent candle (grey candle at the left), most of the intrabar activity lies just beneath the opening price. Note that results will be more accurate when more data is available, which can be done by making the difference between the current timeframe and the intrabar timeframe large enough. 🔹 Price / Volume distribution The price and volume distribution can be helpful for highlighting areas of interest. Here, we can see two areas where intrabar closing prices are mainly positioned. The following example shows three successive bars. The recent bar is displayed on the left side, together with the volume distribution. The boxplot and price distribution are displayed on the right. You can see the difference between volume and price distribution. At the first bar, most price activity is at the top, while most of the volume was generated at the bottom; in other words, the price got briefly in the bottom region, with high volume before it returned. At the second bar, price and volume are relatively equally distributed, which fits for indecisiveness. The third bar shows more volume at a higher region; most intrabar closing prices are above the closing price. Following example shows the same with 'Curve shaped' enabled (Settings: 'Price Distribution') When 'Curve shaped' is enabled, lines/labels are shown with the standard deviation distance. A blue 'guide line' can be enabled for easier interpretation. 🔹 Volume Delta When there is a discrepancy between the delta volume and direction of the candle, this will be displayed as follows: Red candle: when the sum of the volume of green intrabars is higher than the sum of the volume of red intrabars, the 'mean dot' will be coloured green. Green candle: when the sum of the volume of red intrabars is higher than the sum of the volume of green intrabars, the 'mean dot' will be coloured red. 🔶 DETAILS The intrabar values are sorted and split in parts/sections. The number of values in each section is displayed as a white line The same principle applies to volume distribution, where the sum of volume per section is displayed as an orange area. The boxplot displays several price values Last close price Highest / lowest intrabar close price Median p25 / p75 🔹 LTF settings When 'Auto' is enabled (Settings, LTF), the LTF will be the nearest possible x times smaller TF than the current TF. When 'Premium' is disabled, the minimum TF will always be 1 minute to ensure TradingView plans lower than Premium don't get an error. Examples with current Daily TF (when Premium is enabled): 500 : 3 minute LTF 1500 (default): 1 minute LTF 5000: 30 seconds LTF (1 minute if Premium is disabled) 🔶 SETTINGS Location: Chart / Pane (when pane is opted, move the indicator to a separate pane as well) Parts: divides the intrabar close values into parts/sections Offset: offsets every drawing at once Width: width of drawings, only applicable on "location: chart" Label size: size of price labels 🔹 LTF LTF: LTF setting Auto + multiple: Adjusts the initial set LTF Premium: Enable when your TradingView plan is Premium or higher 🔹 Current Bar Display toggle + color setting Offset: offsets only the 'Current Bar' drawing 🔹 Intrabar Boxplot Display toggle + Colors, dependable on different circumstances. Up: Price goes up, with more bullish than bearish intrabar volume. Up-: Price goes up, with more bearish than bullish intrabar volume. Down: Price goes down, with more bearish than bullish intrabar volume. Down+: Price goes down, with more bullish than bearish intrabar volume. Offset: offsets only the 'Boxplot' drawing 🔹 Price distribution Display toggle + Color. Curve Shaped Guide Lines: Display 2 blue lines Display Price: Show price of 'x' standard deviation Offset: offsets only the 'Price distribution' drawing Label size: size of price labels (standard deviation) 🔹 Volume distribution Display toggle + Color. Offset: offsets only the 'Volume distribution' drawing 🔹 Table Show TF: Show intrabar Timeframe. Textcolor Size Table: Text Size
Penunjuk Pine Script®
oleh fikira
6