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X VFI (LB) w absorptiona variation of the On-Balance Volume (OBV) introduced by Markos Katsanos and further refined by LazyBear, is a robust volume-based momentum oscillator designed to measure the strength and direction of money flow. It utilizes advanced filtering mechanisms to enhance signal quality for active trading environments. This version has added an absorption feature. Core Functionality and Enhancements Filtered Volume Flow: The VFI is calculated using the Typical Price (HLC/3) and incorporates filters for Volatility (coef) and Excessive Volume (vcoef). This ensures the indicator responds only to price changes supported by sustained, relevant volume, filtering out market noise and anomalous spikes. Zero-Line Bias: VFI values above zero indicate net accumulation (bullish flow), while values below zero indicate net distribution (bearish flow). Signal Line Timing (vfima): The Exponential Moving Average (EMA) of the VFI acts as the Signal Line. Crossovers between the VFI (fast line) and the Signal Line are primary triggers for trade entries and exits. Absorption/Distribution Signals This customized version introduces unique features to visually isolate periods where underlying volume conviction contradicts immediate price action—the most powerful setups for reversals and strong continuations. Absorption/Distribution Highlighting: The histogram's color is dynamically changed to highlight hidden buying or selling pressure: (Absorption Signal): Indicates strong positive VFI momentum occurring on a bearish (down) candle. This signals aggressive buying absorption of supply, where large traders are accumulating positions despite brief selling pressure, often preceding a sharp upward move. (Distribution Signal): Indicates strong negative VFI momentum occurring on a bullish (up) candle. This signals aggressive selling distribution into demand, where large traders are offloading positions into brief rallies, often preceding a sharp downward move. Volume-Filtered Conviction: The visual intensity (transparency) of the signal color is adjusted based on a Volume Filter (minVolFilter). Darker, solid colors denote high-conviction signals supported by above-average volume, while transparent colors indicate lower-conviction signals. Histogram Magnification: The magnification input allows users to visually increase the height of the histogram bars (e.g., 2x). This enhances the immediate visual recognition of momentum acceleration or deceleration.
Pine Script® indicator
by KinetiCapital
ORB + Fair Value Gaps (FVG/iFVG) Suite with Daily 50% MidlineA complete smart-money–focused price-action toolkit combining the New York Open Range Breakout (ORB), ICT-style Fair Value Gaps, Inverted FVGs, and a dynamic Daily 50% Midline. Designed for traders who want a clean, fast, and highly visual way to track liquidity, imbalances, and intraday directional bias. 📌 Key Features 1. NY Session ORB (09:30–09:45 New York Time) Automatically plots: ORB High ORB Low Labels for ORB high/low Optional 5-minute chart restriction Lines extend forward for easy reference Used to identify breakout conditions, liquidity sweeps, and directional bias into the morning session. 📌 2. ICT-Style Fair Value Gaps (FVGs) Full automated detection of bullish & bearish FVGs based on the classic 3-candle displacement structure: Bullish FVG: high < low Bearish FVG: low > high Each FVG is drawn as a box with: Custom colour Custom border style (solid, dashed, dotted) Automatic extension to the right until filled Optional size text showing the gap in points (font size/colour adjustable) Adjustable max lookback for performance 📌 3. Inverted FVGs (iFVGs) Once price fully fills an FVG, it automatically becomes an iFVG, shown with: Custom iFVG colour Custom border style Extension to the right Once price trades through the zone from the opposite side, the iFVG is considered “consumed” and: It stops extending And optionally auto-deletes based on user settings This makes it easy to track meaningful imbalances that turn into liquidity pockets. 📌 4. “Show Only After ORB” Filter Optionally hide all FVGs/iFVGs formed before the ORB completes. This is especially useful for intraday strategies focused on NY session structure only. 📌 5. Daily 50% Midline (OHLC Midpoint) A dynamic, always-updating midpoint of the current daily candle: Mid = (Daily High + Daily Low) / 2 Features: Custom colour Dashed styling Extends left and right as a horizontal ray Updates live as the daily candle forms Great for bias filters, mean reversion, and daily liquidity zones. 📌 6. Performance-Optimized (Fast!) Built with: Fully configurable max lookback Memory-efficient arrays Auto-cleaning of old FVG/iFVG objects Lightweight daily midline recalculation This allows extremely fast rendering even on 1-minute charts. 📌 7. Alerts Includes a clean alert condition: Price returned to a Fair Value Gap Works for both bullish and bearish FVG revisits. 🎯 Who This Indicator Is For This tool is ideal for traders who use: ICT / SMC concepts Liquidity-based trading ORB strategies Imbalance-driven price action Intraday or NY session-focused setups Futures, crypto, forex, and equities 🎁 Summary This indicator gives you: A clean ORB framework Automatic, dynamic FVG and iFVG analysis Real-time daily candle context Customizable visuals Powerful session filtering Efficient performance All in one clean, intuitive package built for real-time decision making.
Pine Script® indicator
by jiggggggerr
New Day Opening Gaps (1m 3:29-9:15)Precise Day Opening Gap IndicatorThis custom Pine Script indicator is designed for traders who rely on high-precision gap analysis, particularly for markets like the NSE/BSE.🎯 Core Functionality: Precision Gap IdentificationThis indicator calculates and highlights the exact price gap between the previous day's close and the current day's open. Unlike standard gap analysis that relies on higher timeframes, this script ensures accuracy by strictly using 1-minute data:Previous Close: Captured from the 1-minute candle closing at 3:29 PM (15:29).Current Open: Captured from the 1-minute candle opening at 9:15 AM (09:15).The resulting gap zone is plotted and automatically extends to the right, serving as a critical level for current price interaction.✨ Key Features1. Cross-Timeframe PersistenceThe gap markings are calculated based on the 1-minute chart but are displayed correctly and persist across all timeframes, including 5-minute, 15-minute, 1-hour, and even the Daily chart, ensuring consistency no matter how you analyze the price action.2. Controlled Historical ViewAvoid chart clutter with the "Number of Gaps to Show" input. Easily control how many historical day-opening gaps you want to display on your chart, allowing you to focus only on the most recent and relevant levels.3. Full CustomizationCustomize the look and feel to fit your charting style:Gap Zone: Adjust the color and opacity (transparency) of the gap box.Date Label: Toggle the date label display on/off and control its color, background, opacity, and size. The label is optimally placed at the top-right of the gap zone for clear visibility.🛠️ Recommended UseThis tool is perfect for intraday traders looking to:Identify immediate support and resistance zones based on overnight price action.Track where price action reacts to prior day gaps (filling or holding the gap).Maintain a clear visual reference of daily market openings.
Pine Script® indicator
by Aviiinash
Dynamic Equity Allocation Model//@version=6 indicator('Dynamic Equity Allocation Model', shorttitle = 'DEAM', overlay = false, precision = 1, scale = scale.right, max_bars_back = 500) // DYNAMIC EQUITY ALLOCATION MODEL // Quantitative framework for dynamic portfolio allocation between stocks and cash. // Analyzes five dimensions: market regime, risk metrics, valuation, sentiment, // and macro conditions to generate allocation recommendations (0-100% equity). // // Uses real-time data from TradingView including fundamentals (P/E, ROE, ERP), // volatility indicators (VIX), credit spreads, yield curves, and market structure. // INPUT PARAMETERS group1 = 'Model Configuration' model_type = input.string('Adaptive', 'Allocation Model Type', options = , group = group1, tooltip = 'Conservative: Slower to increase equity, Aggressive: Faster allocation changes, Adaptive: Dynamic based on regime') use_crisis_detection = input.bool(true, 'Enable Crisis Detection System', group = group1, tooltip = 'Automatic detection and response to crisis conditions') use_regime_model = input.bool(true, 'Use Market Regime Detection', group = group1, tooltip = 'Identify Bull/Bear/Crisis regimes for dynamic allocation') group2 = 'Portfolio Risk Management' target_portfolio_volatility = input.float(12.0, 'Target Portfolio Volatility (%)', minval = 3, maxval = 20, step = 0.5, group = group2, tooltip = 'Target portfolio volatility (Cash reduces volatility: 50% Equity = ~10% vol, 100% Equity = ~20% vol)') max_portfolio_drawdown = input.float(15.0, 'Maximum Portfolio Drawdown (%)', minval = 5, maxval = 35, step = 2.5, group = group2, tooltip = 'Maximum acceptable PORTFOLIO drawdown (not market drawdown - portfolio with cash has lower drawdown)') enable_portfolio_risk_scaling = input.bool(true, 'Enable Portfolio Risk Scaling', group = group2, tooltip = 'Scale allocation based on actual portfolio risk characteristics (recommended)') risk_lookback = input.int(252, 'Risk Calculation Period (Days)', minval = 60, maxval = 504, group = group2, tooltip = 'Period for calculating volatility and risk metrics') group3 = 'Component Weights (Total = 100%)' w_regime = input.float(35.0, 'Market Regime Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_risk = input.float(25.0, 'Risk Metrics Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_valuation = input.float(20.0, 'Valuation Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_sentiment = input.float(15.0, 'Sentiment Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) w_macro = input.float(5.0, 'Macro Weight (%)', minval = 0, maxval = 100, step = 5, group = group3) group4 = 'Crisis Detection Thresholds' crisis_vix_threshold = input.float(40, 'Crisis VIX Level', minval = 30, maxval = 80, group = group4, tooltip = 'VIX level indicating crisis conditions (COVID peaked at 82)') crisis_drawdown_threshold = input.float(15, 'Crisis Drawdown Threshold (%)', minval = 10, maxval = 30, group = group4, tooltip = 'Market drawdown indicating crisis conditions') crisis_credit_spread = input.float(500, 'Crisis Credit Spread (bps)', minval = 300, maxval = 1000, group = group4, tooltip = 'High yield spread indicating crisis conditions') group5 = 'Display Settings' show_components = input.bool(false, 'Show Component Breakdown', group = group5, tooltip = 'Display individual component analysis lines') show_regime_background = input.bool(true, 'Show Dynamic Background', group = group5, tooltip = 'Color background based on allocation signals') show_reference_lines = input.bool(false, 'Show Reference Lines', group = group5, tooltip = 'Display allocation percentage reference lines') show_dashboard = input.bool(true, 'Show Analytics Dashboard', group = group5, tooltip = 'Display comprehensive analytics table') show_confidence_bands = input.bool(false, 'Show Confidence Bands', group = group5, tooltip = 'Display uncertainty quantification bands') smoothing_period = input.int(3, 'Smoothing Period', minval = 1, maxval = 10, group = group5, tooltip = 'Smoothing to reduce allocation noise') background_intensity = input.int(95, 'Background Intensity (%)', minval = 90, maxval = 99, group = group5, tooltip = 'Higher values = more transparent background') // Styling Options color_scheme = input.string('EdgeTools', 'Color Theme', options = , group = 'Appearance', tooltip = 'Professional color themes') use_dark_mode = input.bool(true, 'Optimize for Dark Theme', group = 'Appearance') main_line_width = input.int(3, 'Main Line Width', minval = 1, maxval = 5, group = 'Appearance') // DATA RETRIEVAL // Market Data sp500 = request.security('SPY', timeframe.period, close) sp500_high = request.security('SPY', timeframe.period, high) sp500_low = request.security('SPY', timeframe.period, low) sp500_volume = request.security('SPY', timeframe.period, volume) // Volatility Indicators vix = request.security('VIX', timeframe.period, close) vix9d = request.security('VIX9D', timeframe.period, close) vxn = request.security('VXN', timeframe.period, close) // Fixed Income and Credit us2y = request.security('US02Y', timeframe.period, close) us10y = request.security('US10Y', timeframe.period, close) us3m = request.security('US03MY', timeframe.period, close) hyg = request.security('HYG', timeframe.period, close) lqd = request.security('LQD', timeframe.period, close) tlt = request.security('TLT', timeframe.period, close) // Safe Haven Assets gold = request.security('GLD', timeframe.period, close) usd = request.security('DXY', timeframe.period, close) yen = request.security('JPYUSD', timeframe.period, close) // Financial data with fallback values get_financial_data(symbol, fin_id, period, fallback) => data = request.financial(symbol, fin_id, period, ignore_invalid_symbol = true) na(data) ? fallback : data // SPY fundamental metrics spy_earnings_per_share = get_financial_data('AMEX:SPY', 'EARNINGS_PER_SHARE_BASIC', 'TTM', 20.0) spy_operating_earnings_yield = get_financial_data('AMEX:SPY', 'OPERATING_EARNINGS_YIELD', 'FY', 4.5) spy_dividend_yield = get_financial_data('AMEX:SPY', 'DIVIDENDS_YIELD', 'FY', 1.8) spy_buyback_yield = get_financial_data('AMEX:SPY', 'BUYBACK_YIELD', 'FY', 2.0) spy_net_margin = get_financial_data('AMEX:SPY', 'NET_MARGIN', 'TTM', 12.0) spy_debt_to_equity = get_financial_data('AMEX:SPY', 'DEBT_TO_EQUITY', 'FY', 0.5) spy_return_on_equity = get_financial_data('AMEX:SPY', 'RETURN_ON_EQUITY', 'FY', 15.0) spy_free_cash_flow = get_financial_data('AMEX:SPY', 'FREE_CASH_FLOW', 'TTM', 100000000) spy_ebitda = get_financial_data('AMEX:SPY', 'EBITDA', 'TTM', 200000000) spy_pe_forward = get_financial_data('AMEX:SPY', 'PRICE_EARNINGS_FORWARD', 'FY', 18.0) spy_total_debt = get_financial_data('AMEX:SPY', 'TOTAL_DEBT', 'FY', 500000000) spy_total_equity = get_financial_data('AMEX:SPY', 'TOTAL_EQUITY', 'FY', 1000000000) spy_enterprise_value = get_financial_data('AMEX:SPY', 'ENTERPRISE_VALUE', 'FY', 30000000000) spy_revenue_growth = get_financial_data('AMEX:SPY', 'REVENUE_ONE_YEAR_GROWTH', 'TTM', 5.0) // Market Breadth Indicators nya = request.security('NYA', timeframe.period, close) rut = request.security('IWM', timeframe.period, close) // Sector Performance xlk = request.security('XLK', timeframe.period, close) xlu = request.security('XLU', timeframe.period, close) xlf = request.security('XLF', timeframe.period, close) // MARKET REGIME DETECTION // Calculate Market Trend sma_20 = ta.sma(sp500, 20) sma_50 = ta.sma(sp500, 50) sma_200 = ta.sma(sp500, 200) ema_10 = ta.ema(sp500, 10) // Market Structure Score trend_strength = 0.0 trend_strength := trend_strength + (sp500 > sma_20 ? 1 : -1) trend_strength := trend_strength + (sp500 > sma_50 ? 1 : -1) trend_strength := trend_strength + (sp500 > sma_200 ? 2 : -2) trend_strength := trend_strength + (sma_50 > sma_200 ? 2 : -2) // Volatility Regime returns = math.log(sp500 / sp500 ) realized_vol_20d = ta.stdev(returns, 20) * math.sqrt(252) * 100 realized_vol_60d = ta.stdev(returns, 60) * math.sqrt(252) * 100 ewma_vol = ta.ema(math.pow(returns, 2), 20) realized_vol = math.sqrt(ewma_vol * 252) * 100 vol_premium = vix - realized_vol // Drawdown Calculation running_max = ta.highest(sp500, risk_lookback) current_drawdown = (running_max - sp500) / running_max * 100 // Regime Score regime_score = 0.0 // Trend Component (40%) if trend_strength >= 4 regime_score := regime_score + 40 regime_score else if trend_strength >= 2 regime_score := regime_score + 30 regime_score else if trend_strength >= 0 regime_score := regime_score + 20 regime_score else if trend_strength >= -2 regime_score := regime_score + 10 regime_score else regime_score := regime_score + 0 regime_score // Volatility Component (30%) if vix < 15 regime_score := regime_score + 30 regime_score else if vix < 20 regime_score := regime_score + 25 regime_score else if vix < 25 regime_score := regime_score + 15 regime_score else if vix < 35 regime_score := regime_score + 5 regime_score else regime_score := regime_score + 0 regime_score // Drawdown Component (30%) if current_drawdown < 3 regime_score := regime_score + 30 regime_score else if current_drawdown < 7 regime_score := regime_score + 20 regime_score else if current_drawdown < 12 regime_score := regime_score + 10 regime_score else if current_drawdown < 20 regime_score := regime_score + 5 regime_score else regime_score := regime_score + 0 regime_score // Classify Regime market_regime = regime_score >= 80 ? 'Strong Bull' : regime_score >= 60 ? 'Bull Market' : regime_score >= 40 ? 'Neutral' : regime_score >= 20 ? 'Correction' : regime_score >= 10 ? 'Bear Market' : 'Crisis' // RISK-BASED ALLOCATION // Calculate Market Risk parkinson_hl = math.log(sp500_high / sp500_low) parkinson_vol = parkinson_hl / (2 * math.sqrt(math.log(2))) * math.sqrt(252) * 100 garman_klass_vol = math.sqrt((0.5 * math.pow(math.log(sp500_high / sp500_low), 2) - (2 * math.log(2) - 1) * math.pow(math.log(sp500 / sp500 ), 2)) * 252) * 100 market_volatility_20d = math.max(ta.stdev(returns, 20) * math.sqrt(252) * 100, parkinson_vol) market_volatility_60d = ta.stdev(returns, 60) * math.sqrt(252) * 100 market_drawdown = current_drawdown // Initialize risk allocation risk_allocation = 50.0 if enable_portfolio_risk_scaling // Volatility-based allocation vol_based_allocation = target_portfolio_volatility / math.max(market_volatility_20d, 5.0) * 100 vol_based_allocation := math.max(0, math.min(100, vol_based_allocation)) // Drawdown-based allocation dd_based_allocation = 100.0 if market_drawdown > 1.0 dd_based_allocation := max_portfolio_drawdown / market_drawdown * 100 dd_based_allocation := math.max(0, math.min(100, dd_based_allocation)) dd_based_allocation // Combine (conservative) risk_allocation := math.min(vol_based_allocation, dd_based_allocation) // Dynamic adjustment current_equity_estimate = 50.0 estimated_portfolio_vol = current_equity_estimate / 100 * market_volatility_20d estimated_portfolio_dd = current_equity_estimate / 100 * market_drawdown vol_utilization = estimated_portfolio_vol / target_portfolio_volatility dd_utilization = estimated_portfolio_dd / max_portfolio_drawdown risk_utilization = math.max(vol_utilization, dd_utilization) risk_adjustment_factor = 1.0 if risk_utilization > 1.0 risk_adjustment_factor := math.exp(-0.5 * (risk_utilization - 1.0)) risk_adjustment_factor := math.max(0.5, risk_adjustment_factor) risk_adjustment_factor else if risk_utilization < 0.9 risk_adjustment_factor := 1.0 + 0.2 * math.log(1.0 / risk_utilization) risk_adjustment_factor := math.min(1.3, risk_adjustment_factor) risk_adjustment_factor risk_allocation := risk_allocation * risk_adjustment_factor risk_allocation else vol_scalar = target_portfolio_volatility / math.max(market_volatility_20d, 10) vol_scalar := math.min(1.5, math.max(0.2, vol_scalar)) drawdown_penalty = 0.0 if current_drawdown > max_portfolio_drawdown drawdown_penalty := (current_drawdown - max_portfolio_drawdown) / max_portfolio_drawdown drawdown_penalty := math.min(1.0, drawdown_penalty) drawdown_penalty risk_allocation := 100 * vol_scalar * (1 - drawdown_penalty) risk_allocation risk_allocation := math.max(0, math.min(100, risk_allocation)) // VALUATION ANALYSIS // Valuation Metrics actual_pe_ratio = spy_earnings_per_share > 0 ? sp500 / spy_earnings_per_share : spy_pe_forward actual_earnings_yield = nz(spy_operating_earnings_yield, 0) > 0 ? spy_operating_earnings_yield : 100 / actual_pe_ratio total_shareholder_yield = spy_dividend_yield + spy_buyback_yield // Equity Risk Premium (multi-method calculation) method1_erp = actual_earnings_yield - us10y method2_erp = actual_earnings_yield + spy_buyback_yield - us10y payout_ratio = spy_dividend_yield > 0 and actual_earnings_yield > 0 ? spy_dividend_yield / actual_earnings_yield : 0.4 sustainable_growth = spy_return_on_equity * (1 - payout_ratio) / 100 method3_erp = spy_dividend_yield + sustainable_growth * 100 - us10y implied_growth = spy_revenue_growth * 0.7 method4_erp = total_shareholder_yield + implied_growth - us10y equity_risk_premium = method1_erp * 0.35 + method2_erp * 0.30 + method3_erp * 0.20 + method4_erp * 0.15 ev_ebitda_ratio = spy_enterprise_value > 0 and spy_ebitda > 0 ? spy_enterprise_value / spy_ebitda : 15.0 debt_equity_health = spy_debt_to_equity < 1.0 ? 1.2 : spy_debt_to_equity < 2.0 ? 1.0 : 0.8 // Valuation Score base_valuation_score = 50.0 if equity_risk_premium > 4 base_valuation_score := 95 base_valuation_score else if equity_risk_premium > 3 base_valuation_score := 85 base_valuation_score else if equity_risk_premium > 2 base_valuation_score := 70 base_valuation_score else if equity_risk_premium > 1 base_valuation_score := 55 base_valuation_score else if equity_risk_premium > 0 base_valuation_score := 40 base_valuation_score else if equity_risk_premium > -1 base_valuation_score := 25 base_valuation_score else base_valuation_score := 10 base_valuation_score growth_adjustment = spy_revenue_growth > 10 ? 10 : spy_revenue_growth > 5 ? 5 : 0 margin_adjustment = spy_net_margin > 15 ? 5 : spy_net_margin < 8 ? -5 : 0 roe_adjustment = spy_return_on_equity > 20 ? 5 : spy_return_on_equity < 10 ? -5 : 0 valuation_score = base_valuation_score + growth_adjustment + margin_adjustment + roe_adjustment valuation_score := math.max(0, math.min(100, valuation_score * debt_equity_health)) // SENTIMENT ANALYSIS // VIX Term Structure vix_term_structure = vix9d > 0 ? vix / vix9d : 1 backwardation = vix_term_structure > 1.05 steep_backwardation = vix_term_structure > 1.15 // Safe Haven Flows gold_momentum = ta.roc(gold, 20) dollar_momentum = ta.roc(usd, 20) yen_momentum = ta.roc(yen, 20) treasury_momentum = ta.roc(tlt, 20) safe_haven_flow = gold_momentum * 0.3 + treasury_momentum * 0.3 + dollar_momentum * 0.25 + yen_momentum * 0.15 // Advanced Sentiment Analysis vix_percentile = ta.percentrank(vix, 252) vix_zscore = (vix - ta.sma(vix, 252)) / ta.stdev(vix, 252) vix_momentum = ta.roc(vix, 5) vvix_proxy = ta.stdev(vix_momentum, 20) * math.sqrt(252) risk_reversal_proxy = (vix - realized_vol) / realized_vol // Sentiment Score base_sentiment = 50.0 vix_adjustment = 0.0 if vix_zscore < -1.5 vix_adjustment := 40 vix_adjustment else if vix_zscore < -0.5 vix_adjustment := 20 vix_adjustment else if vix_zscore < 0.5 vix_adjustment := 0 vix_adjustment else if vix_zscore < 1.5 vix_adjustment := -20 vix_adjustment else vix_adjustment := -40 vix_adjustment term_structure_adjustment = backwardation ? -15 : steep_backwardation ? -30 : 5 vvix_adjustment = vvix_proxy > 2.0 ? -10 : vvix_proxy < 1.0 ? 10 : 0 sentiment_score = base_sentiment + vix_adjustment + term_structure_adjustment + vvix_adjustment sentiment_score := math.max(0, math.min(100, sentiment_score)) // MACRO ANALYSIS // Yield Curve yield_spread_2_10 = us10y - us2y yield_spread_3m_10 = us10y - us3m // Credit Conditions hyg_return = ta.roc(hyg, 20) lqd_return = ta.roc(lqd, 20) tlt_return = ta.roc(tlt, 20) hyg_duration = 4.0 lqd_duration = 8.0 tlt_duration = 17.0 hyg_log_returns = math.log(hyg / hyg ) lqd_log_returns = math.log(lqd / lqd ) hyg_volatility = ta.stdev(hyg_log_returns, 20) * math.sqrt(252) lqd_volatility = ta.stdev(lqd_log_returns, 20) * math.sqrt(252) hyg_yield_proxy = -math.log(hyg / hyg ) * 100 lqd_yield_proxy = -math.log(lqd / lqd ) * 100 tlt_yield = us10y hyg_spread = (hyg_yield_proxy - tlt_yield) * 100 lqd_spread = (lqd_yield_proxy - tlt_yield) * 100 hyg_distance = (hyg - ta.lowest(hyg, 252)) / (ta.highest(hyg, 252) - ta.lowest(hyg, 252)) lqd_distance = (lqd - ta.lowest(lqd, 252)) / (ta.highest(lqd, 252) - ta.lowest(lqd, 252)) default_risk_proxy = 2.0 - (hyg_distance + lqd_distance) credit_spread = hyg_spread * 0.5 + (hyg_volatility - lqd_volatility) * 1000 * 0.3 + default_risk_proxy * 200 * 0.2 credit_spread := math.max(50, credit_spread) credit_market_health = hyg_return > lqd_return ? 1 : -1 flight_to_quality = tlt_return > (hyg_return + lqd_return) / 2 // Macro Score macro_score = 50.0 yield_curve_score = 0 if yield_spread_2_10 > 1.5 and yield_spread_3m_10 > 2 yield_curve_score := 40 yield_curve_score else if yield_spread_2_10 > 0.5 and yield_spread_3m_10 > 1 yield_curve_score := 30 yield_curve_score else if yield_spread_2_10 > 0 and yield_spread_3m_10 > 0 yield_curve_score := 20 yield_curve_score else if yield_spread_2_10 < 0 or yield_spread_3m_10 < 0 yield_curve_score := 10 yield_curve_score else yield_curve_score := 5 yield_curve_score credit_conditions_score = 0 if credit_spread < 200 and not flight_to_quality credit_conditions_score := 30 credit_conditions_score else if credit_spread < 400 and credit_market_health > 0 credit_conditions_score := 20 credit_conditions_score else if credit_spread < 600 credit_conditions_score := 15 credit_conditions_score else if credit_spread < 1000 credit_conditions_score := 10 credit_conditions_score else credit_conditions_score := 0 credit_conditions_score financial_stability_score = 0 if spy_debt_to_equity < 0.5 and spy_return_on_equity > 15 financial_stability_score := 20 financial_stability_score else if spy_debt_to_equity < 1.0 and spy_return_on_equity > 10 financial_stability_score := 15 financial_stability_score else if spy_debt_to_equity < 1.5 financial_stability_score := 10 financial_stability_score else financial_stability_score := 5 financial_stability_score macro_score := yield_curve_score + credit_conditions_score + financial_stability_score macro_score := math.max(0, math.min(100, macro_score)) // CRISIS DETECTION crisis_indicators = 0 if vix > crisis_vix_threshold crisis_indicators := crisis_indicators + 1 crisis_indicators if vix > 60 crisis_indicators := crisis_indicators + 2 crisis_indicators if current_drawdown > crisis_drawdown_threshold crisis_indicators := crisis_indicators + 1 crisis_indicators if current_drawdown > 25 crisis_indicators := crisis_indicators + 1 crisis_indicators if credit_spread > crisis_credit_spread crisis_indicators := crisis_indicators + 1 crisis_indicators sp500_roc_5 = ta.roc(sp500, 5) tlt_roc_5 = ta.roc(tlt, 5) if sp500_roc_5 < -10 and tlt_roc_5 < -5 crisis_indicators := crisis_indicators + 2 crisis_indicators volume_spike = sp500_volume > ta.sma(sp500_volume, 20) * 2 sp500_roc_1 = ta.roc(sp500, 1) if volume_spike and sp500_roc_1 < -3 crisis_indicators := crisis_indicators + 1 crisis_indicators is_crisis = crisis_indicators >= 3 is_severe_crisis = crisis_indicators >= 5 // FINAL ALLOCATION CALCULATION // Convert regime to base allocation regime_allocation = market_regime == 'Strong Bull' ? 100 : market_regime == 'Bull Market' ? 80 : market_regime == 'Neutral' ? 60 : market_regime == 'Correction' ? 40 : market_regime == 'Bear Market' ? 20 : 0 // Normalize weights total_weight = w_regime + w_risk + w_valuation + w_sentiment + w_macro w_regime_norm = w_regime / total_weight w_risk_norm = w_risk / total_weight w_valuation_norm = w_valuation / total_weight w_sentiment_norm = w_sentiment / total_weight w_macro_norm = w_macro / total_weight // Calculate Weighted Allocation weighted_allocation = regime_allocation * w_regime_norm + risk_allocation * w_risk_norm + valuation_score * w_valuation_norm + sentiment_score * w_sentiment_norm + macro_score * w_macro_norm // Apply Crisis Override if use_crisis_detection if is_severe_crisis weighted_allocation := math.min(weighted_allocation, 10) weighted_allocation else if is_crisis weighted_allocation := math.min(weighted_allocation, 25) weighted_allocation // Model Type Adjustment model_adjustment = 0.0 if model_type == 'Conservative' model_adjustment := -10 model_adjustment else if model_type == 'Aggressive' model_adjustment := 10 model_adjustment else if model_type == 'Adaptive' recent_return = (sp500 - sp500 ) / sp500 * 100 if recent_return > 5 model_adjustment := 5 model_adjustment else if recent_return < -5 model_adjustment := -5 model_adjustment // Apply adjustment and bounds final_allocation = weighted_allocation + model_adjustment final_allocation := math.max(0, math.min(100, final_allocation)) // Smooth allocation smoothed_allocation = ta.sma(final_allocation, smoothing_period) // Calculate portfolio risk metrics (only for internal alerts) actual_portfolio_volatility = smoothed_allocation / 100 * market_volatility_20d actual_portfolio_drawdown = smoothed_allocation / 100 * current_drawdown // VISUALIZATION // Color definitions var color primary_color = #2196F3 var color bullish_color = #4CAF50 var color bearish_color = #FF5252 var color neutral_color = #808080 var color text_color = color.white var color bg_color = #000000 var color table_bg_color = #1E1E1E var color header_bg_color = #2D2D2D switch color_scheme // Apply color scheme 'Gold' => primary_color := use_dark_mode ? #FFD700 : #DAA520 bullish_color := use_dark_mode ? #FFA500 : #FF8C00 bearish_color := use_dark_mode ? #FF5252 : #D32F2F neutral_color := use_dark_mode ? #C0C0C0 : #808080 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #1A1A00 : #FFFEF0 header_bg_color := use_dark_mode ? #2D2600 : #F5F5DC header_bg_color 'EdgeTools' => primary_color := use_dark_mode ? #4682B4 : #1E90FF bullish_color := use_dark_mode ? #4CAF50 : #388E3C bearish_color := use_dark_mode ? #FF5252 : #D32F2F neutral_color := use_dark_mode ? #708090 : #696969 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #0F1419 : #F0F8FF header_bg_color := use_dark_mode ? #1E2A3A : #E6F3FF header_bg_color 'Behavioral' => primary_color := #808080 bullish_color := #00FF00 bearish_color := #8B0000 neutral_color := #FFBF00 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #1A1A1A : #F8F8F8 header_bg_color := use_dark_mode ? #2D2D2D : #E8E8E8 header_bg_color 'Quant' => primary_color := #808080 bullish_color := #FFA500 bearish_color := #8B0000 neutral_color := #4682B4 text_color := use_dark_mode ? color.white : color.black bg_color := use_dark_mode ? #000000 : #FFFFFF table_bg_color := use_dark_mode ? #0D0D0D : #FAFAFA header_bg_color := use_dark_mode ? #1A1A1A : #F0F0F0 header_bg_color 'Ocean' => primary_color := use_dark_mode ? #20B2AA : #008B8B bullish_color := use_dark_mode ? #00CED1 : #4682B4 bearish_color := use_dark_mode ? #FF4500 : #B22222 neutral_color := use_dark_mode ? #87CEEB : #2F4F4F text_color := use_dark_mode ? #F0F8FF : #191970 bg_color := use_dark_mode ? #001F3F : #F0F8FF table_bg_color := use_dark_mode ? #001A2E : #E6F7FF header_bg_color := use_dark_mode ? #002A47 : #CCF2FF header_bg_color 'Fire' => primary_color := use_dark_mode ? #FF6347 : #DC143C bullish_color := use_dark_mode ? #FFD700 : #FF8C00 bearish_color := use_dark_mode ? #8B0000 : #800000 neutral_color := use_dark_mode ? #FFA500 : #CD853F text_color := use_dark_mode ? #FFFAF0 : #2F1B14 bg_color := use_dark_mode ? #2F1B14 : #FFFAF0 table_bg_color := use_dark_mode ? #261611 : #FFF8F0 header_bg_color := use_dark_mode ? #3D241A : #FFE4CC header_bg_color 'Matrix' => primary_color := use_dark_mode ? #00FF41 : #006400 bullish_color := use_dark_mode ? #39FF14 : #228B22 bearish_color := use_dark_mode ? #FF073A : #8B0000 neutral_color := use_dark_mode ? #00FFFF : #008B8B text_color := use_dark_mode ? #C0FF8C : #003300 bg_color := use_dark_mode ? #0D1B0D : #F0FFF0 table_bg_color := use_dark_mode ? #0A1A0A : #E8FFF0 header_bg_color := use_dark_mode ? #112B11 : #CCFFCC header_bg_color 'Arctic' => primary_color := use_dark_mode ? #87CEFA : #4169E1 bullish_color := use_dark_mode ? #00BFFF : #0000CD bearish_color := use_dark_mode ? #FF1493 : #8B008B neutral_color := use_dark_mode ? #B0E0E6 : #483D8B text_color := use_dark_mode ? #F8F8FF : #191970 bg_color := use_dark_mode ? #191970 : #F8F8FF table_bg_color := use_dark_mode ? #141B47 : #F0F8FF header_bg_color := use_dark_mode ? #1E2A5C : #E0F0FF header_bg_color // Transparency settings bg_transparency = use_dark_mode ? 85 : 92 zone_transparency = use_dark_mode ? 90 : 95 band_transparency = use_dark_mode ? 70 : 85 table_transparency = use_dark_mode ? 80 : 15 // Allocation color alloc_color = smoothed_allocation >= 80 ? bullish_color : smoothed_allocation >= 60 ? color.new(bullish_color, 30) : smoothed_allocation >= 40 ? primary_color : smoothed_allocation >= 20 ? color.new(bearish_color, 30) : bearish_color // Dynamic background var color dynamic_bg_color = na if show_regime_background if smoothed_allocation >= 70 dynamic_bg_color := color.new(bullish_color, background_intensity) dynamic_bg_color else if smoothed_allocation <= 30 dynamic_bg_color := color.new(bearish_color, background_intensity) dynamic_bg_color else if smoothed_allocation > 60 or smoothed_allocation < 40 dynamic_bg_color := color.new(primary_color, math.min(99, background_intensity + 2)) dynamic_bg_color bgcolor(dynamic_bg_color, title = 'Allocation Signal Background') // Plot main allocation line plot(smoothed_allocation, 'Equity Allocation %', color = alloc_color, linewidth = math.max(1, main_line_width)) // Reference lines (static colors for hline) hline_bullish_color = color_scheme == 'Gold' ? use_dark_mode ? #FFA500 : #FF8C00 : color_scheme == 'EdgeTools' ? use_dark_mode ? #4CAF50 : #388E3C : color_scheme == 'Behavioral' ? #00FF00 : color_scheme == 'Quant' ? #FFA500 : color_scheme == 'Ocean' ? use_dark_mode ? #00CED1 : #4682B4 : color_scheme == 'Fire' ? use_dark_mode ? #FFD700 : #FF8C00 : color_scheme == 'Matrix' ? use_dark_mode ? #39FF14 : #228B22 : color_scheme == 'Arctic' ? use_dark_mode ? #00BFFF : #0000CD : #4CAF50 hline_bearish_color = color_scheme == 'Gold' ? use_dark_mode ? #FF5252 : #D32F2F : color_scheme == 'EdgeTools' ? use_dark_mode ? #FF5252 : #D32F2F : color_scheme == 'Behavioral' ? #8B0000 : color_scheme == 'Quant' ? #8B0000 : color_scheme == 'Ocean' ? use_dark_mode ? #FF4500 : #B22222 : color_scheme == 'Fire' ? use_dark_mode ? #8B0000 : #800000 : color_scheme == 'Matrix' ? use_dark_mode ? #FF073A : #8B0000 : color_scheme == 'Arctic' ? use_dark_mode ? #FF1493 : #8B008B : #FF5252 hline_primary_color = color_scheme == 'Gold' ? use_dark_mode ? #FFD700 : #DAA520 : color_scheme == 'EdgeTools' ? use_dark_mode ? #4682B4 : #1E90FF : color_scheme == 'Behavioral' ? #808080 : color_scheme == 'Quant' ? #808080 : color_scheme == 'Ocean' ? use_dark_mode ? #20B2AA : #008B8B : color_scheme == 'Fire' ? use_dark_mode ? #FF6347 : #DC143C : color_scheme == 'Matrix' ? use_dark_mode ? #00FF41 : #006400 : color_scheme == 'Arctic' ? use_dark_mode ? #87CEFA : #4169E1 : #2196F3 hline(show_reference_lines ? 100 : na, '100% Equity', color = color.new(hline_bullish_color, 70), linestyle = hline.style_dotted, linewidth = 1) hline(show_reference_lines ? 80 : na, '80% Equity', color = color.new(hline_bullish_color, 40), linestyle = hline.style_dashed, linewidth = 1) hline(show_reference_lines ? 60 : na, '60% Equity', color = color.new(hline_bullish_color, 60), linestyle = hline.style_dotted, linewidth = 1) hline(50, '50% Balanced', color = color.new(hline_primary_color, 50), linestyle = hline.style_solid, linewidth = 2) hline(show_reference_lines ? 40 : na, '40% Equity', color = color.new(hline_bearish_color, 60), linestyle = hline.style_dotted, linewidth = 1) hline(show_reference_lines ? 20 : na, '20% Equity', color = color.new(hline_bearish_color, 40), linestyle = hline.style_dashed, linewidth = 1) hline(show_reference_lines ? 0 : na, '0% Equity', color = color.new(hline_bearish_color, 70), linestyle = hline.style_dotted, linewidth = 1) // Component plots plot(show_components ? regime_allocation : na, 'Regime', color = color.new(#4ECDC4, 70), linewidth = 1) plot(show_components ? risk_allocation : na, 'Risk', color = color.new(#FF6B6B, 70), linewidth = 1) plot(show_components ? valuation_score : na, 'Valuation', color = color.new(#45B7D1, 70), linewidth = 1) plot(show_components ? sentiment_score : na, 'Sentiment', color = color.new(#FFD93D, 70), linewidth = 1) plot(show_components ? macro_score : na, 'Macro', color = color.new(#6BCF7F, 70), linewidth = 1) // Confidence bands upper_band = plot(show_confidence_bands ? math.min(100, smoothed_allocation + ta.stdev(smoothed_allocation, 20)) : na, color = color.new(neutral_color, band_transparency), display = display.none, title = 'Upper Band') lower_band = plot(show_confidence_bands ? math.max(0, smoothed_allocation - ta.stdev(smoothed_allocation, 20)) : na, color = color.new(neutral_color, band_transparency), display = display.none, title = 'Lower Band') fill(upper_band, lower_band, color = show_confidence_bands ? color.new(neutral_color, zone_transparency) : na, title = 'Uncertainty') // DASHBOARD if show_dashboard and barstate.islast var table dashboard = table.new(position.top_right, 2, 20, border_width = 1, bgcolor = color.new(table_bg_color, table_transparency)) table.clear(dashboard, 0, 0, 1, 19) // Header header_color = color.new(header_bg_color, 20) dashboard_text_color = text_color table.cell(dashboard, 0, 0, 'DEAM', text_color = dashboard_text_color, bgcolor = header_color, text_size = size.normal) table.cell(dashboard, 1, 0, model_type, text_color = dashboard_text_color, bgcolor = header_color, text_size = size.normal) // Core metrics table.cell(dashboard, 0, 1, 'Equity Allocation', text_color = dashboard_text_color, text_size = size.small) table.cell(dashboard, 1, 1, str.tostring(smoothed_allocation, '##.#') + '%', text_color = alloc_color, text_size = size.small) table.cell(dashboard, 0, 2, 'Cash Allocation', text_color = dashboard_text_color, text_size = size.small) cash_color = 100 - smoothed_allocation > 70 ? bearish_color : primary_color table.cell(dashboard, 1, 2, str.tostring(100 - smoothed_allocation, '##.#') + '%', text_color = cash_color, text_size = size.small) // Signal signal_text = 'NEUTRAL' signal_color = primary_color if smoothed_allocation >= 70 signal_text := 'BULLISH' signal_color := bullish_color signal_color else if smoothed_allocation <= 30 signal_text := 'BEARISH' signal_color := bearish_color signal_color table.cell(dashboard, 0, 3, 'Signal', text_color = dashboard_text_color, text_size = size.small) table.cell(dashboard, 1, 3, signal_text, text_color = signal_color, text_size = size.small) // Market Regime table.cell(dashboard, 0, 4, 'Regime', text_color = dashboard_text_color, text_size = size.small) regime_color_display = market_regime == 'Strong Bull' or market_regime == 'Bull Market' ? bullish_color : market_regime == 'Neutral' ? primary_color : market_regime == 'Crisis' ? bearish_color : bearish_color table.cell(dashboard, 1, 4, market_regime, text_color = regime_color_display, text_size = size.small) // VIX table.cell(dashboard, 0, 5, 'VIX Level', text_color = dashboard_text_color, text_size = size.small) vix_color_display = vix < 20 ? bullish_color : vix < 30 ? primary_color : bearish_color table.cell(dashboard, 1, 5, str.tostring(vix, '##.##'), text_color = vix_color_display, text_size = size.small) // Market Drawdown table.cell(dashboard, 0, 6, 'Market DD', text_color = dashboard_text_color, text_size = size.small) market_dd_color = current_drawdown < 5 ? bullish_color : current_drawdown < 10 ? primary_color : bearish_color table.cell(dashboard, 1, 6, '-' + str.tostring(current_drawdown, '##.#') + '%', text_color = market_dd_color, text_size = size.small) // Crisis Detection table.cell(dashboard, 0, 7, 'Crisis Detection', text_color = dashboard_text_color, text_size = size.small) crisis_text = is_severe_crisis ? 'SEVERE' : is_crisis ? 'CRISIS' : 'Normal' crisis_display_color = is_severe_crisis or is_crisis ? bearish_color : bullish_color table.cell(dashboard, 1, 7, crisis_text, text_color = crisis_display_color, text_size = size.small) // Real Data Section financial_bg = color.new(primary_color, 85) table.cell(dashboard, 0, 8, 'REAL DATA', text_color = dashboard_text_color, bgcolor = financial_bg, text_size = size.small) table.cell(dashboard, 1, 8, 'Live Metrics', text_color = dashboard_text_color, bgcolor = financial_bg, text_size = size.small) // P/E Ratio table.cell(dashboard, 0, 9, 'P/E Ratio', text_color = dashboard_text_color, text_size = size.small) pe_color = actual_pe_ratio < 18 ? bullish_color : actual_pe_ratio < 25 ? primary_color : bearish_color table.cell(dashboard, 1, 9, str.tostring(actual_pe_ratio, '##.#'), text_color = pe_color, text_size = size.small) // ERP table.cell(dashboard, 0, 10, 'ERP', text_color = dashboard_text_color, text_size = size.small) erp_color = equity_risk_premium > 2 ? bullish_color : equity_risk_premium > 0 ? primary_color : bearish_color table.cell(dashboard, 1, 10, str.tostring(equity_risk_premium, '##.##') + '%', text_color = erp_color, text_size = size.small) // ROE table.cell(dashboard, 0, 11, 'ROE', text_color = dashboard_text_color, text_size = size.small) roe_color = spy_return_on_equity > 20 ? bullish_color : spy_return_on_equity > 10 ? primary_color : bearish_color table.cell(dashboard, 1, 11, str.tostring(spy_return_on_equity, '##.#') + '%', text_color = roe_color, text_size = size.small) // D/E Ratio table.cell(dashboard, 0, 12, 'D/E Ratio', text_color = dashboard_text_color, text_size = size.small) de_color = spy_debt_to_equity < 0.5 ? bullish_color : spy_debt_to_equity < 1.0 ? primary_color : bearish_color table.cell(dashboard, 1, 12, str.tostring(spy_debt_to_equity, '##.##'), text_color = de_color, text_size = size.small) // Shareholder Yield table.cell(dashboard, 0, 13, 'Dividend+Buyback', text_color = dashboard_text_color, text_size = size.small) yield_color = total_shareholder_yield > 4 ? bullish_color : total_shareholder_yield > 2 ? primary_color : bearish_color table.cell(dashboard, 1, 13, str.tostring(total_shareholder_yield, '##.#') + '%', text_color = yield_color, text_size = size.small) // Component Scores component_bg = color.new(neutral_color, 80) table.cell(dashboard, 0, 14, 'Components', text_color = dashboard_text_color, bgcolor = component_bg, text_size = size.small) table.cell(dashboard, 1, 14, 'Scores', text_color = dashboard_text_color, bgcolor = component_bg, text_size = size.small) table.cell(dashboard, 0, 15, 'Regime', text_color = dashboard_text_color, text_size = size.small) regime_score_color = regime_allocation > 60 ? bullish_color : regime_allocation < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 15, str.tostring(regime_allocation, '##'), text_color = regime_score_color, text_size = size.small) table.cell(dashboard, 0, 16, 'Risk', text_color = dashboard_text_color, text_size = size.small) risk_score_color = risk_allocation > 60 ? bullish_color : risk_allocation < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 16, str.tostring(risk_allocation, '##'), text_color = risk_score_color, text_size = size.small) table.cell(dashboard, 0, 17, 'Valuation', text_color = dashboard_text_color, text_size = size.small) val_score_color = valuation_score > 60 ? bullish_color : valuation_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 17, str.tostring(valuation_score, '##'), text_color = val_score_color, text_size = size.small) table.cell(dashboard, 0, 18, 'Sentiment', text_color = dashboard_text_color, text_size = size.small) sent_score_color = sentiment_score > 60 ? bullish_color : sentiment_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 18, str.tostring(sentiment_score, '##'), text_color = sent_score_color, text_size = size.small) table.cell(dashboard, 0, 19, 'Macro', text_color = dashboard_text_color, text_size = size.small) macro_score_color = macro_score > 60 ? bullish_color : macro_score < 40 ? bearish_color : primary_color table.cell(dashboard, 1, 19, str.tostring(macro_score, '##'), text_color = macro_score_color, text_size = size.small) // ALERTS // Major allocation changes alertcondition(smoothed_allocation >= 80 and smoothed_allocation < 80, 'High Equity Allocation', 'Equity allocation reached 80% - Bull market conditions') alertcondition(smoothed_allocation <= 20 and smoothed_allocation > 20, 'Low Equity Allocation', 'Equity allocation dropped to 20% - Defensive positioning') // Crisis alerts alertcondition(is_crisis and not is_crisis , 'CRISIS DETECTED', 'Crisis conditions detected - Reducing equity allocation') alertcondition(is_severe_crisis and not is_severe_crisis , 'SEVERE CRISIS', 'Severe crisis detected - Maximum defensive positioning') // Regime changes regime_changed = market_regime != market_regime alertcondition(regime_changed, 'Regime Change', 'Market regime has changed') // Risk management alerts risk_breach = enable_portfolio_risk_scaling and (actual_portfolio_volatility > target_portfolio_volatility * 1.2 or actual_portfolio_drawdown > max_portfolio_drawdown * 1.2) alertcondition(risk_breach, 'Risk Breach', 'Portfolio risk exceeds target parameters') // USAGE // The indicator displays a recommended equity allocation percentage (0-100%). // Example: 75% allocation = 75% stocks, 25% cash/bonds. // // The model combines market regime analysis (trend, volatility, drawdowns), // risk management (portfolio-level targeting), valuation metrics (P/E, ERP), // sentiment indicators (VIX term structure), and macro factors (yield curve, // credit spreads) into a single allocation signal. // // Crisis detection automatically reduces exposure when multiple warning signals // converge. Alerts available for major allocation shifts and regime changes. // // Designed for SPY/S&P 500 portfolio allocation. Adjust component weights and // risk parameters in settings to match your risk tolerance. View in Pine
Pine Script® indicator
by Alfred773
Pivot Points Standard + 9/20/50/200 EMA by NK//@version=6 indicator("Pivot Points Standard + 9/20/50/200 EMA", "Pivots+EMA", overlay=true, max_lines_count=500, max_labels_count=500) // --- EMA calculations and plots ema9 = ta.ema(close, 9) ema20 = ta.ema(close, 20) ema50 = ta.ema(close, 50) ema200 = ta.ema(close, 200) plot(ema9, color=color.green, linewidth=2, title="EMA 9") plot(ema20, color=color.red, linewidth=2, title="EMA 20") plot(ema50, color=color.new(color.blue, 0), linewidth=2, title="EMA 50") // dark blue plot(ema200, color=color.black, linewidth=2, title="EMA 200") // --- Pivots Inputs pivotTypeInput = input.string(title="Type", defval="Traditional", options= ) pivotAnchorInput = input.string(title="Pivots Timeframe", defval="Auto", options= ) maxHistoricalPivotsInput = input.int(title="Number of Pivots Back", defval=15, minval=1, maxval=200, display = display.data_window) isDailyBasedInput = input.bool(title="Use Daily-based Values", defval=true, display = display.data_window, tooltip="When this option is unchecked, Pivot Points will use intraday data while calculating on intraday charts. If Extended Hours are displayed on the chart, they will be taken into account during the pivot level calculation. If intraday OHLC values are different from daily-based values (normal for stocks), the pivot levels will also differ.") showLabelsInput = input.bool(title="Show Labels", defval=true, group="labels", display = display.data_window) showPricesInput = input.bool(title="Show Prices", defval=true, group="labels", display = display.data_window) positionLabelsInput = input.string("Left", "Labels Position", options= , group="labels", display = display.data_window, active = showLabelsInput or showPricesInput) linewidthInput = input.int(title="Line Width", defval=1, minval=1, maxval=100, group="levels", display = display.data_window) DEFAULT_COLOR = #FB8C00 showLevel2and3 = pivotTypeInput != "DM" showLevel4 = pivotTypeInput != "DM" and pivotTypeInput != "Fibonacci" showLevel5 = pivotTypeInput == "Traditional" or pivotTypeInput == "Camarilla" pColorInput = input.color(DEFAULT_COLOR, "P ", inline="P", group="levels", display = display.data_window) pShowInput = input.bool(true, "", inline="P", group="levels", display = display.data_window) s1ColorInput = input.color(DEFAULT_COLOR, "S1", inline="S1/R1" , group="levels", display = display.data_window) s1ShowInput = input.bool(true, "", inline="S1/R1", group="levels", display = display.data_window) r1ColorInput = input.color(DEFAULT_COLOR, " R1", inline="S1/R1", group="levels", display = display.data_window) r1ShowInput = input.bool(true, "", inline="S1/R1", group="levels", display = display.data_window) s2ColorInput = input.color(DEFAULT_COLOR, "S2", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3) s2ShowInput = input.bool(true, "", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3) r2ColorInput = input.color(DEFAULT_COLOR, " R2", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3) r2ShowInput = input.bool(true, "", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3) s3ColorInput = input.color(DEFAULT_COLOR, "S3", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3) s3ShowInput = input.bool(true, "", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3) r3ColorInput = input.color(DEFAULT_COLOR, " R3", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3) r3ShowInput = input.bool(true, "", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3) s4ColorInput = input.color(DEFAULT_COLOR, "S4", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4) s4ShowInput = input.bool(true, "", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4) r4ColorInput = input.color(DEFAULT_COLOR, " R4", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4) r4ShowInput = input.bool(true, "", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4) s5ColorInput = input.color(DEFAULT_COLOR, "S5", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5) s5ShowInput = input.bool(true, "", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5) r5ColorInput = input.color(DEFAULT_COLOR, " R5", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5) r5ShowInput = input.bool(true, "", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5) type graphicSettings string levelName color levelColor bool showLevel var graphicSettingsArray = array.from( graphicSettings.new(" P", pColorInput, pShowInput), graphicSettings.new("R1", r1ColorInput, r1ShowInput), graphicSettings.new("S1", s1ColorInput, s1ShowInput), graphicSettings.new("R2", r2ColorInput, r2ShowInput), graphicSettings.new("S2", s2ColorInput, s2ShowInput), graphicSettings.new("R3", r3ColorInput, r3ShowInput), graphicSettings.new("S3", s3ColorInput, s3ShowInput), graphicSettings.new("R4", r4ColorInput, r4ShowInput), graphicSettings.new("S4", s4ColorInput, s4ShowInput), graphicSettings.new("R5", r5ColorInput, r5ShowInput), graphicSettings.new("S5", s5ColorInput, s5ShowInput)) autoAnchor = switch timeframe.isintraday => timeframe.multiplier <= 15 ? "1D" : "1W" timeframe.isdaily => "1M" => "12M" pivotTimeframe = switch pivotAnchorInput "Auto" => autoAnchor "Daily" => "1D" "Weekly" => "1W" "Monthly" => "1M" "Quarterly" => "3M" => "12M" pivotYearMultiplier = switch pivotAnchorInput "Biyearly" => 2 "Triyearly" => 3 "Quinquennially" => 5 "Decennially" => 10 => 1 numOfPivotLevels = switch pivotTypeInput "Traditional" => 11 "Camarilla" => 11 "Woodie" => 9 "Classic" => 9 "Fibonacci" => 7 "DM" => 3 type pivotGraphic line pivotLine label pivotLabel method delete(pivotGraphic graphic) => graphic.pivotLine.delete() graphic.pivotLabel.delete() var drawnGraphics = matrix.new() localPivotTimeframeChange = timeframe.change(pivotTimeframe) and year % pivotYearMultiplier == 0 securityPivotTimeframeChange = timeframe.change(timeframe.period) and year % pivotYearMultiplier == 0 pivotTimeframeChangeCounter(condition) => var count = 0 if condition and bar_index > 0 count += 1 count localPivots = ta.pivot_point_levels(pivotTypeInput, localPivotTimeframeChange) securityPivotPointsArray = ta.pivot_point_levels(pivotTypeInput, securityPivotTimeframeChange) securityTimeframe = timeframe.isintraday ? "1D" : timeframe.period = request.security(syminfo.tickerid, pivotTimeframe, , lookahead = barmerge.lookahead_on) pivotPointsArray = isDailyBasedInput ? securityPivots : localPivots affixOldPivots(endTime) => if drawnGraphics.rows() > 0 lastGraphics = drawnGraphics.row(drawnGraphics.rows() - 1) for graphic in lastGraphics graphic.pivotLine.set_x2(endTime) if positionLabelsInput == "Right" graphic.pivotLabel.set_x(endTime) drawNewPivots(startTime) => newGraphics = array.new() for in pivotPointsArray levelSettings = graphicSettingsArray.get(index) if not na(coord) and levelSettings.showLevel lineEndTime = startTime + timeframe.in_seconds(pivotTimeframe) * 1000 * pivotYearMultiplier pivotLine = line.new(startTime, coord, lineEndTime, coord, xloc = xloc.bar_time, color=levelSettings.levelColor, width=linewidthInput) pivotLabel = label.new(x = positionLabelsInput == "Left" ? startTime : lineEndTime, y = coord, text = (showLabelsInput ? levelSettings.levelName + " " : "") + (showPricesInput ? "(" + str.tostring(coord, format.mintick) + ")" : ""), style = positionLabelsInput == "Left" ? label.style_label_right : label.style_label_left, textcolor = levelSettings.levelColor, color = #00000000, xloc=xloc.bar_time) newGraphics.push(pivotGraphic.new(pivotLine, pivotLabel)) drawnGraphics.add_row(array_id = newGraphics) if drawnGraphics.rows() > maxHistoricalPivotsInput oldGraphics = drawnGraphics.remove_row(0) for graphic in oldGraphics graphic.delete() localPivotDrawConditionStatic = not isDailyBasedInput and localPivotTimeframeChange securityPivotDrawConditionStatic = isDailyBasedInput and securityPivotCounter != securityPivotCounter var isMultiYearly = array.from("Biyearly", "Triyearly", "Quinquennially", "Decennially").includes(pivotAnchorInput) localPivotDrawConditionDeveloping = not isDailyBasedInput and time_close == time_close(pivotTimeframe) and not isMultiYearly securityPivotDrawConditionDeveloping = false if (securityPivotDrawConditionStatic or localPivotDrawConditionStatic) affixOldPivots(time) drawNewPivots(time) var FIRST_BAR_TIME = time if (barstate.islastconfirmedhistory and drawnGraphics.columns() == 0) if not na(securityPivots) and securityPivotCounter > 0 if isDailyBasedInput drawNewPivots(FIRST_BAR_TIME) else runtime.error("Not enough intraday data to calculate Pivot Points. Lower the Pivots Timeframe or turn on the 'Use Daily-based Values' option in the indicator settings.") else runtime.error("Not enough data to calculate Pivot Points. Lower the Pivots Timeframe in the indicator settings.")
Pine Script® indicator
by NagsenKhade
RSI Rate of Change (ROC of RSI)The RSI Rate of Change (ROC of RSI) indicator measures the speed and momentum of changes in the RSI, helping traders identify early trend shifts, strength of price moves, and potential reversals before they appear on the standard RSI. While RSI shows overbought and oversold conditions, the ROC of RSI reveals how fast RSI itself is rising or falling, offering a deeper view of market momentum. How the Indicator Works 1. RSI Calculation The indicator first calculates the classic Relative Strength Index (RSI) using the selected length (default 14). This measures the strength of recent price movements. 2. Rate of Change (ROC) of RSI Next, it computes the Rate of Change (ROC) of the RSI over a user-defined period. This shows: Positive ROC → RSI increasing quickly → strong bullish momentum Negative ROC → RSI decreasing quickly → strong bearish momentum ROC crossing above/below 0 → potential early trend shift What You See on the Chart Blue Line: RSI Red Line: ROC of RSI Grey dotted Zero Line: Momentum reference Why Traders Use It The RSI ROC helps you: Detect momentum reversals early Spot bullish and bearish accelerations not visible on RSI alone Identify exhaustion points before RSI reaches extremes Improve entry/exit precision in trend and swing trading Validate price breakouts or breakdowns with momentum confirmation Best For Swing traders Momentum traders Reversal traders Trend-following systems needing early confirmation signals
Pine Script® indicator
by vijendersb
EMV// This Pine Script® code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org //@version=5 indicator("EMV", overlay=false) N = input.int(14, "N") M = input.int(9, "M") // ==== VOLUME ==== maVol = ta.sma(volume, N) VOLUME = maVol / volume // ==== MID ==== hl = high + low MID = 100 * (hl - hl ) / hl // ==== HL_RANGE ==== HL_RANGE = ta.sma(high - low, N) // ==== EMV ==== EMV_raw = MID * VOLUME * (high - low) / HL_RANGE EMV = ta.sma(EMV_raw, N) // ==== MAEMV ==== MAEMV = ta.sma(EMV, M) plot(EMV, color=color.blue, title="EMV") plot(MAEMV, color=color.orange, title="MAEMV")
Pine Script® indicator
by Handson2022
Hardwaybets' Protected Highs / Protected Lows TradingProtected Highs & Lows – Multi-Condition Structural Marker This indicator identifies specific candle formations where price breaks a previous candle’s high or low, fails to maintain that break, and confirms the rejection with an additional condition involving prior candles. These marked locations offer a visual reference for areas where price attempted directional expansion but did not sustain it. All levels remain visible until later invalidated by price movement. Protected High – Detection Logic A Protected High is marked only when all three of the following conditions occur: 1. Break of Previous High The current candle trades above the prior candle’s high. 2. Close Back Inside Range The current candle closes within the high-to-low range of the previous candle, indicating the upward expansion was not sustained. 3. Reversal Through Prior Bullish Structure After forming the high, price closes below the opening price of one or more bullish candles that were part of the upward movement into that high. This reflects a shift away from the prior upward structure. When all three conditions are met, the high of the candle that created the event is marked on the chart. Protected Low – Detection Logic A Protected Low is marked only when all three of the following conditions occur: 1. Break of Previous Low The current candle trades below the prior candle’s low. 2. Close Back Inside Range The current candle closes within the high-to-low range of the previous candle, indicating the downward expansion was not sustained. 3. Reversal Through Prior Bearish Structure After forming the low, price closes above the opening price of one or more bearish candles that were part of the downward movement into that low. This reflects a shift away from the prior downward structure. When all three conditions are met, the low of the candle that created the event is marked on the chart. Level Management * Marked highs and lows remain active as long as price does not trade beyond them. * If price moves past a marked level, that level is removed. * Only active, unviolated structural reference points remain displayed. Market Structure Context (Strictly Non-Signaling) Protected highs and lows can help traders observe areas where: * Price briefly exceeded a previous high or low * That expansion was not maintained * Price then moved back through recent candles associated with the prior direction These observations can be used by traders to understand how price interacts with nearby structural reference points. The indicator itself does not provide trade entries, exits, or directional guidance. Customization Options The indicator provides adjustable settings for: * Marker style (labels or shapes) * Shape type (circle, square, diamond, etc.) * Colors for highs and lows * Vertical spacing between markers and candles These options help maintain clarity on different chart types and timeframes. Intended Use The indicator does not generate forecasts or trading signals. Its purpose is to visually highlight multi-condition candle formations where price briefly exceeded a prior high or low, failed to sustain that expansion, and later reversed through nearby structural points. Compatibility Suitable for all assets and timeframes.
Pine Script® indicator
by hardwaybets
10
ITFI Lite — Smart Money Dashboard✅ ITFI Lite is now live — ITFI Pro (full confluence) coming soon 🔒 Follow to get early access. This is the Lite version of the upcoming ITFI Pro system. Designed for traders who want clean higher-timeframe context without clutter, complexity, or repainting. Included in ITFI Lite: • D1 & H4 bias (EMA-based) • M15 equilibrium + premium/discount zones • PB/BO Lite signals (no scoring) • UTC session with volatility phase • Compact dashboard for fast decision-making Not included (Pro features): • HTF FVG / OB mapping • Scoring engine • Ready / Wait / Blocked system • Advanced liquidity model • Entry timing assistant • Full multi-timeframe confluence 📌 Pro version is currently in development — Coming Soon 🔒 Follow for updates and early access.
Pine Script® indicator
by dr-mubashir
1
Support/Resistance (OI) + 9/20 EMA//@version=6 indicator("Support/Resistance (OI) + 9/20 EMA", overlay=true) ema9 = ta.ema(close, 9) ema20 = ta.ema(close, 20) plot(ema9, color=color.blue, linewidth=2, title="EMA 9") plot(ema20, color=color.orange, linewidth=2, title="EMA 20") // Update these levels daily based on your OI analysis s1 = 25850 s2 = 25800 s3 = 25500 r1 = 26000 r2 = 25950 r3 = 26100 // Use hline for persistent horizontal levels hline(s1, 'Support 1', color=color.green, linestyle=hline.style_dashed, linewidth=2) hline(s2, 'Support 2', color=color.green, linestyle=hline.style_dashed, linewidth=2) hline(s3, 'Support 3', color=color.green, linestyle=hline.style_dashed, linewidth=2) hline(r1, 'Resistance 1', color=color.red, linestyle=hline.style_dashed, linewidth=2) hline(r2, 'Resistance 2', color=color.red, linestyle=hline.style_dashed, linewidth=2) hline(r3, 'Resistance 3', color=color.red, linestyle=hline.style_dashed, linewidth=2)
Pine Script® indicator
by NagsenKhade
Relative Performance Analyzer [AstrideUnicorn]Relative Performance Analyzer (RPA) is a performance analysis tool inspired by the data comparison features found in professional trading terminals. The RPA replicates the analytical approach used by portfolio managers and institutional analysts who routinely compare multiple securities or other types of data to identify relative strength opportunities, make allocation decisions, choose the most optimal investment from several alternatives, and much more. Key Features: Multi-Symbol Comparison: Track up to 5 different symbols simultaneously across any asset class or dataset Two Performance Calculation Methods: Choose between percentage returns or risk-adjusted returns Interactive Analysis: Drag the start date line on the chart or manually choose the start date in the settings Professional Visualization: High-contrast color scheme designed for both dark and light chart themes Live Performance Table: Real-time display of current return values sorted from the top to the worst performers Practical Use Cases: ETF Selection: Compare similar ETFs (e.g., SPY vs IVV vs VOO) to identify the most efficient investment Sector Rotation: Analyze which sectors are showing relative strength for strategic allocation Competitive Analysis: Compare companies within the same industry to identify leaders (e.g., APPLE vs SAMSUNG vs XIAOMI) Cross-Asset Allocation: Evaluate performance across stocks, bonds, commodities, and currencies to guide portfolio rebalancing Risk-Adjusted Decisions: Use risk-adjusted performance to find investments with the best returns per unit of risk Example Scenarios: Analyze whether tech stocks are outperforming the broader market by comparing XLK to SPY Evaluate which emerging market ETF (EEM vs VWO) has provided better risk-adjusted returns over the past year HOW DOES IT WORK The indicator calculates and visualizes performance from a user-defined starting point using two methodologies: Percentage Returns: Standard total return calculation showing percentage change from the start date Risk-Adjusted Returns: Cumulative returns divided by the volatility (standard deviation), providing insight into the efficiency of performance. An expanding window is used to calculate the volatility, ensuring accurate risk-adjusted comparisons throughout the analysis period. HOW TO USE Setup Your Comparison: Enable up to 5 assets and input their symbols in the settings Set Analysis Period: When you first launch the indicator, select the start date by clicking on the price chart. The vertical start date line will appear. Drag it on the chart or manually input a specific date to change the start date. Choose Return Type: Select between percentage or risk-adjusted returns based on your analysis needs Interpret Results Use the real-time table for precise current values SETTINGS Assets 1-5: Toggle on/off and input symbols for comparison (stocks, ETFs, indices, forex, crypto, fundamental data, etc.) Start Date: Set the initial point for return calculations (drag on chart or input manually) Return Type: Choose between "Percentage" or "Risk-Adjusted" performance.
Pine Script® indicator
by AstrideUnicorn