Cyber Mondayลดสูงสุดถึง 80%

Candlestick analysis

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
อินดิเคเตอร์ Pine Script®
โดย Alfred773
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.
อินดิเคเตอร์ Pine Script®
โดย hardwaybets
10
Mambo MA & HAMambo MA & HA is a combined trend-view indicator that overlays Heikin Ashi direction markers and up to eight customizable moving averages on any chart. The goal is to give a clear, uncluttered visual summary of short-term and long-term trend direction using both regular chart data and Heikin Ashi structure. This indicator displays: Heikin Ashi (HA) directional markers on the chart timeframe Optional Heikin Ashi markers from a second, higher timeframe Up to eight different moving averages (SMA, EMA, SMMA/RMA, WMA, VWMA) Adjustable colors and transparency for visual layering Offset controls for HA markers to prevent overlap with price candles It is designed for visual clarity without altering the underlying price candles. Heikin Ashi Direction Markers (Chart Timeframe) The indicator generates HA OHLC values internally and compares the HA open and close: Green (bullish) HA candle → triangle-up marker plotted above the bar Red (bearish) HA candle → triangle-down marker plotted above the bar The triangles use soft pastel colors for minimal obstruction: Up marker: light green (rgb 204, 232, 204) Down marker: light red (rgb 255, 204, 204) The “HA Offset (chart TF ticks)” input lets users shift the triangle vertically in price terms to avoid overlapping the real candles or MAs. Heikin Ashi Markers from a Second Timeframe An optional second timeframe (default: 60m) shows additional HA direction: Green HA (higher timeframe) → tiny triangle-up below the bar Red HA (higher timeframe) → tiny triangle-down below the bar This allows a trader to see higher-timeframe HA structure without switching charts. The offset for the second timeframe is independent (“HA Offset (extra TF ticks)”). Custom Moving Averages (Up to Eight) The indicator includes eight individually configurable MAs, each with: On/off visibility toggle MA type SMA EMA SMMA / RMA WMA VWMA Source Length Color (with preset 70% transparency for visual stacking) The default MA lengths are: 10, 20, 50, 100, 150, 200, 250, 300. All MA colors are slightly transparent by design to avoid obscuring price bars and HA markers. Purpose of the Indicator This tool provides a simple combined view of: Immediate trend direction (chart-TF HA markers) Higher-timeframe HA trend bias (extra-TF markers) Overall moving-average structure from short to very long periods It is particularly useful for: Monitoring trend continuation vs. reversal Confirming entries with multi-TF Heikin Ashi direction Identifying pullbacks relative to layered moving averages Viewing trend context without switching timeframes There are no signals, alerts, or strategy components. It is strictly a visual trend-context tool. Key Features Summary Two-timeframe Heikin Ashi direction Separate offsets for HA markers Eight fully configurable MAs Clean color scheme with low opacity Non-intrusive overlays Compatible with all markets and chart types
อินดิเคเตอร์ Pine Script®
โดย alejandroscotti
AP Capital – Volatility + High/Low Projection v1.1📌 AP Capital – Volatility + High/Low Projection v1.1 Predictive Daily Volatility • Session Logic • High/Low Projection Indicator This indicator is designed to help traders visually understand daily volatility conditions, identify session-based turning points, and anticipate potential highs and lows of the day using statistical behavior observed across thousands of bars of intraday data. It combines intraday session structure, volatility regime classification, and context from the previous day’s expansion to highlight high-probability areas where the market may set its daily high or daily low. 🔍 What This Indicator Does 1. Volatility Regime Detection Each day is classified into: 🔴 High Volatility (trend continuation & expansion likely) 🟡 Normal Volatility 🔵 Low Volatility (chop, false breaks, mean-reversion common) The background color automatically adapts so you always know what environment you're trading in. 2. Session-Based High/Low Identification Different global sessions tend to create different market behaviors: Asia session frequently sets the LOW of day New York & Late US sessions frequently set the HIGH of day This indicator uses those probabilities to highlight potential turning points. 3. Potential High / Low of Day Projections The script plots: 🟢 Potential LOW of Day 🔴 Potential HIGH of Day These appear only when: Price hits the session-statistical turning zone Volatility conditions match Yesterday’s expansion or compression context agrees This keeps signals clean and prevents over-marking. 4. Clean Visuals Instead of cluttering the chart, highs and lows are marked only when conditions align, making this tool ideal for: Session scalpers Day traders Gold / NAS100 / FX intraday traders High-probability reversal traders 🧠 How It Works The engine combines: Daily range vs 20-day average Real-time intraday high/low formation Session-specific probability weighting Previous day expansion and volatility filters This results in highly reliable signals for: Fade trades Reversal setups Timing entries more accurately ✔️ Best Uses Identifying where the day’s range is likely to complete Avoiding trades during low-volatility compression days Detecting where the market is likely to turn during major sessions Using potential HIGH/LOW levels as take-profit zones Enhancing breakout or reversal strategies ⚠️ Disclaimer This indicator does not repaint, but it is not a standalone entry tool. It is designed to provide context, session awareness, and volatility-driven turning points to assist your existing strategy. Always combine with sound risk management.
อินดิเคเตอร์ Pine Script®
โดย APCapitalTrading
Advanced Supply and DemandThe Supply and Demand Visible Range indicator displays areas & levels on the user's chart for the visible range using a novel volume-based method. The script also makes use of intra-bar data to create precise Supply & Demand zones. 🔶 SETTINGS Threshold %: Percentage of the total visible range volume used as a threshold to set supply/demand areas. Higher values return wider areas. Resolution: Determines the number of bins used to find each area. Higher values will return more precise results. Intra-bar TF: Timeframe used to obtain intra-bar data. The supply/demand areas and levels displayed by the script are aimed at providing potential supports/resistances for users. The script's behavior makes it recalculate each time the visible chart interval/range changes, as such this script is more suited as a descriptive tool. Price reaching a supply (upper) area that might have been tested a few times might be indicative of a potential reversal down, while price reaching a demand (lower) area that might have been tested a few times could be indicative of a potential reversal up. The width of each area can also indicate which areas are more liquid, with thinner areas indicating more significant liquidity. The user can control the width of each area using the Threshold % setting, with a higher setting returning wider areas. The precision setting can also return wider supply/demand areas if very low values are used and has the benefit of improving the script execution time at the cost of precision. The Supply and Demand Zones indicator returns various levels. The solid-colored levels display the average of each area, while dashed colored lines display the weighted averages of each area. These weighted averages can highlight more liquid price levels within the supply/demand areas. Central solid/dashed lines display the average between the areas' averages and weighted averages. 🔶 DETAILS Each supply/demand area is constructed from volume data. The calculation is done as follows: The accumulated volume within the chart visible range is calculated. The chart visible range is divided into N bins of equal width (where N is the resolution setting) Calculation start from the highest visible range price value for the supply area, and lowest value for the demand area. The volume within each bin after the starting calculation level is accumulated, once this accumulated volume is equal or exceed the threshold value (p % of the total visible range volume) the area is set. Each bin volume accumulation within an area is displayed on the left, this can help indicate how fast volume accumulates within an area. 🔶 LIMITATIONS The script execution time is dependent on all of the script's settings, using more demanding settings might return errors so make sure to be aware of the potential scenarios that might make the script exceed the allowed execution time: Having a chart's visible range including a high number of bars. Using a high number of bins (high resolution value) will increase computation time, this can be worsened by using a high threshold %. Using very low intra-bar timeframe can drastically increase computation time but can also simply throw an error if the chart timeframe is high.
อินดิเคเตอร์ Pine Script®
โดย Crxzx
bows//@version=5 indicator("NQ EMA+RSI+ATR Alerts with SL/TP", overlay=true, shorttitle="NQ Alerts SLTP") // === Inputs ===a fastLen = input.int(9, "Fast EMA", minval=1) slowLen = input.int(21, "Slow EMA", minval=1) rsiLen = input.int(14, "RSI Length", minval=1) rsiLongMax = input.int(70, "Max RSI to allow LONG", minval=50, maxval=90) rsiShortMin = input.int(30, "Min RSI to allow SHORT", minval=10, maxval=50) atrLen = input.int(14, "ATR Length", minval=1) atrMultSL = input.float(1.5, "ATR Stop-Loss Multiplier", step=0.1) atrMultTP = input.float(2.5, "ATR Take-Profit Multiplier", step=0.1) // === Indicator calculations === price = close fastEMA = ta.ema(price, fastLen) slowEMA = ta.ema(price, slowLen) rsiVal = ta.rsi(price, rsiLen) atr = ta.atr(atrLen) // === Entry signals === longSignal = ta.crossover(fastEMA, slowEMA) and rsiVal < rsiLongMax shortSignal = ta.crossunder(fastEMA, slowEMA) and rsiVal > rsiShortMin // === SL/TP Levels === longSL = price - atr * atrMultSL longTP = price + atr * atrMultTP shortSL = price + atr * atrMultSL shortTP = price - atr * atrMultTP // === Plotting === plot(fastEMA, color=color.orange, title="Fast EMA") plot(slowEMA, color=color.blue, title="Slow EMA") plotshape(longSignal, title="Buy Signal", style=shape.triangleup, color=color.new(color.green, 0), location=location.belowbar, size=size.tiny) plotshape(shortSignal, title="Sell Signal", style=shape.triangledown, color=color.new(color.red, 0), location=location.abovebar, size=size.tiny) // Optional visualization of SL/TP plot(longSignal ? longSL : na, "Long Stop-Loss", color=color.new(color.red, 50), style=plot.style_linebr) plot(longSignal ? longTP : na, "Long Take-Profit", color=color.new(color.green, 50), style=plot.style_linebr) plot(shortSignal ? shortSL : na, "Short Stop-Loss", color=color.new(color.red, 50), style=plot.style_linebr) plot(shortSignal ? shortTP : na, "Short Take-Profit", color=color.new(color.green, 50), style=plot.style_linebr) // === Alerts with SL/TP info === alertcondition(longSignal, title="BUY Signal", message="BUY Alert — NQ LONG: Entry @ {{close}} | SL: {{plot_1}} | TP: {{plot_2}} | {{ticker}}") alertcondition(shortSignal, title="SELL Signal", message="SELL Alert — NQ SHORT: Entry @ {{close}} | SL: {{plot_3}} | TP: {{plot_4}} | {{ticker}}") // === Visual labels === if (longSignal) label.new(bar_index, low, "BUY SL: " + str.tostring(longSL, format.mintick) + " TP: " + str.tostring(longTP, format.mintick), style=label.style_label_up, color=color.new(#be14c4, 0), textcolor=color.white) if (shortSignal) label.new(bar_index, high, "SELL SL: " + str.tostring(shortSL, format.mintick) + " TP: " + str.tostring(shortTP, format.mintick), style=label.style_label_down, color=color.new(color.red, 0), textcolor=color.white)
อินดิเคเตอร์ Pine Script®
โดย ProfitsJibee
Structural Liquidity ZonesTitle: Structural Liquidity Zones Description: This script is a technical analysis system designed to map market structure (Liquidity) using dynamic, volatility-adjusted zones, while offering an optional Trend Confluence filter to assist with trade timing. Concept & Originality: Standard support and resistance indicators often clutter the chart with historical lines that are no longer relevant. This script solves that issue by utilizing Pine Script Arrays and User-Defined Types to manage the "Lifecycle" of a zone. It automatically detects when a structure is broken by price action and removes it from the chart, ensuring traders only see valid, fresh levels. By combining this structural mapping with an optional EMA Trend Filter, the script serves as a complete "Confluence System," helping traders answer both "Where to trade?" (Structure) and "When to trade?" (Trend). Key Features: 1. Dynamic Structure (The Array Engine) Pivot Logic: The script identifies major turning points using a customizable lookback period. Volatility Zones: Instead of thin lines, zones are projected using the ATR (Average True Range). This creates a "breathing room" for price, visualizing potential invalidation areas. Active Management: The script maintains a memory of active zones. As new bars form, the zones extend forward. If price closes beyond a zone, the script's garbage collection logic removes the level, keeping the chart clean. 2. Trend Confluence (Optional) EMA System: Includes a Fast (9) and Slow (21) Exponential Moving Average module. Signals: Visual Buy/Sell labels appear on crossover events. Purpose: This allows for "Filter-based Trading." For example, a trader can choose to take a "Buy" bounce from a Support Zone only if the EMA Trend is also bullish. Settings: Structure Lookback: Controls the sensitivity of the pivot detection. Max Active Zones: Limits the number of lines to optimize performance. ATR Settings: Adjusts the width of the zones based on volatility. Enable Trend Filter: Toggles the EMA lines and signals on/off. Usage: This tool is intended for structural analysis and educational purposes. It visualizes the relationship between price action pivots and momentum trends.
อินดิเคเตอร์ Pine Script®
โดย siddarth_banakar3
3
Second chartThis is a trend-following momentum confirmation indicator designed to filter trades in the direction of the dominant trend while timing entries using RSI momentum shifts. Best suited for: ✅ Forex & Crypto ✅ 5m – 1H timeframes ✅ Trend continuation strategies ⚙ Inputs Explained ▸ Trend MA Length Controls the EMA trend filter Lower value (20–30) → faster, more signals Higher value (50–100) → slower, stronger trend filter ▸ RSI Length Controls responsiveness of momentum Standard setting: 14 Lower → aggressive entries Higher → conservative entries ▸ Show Buy/Sell Signals ON → Displays BUY/SELL labels OFF → Hides all trade signals ▸ Trend Background ON → Green = Bullish / Red = Bearish OFF → Clean chart mode 🧠 Signal Logic Breakdown
อินดิเคเตอร์ Pine Script®
โดย vahidfrb
GK BOS ultimateGK BOS ultimate is a structured Break of Structure tool designed to highlight major shifts in the market structure. The script identifies when price breaks above a significant previous high or below a significant low, using a defined lookback period and a ATR filter to reduce weak or minor breakouts When a major bullish or bearish structure breaks occurs, the indicator marks the chart with a GK BUY or GK SELL label. It also plots a TP1 level based on ATR(14) multiplied by a user-selected factor. This provides a consistent volatility-based reference point that helps traders analyse potential follow-through areas after a structure break. HOW IT WORKS the script calculates the highest high and lowest low over the chosen lookback period A break of structure is confirmed only if the close moves beyond these levels with enough strength relative to ATR, When this happens the indicator Prints GK BUY for bullish structure breaks Prints GK SELL for bearish structure breaks Plots a corresponding TP1 PRINT derived from recent volatility no repainting occurs because calculations are based on confirmed closes this TOOL is intended for educational and analytical purposes only
อินดิเคเตอร์ Pine Script®
โดย hgavin780
1
Delta Signals NO REPINTA (FINAL)📢 New Indicator: Delta Signals NO REPAINT 🔥 Introducing my new indicator based on Order Flow Delta, designed to provide buy and sell signals with absolutely NO repainting — perfect for scalping, day trading, or swing trading. This tool combines two powerful components: ✅ Order Flow Delta — Measures the real strength between buyers and sellers ✅ Smart Trend Filter — Only shows signals in the direction of the dominant trend Together, they deliver cleaner, more accurate and more reliable signals, with clear entry markers on the chart and a delta histogram revealing real market pressure. 🚀 What’s Included? 🔹 Buy/Sell signals with NO repaint 🔹 Intelligent delta calculation 🔹 Trend filter using moving average 🔹 Clear labels on entry points 🔹 Visual delta histogram 🔹 Works great on Crypto, Forex, Indices & Stocks 🔹 Very lightweight and fast on TradingView 🎯 Why is it powerful? Because it doesn't rely on lagging indicators — it reads the actual imbalance between buyers and sellers, often detecting strong moves before traditional indicators do. This type of analysis is used by professional order flow traders, but now you have it on your TradingView chart in a simple, visual format. 🔥 Perfect for: Scalpers who need precision Day traders working breakouts and pullbacks Swing traders seeking strong confirmations Traders who want clean, NO-repaint signals If you want a version with automatic TP/SL, alerts, or full backtesting, I can publish that as well. Just let me know. 🚀📈
อินดิเคเตอร์ Pine Script®
โดย kalibea
Probabilistic Panel - COMPLETE VERSION📘 Probabilistic Panel — User Manual ________________________________________ INTRODUCTION The Probabilistic Panel is an advanced TradingView indicator that merges multiple technical-analysis components to provide a probabilistic evaluation of market direction. It is composed of several sections that assess trend, volume, price zones, support and resistance, multiple timeframes, and candle distribution. ________________________________________ PANEL STRUCTURE 1. HEADER • PROBABILISTIC PANEL: Indicator name. • FULL VERSION: Indicates that all functionalities are enabled. ________________________________________ 2. GENERAL INFORMATION • ASSET: Displays the asset symbol being analyzed. • LIMITS: Shows score thresholds for classifying setups (A+, B, C). ________________________________________ 3. DIRECTION PROBABILITIES • PROB: Displays probability of upward movement (upPct) and downward movement (downPct) in percentage. o Importance: Indicates the direction with the highest probability based on weighted factors. ________________________________________ 4. CONTINUATION BIAS • BIAS: Shows the probability of continuation of the current trend (intrProbCont). o Importance: Evaluates whether the market is likely to continue in the same direction. ________________________________________ 5. MULTI-TIMEFRAME ANALYSIS (MTF) • MTF: Shows trend direction across multiple timeframes (1D, 1H, 15M, 5M, 1M) using arrows (↑ uptrend, ↓ downtrend, → sideways). o Importance: Helps identify convergence or divergence between timeframes. • ALIGNED MTF: Displays the percentage of alignment between timeframes. o Importance: Higher alignment indicates stronger trends. ________________________________________ 6. VOLUME • VOLUME: Indicates whether volume is “INCREASING”, “DECREASING”, or “STABLE.” o Importance: Increasing volume confirms trend strength. ________________________________________ 7. TECHNICAL INDICATORS • RSI/ROC: Displays RSI (Relative Strength Index) and ROC (Rate of Change). o Importance:  RSI > 65 → Overbought  RSI < 35 → Oversold  ROC → Momentum strength indicator ________________________________________ 8. PRICE ZONE • ZONE: Classifies current price as “PREMIUM” (above average), “DISCOUNT” (below average), or “EQUILIBRIUM.” o Importance: Helps identify buying/selling opportunities based on mean-reversion logic. ________________________________________ 9. CANDLE ANALYSIS • AMPLITUDE: Shows current candle size in percentage and ticks. o Importance: Candles above minimum amplitude threshold are considered trade-valid. • FORMATION: Classifies candle as: o HIGH INDECISION o TOP REJECTION o BOTTOM REJECTION o CONVICTION o MIXED o Importance: Reflects market sentiment and psychology. • WICKS: Displays upper and lower wick size in percentage. o Importance: Longer wicks suggest rejection or indecision. • RATIO: Ratio between total wick size and candle body. o Importance: High ratio = indecision; low ratio = conviction. ________________________________________ 10. TRENDS • AMPLITUDE TREND: Indicates if amplitude is “INCREASING,” “DECREASING,” or “STABLE.” o Importance: Increasing amplitude may signal rising volatility. • CONVICTION TREND: Indicates recent candle conviction: o STRONG UP o STRONG DOWN o INDECISIVE o MIXED o Importance: Measures the strength of recent candles. ________________________________________ 11. PROBABILITY DIFFERENCE (DIF PROB) • Shows the percentage difference between upward and downward probabilities, classified as: o EXCELLENT: Very favorable o GOOD: Significant o MEDIUM: Moderate (avoid entering) o MARKET LOSING STRENGTH: Small difference (avoid entering) o UNSTABLE MARKET: Very small difference (do not trade) o Importance: Higher difference = more directional clarity. ________________________________________ 12. CONFIRMATIONS • Shows how many consecutive confirmations of the current signal were achieved relative to the configured requirement. o Importance: More confirmations increase reliability. ________________________________________ 13. SCORE & CLASSIFICATION • SCORE: Final score from 0 to 100, calculated based on multiple factors. o Higher scores = better setups. • CLASSIFICATION: Setup categorized as: o A+ SETUP o B SETUP o C SETUP o DO NOT TRADE o Importance: Defines whether conditions are favorable. ________________________________________ 14. ACTION • ACTION: Suggests “BUY,” “SELL,” or “WAIT.” o Importance: Final actionable signal. ________________________________________ DECISION LOGIC The indicator uses a weighted combination of multiple factors: 1. Trend (wTrend): Based on the price relative to EMA50. 2. Volume (wVol): Based on recent volume vs. its average. 3. Zone (wZona): Based on price position within recent price range. 4. Support/Resistance (wSR): Based on strength of S/R levels. 5. MTF (wMTF): Timeframe alignment. 6. Distribution (wDist): Distribution of bullish, bearish, and neutral candles. The final score integrates: • Probability of upward movement • Continuation bias • MTF conflict • Moving-average alignment • Volume • Extreme RSI conditions ________________________________________ FALSE-SIGNAL FILTERS • Close-Only Mode: Updates calculations only on candle close. • Minimum Candle Size: Ignores very small candles. • Consecutive Confirmations: Requires repeated signal confirmation. • Minimum Probability Difference: Enforces a minimum separation between bullish and bearish probabilities. ________________________________________ CONCLUSION The Probabilistic Panel is a comprehensive tool that integrates multiple technical-analysis dimensions to deliver more reliable trading signals. Parameters must be adjusted according to the asset and timeframe. Remember: no indicator is infallible. Always combine it with risk management and additional confirmations.
อินดิเคเตอร์ Pine Script®
โดย pf0210