Black Friday%80 kadar İNDİRİM

Candlestick analysis

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
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alejandroscotti tarafından
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® göstergesi
Alfred773 tarafından
Buy/Sell Signal v3 BTC/Gold Scalping Strategy - 5M 1H Timeframe Description Advanced trading indicator specifically designed for scalping BTC and Gold on 5-minute and 1-hour timeframes. It uses EMA crossovers with automatic risk management (Stop Loss and Take Profit) based on ATR. Main Features - Automatic entry signals: Identifies optimal buy and sell points based on EMA crossovers - Integrated risk management: Automatically calculates SL and TP according to your preferred risk/reward ratio - Smart invalidation: Cancels previous positions when a new opposite signal appears - Clear visualization: Horizontal lines showing entry, stop loss, and take profit in real time - Informative panel: Table with all information about the active position - Complete alert system: Notifications for entries, exits, and invalidations How It Works The indicator detects trend changes through the crossover of two EMAs: - Fast EMA (default 5 periods): Quickly responds to price changes - Slow EMA (default 13 periods): Filters market noise - BUY Signal: When the fast EMA crosses above the slow EMA - SELL Signal: When the fast EMA crosses below the slow EMA Risk Management - Stop Loss: Placed below/above the low/high of the entry candle, adjusted by ATR - Take Profit: Automatically calculated according to your configured R:R ratio (default 3:1) - Invalidation: If an opposite signal appears before TP or SL is hit, the position closes automatically Visual Elements - 🟢 BUY Label: Buy signal (lime green) - 🔴 SELL Label: Sell signal (red) - ⚠️ INV Icon: Invalidated entry (yellow) - 🎯 TP Icon: Take Profit reached (green) - ❌ SL Icon: Stop Loss triggered (red) - ⚪ White dotted line: Entry price - 🔴 Red line: Stop Loss level - 🟢 Green line: Take Profit level Recommended Settings For BTC: - Fast EMA: 5 - Slow EMA: 13 - ATR: 14 - SL Multiplier: 0.5 - Risk:Reward: 3.0 - Candle confirmation: Enabled For Gold (XAU/USD): - Fast EMA: 5 - Slow EMA: 13 - ATR: 14 - SL Multiplier: 0.4–0.6 (adjust depending on volatility) - Risk:Reward: 2.5–3.5 - Candle confirmation: Enabled Available Alerts 1. 🟢 BUY Signal: Triggered when a new buy opportunity appears 2. 🔴 SELL Signal: Triggered when a new sell opportunity appears 3. 🎯 Take Profit: Notifies when the profit target is reached 4. ❌ Stop Loss: Alerts when stop loss is triggered 5. ⚠️ Invalidated Entry: Alerts when a position is canceled by an opposite signal Important Notes - ⏰ Recommended timeframe: 5 minutes and 1 hour - 📊 Optimized assets: Bitcoin (BTC/USD) and Gold (XAU/USD), including Futures - ⚠️ No repaint*: Signals do not change once confirmed - 🎓 Educational use: Always perform your own analysis before trading - 💰 Capital management: Never risk more than 1–2% of your account per trade Customizable Parameters Indicators: - Fast EMA period (1–50) - Slow EMA period (1–50) - ATR period (1–50) - Stop Loss multiplier (0.1–5.0) - Risk:Reward ratio (1.0–10.0) - Candle confirmation (On/Off) EMA Visualization: - Show/Hide Fast EMA - Customizable Fast EMA color - Show/Hide Slow EMA - Customizable Slow EMA color How to Use 1. Add the indicator to your 5-minute chart 2. Adjust parameters according to your trading style 3. Set up alerts to receive notifications 4. Wait for clear BUY or SELL signals 5. Manage the trade following the SL and TP levels displayed Disclaimer: This indicator is a technical analysis tool. It does not constitute financial advice. Trading carries significant risks and you may lose your capital. Always trade responsibly with proper risk management.
Pine Script® göstergesi
kelfry98 tarafından
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.
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pf0210 tarafından
纳斯达克涨2.5%以上//@version=5 indicator("纳斯达克大涨标记", shorttitle="NASDAQ+2.5%", overlay=true) // 纳斯达克综合指数的符号 // 如果您想使用 E-mini 纳斯达克 100 期货 (NQ!) 或其他相关工具，请更改此符号 symbolName = "NASDAQ:IXIC" // 目标涨幅百分比 targetPercentage = 2.5 // 获取纳斯达克指数的数据 // 使用 security() 函数获取不同品种的数据 nasdaq_close = request.security(symbolName, "D", close ) nasdaq_prev_close = request.security(symbolName, "D", close ) // 确保我们有足够的数据进行计算 isDataAvailable = not na(nasdaq_close) and not na(nasdaq_prev_close) // 计算当天的涨幅百分比 // (今日收盘价 - 昨日收盘价) / 昨日收盘价 * 100 changePercentage = isDataAvailable ? (nasdaq_close - nasdaq_prev_close) / nasdaq_prev_close * 100 : na // 检查条件：涨幅是否大于或等于目标百分比 isBigUpDay = changePercentage >= targetPercentage // 绘制粉红色的点 plotshape(isBigUpDay, title="大涨日", location=location.belowbar, // 绘制在 K 线的下方 color=color.rgb(255, 0, 255, 0), // 纯粉红色 style=shape.circle, size=size.small, text="") // 可以在图表底部显示涨幅百分比作为确认 plot(isBigUpDay ? changePercentage : na, title="当日涨幅%", color=color.rgb(255, 0, 255, 50), style=plot.style_stepline, trackprice=false) // 警报示例 (可选) // if isBigUpDay // alert("纳斯达克当日涨幅达到 " + str.tostring(targetPercentage) + "% 或以上!", alert.freq_once_per_bar_close)
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xjincn tarafından
Prestijlo X v2 — Precision Scalper & Swing Hybrid AlgoPrestijlo X v2 is a hybrid trading algorithm optimized for short-term (SCALP) and medium-term (SWING) trades. It is an ultra-stable system with an EMA 9-21-50 trend filter, ATR-based risk calculation, percentage TP/SL, and advanced signal filtering. • SCALP / SWING mode selection • ATR & % TP/SL checkboxes • EMA 9-21-50 trend filter • Optimized for 1-5-15 minutes • No label error / background color error Usage: Trading planning can be done using the TP/SL boxes after a signal.
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tugceuluhan tarafından
Kai GoNoGo 2mKai GoNoGo 2m is a multi-factor trend confirmation system designed for fast intraday trading on the 2-minute chart. It combines EMAs, MACD, RSI and ADX through a weighted scoring model to generate clear Go / NoGo conditions for both CALL (long) and PUT (short) setups. The indicator paints the candles with pure colors to show the current strength of the trend: Strong Go (Bright Blue): Full bullish alignment across EMAs, momentum and trend strength. Weak Go (Light Blue): Bullish structure but with softer momentum. Weak NoGo (Light Pink): Bearish structure starting to develop. Strong NoGo (Bright Pink): Full bearish alignment across all components. Neutral (Gray): No trend, compression or transition phase. Components included: EMA Trend Structure (9/21/50/100/200) MACD Momentum (12-26-9) RSI Confirmation (14) ADX Trend Strength Filter via DMI (14,14) Scoring system inspired by the original GoNoGo concept, improved for speed-based trading. Designed for: Scalping, 0DTE options, FAST trend continuation entries, and momentum confirmation on QQQ, SPY, NQ, ES and high-beta names. This version uses pure colors (no gradients) for maximum clarity when trading fast charts.
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dgr_abogado tarafından
Psychological Price Level GBPJPY (.250 / .750)This indicator is designed for GBPJPY traders who work with precision and smart-money-based analysis. It automatically plots psychological price levels at .250 and .750, which are known institutional reference points that often influence market structure, price reactions, and liquidity behavior. Unlike typical round-number indicators, this tool focuses specifically on quarter levels, which are frequently used by algorithms, banks, and experienced institutional traders. Fixed and Reliable Levels As price evolves, the levels update automatically and remain fixed on the chart without shifting when you scroll. This ensures that the levels always stay anchored to relevant market structure, making them reliable reference points for planning entries, targets, or stop placements. Customization The indicator allows full customization. You can freely adjust the line color, line thickness, and line style to match your personal trading chart layout. You can also choose whether lines extend left, right, or both directions, making the tool flexible enough to fit minimalist or highly marked-up workspaces. Why These Levels Matter In smart money trading approaches, the .250 and .750 levels often act as magnetic zones. Price frequently gravitates toward them to test liquidity or engineer traps before continuing its move. These levels may serve as rejection points, breakout confirmation zones, or take-profit areas depending on the broader context. Because they frequently align with order blocks, fair value gaps, and market structure shifts, they can add meaningful confluence to directional bias and trade timing. Who Can Benefit This tool is particularly useful for scalpers, day traders, and swing traders who base decisions on liquidity behavior and institutional logic. It works well on any timeframe and complements concepts such as premium and discount models, inefficiencies, fair value gaps, and volume imbalances. Many traders find that these price levels help them identify reactions earlier, refine entries, and improve confidence when executing trades. Final Note If this indicator supports your trading workflow, feel free to leave a comment or mark it as a favorite + give it a BOOST . Your feedback helps guide future improvements and ensures the tool continues evolving for serious GBPJPY traders. Happy trading — and stay precise. 🚀📊
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x9x5gy7s tarafından
SPY/QQQ Customizable Price ConverterThis is a minimalist utility tool designed for Index traders (SPX, NDX, RUT). It allows you to monitor the price of a reference asset (like SPY, QQQ) directly on your main chart without cluttering your screen. Key Features: 1.🖱️ Crosshair Sync for Historical Data (Highlight): Unlike simple info tables that only show the latest price, this script allows for historical inspection. · How it works: Simply move your mouse crosshair over ANY historical candle on your chart. · The script will instantly display the closing price of the reference asset (e.g., SPY) for that specific time in the Status Line (top-left) or the Data Window. Perfect for backtesting and reviewing price action. 2.🔄 Fully Customizable Ticker: Default is set to SPY, but you can change it to anything in the settings. e.g. · Trading NDX Change it to QQQ. · Trading RUT Change it to IWM. 3.📊 Clean Real-Time Dashboard: · A floating table displays the current real-time price of your reference asset. · Color-coded text (Green/Red) indicates price movement. · Fully customizable size, position, and colors to fit your layout.
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Sewell_ tarafından
Blackscrum Adaptive Momentum Line (BAML)Overview The BlackScrum Adaptive Momentum Line (BAML) is a dynamic trend-confirmation tool designed to keep traders aligned with the dominant market direction while filtering out short-term noise. It adapts automatically to market volatility and candle structure, giving clear visual cues for momentum shifts, trend reversals, and entry confirmation. 🔍 How It Works BAML tracks price strength relative to its adaptive moving average and volatility envelope. When momentum turns decisively bullish, the line flips gold, signalling a potential uptrend. When momentum breaks down, it flips blue, showing trend exhaustion or a developing downtrend. In sideways or transitional conditions, the line fades to neutral grey, helping traders avoid false entries. The line uses: An adaptive EMA core (to stay close to price during fast markets). A volatility-weighted filter (to delay signals during chop). Optional smoothing to fine-tune responsiveness. 🎯 How to Use It Trend Direction: Gold Line → Uptrend confirmed. Consider long bias, pullback entries, or trend continuation setups. Blue Line → Downtrend confirmed. Consider short bias or defensive management on longs. Grey/Flat Line → Neutral/transition phase. Wait for confirmation. Entry Timing: Combine BAML with your breakout or swing confirmation rules. For example: Entry when the line turns gold and price closes above it. Exit when it flips blue or price breaks back below. Multi-Timeframe Usage: Works effectively on any timeframe from 15-minute to 1-day charts. Aligning higher-timeframe BAML with lower-timeframe triggers offers confluence for trend trades. ⚙️ Key Advantages ✅ Adaptive to volatility and candle structure — fewer fake flips. ✅ Visually clear color coding for fast trend reading. ✅ Compatible with other BlackScrum indicators (Fear & Greed, FOMO Finder, Swing Boxes). ✅ Ideal for swing, position, or momentum traders seeking clarity in volatile crypto or stock markets. ⚠️ Tips Use alongside volume or sentiment indicators for confirmation. Avoid counter-trend setups when both higher and lower timeframe BAML lines agree. Works best in trending environments; during consolidation it acts as a stay-out filter. 🧠 In Summary The BlackScrum Adaptive Momentum Line turns raw price data into a smooth, trustworthy trend signal. It’s built to help you stay in strong moves longer, avoid fakeouts, and visually track the transition between fear, neutrality, and euphoria in real time.
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tyronemapp tarafından
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
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vahidfrb tarafından
Guardian Pulse + Enhanced All-In-One (FINAL WORKING)GUARDIAN BUY SIGNAL (Lime Candle + Big Green Arrow) All 4 must happen on the same candle: RSI (14) crosses above 35 from below (bouncing out of oversold) Price is above the blue 21 EMA (short-term trend filter) Candle closes green (close > open) Volume is at least 15% above its 20-period average (real buying pressure) → When all four line up = lime candle + huge green “BUY” arrow → That’s your master entry. Buy shares, calls, whatever. GUARDIAN SELL / EXIT SIGNAL (Red Down Arrow) RSI (14) crosses below 65 from above → That’s your “take profits or get out” signal (red down-triangle appears above the bar) Optional Trend Filter (for safety) Only take BUY signals when the overall trend is Bullish (9 EMA > 21 EMA > 200 EMA) The info box in the top-right will say “Bullish” in green when it’s safe.
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dustinappenzeller1987 tarafından