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Daily RDR (Prev Day H/L, Intraday)This indicator identifies intraday Range-Deviation Reversal (RDR) signals using the previous day’s high and low. At each new session, it stores yesterday’s levels and resets today’s range tracking. During the day, it detects when price first breaks above the prior high or below the prior low, then waits for a reversal: a bearish RDR triggers when price exceeds yesterday’s high and then closes back below it, while a bullish RDR triggers when price undercuts yesterday’s low and then closes back above it. The script plots the previous day’s levels and marks RDR reversals with small up/down triangles.
Pine Script® göstergesi
aykutfirat17331 tarafından
Float Rotation TrackerFloat Rotation Tracker - Quick Reference Guide What is Float Rotation? Float Rotation = Cumulative Daily Volume ÷ Float Example: Float = 5,000,000 shares Day Volume = 7,500,000 shares Rotation = 7.5M ÷ 5M = 1.5x (150%) When rotation hits 1x (100%), every available share has theoretically changed hands at least once during the trading day. Why It Matters RotationMeaningImplication0.5x50% of float tradedInterest building1.0x 🔥Full rotationExtreme interest confirmed2.0x 🔥🔥Double rotationVery high volatility3.0x 🔥🔥🔥Triple rotationRare - maximum volatility Key insight: High rotation on a low-float stock = explosive potential Float Classification Float SizeClassificationRotation Impact≤ 2M🔥 MICROExtremely volatile, fast rotation≤ 5M🔥 VERY LOWExcellent momentum potential≤ 10MLOWGood for rotation plays> 10MNORMALNeeds massive volume to rotate Rule of thumb: Focus on stocks with float under 10M for meaningful rotation signals. Reading the Indicator Rotation Line (Yellow) Shows current rotation level Rises throughout the day as volume accumulates Crosses horizontal level lines at milestones Level Lines LineColorMeaning0.5Gray dotted50% rotation1.0Orange solidFull rotation2.0Red solidDouble rotation3.0Fuchsia solidTriple rotation Volume Bars (Bottom) ColorMeaningGrayBelow average volumeBlueNormal volume (1-2x avg)GreenHigh volume (2-5x avg)LimeExtreme volume (5x+ avg) Milestone Markers Circles appear when rotation crosses key levels Labels show "50%", "1x", "2x", "3x🔥" Background Color Changes as rotation increases Darker = higher rotation level Info Table Explained FieldDescriptionFloatShare count + classification (MICRO/LOW/NORMAL)SourceAuto ✓ = TradingView data / Manual = user enteredRotationCurrent rotation with emoji indicatorRotation %Same as rotation × 100Day VolumeCumulative volume todayTo XxVolume needed to reach next milestoneBar RVolCurrent bar's relative volumeMilestonesWhich levels have been hit todayPer RotationShares equal to one full rotationEst. TimeBars until next milestone (at current pace) Trading with Float Rotation Entry Signals Early Entry (Higher Risk, Higher Reward) Rotation approaching 0.5x Strong price action (bull flag, breakout) Rising relative volume bars Confirmation Entry (Lower Risk) Rotation at or above 1x Price holding above VWAP Continuous green/lime volume bars Late Entry (Highest Risk) Rotation above 2x Only enter on clear pullback pattern Tight stop required Exit Signals Warning Signs: Rotation very high (2x+) with declining volume bars Reversal candle after milestone Price breaking below key support Volume bars turning gray/blue after being green/lime Take Profits: Partial profit at each rotation milestone Trail stop as rotation increases Full exit on reversal pattern after 2x+ rotation Best Setups Ideal Float Rotation Play ✓ Float under 10M (preferably under 5M) ✓ Stock up 5%+ on the day ✓ News catalyst driving interest ✓ Rotation approaching or exceeding 1x ✓ Price above VWAP ✓ Volume bars green or lime ✓ Clear chart pattern (bull flag, flat top) Red Flags to Avoid ✗ Float over 50M (hard to rotate meaningfully) ✗ Rotation high but price declining ✗ Volume bars turning gray after spike ✗ No clear catalyst ✗ Price below VWAP with high rotation ✗ Late in day (3pm+) after 2x rotation Float Data Sources If auto-detect doesn't work, get float from: SourceHow to FindFinvizfinviz.com → ticker → "Shs Float"Yahoo FinanceFinance.yahoo.com → Statistics → "Float"MarketWatchMarketwatch.com → ticker → ProfileYour BrokerUsually in stock details/fundamentals Note: Float can change due to offerings, buybacks, lockup expirations. Check recent data. Settings Guide Conservative Settings Alert Level 1: 0.75 (75%) Alert Level 2: 1.0 (100%) Alert Level 3: 2.0 (200%) Alert Level 4: 3.0 (300%) High Vol Multiplier: 2.0 Extreme Vol Multiplier: 5.0 Aggressive Settings Alert Level 1: 0.3 (30%) Alert Level 2: 0.5 (50%) Alert Level 3: 1.0 (100%) Alert Level 4: 2.0 (200%) High Vol Multiplier: 1.5 Extreme Vol Multiplier: 3.0 Alert Setup Recommended Alerts 100% Rotation (1x) - Primary signal Most important milestone Confirms extreme interest High Rotation + Extreme Volume Combined condition Very high probability signal How to Set Right-click chart → Add Alert Condition: Float Rotation Tracker Select desired milestone Set notification (popup/email/phone) Set expiration Common Questions Q: Why is my float showing "Manual (no data)"? A: TradingView doesn't have float data for this stock. Enter the float manually in settings after looking it up on Finviz or Yahoo Finance. Q: The rotation seems too high/low - is the float wrong? A: Possibly. Cross-check float on Finviz. Recent offerings or share structure changes may not be reflected in TradingView's data. Q: What if float rotates early in the day? A: Early 1x rotation (within first hour) is very bullish - indicates massive interest. Watch for continuation patterns. Q: High rotation but price is dropping? A: This is distribution - large holders are selling into demand. High rotation doesn't guarantee price direction, just volatility. Q: Can I use this for swing trading? A: The indicator resets daily, so it's designed for intraday use. You could note multi-day rotation patterns manually. Quick Decision Matrix RotationPrice ActionVolumeDecision<0.5xStrong upHighWatch, early stage0.5-1xConsolidatingSteadyPrepare entry1x+Breaking outIncreasingEntry on pattern1x+DroppingHighAvoid - distribution2x+Strong upExtremePartial profit, trail stop2x+Reversal candleDecliningExit or avoid Workflow Integration MORNING ROUTINE: 1. Scan for gappers (5%+, high volume) 2. Check float on each candidate 3. Apply Float Rotation Tracker 4. Prioritize lowest float with building rotation DURING SESSION: 5. Watch rotation levels on active trades 6. Enter on patterns when rotation confirms (0.5-1x) 7. Scale out as rotation increases 8. Exit or trail after 2x rotation END OF DAY: 9. Note which stocks hit 2x+ rotation 10. Review rotation vs price action 11. Learn patterns for future trades Combining with Other Indicators IndicatorHow to Use Together5 PillarsScreen for low-float stocks firstGap & GoCheck rotation on gappersBull FlagEnter bull flags with 1x+ rotationVWAPOnly trade rotation plays above VWAPRSIWatch for divergence at high rotation Key Takeaways Float size matters - Lower float = faster rotation = more volatility 1x is the key level - Full rotation confirms extreme interest Volume quality matters - Green/lime bars better than gray Combine with price action - Rotation confirms, patterns trigger Know when you're late - 2x+ rotation is late stage Check your float data - Wrong float = wrong rotation calculation Happy Trading! 🔥
Pine Script® göstergesi
DayTradingMD1992 tarafından
Liquidity LayoutLiquidity Layout The Liquidity Layout is a comprehensive macroeconomic indicator that tracks global liquidity conditions by aggregating multiple financial data streams from major economies (US, EU, China, Japan, UK, Canada, Switzerland). It provides traders with a macro view of market liquidity to help identify favorable conditions for risk assets ⚠️ Important: Timeframe Settings This indicator is designed for the 1W (weekly) timeframe. If you use other timeframes, you must adjust the offset parameter in the settings to properly align the data with price action. The default offset of 12 is calibrated for weekly charts. What It Measures This indicator combines seven key components of global liquidity: 1. Global M2 Money Supply - Tracks broad money supply (M2) plus 10% of narrow money supply (M1) across major economies, weighted by currency strength. This represents the total amount of money circulating in the private sector. 2. Central Bank Balance Sheets (CBBS) - Monitors the combined balance sheets of major central banks (Fed, ECB, BoJ, PBoC, etc.), reflecting quantitative easing and monetary expansion policies. 3. Foreign Exchange Reserves (FER) - Aggregates forex reserves held by central banks, indicating international liquidity buffers and capital flows. 4. Current Account + Capital Flows (CA) - Combines current account balances with capital flows to measure cross-border money movement and trade liquidity. 5. Government Spending (GSP) - Tracks government expenditure minus a portion of federal expenses, representing fiscal stimulus and public sector liquidity injection. 6. World Currency Unit (WCU) - A custom forex composite that weights major and emerging market currencies to capture global currency strength dynamics. 7. Bond Market Conditions - Analyzes yield curves, spreads, and bond indices to assess credit conditions and risk appetite in fixed income markets. The Formula The indicator uses two main calculation modes: ADJ Global Liquidity (Default): × This multiplies liquidity components by currency and bond market factors to capture the interactive effects between monetary conditions and market sentiment. TPI (Trend Power Index) Mode: A normalized version that combines all components with optimized weights: Global Liquidity Index: 10% Bonds: 17.5% Bond Yields: 25% Currency Strength: 25% Government Spending: 5% Current Account: 5% M2: 2.5% Central Bank Balance Sheets: 2.5% Forex Reserves: 5% Oil (macro risk indicator): 2.5% How to Use It Visualization Modes: Background Mode (default): Orange background appears when TPI is positive (favorable liquidity conditions) Line Mode: Displays the indicator as an orange line with customizable offset Interpreting the Signal: Positive/Rising = Expanding liquidity, generally bullish for risk assets Negative/Falling = Contracting liquidity, risk-off environment TPI > 1 = Extremely favorable conditions (upper threshold) TPI < -1 = Severe liquidity stress (lower threshold) Best Practices: Use on higher timeframes (daily, weekly) for macro trend analysis Combine with price action - liquidity often leads market moves by weeks or months Watch for divergences between liquidity and asset prices Particularly relevant for Bitcoin, equities, and risk assets Data Sources The indicator pulls real-time economic data from TradingView's ECONOMICS database and major market indices, including central bank statistics, government reports, and forex rates across G7 and major emerging markets. Settings Data Plot: Choose which liquidity component to display Plot Type: Switch between raw Index values or normalized TPI Offset: Shift the plot forward/backward for alignment (default: 12 for weekly charts) Style: Background shading or line plot Notes This is a macro-level indicator best suited for understanding the broader liquidity environment rather than short-term trading signals. It helps answer the question: "Is the global financial system expanding or contracting liquidity?"
Pine Script® göstergesi
Unicorpus 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
Support/Resistance (OI) + 9/20 EMA//@version=6 indicator("Support/Resistance (OI) + 9/20 EMA", overlay=true) ema9 = ta.ema(close, 9) ema20 = ta.ema(close, 20) plot(ema9, color=color.blue, linewidth=2, title="EMA 9") plot(ema20, color=color.orange, linewidth=2, title="EMA 20") // Update these levels daily based on your OI analysis s1 = 25850 s2 = 25800 s3 = 25500 r1 = 26000 r2 = 25950 r3 = 26100 // Use hline for persistent horizontal levels hline(s1, 'Support 1', color=color.green, linestyle=hline.style_dashed, linewidth=2) hline(s2, 'Support 2', color=color.green, linestyle=hline.style_dashed, linewidth=2) hline(s3, 'Support 3', color=color.green, linestyle=hline.style_dashed, linewidth=2) hline(r1, 'Resistance 1', color=color.red, linestyle=hline.style_dashed, linewidth=2) hline(r2, 'Resistance 2', color=color.red, linestyle=hline.style_dashed, linewidth=2) hline(r3, 'Resistance 3', color=color.red, linestyle=hline.style_dashed, linewidth=2)
Pine Script® göstergesi
NagsenKhade tarafından
ATR-Multiple from 50SMAATR-Multiple from 50SMA is a momentum extension indicator that measures how far price has traveled from the 50-day simple moving average, expressed as a multiple of the Average True Range (ATR). This normalized approach provides a volatility-adjusted view of price extension, making it comparable across different stocks and market conditions. The indicator displays as a histogram with intuitive color-coded zones: values below zero indicate price is trading under the 50SMA, the 0-4 range (green) represents optimal entry territory, 4-7 (yellow) signals it's time to prepare for profit-taking, 7-10 (orange) marks the profit-taking threshold where positions should be actively managed, and above 10 (red) indicates extreme extension where risk is elevated and new entries are typically unwise. Choose between smooth gradient colors or distinct step-based coloring to match your visual preference. Reference lines at 0, 4, 7, and 10 provide clear visual boundaries for each zone. By default, calculations use daily timeframe data regardless of your chart timeframe, ensuring consistent readings—though this can be toggled off for timeframe-specific analysis. Built-in alert conditions notify you when price reaches profit-taking (7+) or extreme extension (10+) levels. Ideal for swing traders and position traders looking to time entries, manage risk, and avoid chasing overextended moves.
Pine Script® göstergesi
McDude tarafından
Dimagi72 Trend Suite (EMA/SMA + 52W + Cross Signals)Dimagi72 Trend Suite is an advanced trend analysis tool designed to give traders a clear picture of market direction, momentum, and major structural turning points. It combines the most reliable long-term and short-term signals into one clean, easy-to-read indicator. Features • EMA9 & EMA21 for short-term momentum • SMA50, SMA100, SMA200 for medium & long-term trend structure • 52-Week High & Low levels for institutional support/resistance • Golden Cross / Death Cross signals (SMA50 vs SMA200) • Trend Strength Meter, shown directly on the chart • Clean labels without clutter • Designed for crypto, stocks, and forex on all timeframes (best on Daily) How it works The indicator measures alignment between EMAs and SMAs, tracks long-term institutional levels, and highlights major trend reversals through cross signals. The Trend Strength Meter calculates a score from -4 to +4, making trend direction instantly visible. Why use this indicator This suite brings together the most widely used trend-following tools into one unified system. It helps traders quickly determine when the market is bullish, bearish, or neutral — and when major reversals may be forming. Best for: Swing traders, long-term trend followers, crypto traders, and anyone who wants a clean visual overview of the trend without using multiple separate indicators. Tags (use these to show up in search) trend ema sma trend-following golden cross death cross momentum trend strength 52 week high crypto stocks market structure
Pine Script® göstergesi
Dimagi72 tarafından
Smart Money Flow - Exchange & TVL Composite# Smart Money Flow - Exchange & TVL Composite Indicator ## Overview The **Smart Money Flow (SMF)** indicator combines two powerful on-chain metrics - **Exchange Flows** and **Total Value Locked (TVL)** - to create a composite index that tracks institutional and "smart money" movement in the cryptocurrency market. This indicator helps traders identify accumulation and distribution phases by analyzing where capital is flowing. ## What It Does This indicator normalizes and combines: - **Exchange Net Flow** (from IntoTheBlock): Tracks Bitcoin/Ethereum movement to and from exchanges - **Total Value Locked** (from DefiLlama): Measures capital locked in DeFi protocols The composite index is displayed on a 0-100 scale with clear zones for overbought/oversold conditions. ## Core Concept ### Exchange Flows - **Negative Flow (Outflows)** = Bullish Signal - Coins moving OFF exchanges → Long-term holding/accumulation - Indicates reduced selling pressure - **Positive Flow (Inflows)** = Bearish Signal - Coins moving TO exchanges → Preparation for selling - Indicates potential distribution phase ### Total Value Locked (TVL) - **Rising TVL** = Bullish Signal - Capital flowing into DeFi protocols - Increased ecosystem confidence - **Falling TVL** = Bearish Signal - Capital exiting DeFi protocols - Decreased ecosystem confidence ### Combined Signals **🟢 Strong Bullish (70-100):** - Exchange outflows + Rising TVL - Smart money accumulating and deploying capital **🔴 Strong Bearish (0-30):** - Exchange inflows + Falling TVL - Smart money preparing to sell and exiting positions **⚪ Neutral (40-60):** - Mixed or balanced flows ## Key Features ### ✅ Auto-Detection - Automatically detects chart symbol (BTC/ETH) - Uses appropriate exchange flow data for each asset ### ✅ Weighted Composite - Customizable weights for Exchange Flow and TVL components - Default: 50/50 balance ### ✅ Normalized Scale - 0-100 index scale - Configurable lookback period for normalization (default: 90 days) ### ✅ Signal Zones - **Overbought**: 70+ (Strong bullish pressure) - **Oversold**: 30- (Strong bearish pressure) - **Extreme**: 85+ / 15- (Very strong signals) ### ✅ Clean Interface - Minimal visual clutter by default - Only main index line and MA visible - Optional elements can be enabled: - Background color zones - Divergence signals - Trend change markers - Info table with detailed metrics ### ✅ Divergence Detection - Identifies when price diverges from smart money flows - Potential reversal warning signals ### ✅ Alerts - Extreme overbought/oversold conditions - Trend changes (crossing 50 line) - Bullish/bearish divergences ## How to Use ### 1. Trend Confirmation - Index above 50 = Bullish trend - Index below 50 = Bearish trend - Use with price action for confirmation ### 2. Reversal Signals - **Extreme readings** (>85 or <15) suggest potential reversal - Look for divergences between price and indicator ### 3. Accumulation/Distribution - **70+**: Accumulation phase - smart money buying/holding - **30-**: Distribution phase - smart money selling ### 4. DeFi Health - Monitor TVL component for DeFi ecosystem strength - Combine with exchange flows for complete picture ## Settings ### Data Sources - **Exchange Flow**: IntoTheBlock real-time data - **TVL**: DefiLlama aggregated DeFi TVL - **Manual Mode**: For testing or custom data ### Indicator Settings - **Smoothing Period (MA)**: Default 14 periods - **Normalization Lookback**: Default 90 days - **Exchange Flow Weight**: Adjustable 0-100% - **Overbought/Oversold Levels**: Customizable thresholds ### Visual Options - Show/Hide Moving Average - Show/Hide Zone Lines - Show/Hide Background Colors - Show/Hide Divergence Signals - Show/Hide Trend Markers - Show/Hide Info Table ## Data Requirements ⚠️ **Important Notes:** - Uses **daily data** from IntoTheBlock and DefiLlama - Works on any chart timeframe (data updates daily) - Auto-switches between BTC and ETH based on chart - All other crypto charts default to BTC exchange flow data ## Best Practices 1. **Use on Daily+ Timeframes** - On-chain data is daily, most effective on D/W/M charts 2. **Combine with Price Action** - Use as confirmation, not standalone signals 3. **Watch for Divergences** - Price making new highs while indicator falling = warning 4. **Monitor Extreme Zones** - Sustained readings >85 or <15 indicate strong conviction 5. **Context Matters** - Consider broader market conditions and fundamentals ## Calculation 1. **Exchange Net Flow** = Inflows - Outflows (inverted for index) 2. **TVL Rate of Change** = % change over smoothing period 3. **Normalize** both metrics to 0-100 scale 4. **Composite Index** = (ExchangeFlow × Weight) + (TVL × Weight) 5. **Smooth** with moving average ## Disclaimer This indicator uses on-chain data for analysis. While valuable, it should not be used as the sole basis for trading decisions. Always combine with other technical analysis tools, fundamental analysis, and proper risk management. On-chain data reflects blockchain activity but may lag price action. Use this indicator as part of a comprehensive trading strategy. --- ## Credits **Data Sources:** - IntoTheBlock: Exchange flow metrics - DefiLlama: Total Value Locked data **Indicator by:** @iCD_creator **Version:** 1.0 **Pine Script™ Version:** 6 --- ## Updates & Support For questions, suggestions, or bug reports, please comment below or message the author. **Like this indicator? Leave a 👍 and share your feedback!**
Pine Script® göstergesi
iCD_creator tarafından
Global Macro IndexGlobal Macro Index The Global Macro Index is a comprehensive economic sentiment indicator that aggregates 23 real-time macroeconomic data points from the world's largest economies (US, EU, China, Japan, Taiwan). It provides a single normalized score that reflects the overall health and momentum of the global economy, helping traders identify macro trends that drive asset prices. ⚠️ Important: Timeframe Settings This indicator is designed exclusively for the 1W (weekly) timeframe. The indicator is hardcoded to pull weekly data and will not function correctly on other timeframes. What It Measures The indicator tracks normalized Trend Power Index (TPI) values across multiple economic categories: United States (7 components) Business Confidence Index (BCOI) - Business sentiment and outlook Composite Leading Indicator (CLI) - Forward-looking economic indicators Consumer Confidence Index (CCI) - Consumer sentiment and spending intentions Terms of Trade (TOT) - Import/export price relationships Manufacturing Composite - Combines business confidence, production, and new orders Comprehensive Economic Composite - Broad aggregation including employment, business activity, and regional indicators Business Inventory (BI) - Stock levels and supply chain health European Union (10 components) Sentiment Survey (SS) - Overall economic sentiment Business Confidence Index - EU business outlook Economic Sentiment Indicator (ESI) - Combined confidence metrics Manufacturing Production (MPRYY) - Industrial output year-over-year New Orders - Germany, France, Netherlands, Spain manufacturing orders Composite Leading Indicators - Germany, France forward-looking metrics Business Climate Index (BCLI) - France business conditions Asia (6 components) New Orders - China, Japan, Taiwan manufacturing demand Composite Leading Indicators - China, Japan economic momentum The Formula The indicator calculates a weighted average of normalized TPI scores: Global Macro Index = (1/23) × Σ Each of the 23 economic indicators is: Converted to a Trend Power Index (TPI) using 4-day Bitcoin normalization Weighted equally (1/23 ≈ 4.35% each) Summed and smoothed with a 1-period SMA The result is a single oscillator that ranges typically between -1 and +1, with extreme readings beyond ±0.6. Z-Score Signal System The indicator includes an optional Z-Score overlay that identifies extreme macro conditions: Calculation: Z-Score = (Current Value - 50-period Mean) / Standard Deviation Smoothed with 35-period Hull Moving Average Inverted for intuitive interpretation Signals: Green background (Z-Score ≥ 2) = Extremely positive macro conditions, potential overbought Red background (Z-Score ≤ -2) = Extremely negative macro conditions, potential oversold These extreme readings occur approximately 5% of the time statistically How to Use It Interpreting the Main Plot (Red Line): Above 0 = Positive macro momentum, risk-on environment Below 0 = Negative macro momentum, risk-off environment Above +0.6 = Strong expansion, bullish for equities and crypto Below -0.6 = Severe contraction, bearish conditions Trend direction = More important than absolute level Z-Score Signals: Z ≥ 2 (Green) = Macro sentiment extremely positive, consider taking profits or preparing for pullback Z ≤ -2 (Red) = Macro sentiment extremely negative, potential buying opportunity for contrarians Works best as a regime filter, not precise timing tool Best Practices: Use as a macro regime filter for other strategies Combines well with liquidity indicators and price action Leading indicator for risk assets (equities, Bitcoin, emerging markets) Lagging indicator - confirms macro trends rather than predicting reversals Watch for divergences: price making new highs while macro weakens (bearish) or vice versa (bullish) Settings Show Zscore Signals: Toggle green/red background shading for extreme readings Overlay Zscore Signals: Display Z-Score signals on the price chart as well as the indicator panel Reference Lines 0 (gray) = Neutral macro conditions +0.6 (green) = Strong positive threshold -0.6 (red) = Strong negative threshold Data Sources Real-time economic data from TradingView's ECONOMICS database, including: OECD leading indicators Manufacturing PMIs and new orders Consumer and business confidence surveys Trade and inventory metrics Regional economic sentiment indices Notes This is a macro trend indicator, not a day-trading tool. Economic data updates weekly and reflects the aggregate health of global growth. Best used on weekly timeframes to identify favorable or unfavorable macro regimes for risk asset allocation. The indicator distills complex global economic data into a single actionable score, answering: "Is the global economy expanding or contracting right now?"
Pine Script® göstergesi
Unicorpus 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.
Pine Script® göstergesi
tugceuluhan tarafından
知行趋势指标根据Z哥给的通达信指标翻译为pine script，去掉了TV没有的行业板块概念信息。 知行趋势指标（Zhixing Trend Indicato） 知行趋势指标是一种基于多重均线的趋势跟踪工具，结合短期 EMA 与多周期 SMA，以判断市场的短期和中长期趋势。 指标组成： 知行短期趋势线（zx_short）：采用双 EMA（EMA(EMA(Close, 10),10)）计算，反应价格的短期波动和趋势。 知行多空线（zx_trend）：由四条不同周期的 SMA 平均计算（默认周期 M1=14, M2=28, M3=57, M4=114），用于判断市场的多空方向。 使用说明： 指标只在日线及以上周期显示，分钟和小时级别周期自动隐藏。 短期趋势线可以捕捉快速的价格变化，而多空线用于确认整体趋势方向。 可通过调整四条 SMA 周期，适应不同市场和品种的波动特点。 Zhixing Trend Indicator The Zhixing Trend Indicator is a trend-following tool based on multiple moving averages. It combines short-term EMA with multi-period SMA to identify both short-term and medium-to-long-term market trends. Components: Short-Term Trend Line (zx_short): Calculated using a double EMA (EMA(EMA(Close,10),10)), reflecting short-term price fluctuations and trend. Bull-Bear Line (zx_trend): Calculated as the average of four SMAs with different periods (default M1=14, M2=28, M3=57, M4=114), used to determine overall market direction. Usage Notes: This indicator only displays on daily or higher timeframes; intraday (minute/hour) charts are automatically hidden. The short-term trend line captures fast price movements, while the bull-bear line confirms the overall trend. SMA periods can be adjusted to suit different markets or trading instruments.
Pine Script® göstergesi
xianzheTM tarafından
DMI_HMA Oscillator Smoothed by HMA v2The script provided, titled "DMI_HMA Oscillator Smoothed by HMA v2," is a technical analysis tool written in Pine Script (Version 6) for the TradingView platform. It represents a hybrid approach to momentum trading, combining the directional insight of the Directional Movement Index (DMI) with the noise-reduction capabilities of the Hull Moving Average (HMA). The purpose of this indicator is to provide a cleaner, lag-reduced view of market trend strength and direction, filtering out the erratic noise often associated with raw DMI readings. Using this indicator is highly appropriate for traders who struggle with the "noise" of standard momentum indicators but cannot afford the delay of traditional smoothing. Noise Reduction: Standard DMI crossovers can trigger frequently in sideways markets. The HMA smoothing suppresses these minor fluctuations, helping traders stay in a trade longer without being shaken out by temporary volatility. Lag Management: Most smoothing techniques (like SMA or EMA) introduce significant lag, making signals arrive too late. The HMA is specifically designed to minimize this, making it an excellent choice for a leading indicator like DMI. Clarity: By converting the two-line DMI system into a single oscillator relative to a zero line, the visual complexity is reduced. A trader simply needs to observe if the teal line is above or below the gray dashed zero line. Conclusion: The DMI_HMA Oscillator is a sophisticated yet elegant tool. It effectively addresses the primary weakness of the standard DMI (noise) without sacrificing its primary strength (responsiveness). It is best used as a trend-confirmation tool in conjunction with price action analysis, serving as a reliable filter for determining the prevailing market bias.
Pine Script® göstergesi
vlenarcic tarafından
Sniper BB + VWAP System (with SMT Divergence Arrows)STEP 1: Load two correlated futures charts. Example: CL + RB/SI+GC/ NQ+ES STEP 2: Add Bollinger Bands (20, 2.0) on both. Optional add (20, 3.0). STEP 3: Watch for a BB tag on one chart but not the other. STEP 4: Wait for a reclaim candle back inside the band. STEP 5: Enter with stop below/above the wick + 3.0 BB. STEP 6: Scale out midline, then opposite band. STEP 7: Hold partials when both pairs confirm trend. *You can take the vwap bands off the chart if it is too cluttered.
Pine Script® göstergesi
the1nepercent tarafından
1