Acc/Dist. Cloud with Fractal Deviation Bands by @XeL_ArjonaACCUMULATION / DISTRIBUTION CLOUD with MORPHIC DEVIATION BANDS
Ver. 2.0.beta.23:08:2015
by Ricardo M. Arjona @XeL_Arjona
DISCLAIMER
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
Pine Script code MOD's and adaptations by @XeL_Arjona with special mention in regard of:
Buy (Bull) and Sell (Bear) "Power Balance Algorithm by Vadim Gimelfarb published at Stocks & Commodities V. 21:10 (68-72).
Custom Weighting Coefficient for Exponential Moving Average (nEMA) adaptation work by @XeL_Arjona with contribution help from @RicardoSantos at TradingView @pinescript chat room.
Morphic Numbers (PHI & Plastic) Pine Script adaptation from it's algebraic generation formulas by @XeL_Arjona
Fractal Deviation Bands idea by @XeL_Arjona
CHANGE LOG:
ACCUMULATION / DISTRIBUTION CLOUD: I decided to change it's name from the Buy to Sell Pressure. The code is essentially the same as older versions and they are the center core (VORTEX?) of all derived New stuff which are:
MORPHIC NUMBERS: The "Golden Ratio" expressed by the result of the constant "PHI" and the newer and same in characteristics "Plastic Number" expressed as "PN". For more information about this regard take a look at: HERE!
CUSTOM(K) EXPONENTIAL MOVING AVERAGE: Some code has cleaned from last version to include as custom function the nEMA , which use an additional input (K) to customise the way the "exponentially" is weighted from the custom array. For the purpose of this indicator, I implement a volatility algorithm using the Average True Range of last 9 periods multiplied by the morphic number used in the fractal study. (Golden Ratio as default) The result is very similar in response to classic EMA but tend to accelerate or decelerate much more responsive with wider bars presented in trending average.
FRACTAL DEVIATION BANDS: The main idea is based on the so useful Standard Deviation process to create Bands in favor of a multiplier (As John Bollinger used in it's own bands) from a custom array, in which for this case is the "Volume Pressure Moving Average" as the main Vortex for the "Fractallitly", so then apply as many "Child bands" using the older one as the new calculation array using the same morphic constant as multiplier (Like Fibonacci but with other approach rather than %ratios). Results are AWSOME! Market tend to accelerate or decelerate their Trend in favor of a Fractal approach. This bands try to catch them, so please experiment and feedback me your own observations.
EXTERNAL TICKER FOR VOLUME DATA: I Added a way to input volume data for this kind of study from external tickers. This is just a quicky-hack given that currently TradingView is not adding Volume to their Indexes so; maybe this is temporary by now. It seems that this part of the code is conflicting with intraday timeframes, so You are advised.
This CODE is versioned as BETA FOR TESTING PROPOSES. By now TradingView Admins are changing lot's of things internally, so maybe this could conflict with correct rendering of this study with special tickers or timeframes. I will try to code by itself just the core parts of this study in order to use them at discretion in other areas. ALL NEW IDEAS OR MODIFICATIONS to these indicator(s) are Welcome in favor to deploy a better and more accurate readings. I will be very glad to be notified at Twitter or TradingView accounts at: @XeL_Arjona
在脚本中搜索"GOLD"
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.
PyraTime: Tesla Trinity [LITE] | 3-6-9 Time CyclesThe Algorithm of the Universe
"If you knew the magnificence of the 3, 6, and 9, you would have a key to the universe." — Nikola Tesla
Most traders analyze Price (the Y-axis) but completely ignore the most critical dimension: Time (the X-axis).
PyraTime: Tesla Trinity is a harmonic time projector. It moves beyond standard technical analysis by translating the ancient Solfeggio Frequencies directly into time cycles on your chart.
How It Works
This indicator projects the 9 core Solfeggio frequencies as specific time intervals anchored to a user-defined "Origin Pivot." When these invisible time harmonics align, they create "Time Clusters"—high-probability zones where market energy is likely to pivot, reverse, or expand.
The 3-6-9 Sequences
The indicator visualizes three distinct energy groups:
🔴 The Grounding Trinity (174Hz - 396Hz): Foundation & Stability.
🟢 The Transformation Trinity (417Hz - 639Hz): Change & Acceleration.
🔵 The Awakening Trinity (741Hz - 963Hz): Spiritual Peak & Completion.
How to Use (LITE Version)
Find the Origin: Identify a major market structure point (e.g., a Swing High or Low).
Set the Anchor: In the settings, input the exact time of that pivot into the "Origin Pivot" field.
Watch the Clusters: Look for areas where multiple frequency lines converge.
Example: A Grounding line (Red) and an Awakening line (Blue) overlapping often signals a trend reversal.
Included in LITE Version
✅ 1-Minute Fractals: For scalping and micro-cycle analysis.
✅ 15-Minute Fractals: For intraday trend identification.
✅ Full Harmonic Spectrum: All 9 Solfeggio frequencies included.
Want the Master Edition?
The Master Edition unlocks the "Hidden Fractals" used by institutional harmonic traders:
🔓 The "4x" Suite: 4-minute, 40-minute, and 4-hour projection cycles.
🔓 Macro Cycles: Hourly and Daily projections for Swing Trading.
🔓 Golden Anchor: Advanced precision tools.
"Search 'PyraTime' for the Master Edition."
Disclaimer: This tool is for educational and experimental purposes only. Trading involves risk.
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.
Ultimate Trend System — Flagship Full VersionUltimate Trend System — Flagship Full Version
The most complete intraday trend detection system, designed for traders who need fast and reliable directional signals.
🔥 Core Features
BUY / SELL / STOP signals
True Breakout Detection (high/low confirmation + volatility filter)
Fakeout Recognition (stop-hunt / liquidity sweep detection)
Dynamic Trend Strength Rating (0–3 stars, real-time updated)
SuperTrend + QQE + ATR + CHOP fusion model
Automatic Trend Background Coloring
Compact Info Panel (trend, momentum, volatility, regime)
Continuation-safe Pine v6 code (no line errors)
🚀 What This System Does
This indicator identifies:
The main trend direction
Trend strength
High-probability breakout zones
Areas to completely avoid trading
Fake breakouts caused by bots/liquidity sweeps
All signals update in real time and work extremely well for fast-moving assets such as Gold (MGC), Silver (SIL), Crude Oil, NASDAQ, and FX pairs.
⭐ Signal Logic
A BUY or SELL is triggered only when:
SuperTrend agrees
QQE momentum confirms
ATR expansion appears
Market regime (CHOP) allows trend following
This greatly filters noise and improves win rate.
📌 Ideal For
Scalpers (1m / 2m / 5m)
Intraday traders
Trend followers
Breakout traders
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
AP Capital – Volatility + High/Low Projection v1.1📌 AP Capital – Volatility + High/Low Projection v1.1
Predictive Daily Volatility • Session Logic • High/Low Projection Indicator
This indicator is designed to help traders visually understand daily volatility conditions, identify session-based turning points, and anticipate potential highs and lows of the day using statistical behavior observed across thousands of bars of intraday data.
It combines intraday session structure, volatility regime classification, and context from the previous day’s expansion to highlight high-probability areas where the market may set its daily high or daily low.
🔍 What This Indicator Does
1. Volatility Regime Detection
Each day is classified into:
🔴 High Volatility (trend continuation & expansion likely)
🟡 Normal Volatility
🔵 Low Volatility (chop, false breaks, mean-reversion common)
The background color automatically adapts so you always know what environment you're trading in.
2. Session-Based High/Low Identification
Different global sessions tend to create different market behaviors:
Asia session frequently sets the LOW of day
New York & Late US sessions frequently set the HIGH of day
This indicator uses those probabilities to highlight potential turning points.
3. Potential High / Low of Day Projections
The script plots:
🟢 Potential LOW of Day
🔴 Potential HIGH of Day
These appear only when:
Price hits the session-statistical turning zone
Volatility conditions match
Yesterday’s expansion or compression context agrees
This keeps signals clean and prevents over-marking.
4. Clean Visuals
Instead of cluttering the chart, highs and lows are marked only when conditions align, making this tool ideal for:
Session scalpers
Day traders
Gold / NAS100 / FX intraday traders
High-probability reversal traders
🧠 How It Works
The engine combines:
Daily range vs 20-day average
Real-time intraday high/low formation
Session-specific probability weighting
Previous day expansion and volatility filters
This results in highly reliable signals for:
Fade trades
Reversal setups
Timing entries more accurately
✔️ Best Uses
Identifying where the day’s range is likely to complete
Avoiding trades during low-volatility compression days
Detecting where the market is likely to turn during major sessions
Using potential HIGH/LOW levels as take-profit zones
Enhancing breakout or reversal strategies
⚠️ Disclaimer
This indicator does not repaint, but it is not a standalone entry tool.
It is designed to provide context, session awareness, and volatility-driven turning points to assist your existing strategy.
Always combine with sound risk management.
Debt-Cycle vs Bitcoin-CycleDebt-Cycle vs Bitcoin-Cycle Indicator
The Debt-Cycle vs Bitcoin-Cycle indicator is a macro-economic analysis tool that compares traditional financial market cycles (debt/credit cycles) against Bitcoin market cycles. It uses Z-score normalization to track the relative positioning of global financial conditions versus cryptocurrency market sentiment, helping identify potential turning points and divergences between traditional finance and digital assets.
Key Features
Dual-Cycle Analysis: Simultaneously tracks traditional financial cycles and Bitcoin-specific cycles
Z-Score Normalization: Standardizes diverse data sources for meaningful comparison
Multi-Asset Coverage: Analyzes currencies, commodities, bonds, monetary aggregates, and on-chain metrics
Divergence Detection: Identifies when Bitcoin cycles move independently from traditional finance
21-Day Timeframe: Optimized for Long-term cycle analysis
What It Measures
Finance-Cycle (White Line)
Tracks traditional financial market health through:
Currencies: USD strength (DXY), global currency weights (USDWCU, EURWCU)
Commodities: Oil, gold, natural gas, agricultural products, and Bitcoin price
Corporate Bonds: Investment-grade spreads, high-yield spreads, credit conditions
Monetary Aggregates: M2 money supply, foreign exchange reserves (weighted by currency)
Treasury Bonds: Yield curve (2Y/10Y, 3M/10Y), term premiums, long-term rates
Bitcoin-Cycle (Orange Line)
Tracks Bitcoin market positioning through:
On-Chain Metrics:
MVRV Ratio (Market Value to Realized Value)
NUPL (Net Unrealized Profit/Loss)
Profit/Loss Address Distribution
Technical Indicators:
Bitcoin price Z-score
Moving average deviation
Relative Strength:
ETH/BTC ratio (altcoin strength indicator)
Visual Elements
White Line: Finance-Cycle indicator (positive = expansionary conditions, negative = contractionary)
Orange Line: Bitcoin-Cycle indicator (positive = bullish positioning, negative = bearish)
Zero Line: Neutral reference point
Interpretation
Cycle Alignment
Both positive: Risk-on environment, favorable for crypto
Both negative: Risk-off environment, caution warranted
Divergence: Potential opportunities or warning signals
Divergence Signals
Finance positive, Bitcoin negative: Bitcoin may be undervalued relative to macro conditions
Finance negative, Bitcoin positive: Bitcoin may be overextended or decoupling from traditional finance
Important Limitations
This indicator uses some technical and macro data but still has significant gaps:
⚠️ Limited monetary data - missing:
Funding rates (repo, overnight markets)
Comprehensive bond spread analysis
Collateral velocity and quality metrics
Central bank balance sheet details
⚠️ Basic economic coverage - missing:
GDP growth rates
Inflation expectations
Employment data
Manufacturing indices
Consumer confidence
⚠️ Simplified on-chain analysis - missing:
Exchange flow data
Whale wallet movements
Mining difficulty adjustments
Hash rate trends
Network fee dynamics
⚠️ No sentiment data - missing:
Fear & Greed Index
Options positioning
Futures open interest
Social media sentiment
The indicator provides a high-level cycle comparison but should be combined with comprehensive fundamental analysis, detailed on-chain research, and proper risk management.
Settings
Offset: Adjust the horizontal positioning of the indicators (default: 0)
Timeframe: Fixed at 21 days for optimal cycle detection
Use Cases
Macro-crypto correlation analysis: Understand when Bitcoin moves with or against traditional markets
Cycle timing: Identify potential tops and bottoms in both cycles
Risk assessment: Gauge overall market conditions across asset classes
Divergence trading: Spot opportunities when cycles diverge significantly
Portfolio allocation: Balance traditional and crypto assets based on cycle positioning
Technical Notes
Uses Z-score normalization with varying lookback periods (40-60 bars)
Applies HMA (Hull Moving Average) smoothing to reduce noise
Asymmetric multipliers for upside/downside movements in certain metrics
Requires access to FRED economic data, Glassnode, CoinMetrics, and IntoTheBlock feeds
21-day timeframe optimized for cycle analysis
Strategy Applications
This indicator is particularly useful for:
Cross-asset allocation - Decide between traditional finance and crypto exposure
Cycle positioning - Identify where we are in credit/debt cycles vs. Bitcoin cycles
Regime changes - Detect shifts in market leadership and correlation patterns
Risk management - Reduce exposure when both cycles turn negative
Disclaimer: This indicator is a cycle analysis tool and should not be used as the sole basis for investment decisions. It has limited coverage of monetary conditions, economic fundamentals, and on-chain metrics. The indicator provides directional insight but cannot predict exact timing or magnitude of market moves. Always conduct thorough research, consider multiple data sources, and maintain proper risk management in all investment decisions.
Technology Stocks RSPSTechnology Stocks RSPS Indicator - TradingView Description
Overview
The Technology Stocks RSPS (Relative Strength Portfolio System) indicator is a sophisticated portfolio allocation tool designed specifically for technology sector stocks. It calculates relative strength positions and provides dynamic allocation recommendations based on technical price momentum analysis.
Key Features
- Relative Strength Analysis: Compares 15 major technology stocks and the XLK sector ETF
against each other and gold as a baseline
- Dynamic Portfolio Allocation: Automatically calculates optimal position sizes based on relative
performance
- Visual Portfolio Performance: Tracks cumulative portfolio returns with color-coded
performance indicators
- Customizable Table Display: Shows real-time allocation percentages and optional cash values
for each position
- Technical Momentum Filtering: Uses normalized indicators to identify strength and filter out
weak positions
Included Assets
Sector ETF: XLK
Major Tech Stocks: AAPL, MSFT, NVDA, AVGO, CRM, ORCL, CSCO, ADBE, ACN, AMD, IBM, INTC, NOW, TXN
Benchmark: Gold (TVC:GOLD)
How It Works
The indicator calculates a relative strength score for each asset by comparing it against:
Gold (baseline commodity)
All other technology stocks in the pool
The XLK sector ETF
Assets with positive relative strength receive portfolio allocations proportional to their strength scores. Weak or negative performers are automatically filtered out (allocated 0%).
Visual Elements
Red Area: Aggregate strength of major technology stocks
Navy Blue Area: Overall technical positioning index (TPI)
Performance Line: Cumulative portfolio return (blue = cash-heavy, red = equity-heavy)
Allocation Table: Bottom-left display showing current recommended positions
Important Limitations
This indicator primarily uses technical data and has significant limitations:
❌ No fundamental economic data (ISM, CLI, etc.)
❌ Limited monetary data - missing critical components:
comprehensive monetary data
Funding rates
Detailed bond spreads analysis
Collateral data
❌ No sentiment indicators
❌ No options flow or derivatives data
❌ No earnings or valuation metrics
The indicator focuses purely on price-based relative strength and technical momentum. Users should combine this tool with fundamental analysis, economic data, and proper risk management for complete investment decisions.
Settings
Plot Table: Toggle allocation table visibility
Use Cash: Enable to display dollar amounts based on portfolio size
Cash Amount: Set your total portfolio value for cash allocation calculations
Use Cases
Sector rotation within technology stocks
Relative strength-based portfolio rebalancing
Technical momentum screening for tech sector
Dynamic position sizing based on price trends
Technical Notes
The script avoids for-loops to reduce calculation errors and noise
Uses semi-individual calculations for each asset
Requires the Unicorpus/NormalizedIndicators/1 library for normalized momentum calculations
Maximum lookback: 100 bars
Disclaimer: This indicator is a technical tool only and should not be used as the sole basis for investment decisions. It does not incorporate fundamental, economic, or comprehensive monetary data. Always conduct thorough research and consider your risk tolerance before making investment decisions.
Stablecoin Total Index V4**Stablecoin Total Index V4 - Full History + Full Coverage**
This indicator provides the **best of both worlds**: long historical data AND complete stablecoin coverage.
**How it works:**
- **Before May 2025:** Manual sum of 35 major stablecoins (~90% coverage)
- **After May 2025:** Switches to STABLE.C index (100 stablecoins, 100% coverage)
**Why this approach?**
TradingView's official STABLE.C index was only created on May 19, 2025. This indicator gives you **years of historical data** going back to 2017-2018, then seamlessly transitions to the official index for complete accuracy.
**Note:** There is a ~$30B jump at the May 2025 transition point. This is NOT an error - it represents the ~65 smaller stablecoins that are included in STABLE.C but don't have individual CRYPTOCAP symbols for manual tracking.
**Pre-May 2025 Coverage (35 stablecoins):**
- **Tier 1:** USDT, USDC
- **Tier 2:** DAI, USDe, USDS, FDUSD
- **Tier 3:** TUSD, USDP, GUSD, FRAX, PYUSD, LUSD, BUSD
- **Tier 4 (2024-2025):** USD1, RLUSD, GHO, crvUSD, sUSDe, USDY, USDM
- **Tier 5 (Euro):** EURC, EURT, EURS
- **Tier 6 (DeFi):** USDD, MIM, DOLA, OUSD, alUSD, sUSD, cUSD
- **Tier 7:** HUSD, USDX, USTC
- **Gold-Backed:** PAXG, XAUT
**Post-May 2025:** Full STABLE.C (100 stablecoins)
**Features:**
- Green/Red color based on direction
- 20-period SMA
- Reference lines at $100B, $200B, $300B
**Best used on Daily timeframe or higher.**
Stablecoin Total Index V3**Stablecoin Total Index V4 - Full History + Full Coverage**
This indicator provides the **best of both worlds**: long historical data AND complete stablecoin coverage.
**How it works:**
- **Before May 2025:** Manual sum of 35 major stablecoins (~90% coverage)
- **After May 2025:** Switches to STABLE.C index (100 stablecoins, 100% coverage)
**Why this approach?**
TradingView's official STABLE.C index was only created on May 19, 2025. This indicator gives you **years of historical data** going back to 2017-2018, then seamlessly transitions to the official index for complete accuracy.
**Note:** There is a ~$30B jump at the May 2025 transition point. This is NOT an error - it represents the ~65 smaller stablecoins that are included in STABLE.C but don't have individual CRYPTOCAP symbols for manual tracking.
**Pre-May 2025 Coverage (35 stablecoins):**
- **Tier 1:** USDT, USDC
- **Tier 2:** DAI, USDe, USDS, FDUSD
- **Tier 3:** TUSD, USDP, GUSD, FRAX, PYUSD, LUSD, BUSD
- **Tier 4 (2024-2025):** USD1, RLUSD, GHO, crvUSD, sUSDe, USDY, USDM
- **Tier 5 (Euro):** EURC, EURT, EURS
- **Tier 6 (DeFi):** USDD, MIM, DOLA, OUSD, alUSD, sUSD, cUSD
- **Tier 7:** HUSD, USDX, USTC
- **Gold-Backed:** PAXG, XAUT
**Post-May 2025:** Full STABLE.C (100 stablecoins)
**Features:**
- Green/Red color based on direction
- 20-period SMA
- Reference lines at $100B, $200B, $300B
**Best used on Daily timeframe or higher.**
Ultimate Multi-Asset Correlation System by able eiei Ultimate Multi-Asset Correlation System - User Guide
Overview
This advanced TradingView indicator combines WaveTrend oscillator analysis with comprehensive multi-asset correlation tracking. It helps traders understand market relationships, identify regime changes, and spot high-probability trading opportunities across different asset classes.
Key Features
1. WaveTrend Oscillator
Main Signal Lines: WT1 (blue) and WT2 (red) plot momentum and its moving average
Overbought/Oversold Zones: Default levels at +60/-60
Cross Signals:
🟢 Bullish: WT1 crosses above WT2 in oversold territory
🔴 Bearish: WT1 crosses below WT2 in overbought territory
Higher Timeframe (HTF) Analysis: Shows WT1 from 4H, Daily, and Weekly timeframes for trend confirmation
2. Multi-Asset Correlation Tracking
Monitors relationships between:
Major Assets: Gold (XAUUSD), Dollar Index (DXY), US 10-Year Yield, S&P 500
Crypto Assets: Bitcoin, Ethereum, Solana, BNB
Cross-Asset Analysis: Correlation between traditional markets and crypto
3. Market Regime Detection
Automatically identifies market conditions:
Risk-On: High correlation + positive sentiment (🟢 Green background)
Risk-Off: High correlation + negative sentiment (🔴 Red background)
Crypto-Risk-On: Strong crypto correlations (🟠 Orange background)
Low-Correlation: Divergent market behavior (⚪ Gray background)
Neutral: Mixed signals (🟡 Yellow background)
How to Use
Basic Setup
Add to Chart: Apply the indicator to any chart (works on all timeframes)
Choose Display Mode (Display Options):
All: Shows everything (recommended for comprehensive analysis)
WaveTrend Only: Focus on momentum signals
Correlation Only: View market relationships
Heatmap Only: Simplified correlation view
Enable Asset Groups:
✅ Major Assets: Traditional markets (stocks, bonds, commodities)
✅ Crypto Assets: Digital currencies
Mix and match based on your trading focus
Reading the Charts
WaveTrend Section (Bottom Panel)
Above 0 = Bullish momentum
Below 0 = Bearish momentum
Above +60 = Overbought (potential reversal)
Below -60 = Oversold (potential bounce)
Lighter lines = Higher timeframe trends
Correlation Histogram (Colored Bars)
Blue bars: Major asset correlations
Orange bars: Crypto correlations
Purple bars: Cross-asset correlations
Bar height: Correlation strength (-50 to +50 scale)
Background Color
Intensity reflects correlation strength
Color shows market regime
Dashboard Elements
🎯 Market Regime Analysis (Top Left)
Current Regime: Overall market condition
Average Correlation: Strength of relationships (0-1 scale)
Risk Sentiment: -100% (risk-off) to +100% (risk-on)
HTF Alignment: Multi-timeframe trend agreement
Signal Quality: Confidence level for current signals
📊 Correlation Matrix (Top Right)
Shows correlation values between asset pairs:
1.00: Perfect positive correlation
0.75+: Strong correlation (🟢 Green)
0.50+: Medium correlation (🟡 Yellow)
0.25+: Weak correlation (🟠 Orange)
Below 0.25: Negative/no correlation (🔴 Red)
🔥 Correlation Heatmap (Bottom Right)
Visual matrix showing:
Gold vs. DXY, BTC, ETH
DXY vs. BTC, ETH
BTC vs. ETH
Color-coded strength
📈 Performance Tracker (Bottom Left)
Tracks individual asset momentum:
WT1 Values: Current momentum reading
Status: OB (overbought) / OS (oversold) / Normal
Trading Strategies
1. High-Probability Trend Following
✅ Entry Conditions:
WaveTrend bullish/bearish cross
HTF Alignment matches signal direction
Signal Quality > 70%
Correlation supports direction
2. Regime Change Trading
🎯 Watch for regime shifts:
Risk-Off → Risk-On = Consider long positions
High correlation → Low correlation = Reduce position size
Crypto-Risk-On = Focus on crypto longs
3. Divergence Trading
🔍 Look for:
Strong correlation breakdown = Potential volatility
Cross-asset correlation surge = Follow the leader
Volume-price correlation extremes = Trend confirmation
4. Overbought/Oversold Reversals
⚡ Trade reversals when:
WT crosses in extreme zones (-60/+60)
HTF alignment shows opposite trend weakening
Correlation confirms mean reversion setup
Customization Tips
Fine-Tuning Parameters
WaveTrend Core:
Channel Length (10): Lower = more sensitive, Higher = smoother
Average Length (21): Adjust for your timeframe
Correlation Settings:
Length (50): Longer = more stable, Shorter = more responsive
Smoothing (5): Reduce noise in correlation readings
Market Regime:
Risk-On Threshold (0.6): Lower = earlier regime signals
High Correlation Threshold (0.75): Adjust sensitivity
Custom Asset Selection
Replace default symbols with your preferred markets:
Major Assets: Any forex, indices, bonds
Crypto: Any digital currencies
Must use correct exchange prefix (e.g., BINANCE:BTCUSDT)
Alert System
Enable "Advanced Alerts" to receive notifications for:
✅ Market regime changes
✅ Correlation breakdowns/surges
✅ Strong signals with high correlation
✅ Extreme volume-price correlation
✅ Complete HTF alignment
Correlation Interpretation Guide
ValueMeaningTrading Implication+0.75 to +1.0Strong positiveAssets move together+0.5 to +0.75Moderate positiveGenerally aligned+0.25 to +0.5Weak positiveLoose relationship-0.25 to +0.25No correlationIndependent movements-0.5 to -0.25Weak negativeSlight inverse relationship-0.75 to -0.5Moderate negativeTend to move opposite-1.0 to -0.75Strong negativeStrongly inversely correlated
Best Practices
Use Multiple Timeframes: Check HTF alignment before trading
Confirm with Correlation: Strong signals work best with supportive correlations
Watch Regime Changes: Adjust strategy based on market conditions
Volume Matters: Enable volume-price correlation for confirmation
Quality Over Quantity: Trade only high-quality setups (>70% signal quality)
Common Patterns to Watch
🔵 Risk-On Environment:
Gold-BTC positive correlation
DXY negative correlation with risk assets
High crypto correlations
🔴 Risk-Off Environment:
Flight to safety (Gold up, stocks down)
DXY strength
Correlation breakdowns
🟡 Transition Periods:
Low correlation across assets
Mixed HTF signals
Use caution, reduce position sizes
Technical Notes
Calculation Period: Uses HLC3 (average of high, low, close)
Correlation Window: Rolling correlation over specified length
HTF Data: Accurately calculated using security() function
Performance: Optimized for real-time calculation on all timeframes
Support
For optimal performance:
Use on 15-minute to daily timeframes
Enable only needed asset groups
Adjust correlation length based on trading style
Combine with your existing strategy for confirmation
Enjoy comprehensive multi-asset analysis! 🚀
Smoothed Heikin Ashi + MA MTF📊 MTF Smoothed Heikin Ashi + MA Cross (SHA_MA)This indicator combines a double-smoothed Heikin Ashi (SHA) with the popular 50 and 200 Simple Moving Averages (SMAs), all wrapped in a robust Multi-Timeframe (MTF) engine.This tool is designed to help traders identify and confirm trend direction across multiple timeframes, providing cleaner signals than standard Heikin Ashi candles.
Key Features and BenefitsDouble-Smoothed Heikin Ashi (SHA):SHA candles are created by applying an Exponential Moving Average (EMA) twice to the standard Heikin Ashi calculation.This significantly filters out market noise and choppy price action, making trend reversals and continuations clearer and more reliable.Color Logic: Candles are colored green (lime) when the SHA Close is above the SHA Open (Bullish) and red when the Close is below the Open (Bearish).Multi-Timeframe (MTF) Capability:You can set the SHA candles, MA50, and MA200 to calculate on a higher timeframe (e.g., 4-Hour) while viewing your main chart on a lower timeframe (e.g., 15-Minute).This is crucial for ensuring your trades are aligned with the overall larger trend direction (top-down analysis).Self-Correction: The script automatically prevents errors by reverting the indicator to the chart's native timeframe if a timeframe lower than the chart's is mistakenly selected.Key Moving Averages:Includes the industry-standard 50 SMA (Fast) and 200 SMA (Slow).The crossover between the two MAs (Golden/Death Cross) acts as a powerful confirmation signal for the SHA trend.
⚙️ How to Use ItTrend Confirmation: Use the SHA candle color (Green/Red) to confirm the short-term trend.Long-Term Bias: Use the MA200 as the primary filter. Only look for long entries when price is above the MA200, and short entries when price is below it.MTF Alignment: Set the "Indicator Timeframe" input to a higher level (e.g., 4H or 1D) to ensure your entry timeframe is trading in the direction of the macro trend.CustomizationSettingDefault ValuePurposeIndicator TimeframeChartSet to a higher TF (e.g., 1h, 4h) for MTF analysis.SHA EMA Length 1 & 210Controls the level of smoothing applied to the candles. Higher values mean less noise but more lag.MA 50 & 200 Length50 & 200Standard Moving Average periods.\
XAUUSD Multi-Timeframe Bias Scanner🎯 Purpose & Overview
This is a sophisticated trading indicator that analyzes XAUUSD (Gold) across 5 different timeframes simultaneously to determine market bias and trend direction.
⚙️ Core Components
2. Bias Calculation Engine
The heart of the indicator uses 5 technical factors to score each timeframe:
Technical Factors (Weighted):
Moving Average Alignment (30 points)
Bullish: EMA(9) > EMA(21) > EMA(50)
Bearish: EMA(9) < EMA(21) < EMA(50)
Price vs MA Position (20 points)
Score increases when price above MAs
Score decreases when price below MAs
RSI Momentum (20 points)
Bullish: RSI > 60 or > 50
Bearish: RSI < 40 or < 50
MACD Signals (15 points)
Bullish: MACD line > Signal line AND > 0
Bearish: MACD line < Signal line AND < 0
Volume Confirmation (15 points)
Volume spikes with price movement add confirmation
📊 Timeframe Analysis
Five Timeframes Monitored:
5-minute - Short-term noise (10% weight)
15-minute - Intraday direction (15% weight)
1-hour - Key intraday bias (25% weight)
4-hour - Primary directional bias (30% weight)
1-day - Overall trend context (20% weight)
Bias Scoring System:
0-100 Scale (50 = Neutral)
STRONG BULLISH: ≥70 (Green)
BULLISH: 55-69 (Lime)
NEUTRAL: 46-54 (Gray)
BEARISH: 31-45 (Orange)
STRONG BEARISH: ≤30 (Red)
🎨 Visual Features
1. Comprehensive Table Display
pinescript
var table biasTable = table.new(position.top_right, 3, 7, ...)
Shows a color-coded table with:
Timeframe name
Numerical bias score (0-100)
Strength description with color coding
2. Chart Visual Indicators
Background coloring based on overall bias
Label markers for strong bullish/bearish conditions
Real-time label showing all timeframe scores
3. Alert System
Triggers when overall bias crosses 70 (bullish) or 30 (bearish)
Configurable with sound options
🔄 How It Processes Data
Data Flow:
Requests security data for each timeframe using request.security()
Calculates technical indicators for each TF separately
Scores each TF based on 5 technical factors
Computes weighted overall bias
Updates visual displays and checks alert conditions
💡 Trading Applications
Bullish Scenarios:
Multiple timeframes show bullish alignment
Higher timeframe bias supports lower timeframe direction
Overall score > 70 indicates strong bullish conviction
Bearish Scenarios:
Multiple timeframes show bearish alignment
Higher timeframe bias confirms lower timeframe moves
Overall score < 30 indicates strong bearish conviction
Conflict Detection:
When timeframes show conflicting biases
Caution required - market may be consolidating
Wait for alignment before taking trades
🎚️ Customization Options
Users can modify:
Timeframe weights
Technical indicator parameters
Alert thresholds
Visual display preferences
Scoring sensitivity
📈 XAUUSD Specific Optimizations
The indicator considers Gold's unique characteristics:
High volatility periods
ATR-based volatility adjustments
Volume confirmation for breakouts
Multiple timeframe confirmation for trend reliability
This creates a powerful tool for identifying high-probability trade setups in XAUUSD by ensuring traders have a complete multi-timeframe perspective before entering positions.
Thirdeyechart Volume Gold//@version=6
indicator("MT5 Style Quotes – Custom Pair Table", overlay=true, max_labels_count=500)
// ==== USER INPUTS ====
// Masukkan pair tambahan kat sini (pisahkan dengan koma)
extraPairsInput = input.string("XAUUSD,XAUJPY,USDJPY,EURJPY", "Custom Pairs (comma separated)")
// Convert input → array
string extraPairs = str.split(extraPairsInput, ",")
// Function kira % change
f_change(sym, tf) =>
o = request.security(sym, tf, open)
c = request.security(sym, tf, close)
pct = ((c - o) / o) * 100
pct
// Table setup
rowCount = array.size(extraPairs) + 1
var tbl = table.new(position.top_right, 4, rowCount, border_width=1)
// Header row
table.cell(tbl, 0, 0, "Symbol", bgcolor=color.new(color.white, 90))
table.cell(tbl, 1, 0, "Day %", bgcolor=color.new(color.white, 90))
table.cell(tbl, 2, 0, "H1 %", bgcolor=color.new(color.white, 90))
table.cell(tbl, 3, 0, "H4 %", bgcolor=color.new(color.white, 90))
// Loop setiap pair
for i = 0 to array.size(extraPairs)-1
sym = str.trim(array.get(extraPairs, i))
day = f_change(sym, "D")
h1 = f_change(sym, "60")
h4 = f_change(sym, "240")
col_day = day >= 0 ? color.blue : color.red
col_h1 = h1 >= 0 ? color.blue : color.red
col_h4 = h4 >= 0 ? color.blue : color.red
table.cell(tbl, 0, i+1, sym)
table.cell(tbl, 1, i+1, str.tostring(day, format.percent), text_color=col_day)
table.cell(tbl, 2, i+1, str.tostring(h1, format.percent), text_color=col_h1)
table.cell(tbl, 3, i+1, str.tostring(h4, format.percent), text_color=col_h4)
EP CPR Future CPR + 4 MA
1. CPR Trend Direction(Bias):
Bullish: If the current day's price is trading above the TC, it suggests a strong bullish trend where the CPR acts as a support zone.
Bearish: If the current day's price is trading below the BC, it suggests a strong bearish trend where the CPR acts as a resistance zone.
Range-Bound/Consolidation: If the price is trading within the CPR lines, it indicates a lack of clear directional bias and suggests a likely sideways or accumulation phase.
2. Moving average Trend Identification
Uptrend: If the price is above a moving average (and the MA line is sloping up), it confirms a bullish trend.
Downtrend: If the price is below a moving average (and the MA line is sloping down), it confirms a bearish trend.
Crossovers (Trading Signals)
A popular strategy involves using two moving averages—a short-term MA (e.g., 50-period) and a long-term MA (e.g., 200-period).
Golden Cross (Bullish Signal): Occurs when the shorter-term MA crosses above the longer-term MA.
Death Cross (Bearish Signal): Occurs when the shorter-term MA crosses below the longer-term MA.
BTC GOD — DEFINITIVE BTC MULTI INDICATORBTC GOD — The Ultimate Bitcoin Cycle Indicator (2025 Edition)
The one indicator every serious BTC holder and trader has been waiting for.
A single script that perfectly combines the 5 most powerful and accurate Bitcoin indicators ever created — all 100 % official versions:
- Official Pi Cycle Top (LookIntoBitcoin) → in 2013, 2017 & 2021 (3/3 hits)
- Official MVRV Z-Score (Glassnode / LookIntoBitcoin) → every major bottom (2015, 2018–19, 2022)
- Dynamic Bull/Bear background (red bear-market when price drops X % from cycle ATH + monthly RSI filter)
- Monthly Golden/Death Cross (50-month EMA vs 200-week EMA) → huge, unmistakable signals
- SuperTrend + 200-week EMA + 50-month EMA
- Cycle ATH/ATL tracking with flashing alert in the table when new highs/lows are made
- Exact days to/from the next halving + optimal accumulation zone (200–750 days post-halving)
- Fully customizable inputs for experienced traders
Zero repainting. Zero errors. Works on every timeframe.
This is the indicator used by people who truly understand Bitcoin’s 4-year cycles.
If you could only keep ONE Bitcoin indicator for the rest of your life… this would be it.
Save it, test it, and you’ll instantly see why it’s called BTC GOD.
Built with love and obsession for Bitcoin cycles.
Last update: November 2025
Multiple Cross Signals (Predictive)The indicator calculates and displays the Golden/Death Cross signals and the buy/sell signals from the following oscillators on the chart:
MACD (Moving Average Convergence Divergence)
RSI (Relative Strength Index)
Stochastic (Stochastic Oscillator)
In addition to the actual cross signals (shown with arrows), it incorporates two key features:
Cross Prediction (Predictive): Identifies when the oscillator lines (MACD, RSI, Stochastic) are very close and converging towards a cross, suggesting that a real signal might occur soon.
False Signal Detection: Attempts to identify when an initial buy/sell signal fails when the low/high of the signal candle is broken within a confirmation period.
Pin Bar Fib Pullback + Engulfing + Pin Reversal (21 EMA + VWAP)21 EMA = trend filter
VWAP = intraday mean price filter
Fibs = 0.50 & 0.618 “golden pocket” from latest swing range
Signals = Pin bars + Engulfing candles inside that Fib zone, with trend + VWAP filter
Vital Wave 20-50Simplicity is almost always the most effective approach, and here I’m giving you a trend-following system that exploits the bullish bias of traditional markets and their trending nature, with very basic rules.
Rules (long entries only)
• Market entry: When the EMA 20 crosses above the EMA 50 (from below)
• Main market exit: When the EMA 20 crosses below the EMA 50 (from above)
• Fixed Stop Loss: Placed at the price level of the Lower Bollinger Band at the moment the trade is entered.
In my strategy, the primary exit is when the EMA 20 crosses below the EMA 50. However, this crossover can sometimes take a while to occur, and in the meantime the price may have already dropped significantly. The Stop Loss based on the Lower Bollinger Band is designed to limit losses in case the market moves sharply against the position without giving the bearish crossover signal in time. Having two exit conditions makes the strategy much more robust in terms of risk management.
Risk Management:
• Initial capital: $10,000
• Position size: 10% of available capital per trade
• Commissions: 0.1% on traded volume
• Stop Loss: Based on the Lower Bollinger Band
• Take Profit / Exit: When EMA 20 crosses below EMA 50
Recommended Markets:
XAUUSD (OANDA) (Daily)
Period: January 3, 1833 – November 23, 2025
Total Profit & Loss: +$6,030.62 USD (+57.57%)
Maximum Drawdown: $541.53 USD (3.83%)
Total Trades: 136
Winning Trades (Win Rate): 36.03% (49/136)
Profit Factor: 2.483
XAUUSD (OANDA) (12-hour)
Period: March 19, 2006 – November 23, 2025
Total Profit & Loss: +$1,209.56 USD (+11.89%)
Maximum Drawdown: $384.58 USD (3.61%)
Total Trades: 97
Winning Trades (Win Rate): 35.05% (34/97)
Profit Factor: 1.676
XAUUSD (OANDA) (8-hour)
Period: March 19, 2006 – November 23, 2025
Total Profit & Loss: +$1,179.36 USD (+11.81%)
Maximum Drawdown: $246.88 USD (2.32%)
Total Trades: 147
Winning Trades (Win Rate): 31.97% (47/147)
Profit Factor: 1.626
Tesla (NASDAQ) (4-hour)
Period: June 29, 2010 – November 23, 2025
Total Profit & Loss (Absolute): +$11,687.90 USD (+116.88%)
Maximum Drawdown: $922.05 USD (6.50%)
Total Trades: 68
Winning Trades (Win Rate): 39.71% (27/68)
Profit Factor: 4.156
Tesla (NASDAQ) (3-hour)
Total Profit & Loss: +$11,522.33 USD (+115.22%)
Maximum Drawdown: $1,247.60 USD (8.80%)
Total Trades: 114
Winning Trades: 33.33% (38/114)
Profit Factor: 2.811
Additional Recommendations
(These assets have shown good trending behavior with the same strategy across multiple timeframes):
• NVDA (15 min, 30 min, 1h, 2h, 3h, 4h, 6h, 8h, 12h, Daily)
• NFLX (1h, 2h, 3h, 4h, 6h, 8h, 12h, Daily)
• MA (1h, 2h, 3h, 4h, 6h, 8h, 12h, Daily)
• META (1h, 2h, 3h, 4h, 6h, 8h, 12h, Daily)
• AAPL (1h, 2h, 3h, 4h, 6h, 8h, 12h, Daily)
• SPY (12h, Daily)
About the Code
The user can modify:
• EMA periods (20 and 50 by default)
• Bollinger Bands length (20 periods)
• Standard deviation (2.0)
Visualization
• EMA 20: Blue line
• EMA 50: Red line
• Green background when EMA20 > EMA50 (bullish trend)
• Red background when EMA20 < EMA50 (bearish trend)
Important Note:
We can significantly increase the profit factor and overall profitability by risking a fixed percentage per trade instead of a fixed amount. This would prevent losses from fluctuating with changes in volatility.
This could be implemented by reducing position size or adjusting leverage based on the volatility percentage required for each trade, but I’m not sure if this is fully possible in Pine Script. In my other script, “ Golden Cross 50/200 EMA ,” I go deeper into this topic and provide examples.
I hope you enjoy this contribution. Best regards!
Chop + MSS/FVG Retest (Ace v1.6) – IndicatorWhat this indicator does
Name: Chop + MSS/FVG Retest (Ace v1.6) – Indicator
This is an entry model helper, not just a BOS/MSS marker.
It looks for clean trend-side setups by combining:
MSS (Market Structure Shift) using swing highs/lows
3-bar ICT Fair Value Gaps (FVG)
First retest back into the FVG
A built-in chop / trend filter based on ATR and a moving average
When everything lines up, it plots:
L below the candle = Long candidate
S above the candle = Short candidate
You pair this with a higher-timeframe filter (like the Chop Meter 1H/30M/15M) to avoid pressing the button in garbage environments.
How it works (simple explanation)
Chop / Trend filter
Computes ATR and compares each bar’s range to ATR.
If the bar is small vs ATR → more likely CHOP.
If the bar is big vs ATR → more likely TREND.
Uses a moving average:
Above MA + TREND → trendLong zone
Below MA + TREND → trendShort zone
MSS (Market Structure Shift)
Uses swing highs/lows (left/right bars) to track the last significant high/low.
Bullish MSS: close breaks above last swing high with displacement.
Bearish MSS: close breaks below last swing low with displacement.
Those events are marked as tiny triangles (MSS up/down).
A MSS only stays “valid” for a certain number of bars (Bars after MSS allowed).
3-bar ICT FVG
Bullish FVG: low > high
→ gap between bar 3 high and bar 2 low.
Bearish FVG: high < low
→ gap between bar 3 low and bar 2 high.
The indicator stores the FVG boundaries (top/bottom).
Retest of FVG
Watches for price to trade back into that gap (first touch).
That retest is the “entry zone” after the MSS.
Final Long / Short condition
Long (L) prints when:
Recent bullish MSS
Bullish FVG has formed
Price retests the bullish FVG
Environment = trendLong (ATR + above MA)
Not CHOP
Short (S) prints when:
Recent bearish MSS
Bearish FVG has formed
Price retests the bearish FVG
Environment = trendShort (ATR + below MA)
Not CHOP
So the L/S markers are “model-approved entry candles”, not just any random BOS.
Inputs / Settings
Key inputs you’ll see:
ATR length (chop filter)
How many bars to use for ATR in the chop / trend filter.
Lower = more sensitive, twitchy
Higher = smoother, slower to change
Max chop ratio
If barRange / ATR is below this → treat as CHOP.
Min trend ratio
If barRange / ATR is above this → treat as TREND.
Hide MSS/BOS marks in CHOP?
ON = MSS triangles disappear when the bar is classified as CHOP
Keeps your chart cleaner in consolidation
Swing left / right bars
Controls how tight or wide the swing highs/lows are for MSS:
Smaller = more sensitive, more MSS points
Larger = fewer, more significant swings
Bars after MSS allowed
How many bars after a MSS the indicator will still allow FVG entries.
Small value (e.g. 10) = MSS must deliver quickly or it’s ignored.
Larger (e.g. 20) = MSS idea stays “in play” longer.
Visual RR (for info only)
Just for plotting relative risk-reward in your head.
This is not a strategy tester; it doesn’t manage positions.
What you see on the chart
Small green triangle up = Bullish MSS
Small red triangle down = Bearish MSS
“L” triangle below a bar = Long idea (MSS + FVG retest + trendLong + not chop)
“S” triangle above a bar = Short idea (MSS + FVG retest + trendShort + not chop)
Faint circle plots on price:
When the filter sees CHOP
When it sees Trend Long zone
When it sees Trend Short zone
You do not have to trade every L or S.
They’re there to show “this is where the model would have considered an entry.”
How to use it in your trading
1. Use it with a higher-timeframe filter
Best practice:
Use this with the Chop Meter 1H/30M/15M or some other HTF filter.
Only consider L/S when:
Chop Meter = TRADE / NORMAL, and
This indicator prints L or S in the right location (premium/discount, near OB/FVG, etc.)
If higher-timeframe says NO TRADE, you ignore all L/S.
2. Location > Signal
Treat L/S as confirmation, not the whole story.
For shorts (S):
Look for premium zones (previous highs, OBs, fair value ranges above mid).
Want purge / raid of liquidity + MSS down + bearish FVG retest → then S.
For longs (L):
Look for discount zones (previous lows, OBs/FVGs below mid).
Want stop raid / purge low + MSS up + bullish FVG retest → then L.
If you see L/S firing in the middle of a bigger range, that’s where you skip and let it go.
3. Instrument presets (example)
You can tune the ATR/chop settings per instrument:
MNQ (noisy, 1m chart):
ATR length: 21
Max chop ratio: 0.90
Min trend ratio: 1.40
Bars after MSS allowed: 10
GOLD (cleaner, 3m chart):
ATR length: 14
Max chop ratio: 0.80
Min trend ratio: 1.30
Bars after MSS allowed: 20
You can save those as presets in the TV settings for quick switching.
4. How to practice with it
Open replay on a couple of days.
Check Chop Meter → if NO TRADE, just observe.
When Chop Meter says TRADE:
Mark where L/S printed.
Ask:
Was this in premium/discount?
Was there SMT / purge on HTF?
Did the move actually deliver, or did it die?
Screenshot the A+ L/S and the ugly ones; refine:
ATR length
Chop / trend thresholds
MSS lookback
Your goal is to get it to where:
The L/S marks show up mostly in the same places your eye already likes,
and you ignore the rest.
