Multi-Band Comparison (Uptrend)Multi-Band Comparison
Overview:
The Multi-Band Comparison indicator is engineered to reveal critical levels of support and resistance in strong uptrends. In a healthy upward market, the price action will adhere closely to the 95th percentile line (the Upper Quantile Band), effectively “riding” it. This indicator combines a modified Bollinger Band (set at one standard deviation), quantile analysis (95% and 5% levels), and power‑law math to display a dynamic picture of market structure—highlighting a “golden channel” and robust support areas.
Key Components & Calculations:
The Golden Channel: Upper Bollinger Band & Upper Std Dev Band of the Upper Quantile
Upper Bollinger Band:
Calculation:
boll_upper=SMA(close,length)+(boll_mult×stdev)
boll_upper=SMA(close,length)+(boll_mult×stdev) Here, the 20-period SMA is used along with one standard deviation of the close, where the multiplier (boll_mult) is 1.0.
Role in an Uptrend:
In a healthy uptrend, price rides near the 95th percentile line. When price crosses above this Upper Bollinger Band, it confirms strong bullish momentum.
Upper Std Dev Band of the Upper Quantile (95th Percentile) Band:
Calculation:
quant_upper_std_up=quant_upper+stdev
quant_upper_std_up=quant_upper+stdev The Upper Quantile Band, quant_upperquant_upper, is calculated as the 95th percentile of recent price data. Adding one standard deviation creates an extension that accounts for normal volatility around this extreme level.
The Golden Channel:
When the price crosses above the Upper Bollinger Band, the Upper Std Dev Band of the Upper Quantile immediately shifts to gold (yellow) and remains gold until price falls below the Bollinger level. Together, these two lines form the “golden channel”—a visual hallmark of a healthy uptrend where the price reliably hugs the 95th percentile level.
Upper Power‑Law Band
Calculation:
The Upper Power‑Law Band is derived in two steps:
Determine the Extreme Return Factor:
power_upper=Percentile(returns,95%)
power_upper=Percentile(returns,95%) where returns are computed as:
returns=closeclose −1.
returns=close close−1.
Scale the Current Price:
power_upper_band=close×(1+power_upper)
power_upper_band=close×(1+power_upper)
Rationale and Correlation:
By focusing on the upper 5% of returns (reflecting “fat tails”), the Upper Power‑Law Band captures extreme but statistically expected movements. In an uptrend, its value often converges with the Upper Std Dev Band of the Upper Quantile because both measures reflect heightened volatility and extreme price levels. When the Upper Power‑Law Band exceeds the Upper Std Dev Band, it can signal a temporary overextension.
Upper Quantile Band (95% Percentile)
Calculation:
quant_upper=Percentile(price,95%)
quant_upper=Percentile(price,95%) This level represents where 95% of past price data falls below, and in a robust uptrend the price action practically rides this line.
Color Logic:
Its color shifts from a neutral (blackish) tone to a vibrant, bullish hue when the Upper Power‑Law Band crosses above it—signaling extra strength in the trend.
Lower Quantile and Its Support
Lower Quantile Band (5% Percentile):
Calculation:
quant_lower=Percentile(price,5%)
quant_lower=Percentile(price,5%)
Behavior:
In a healthy uptrend, price remains well above the Lower Quantile Band. It turns red only when price touches or crosses it, serving as a warning signal. Under normal conditions it remains bright green, indicating the market is not nearing these extreme lows.
Lower Std Dev Band of the Lower Quantile:
This line is calculated by subtracting one standard deviation from quant_lowerquant_lower and typically serves as absolute support in nearly all conditions (except during gap or near-gap moves). Its consistent role as support provides traders with a robust level to monitor.
How to Use the Indicator:
Golden Channel and Trend Confirmation:
As price rides the Upper Quantile (95th percentile) perfectly in a healthy uptrend, the Upper Bollinger Band (1 stdev above SMA) and the Upper Std Dev Band of the Upper Quantile form a “golden channel” once price crosses above the Bollinger level. When this occurs, the Upper Std Dev Band remains gold until price dips back below the Bollinger Band. This visual cue reinforces trend strength.
Power‑Law Insights:
The Upper Power‑Law Band, which is based on extreme (95th percentile) returns, tends to align with the Upper Std Dev Band. This convergence reinforces that extreme, yet statistically expected, price moves are occurring—indicating that even though the price rides the 95th percentile, it can only stretch so far before a correction or consolidation.
Support Indicators:
Primary and Secondary Support in Uptrends:
The Upper Bollinger Band and the Lower Std Dev Band of the Upper Quantile act as support zones for minor retracements in the uptrend.
Absolute Support:
The Lower Std Dev Band of the Lower Quantile serves as an almost invariable support area under most market conditions.
Conclusion:
The Multi-Band Comparison indicator unifies advanced statistical techniques to offer a clear view of uptrend structure. In a healthy bull market, price action rides the 95th percentile line with precision, and when the Upper Bollinger Band is breached, the corresponding Upper Std Dev Band turns gold to form a “golden channel.” This, combined with the Power‑Law analysis that captures extreme moves, and the robust lower support levels, provides traders with powerful, multi-dimensional insights for managing entries, exits, and risk.
Disclaimer:
Trading involves risk. This indicator is for educational purposes only and does not constitute financial advice. Always perform your own analysis before making trading decisions.
Поиск скриптов по запросу "GOLD"
Enhanced Economic Composite with Dynamic WeightEnhanced Economic Composite with Dynamic Weight
Overview of the Indicator :
The "Enhanced Economic Composite with Dynamic Weight" is a comprehensive tool that combines multiple economic indicators, technical signals, and dynamic weighting to provide insights into market and economic health. It adjusts based on current volatility and recession risk, offering a detailed view of market conditions.
What This Indicator Does :
Tracks Economic Health: Uses key economic and market indicators to assess overall market conditions.
Dynamic Weighting: Adjusts the importance of components like stock indices, gold, and bonds based on volatility (VIX) and yield curve inversion.
Technical Signals: Identifies market momentum shifts through key crossovers like the Golden Cross, Death Cross, Silver Cross, and Hospice Cross.
Recession Shading: Marks known recessions for historical context.
Economic Factors Considered :
TIP (Treasury Inflation-Protected Securities): Reflects inflation expectations.
Gold: A safe-haven asset, increases in weight during volatility or rising momentum.
US Dollar Index (DXY): Measures USD strength, fixed weight of 10%, smoothed with EMA.
Commodities (DBC): Indicates global demand; weight increases with momentum or volatility.
Volatility Index (VIX): Reflects market risk, inversely related to market confidence.
Stock Indices (S&P 500, DJIA, NASDAQ, Russell 2000): Represent market performance, with weights reduced during high volatility or negative yield spread.
Yield Spread (10Y - 2Y Treasuries): Predicts recessions; negative spread reduces stock weighting.
Credit Spread (HYG - TLT): Indicates market risk through corporate vs. government bond yields.
How and Why Factors are Weighted:
Stock Indices get more weight in stable markets (low VIX, positive yield spread), while safe-haven assets like gold and bonds gain weight in volatile markets or during yield curve inversions. This dynamic adjustment ensures the composite reflects current market sentiment.
Technical Signals:
Golden Cross: 50 EMA crossing above 200 SMA, signaling bullish momentum.
Death Cross: 50 EMA below 200 SMA, indicating bearish momentum.
Silver Cross: 21 EMA crossing above 50 EMA, plotted only if below the 200-day SMA, signaling potential upside in downtrend conditions.
Hospice Cross: 50 EMA crosses below 21 EMA, plotted only if 21 EMA is below 200 SMA, a leading bearish signal.
Recession Shading:
Recession periods like the Great Recession, Early 2000s Recession, and COVID-19 Recession are shaded to provide historical context.
Benefits of Using This Indicator:
Comprehensive Analysis: Combines economic fundamentals and technical analysis for a full market view.
Dynamic Risk Adjustment: Weights shift between growth and safe-haven assets based on volatility and recession risk.
Early Signals: The Silver Cross and Hospice Cross provide early warnings of potential market shifts.
Recession Forecasting: Helps predict downturns through the yield curve and recession indicators.
Who Can Benefit:
Traders: Identify market momentum shifts early through crossovers.
Long-term Investors: Use recession warnings and dynamic adjustments to protect portfolios.
Analysts: A holistic tool for analyzing both economic trends and market movements.
This indicator helps users navigate varying market conditions by dynamically adjusting based on economic factors and providing early technical signals for market momentum shifts.
CE - Market Performance TableThe 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is a sophisticated market tool designed to provide valuable insights into the current market trends and the approximate current position in the Macroeconomic Regime.
Furthermore the 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 provides the Correlation Implied Trend for the Asset on the Chart. Lastly it provides information about current "RISK ON" or "RISK OFF" periods.
Methodology:
𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 tracks the 15 underlying Stock ETF's to identify their performance and puts the combined performances together to visualize 42MACRO's GRID Equity Model.
For this it uses the below ETF's:
Dividends (SPHD)
Low Beta (SPLV)
Quality (QUAL)
Defensives (DEF)
Growth (IWF)
High Beta (SPHB)
Cyclicals (IYT, IWN)
Value (IWD)
Small Caps (IWM)
Mid Caps (IWR)
Mega Cap Growth (MGK)
Size (OEF)
Momentum (MTUM)
Large Caps (IWB)
Overall Settings:
The main time values you want to change are:
Correlation Length
- Defines the time horizon for the Correlation Table
ROC Period
- Defines the time horizon for the Performance Table
Normalization lookback
- Defines the time horizon for the Trend calculation of the ETF's
- For longer term Trends over weeks or months a length of 50 is usually pretty accurate
Visuals:
There is a variety of options to change the visual settings of what is being plotted and the two table positions and additional considerations.
Everything that is relevant in the underlying logic that can help comprehension can be visualized with these options.
Market Correlation:
The Market Correlation Table takes the Correlation of the above ETF's to the Asset on the Chart, it furthermore uses the Normalized KAMA Oscillator by IkkeOmar to analyse the current trend of every single ETF.
It then Implies a Correlation based on the Trend and the Correlation to give a probabilistically adjusted expectation for the future Chart Asset Movement. This is strengthened by taking the average of all Implied Trends.
With this the Correlation Table provides valuable insights about probabilistically likely Movement of the Asset, for Traders and Investors alike, over the defined time duration.
Market Performance:
𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is the actual valuable part of this Indicator.
It provides valuable information about the current market environment (whether it's risk on or risk off), the rough GRID models from 42MACRO and the actual market performance.
This allows you to obtain a deeper understanding of how the market works and makes it simple to identify the actual market direction.
Utility:
The 𝓜𝓪𝓻𝓴𝓮𝓽 𝓟𝓮𝓻𝓯𝓸𝓻𝓶𝓪𝓷𝓬𝓮 𝓣𝓪𝓫𝓵𝓮 is divided in 4 Sections which are the GRID regimes:
Economic Growth:
Goldilocks
Reflation
Economic Contraction:
Inflation
Deflation
Top 5 Equity Style Factors:
Are the values green for a specific Column? If so then the market reflects the corresponding GRID behavior.
Bottom 5 Equity Style Factors:
Are the values red for a specific Column? If so then the market reflects the corresponding GRID behavior.
So if we have Goldilocks as current regime we would see green values in the Top 5 Goldilocks Cells and red values in the Bottom 5 Goldilocks Cells.
You will find that Reflation will look similar, as it is also a sign of Economic Growth.
Same is the case for the two Contraction regimes.
MathConstantsLibrary "MathConstants"
Mathematical Constants
E() The number e
Log2E() The number log (e)
Log10E() The number log (e)
Ln2() The number log (2)
Ln10() The number log (10)
LnPi() The number log (pi)
Ln2PiOver2() The number log (2*pi)/2
InvE() The number 1/e
SqrtE() The number sqrt(e)
Sqrt2() The number sqrt(2)
Sqrt3() The number sqrt(3)
Sqrt1Over2() The number sqrt(1/2) = 1/sqrt(2) = sqrt(2)/2
HalfSqrt3() The number sqrt(3)/2
Pi() The number pi
Pi2() The number pi*2
PiOver2() The number pi/2
Pi3Over2() The number pi*3/2
PiOver4() The number pi/4
SqrtPi() The number sqrt(pi)
Sqrt2Pi() The number sqrt(2pi)
SqrtPiOver2() The number sqrt(pi/2)
Sqrt2PiE() The number sqrt(2*pi*e)
LogSqrt2Pi() The number log(sqrt(2*pi))
LogSqrt2PiE() The number log(sqrt(2*pi*e))
LogTwoSqrtEOverPi() The number log(2 * sqrt(e / pi))
InvPi() The number 1/pi
TwoInvPi() The number 2/pi
InvSqrtPi() The number 1/sqrt(pi)
InvSqrt2Pi() The number 1/sqrt(2pi)
TwoInvSqrtPi() The number 2/sqrt(pi)
TwoSqrtEOverPi() The number 2 * sqrt(e / pi)
Degree() The number (pi)/180 - factor to convert from Degree (deg) to Radians (rad).
Grad() The number (pi)/200 - factor to convert from NewGrad (grad) to Radians (rad).
PowerDecibel() The number ln(10)/20 - factor to convert from Power Decibel (dB) to Neper (Np). Use this version when the Decibel represent a power gain but the compared values are not powers (e.g. amplitude, current, voltage).
NeutralDecibel() The number ln(10)/10 - factor to convert from Neutral Decibel (dB) to Neper (Np). Use this version when either both or neither of the Decibel and the compared values represent powers.
Catalan() The Catalan constant
Sum(k=0 -> inf){ (-1)^k/(2*k + 1)2 }
EulerMascheroni() The Euler-Mascheroni constant
lim(n -> inf){ Sum(k=1 -> n) { 1/k - log(n) } }
GoldenRatio() The number (1+sqrt(5))/2, also known as the golden ratio
Glaisher() The Glaisher constant
e^(1/12 - Zeta(-1))
Khinchin() The Khinchin constant
prod(k=1 -> inf){1+1/(k*(k+2))^log(k,2)}
Dynamic Auto Fibonacci - Auto/Manual ModeDynamic Auto Fibonacci - Professional Retracement & Extension Tool
The ultimate Fibonacci tool combining automatic high detection with manual precision for swing low selection.
🎯 Key Features
Hybrid Drawing System
Auto Mode: You manually select your swing low by clicking on the chart, then the indicator automatically finds the highest high after that point - giving you control over your anchor while automating the rest
Manual Mode: Full control - click to select BOTH your swing low (0.0) AND swing high (1.0) for complete precision - perfect for drawing multiple projections to find confluence zones
Logarithmic Scale Support
True logarithmic Fibonacci calculations for accurate levels on log-scale charts
Essential for crypto and growth stocks with significant price appreciation
Smart Level Management
"Key Fibs Only" toggle (ON by default): Shows 13 essential professional levels
All 23 levels unlocked: Turn off Key Fibs to access 10 additional advanced levels including 0.414, 0.707, 0.886, 1.886, 2.272, 3.618, and negative projections
Every level is fully customizable - edit values, toggle on/off, change colors
Essential Fibonacci Levels (Default)
Core: 0.0, 0.236, 0.382, 0.5, 0.618 (Golden), 0.786, 1.0
Extensions: 1.272, 1.382, 1.618 (Golden), 2.0, 2.618 (Golden), 4.236
All golden ratio levels (0.618, 1.618, 2.618, 3.618) highlighted in gold
Professional Display Options
Three display modes: Retracements Only, Extensions Only, or Both
Customizable line styles (Solid/Dashed/Dotted), widths, and lengths
Clean text-only labels or traditional price scale labels
Unified color override for minimalist chart aesthetics
Adjustable label positioning and sizing
Perfect for Professional Trading
Add multiple instances with different manual anchors to identify high-probability confluence zones
Combines the convenience of partial automation with the precision of manual anchor selection
Works on all markets: stocks, forex, crypto, futures
Compatible with all timeframes and markets. Clean code, efficient performance, zero repainting.
ProScalper📊 ProScalper - Professional 1-Minute Scalping System
🎯 Overview
ProScalper is a sophisticated, multi-confluence scalping indicator designed specifically for 1-minute chart trading. Combining advanced technical analysis with intelligent signal filtering, it provides high-probability trade setups with clear entry, stop loss, and take profit levels.
✨ Key Features
🔺 Smart Signal Detection
Range Filter Technology: Fast-responding trend detection (25-period) optimized for 1-minute timeframe
Medium-sized triangles appear above/below candles for clear buy/sell signals
Only most recent signal shown - no chart clutter
Automatically deletes old signals when new ones appear
📋 Real-Time Signal Table
Top-center display shows complete trade breakdown
Grade system: A+, A, B+, B, C+ ratings for every setup
All confluence reasons listed with checkmarks
Score and R:R displayed for instant trade quality assessment
Color-coded: Green for LONG, Red for SHORT
📐 Multi-Confluence Analysis
ProScalper combines 10+ technical factors:
✅ EMA Trend: 4 EMAs (200, 48, 13, 8) for multi-timeframe alignment
✅ VWAP: Dynamic support/resistance
✅ Fibonacci Retracement: Golden ratio (61.8%), 50%, 38.2%, 78.6%
✅ Range Filter: Adaptive trend confirmation
✅ Pivot Points: Smart reversal detection
✅ Volume Analysis: Spike detection and volume profile
✅ Higher Timeframe: 5-minute trend confirmation
✅ HTF Support/Resistance: Key levels from higher timeframes
✅ Liquidity Sweeps: Smart money detection
✅ Opening Range Breakout: First 15-minute range
💰 Complete Trade Management
Entry Lines: Dashed green (LONG) or red (SHORT) showing exact entry
Stop Loss: Red dashed line with price label
Take Profit: Blue dashed line with price label and R:R
Partial Exits: 1R level marked with orange dashed line
All lines extend 10 bars for clean alignment with Fibonacci levels
📊 Dynamic Risk/Reward
Adaptive R:R calculation based on market volatility
Targets adjusted for pivot distances
Minimum 1.2:1 to maximum 3.5:1 for scalping
Position sizing based on account risk percentage
🎨 Professional Visualization
Clean chart layout - no clutter, only essential information
Custom EMA colors: Red (200), Aqua (48), Green (13), White (8)
Gold VWAP line for key support/resistance
Color-coded Fibonacci: Bright yellow (61.8%), white (50%), orange (38.2%), fuchsia (78.6%)
No shaded zones - pure price action focus
📈 Performance Tracking
Real-time statistics table (optional)
Win rate, total trades, P&L tracking
Average R:R and win/loss ratios
Setup-specific performance metrics
⚙️ Settings & Customization
Risk Management
Adjustable account risk per trade (default: 0.5%)
ATR-based stop loss multiplier (default: 0.8 for tight scalping)
Dynamic position sizing
Signal Sensitivity
Confluence Score Threshold: 40-100 (default: 55 for balanced signals)
Range Filter Period: 25 bars (fast signals for 1-min)
Range Filter Multiplier: 2.2 (tighter bands for more signals)
Visual Controls
Toggle signal table on/off
Show/hide Fibonacci levels
Control EMA visibility
Adjust table text size
Partial Exits
1R: 50% (default)
2R: 30% (default)
3R: 20% (default)
Fully customizable percentages
Trailing Stops
ATR-Based (best for scalping)
Pivot-Based
EMA-Based
Breakeven trigger at 0.8R
🎯 Best Use Cases
Ideal For:
✅ 1-minute scalping on liquid instruments
✅ Day traders looking for quick 2-8 minute trades
✅ High-frequency trading with 8-15 signals per session
✅ Trending markets where Range Filter excels
✅ Crypto, Forex, Futures - works on all liquid assets
Trading Style:
Timeframe: 1-minute (can work on 3-5 min with adjusted settings)
Hold Time: 3-8 minutes average
Target: 1.2-3R per trade
Frequency: 8-15 signals per day
Win Rate: 45-55% (with proper risk management)
📋 How to Use
Step 1: Wait for Signal
Watch for green triangle (BUY) or red triangle (SELL)
Signal table appears at top center automatically
Step 2: Review Confluence
Check grade (prefer A+, A, B+ for best quality)
Review all reasons listed in table
Confirm score is above your threshold (55+ recommended)
Note the R:R ratio
Step 3: Enter Trade
Enter at current market price
Set stop loss at red dashed line
Set take profit at blue dashed line
Mark 1R level (orange line) for partial exit
Step 4: Manage Trade
Exit 50% at 1R (orange line)
Move to breakeven after 0.8R
Trail remaining position using your chosen method
Exit fully at TP or opposite signal
🎨 Chart Setup Recommendations
Optimal Display:
Timeframe: 1-minute
Chart Type: Candles or Heikin Ashi
Background: Dark theme for best color visibility
Volume: Enable volume bars below chart
Complementary Indicators (optional):
Order flow/Delta for institutional confirmation
Market profile for key levels
Economic calendar for news avoidance
⚠️ Important Notes
Risk Disclaimer:
Not financial advice - for educational purposes only
Always use proper risk management (0.5-1% per trade max)
Past performance doesn't guarantee future results
Test on demo account before live trading
Best Practices:
✅ Trade during high liquidity hours (9:30-11 AM, 2-4 PM EST)
✅ Avoid news events and market open/close (first/last 2 minutes)
✅ Use tight stops (0.8-1.0 ATR) for 1-minute scalping
✅ Take partial profits quickly (1R = 50% off)
✅ Respect max daily loss limits (3% recommended)
✅ Focus on A and B grade setups for consistency
What Makes This Different:
🎯 Complete system - not just signals, but full trade management
📊 Multi-confluence - 10+ factors analyzed per trade
🎨 Professional visualization - clean, focused chart design
⚡ Optimized for 1-min - settings specifically tuned for fast scalping
📋 Transparent reasoning - see exactly why each trade was taken
🏆 Grade system - instantly know trade quality
🔧 Technical Details
Pine Script Version: 5
Overlay: Yes (plots on price chart)
Max Lines: 500
Max Labels: 100
Non-repainting: All signals confirmed on bar close
Alerts: Compatible with TradingView alerts
📞 Support & Updates
This indicator is actively maintained and optimized for 1-minute scalping. Settings can be adjusted for different timeframes and trading styles, but default configuration is specifically tuned for high-frequency 1-minute scalping.
🚀 Get Started
Add ProScalper to your 1-minute chart
Adjust settings to your risk tolerance
Wait for signals (green/red triangles)
Follow the signal table guidance
Manage trades using provided levels
Track performance with stats table
Happy Scalping! 📊⚡💰
Lynie's V9 SELL🟢🔴 Lynie’s V8 — BUY & SELL (Mirrored, Interlocking System)
Lynie’s V8 is a paired long/short engine built as two mirrored scripts—Lynie’s V8 BUY and Lynie’s V8 SELL—that read price the same way, flip conditions symmetrically, and manage trades with the exact logic on opposite sides. Use either one standalone or run both together for full two-sided automation of entries, re-entries, caution states, and adaptive SL/TP.
✳️ What “mirrored” means here
Supertrend Tri-Stack (10/11/12):
BUY: ST10 primary pierce; ST12 fallback; “PAG Buy” when price pierces any ST while above the other two.
SELL: Exact inverse—ST10 primary pierce down; ST12 fallback; “PAG Sell” when price pierces any ST while below the other two.
Re-Enter Clusters:
BUY: Ratcheted up (Heikin-Ashi green holds/tightens).
SELL: Ratcheted down (Heikin-Ashi red holds/tightens).
Both sides use the same cluster age/decay math, care penalties, session awareness, and fast-candle tightening.
Care Flags (context risk):
Ichimoku, MACD, RSI combine into single and paired flags that tighten or widen offsets on both sides with the same scoring.
VWAP–EMA50 (5m) cluster gate:
Identical distance checks for BUY/SELL. When the mean cluster is present, offsets and labels adapt (tighter/“riskier scalp” messaging).
Golden Pocket A/B/C (prev-day):
Same fib boxes & labeling (gold tone) on both sides to call out TP-friendly zones.
SL/TP Envelope:
Shared dynamic engine: per-bar decay, fast-candle expansion, and care-based compress/relax—all mirrored for up/down.
Caution Labels:
BUY side prints CAUTION SELL if HA flips red inside an active long cluster.
SELL side prints CAUTION BUY if HA flips green inside an active short cluster.
Same latching & auto-release behavior.
🧠 Core workflow (both sides)
Primary trigger via ST10 pierce (structure shift) with an ST12 fallback when ST10 didn’t qualify.
PAG Mode when price is already on the right side of the other two STs—strongest conviction.
Cluster phase begins after a signal: ratcheted re-entry level, session-aware offsets, dynamic tightening on fast bars.
Care system shapes every re-entry & SL/TP label (Ichi/MACD/RSI combos + VWAP/EMA gate + QQE).
Protective layer: SL-wick and SL-body logic, caution flips, and “hold 1 bar” cluster carry after SL to avoid whipsaw spam.
🔎 Labels & messages (shared vocabulary)
Lynie’s / Lynie’s+ / Lynie’s++ — strength tiers (ST12 involvement & clean context).
Re-Enter / Excellent Re-Enter — cluster pullback quality; ratchet shows the “must-hold” zone.
SL&TP (n) — live offset multiplier the engine is using right now.
CAUTION BUY / CAUTION SELL — HA flip against the active side inside the cluster.
Restart Next Candle — visual cue to re-arm after a confirmed signal bar.
⚡ Why run both together
Continuity: When a long cycle ends (SL or caution degradation), the SELL engine is already tracking the inverse without re-tuning.
Symmetry: Same math, same signals, opposite direction—no hidden biases.
Coverage: Trend hand-offs are cleaner; you don’t miss early shorts after a long fade (and vice versa).
🔧 Recommended usage
Intraday futures (ES/NQ) or any liquid market.
Keep the VWAP–EMA cluster ON; it filters FOMO chases.
Honor Caution flips inside cluster—scale down or wait for the next clean re-enter.
Treat Golden Zones as TP magnets, not guaranteed reversals.
📌 Notes
Both scripts are Pine v6 and independent. Load BUY and SELL together for the full experience.
All offsets (re-enter & SL/TP) are visible in labels—so you always know why a zone is where it is.
Alerts are provided for signals, re-enter hits, caution, and SL events on both sides.
Summary: Lynie’s V8 BUY & SELL are vice-versa twins—one framework, two directions—delivering consistent entries, adaptive re-entries, and contextual risk management whether the market is pressing up or breaking down.
Auto Fibonacci LevelsAuto Fibonacci Momentum Zones with Visible Range Table
Overview and Originality
The Auto Fibonacci Momentum Zones indicator offers a streamlined, static overlay of Fibonacci retracement levels inspired by extreme RSI momentum thresholds, enhanced with a dynamic table displaying the high and low of the currently visible chart range. This isn't a repackaged RSI oscillator or basic Fib drawer—common in TradingView's library—but a purposeful fusion of geometric harmony (Fibonacci ratios) with momentum psychology (RSI extremes at 35/85), projected as fixed horizontal reference lines on the price chart. The addition of the visible range table, powered by PineCoders' VisibleChart library, provides real-time context for the chart's current view, enabling traders to quickly assess range compression or expansion relative to these zones.
This script's originality stems from its "static momentum mapping": by hardcoding Fib levels on a dynamic chart, it creates universal psychological support/resistance lines that transcend specific assets or timeframes.
Unlike dynamic Fib tools that auto-adjust to price swings (risking noise in ranging markets) or standalone RSI plots (confined to panes), this delivers clean, bias-adjustable overlays for confluence analysis. The visible range table justifies the library integration—it's not a gratuitous add-on but a complementary tool that quantifies the "screen real estate" of price action, helping users correlate Fib touches with actual volatility. Drawn from original code (no auto-generation or public templates), it builds TradingView's body of knowledge by simplifying multi-tool workflows into one indicator, ideal for discretionary traders who value visual efficiency over algorithmic complexity.
How It Works: Underlying Concepts
Fibonacci retracements, derived from the Fibonacci sequence and the golden ratio (≈0.618), identify potential reversal points based on the idea that markets retrace prior moves in predictable proportions: shallow (23.6%, 38.2%), mid (50%), and deep (61.8%, 78.6%).
Adjustable Outputs
1. The "Invert Fibs" toggle (default: true) for bearish/topping bias, can be flipped aligning with trend context.
2. Fibonacci Levels: Seven semi-transparent horizontal lines are drawn using `hline()`:
- 0.0 at high (gray).
- 0.236: high - (range × 0.236) (light cyan, shallow pullback).
- 0.382: high - (range × 0.382) (teal, common retracement).
- 0.5: midpoint average (green, equilibrium).
- 0.618: high - (range × 0.618) (amber, golden pocket for reversals).
- 0.786: high - (range × 0.786) (orange, deep support).
- 1.0 at low (gray).
Colors progress from cool (shallow) to warm (deep) for intuitive scanning.
3. Optional Fib Labels: Right-edge text labels (e.g., "0.618") appear only if enabled, positioned at the last bar + offset for non-cluttering visibility.
4. Visible Range Table: Leveraging the VisibleChart library's `visible.high()` and `visible.low()` functions, a compact 2x2 table (top-right corner) updates on the last bar to show the extrema of bars currently in view. This mashup enhances utility: Fib zones provide fixed anchors, while the table's dynamic values reveal if price is "pinned" to a zone (e.g., visible high hugging 0.382 signals resistance). The library is invoked sparingly for performance, adding value by bridging static geometry with viewport-aware data—unavailable in built-ins without custom code.
How to Use It
1. Setup:
Add to any chart (e.g., 15M for scalps, Daily for swings). As an overlay, lines appear directly on price candles—adjust chart scaling if needed.
2. Input Tweaks:
Invert Fibs: Enable for downtrends (85 top), disable for uptrends (35 bottom).
Show Fibs: Toggle labels for ratio callouts (off for clean charts).
Show Table: Display/hide the visible high/low summary (red for high, green for low, formatted to 2 decimals).
3. Trading Application:
Zone Confluence: Seek price reactions at each fibonacci level—e.g., a doji at 0.618 + rising volume suggests entry; use 0.0/1.0 as invalidation.
Range Context: Check the table: If visible high/low spans <20% of the Fib arc (e.g., both near 0.5), anticipate breakout; wider spans signal consolidation.
Multi-Timeframe: Overlay on higher TF for bias, lower for precision—e.g., Daily Fibs guide 1H entries.
Enhancements: Pair with volume or candlesticks; set alerts on line crosses via TradingView's built-in tools. Backtest on your symbols to validate (e.g., equities favor 0.382, forex the 0.786).
This indicator automates advanced Fibonacci synthesis dynamically, eliminating manual measurement and calculations.
published by ozzy_livin
Liquidity Swap Detector Ultimate - Cedric JeanjeanAdvanced Smart Money Concepts indicator designed to detect high-probability liquidity sweeps and institutional order flow reversals. This professional-grade tool combines multiple ICT (Inner Circle Trader) strategies to identify optimal entry points.
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📊 KEY FEATURES:
✅ Smart Swing Detection
- Identifies confirmed swing highs and lows using adaptive lookback periods
- Eliminates false signals through double-confirmation logic
- Detects liquidity grabs at key market structure points
✅ Fair Value Gap (FVG) Analysis
- Multi-timeframe FVG detection for enhanced accuracy
- Filters imbalances by minimum size threshold
- Combines current timeframe and higher timeframe FVGs
✅ Advanced Volatility Filter
- ATR-based volatility analysis to avoid low-quality setups
- Adjustable volatility threshold (default 0.35%)
- Ensures entries during optimal market conditions
✅ Precision Signal Generation
- LONG signals: Confirmed swing lows + FVG + volatility confirmation
- SHORT signals: Confirmed swing highs + FVG + volatility confirmation
- Clear visual markers with price labels
✅ Comprehensive Alert System
- Three alert types: Simple, Detailed, JSON (for webhooks)
- Separate LONG/SHORT alert controls
- Compatible with MT5 integration via webhooks
- TradingView native alertcondition support
✅ Professional Dashboard
- Real-time ATR monitoring
- Volatility percentage display
- FVG status indicator
- Alert status tracker
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⚙️ CUSTOMIZABLE PARAMETERS:
🔹 Lookback Swing (1-50): Defines swing detection sensitivity
🔹 ATR Multiplier: Controls wick filter strength
🔹 Volatility Filter: Minimum required market volatility (%)
🔹 FVG Filter: Minimum fair value gap size (%)
🔹 FVG Timeframe: Higher timeframe for multi-TF analysis
🔹 Visual Options: Toggle swing marks, FVG zones, labels
🔹 Alert Controls: Enable/disable LONG/SHORT notifications
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📈 HOW IT WORKS:
1. The indicator scans for confirmed swing points using a robust double-confirmation algorithm
2. Simultaneously analyzes Fair Value Gaps on both current and higher timeframes
3. Validates market volatility to ensure sufficient price movement
4. Generates precise entry signals when all conditions align
5. Triggers customizable alerts for instant notification
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🎯 BEST PRACTICES:
- Use on liquid markets (Forex majors, indices, crypto)
- Recommended timeframes: 15m, 1H, 4H
- Combine with support/resistance for confirmation
- Adjust lookback period based on market volatility
- Test alert settings before live trading
- Use JSON alerts for automated trading integration
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⚡ ALERT CONFIGURATION:
1. Click the Alert icon (bell) in TradingView
2. Select "Liquidity Swap Detector Ultimate - TITAN v6"
3. Choose your preferred alert condition:
- LONG Signal: Only bullish setups
- SHORT Signal: Only bearish setups
- ANY Signal: All trading opportunities
4. Set expiration and notification preferences
5. For MT5 integration: Select "JSON" message type and configure webhook URL
Metallic Retracement ToolI made a version of the Metallic Retracement script where instead of using automatic zig-zag detection, you get to place the points manually. When you add it to the chart, it prompts you to click on two points. These two points become your swing range, and the indicator calculates all the metallic retracement levels from there and plots them on your chart. You can drag the points around afterwards to adjust the range, or just add the indicator to the chart again to place a completely new set of points.
The mathematical foundation is identical to the original Metallic Retracement indicator. You're still working with metallic means, which are the sequence of constants that generalize the golden ratio through the equation x² = kx + 1. When k equals 1, you get the golden ratio. When k equals 2, you get silver. Bronze is 3, and so on forever. Each metallic number generates its own set of retracement ratios by raising alpha to various negative powers, where alpha equals (k + sqrt(k² + 4)) / 2. The script algorithmically calculates these levels instead of hardcoding them, which means you can pick any metallic number you want and instantly get its complete retracement sequence.
What's different here is the control. Automatic zig-zag detection is useful when you want the indicator to find swings for you, but sometimes you have a specific price range in mind that doesn't line up with what the zig-zag algorithm considers significant. Maybe you're analyzing a move that's still developing and hasn't triggered the zig-zag's reversal thresholds yet. Maybe you want to measure retracements from an arbitrary high to an arbitrary low that happened weeks apart with tons of noise in between. Manual placement lets you define exactly which two points matter for your analysis without fighting with sensitivity settings or waiting for confirmation.
The interactive placement system uses TradingView's built-in drawing tools, so clicking the two points feels natural and works the same way as drawing a trendline or fibonacci retracement. First click sets your starting point, second click sets your ending point, and the indicator immediately calculates the range and draws all the metallic levels extending from whichever point you chose as the origin. If you picked a swing low and then a swing high, you get retracement levels projecting upward. If you went from high to low, they project downward.
Moving the points after placement is as simple as grabbing one of them and dragging it to a new location. The retracement levels recalculate in real-time as you move the anchor points, which makes it easy to experiment with different range definitions and see how the levels shift. This is particularly useful when you're trying to figure out which swing points produce retracement levels that line up with other technical features like previous support or resistance zones. You can slide the points around until you find a configuration that makes sense for your analysis.
Adding the indicator to the chart multiple times lets you compare different metallic means on the same price range, or analyze multiple ranges simultaneously with different metallic numbers. You could have golden ratio retracements on one major swing and silver ratio retracements on a smaller correction within that swing. Since each instance of the indicator is independent, you can mix and match metallic numbers and ranges however you want without one interfering with the other.
The settings work the same way as the original script. You select which metallic number to use, control how many power ratios to display above and below the 1.0 level, and adjust how many complete retracement cycles you want drawn. The levels extend from your manually placed swing points just like they would from automatically detected pivots, showing you where price might react based on whichever metallic mean you've selected.
What this version emphasizes is that retracement analysis is subjective in terms of which swing points you consider significant. Automatic detection algorithms make assumptions about what constitutes a meaningful reversal, but those assumptions don't always match your interpretation of the price action. By giving you manual control over point placement, this tool lets you apply metallic retracement concepts to exactly the price ranges you care about, without requiring those ranges to fit someone else's definition of a valid swing. You define the context, the indicator provides the mathematical framework.
ATR Anchored Range %b by TradeSeekersAll time highs got you spooked to enter with no levels in sight?
Stuck in a multi-week range and wondering where the heck the pivots are!?
Wondering if you're longing the top or shorting the potential bottom and about to get smoked, sending you back to burger flipping?!
Fret not trading friends!
I've been crafting the ultimate map for scalpers, slingers, swingers, swindlers, swashbucklers -and traders too.
Why should I care about this, what's an ATR!?
Nearly any trader that's entered the markets has heard of ATR, perhaps even taken a stab at trying to calculate the flux capacity of a weekly ATR on a lower timeframe. Continually calculating things manually sucks!
Ok, so you haven't heard of ATR? It's the average true range... what's the true range!? It's simply the low subtracted from the high (high - low) of any given candle.
How is ATR useful?
The theory is simple, if the ATRs on the daily timeframe for a stock are 5, then traders may have a reasonable expectation that any day in the near future the stock will mostly move +/- 5 pts. This +/- 5 can be used as a possible daily high and low for traders to use.
But ATR changes as time passes, with every billionaire X post, viral cat meme, fed announcement or government shutdown the market makes it's move. This means without this tool, traders need to run the standard lame (sorry) ATR indicator and then hand draw a bunch of important levels (barf).
I'm convinced and ready to join the ATR army, what do I do?
Glad to have you aboard sailor, slap this indicator on your layout - it'll initially display a bottom panel, say nice things to it.
Usage
The lower panel provides a %b plot representative of the current price relative to the timeframe and period ATR. (Defaults to 1D timeframe and 20 - 20 trading days in a month yo)
This %b plot is a map for price against the key ATR based levels and resets each time the timeframe change occurs.
Keep reading! (maybe grab a snack, you're doing great)
If you want to see what the indicator sees, how it maths the math, open the settings and check the "overlay" option... it's amazing, I know.
Main base of operations
This will be the gray area between first red and green lines, imagine this is a future candle for the timeframe anchored. The red would represent the candle high (red means stop/overbought), and the green would represent the candle low (green means go/oversold).
Regardless of the timeframe anchored, this area always represents the area the ATR indicates will be the building area of the current candle being formed. Traders should expect most of the trading to occur within this area.
The mid line
Don't diddle in the middle, this by default is the open price and it's the ultimate bias filter for bull or bear riders.
Extension areas
Beyond the gray area is the extension zone, this provides a whole ATR from the mid line to the extension.
Assembling a trade plan
There are just a couple of key concepts to master in order to become the ultimate ATR samurai warrior, capable of slicing through even the messiest liquidity.
Above the midline and holding, but still within the gray area? Could be a great long entry with targets to upper levels. The same holds true for below open and holding while still being within the lower gray area.
As price makes it's ascension or decline towards the ends of the initial gray ATR range, consider managing trades here. If it's suspected, due to a strong hold of the midline, that the range low or high is the midline, then continue to manage trades towards the extension zones.
Timeframes and periods oh my
The tooltips already provide some hints, but not everyone goes around clicking and hovering everything in sight (maybe I'm the only one that does that?).
There's a thoughtful approach to the default values, I like to consider the big market participants with my day trades, swings trades and beyond.
By default I've chosen the daily timeframe and a period of 20, one for each trading day of the calendar month.
It's no large leap to consider alternatives, what about 1W timeframe and a period of 4 (1 month) or 52 (1 year)?
The possibilities are nearly infinite, comment on any particular favorite combos.
An Italian Special Bonus!!!
...sorry, it's not pizza....
First, did you know the famous Italian Fibonacci's real name was actually Leonardo? I'm not sure how I feel about that. Fun fact, my ancestors are Italian.
Alright, you may have guessed that the special bonus is the mythical Fibonacci inspired "Golden Pocket", maybe it's a foreshadowing of your pockets - one can only hope.
Use this feature to show the commonly referenced Fibonacci levels within each major ATR range. I've seen some totally mathematical epic-ness with these hence the addition.
Once key ATR levels have been hit look for reversals back to golden pockets (you tricksy hobbits) for potential entry back towards the prior hit ATR level.
The %b turns gold if you have the feature enabled and of course the overlay displays them also, how fun!
Final thoughts
I hope you have as much fun using this indicator as I do, it has brought much joy to my trading experience. If you don't have fun with it, well I hope you had fun reading about it at least.
100% human crafted and darn proud of it
- SyntaxGeek
🐬Stochastic_RSIStochastic RSI
The indicator highlights the chart background for two specific signals:
- A bearish deadcross occurring above the upper band.
- A bullish goldencross occurring below the lower band.
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스토캐스틱 RSI
두가지 신호를 배경색으로 나타냅니다.
- 어퍼 밴드 위에서의 데드크로스
- 로우어 밴드 아래에서의 골든크로스
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3/4-Bar GRG / RGR Pattern (Conditional 4th Candle)This indicator can be used to identify the Green-Red-Green or Red-Green-Red pattern.
It is a price action indicator where a price action which identifies the defeat of buyers and sellers.
If the buyers comprehensively defeat the sellers then the price moves up and if the sellers defeat the buyers then the price moves down.
In my trading experience this is what defines the price movement.
It is a 3 or 4 candle pattern, beyond that i.e, 5 or more candles could mean a very sideways market and unnecessary signal generation.
How does it work?
Upside/Green signal
Say candle 1 is Green, which means buyers stepped in, then candle 2 is Red or a Doji, that means sellers brought the price down. Then if candle 3 is forming to be Green and breaks the closing of the 1st candle and opening of the 2nd candle, then a green arrow will appear and that is the place where you want to take your trade.
Here the buyers defeated the sellers.
Sometimes candle 3 falls short but candle 4 breaks candle 1's closing and candle 2's opening price. We can enter on candle 4.
Important - We need to enter the trade as soon as the price moves above the candle 1 and 2's body and should not wait for the 3rd or 4th candle to close. Ignore wicks.
I have restricted it to 4 candles and that is all that is needed. More than that is a longer sideways market.
I call it the +-+ or GRG pattern.
Stop loss can be candle 2's mid for safe traders (that includes me) or candle 2's body low for risky traders.
Back testing suggests that body low will be useless and result in more points in loss because for the bigger move this point will not be touched, so why not get out faster.
Downside/Red signal
Say candle 1 is Red, which means sellers stepped in, then candle 2 is Green or a Doji, that means buyers took the price up. Then if candle 3 is forming to be Red and breaks the closing of the 1st candle and opening of the 2nd candle then a Red arrow will appear and that is the place where you want to take your trade.
Sometimes candle 3 falls short but candle 4 breaks candle 1's closing and candle 2's opening price. We can enter on candle 4.
We need to enter the trade as soon as the price moves below the candle 1 and 2's body and should not wait for the 3rd or 4th candle to close.
I have restricted it to 4 candles and that is all that is needed. More than that is a longer sideways market.
I call it the -+- or RGR pattern.
Stop loss can be candle 2's mid for safe traders ( that includes me) or candle 2's body high for risky traders.
Back testing suggests that body high will be useless and result in more points in loss because for the bigger move this point will not be touched, so why not get out faster.
Important Settings
You can enable or disable the 4th candle signal to avoid the noise, but at times I have noticed that the 4th candle gives a very strong signal or I can say that the strong signal falls on the 4th candle. This is mostly a coincidence.
You can also configure how many previous bars should the signal be generated for. 10 to 30 is good enough. To backtest increase it to 2000 or 5000 for example.
Rest are self explanatory.
Pointers
If after taking the trade, the next candle moves in your direction and closes strong bullish or bearish, then move SL to break even and after that you can trail it.
If a upside trade hits SL and immediately a down side trade signal is generated on the next candle then take it. Vice versa is true.
Trades need to be taken on previous 2 candle's body high or low combined and not the wicks.
The most losses a trader takes is on a sideways day and because in our strategy the stop loss is so small that even on a sideways day we'll get out with a little profit or worst break even.
Hold targets for longer targets and don't panic.
If last 3-4 days have been sideways then there is a good probability that day will be trending so we can hold our trade for longer targets. Target to hold the trade for whole day and not exit till the day closes.
In general avoid trading in the middle of the day for index and stocks. Divide the day into 3 parts and avoid the middle.
Use Support/Resistance, 10, 20, 50, 200 EMA/SMA, Gaps, Whole/Round numbers(very imp) for identifying targets.
Trail your SL.
For indexes I would use 5 min and 15 min timeframe.
For commodities and crypto we can use higher timeframe as well. Look for signals during volatile time durations and avoid trading the whole day. Signal usually gives good targets on those times.
If a GRG or RGR pattern appears on a daily timeframe then this is our time to go big.
Minimum Risk to Reward should be 1:2 and for longer targets can be 1:4 to 1:10.
Trade with small lot size. Money management will happen automatically.
With small lot size and correct Risk-Re ward we can be very profitable. Don't trade with big lot size.
Stay in the market for longer and collect points not money.
Very imp - Watch market and learn to generate a market view.
Very imp - Only 4 candles are needed in trading - strong bullish, strong bearish, hammer, inverse hammer and doji.
Go big on bearish days for option traders. Puts are better bought and Calls are better sold.
Cluster of green signals can lead to bigger move on the upside and vice versa for red signals.
Most of this is what I learned from successful traders (from the top 2%) only the indicator is mine.
Alt buy signal 1H Entry + 4H Confirm (MACD + Stoch RSI + HMA)This indicator is a multi-timeframe (MTF) analysis tool designed for the ALT trading , capturing entry signals on the 1-hour (1H) timeframe and confirming trends on the 4-hour (4H) timeframe. It combines MACD, Stoch RSI, and Hull Moving Average (HMA) to identify precise buy opportunities, particularly at reversal points after a downtrend or during trend shifts. It visually marks both past and current BUY signals for easy reference.
Key Features:
1H Entry Signal (Early Ping): Triggers on a MACD golden cross (below 0) combined with a Stoch RSI oversold cross (below 20), offering an initial buy opportunity.
4H Trend Confirmation (Entry Ready): Validates the trend with a 4H MACD histogram rising (in negative territory) or a golden cross, plus a Stoch RSI turn-up (above 30).
Past BUY Display: Labels past data points where these conditions were met as "1H BUY" or "FULL BUY," facilitating backtesting.
HMA Filter: Optional HMA(16) to confirm price breakouts, enhancing trend validation.
Purpose: Ideal for short-term scalping and swing trading. Supports a two-step strategy: initial partial entry on 1H signals, followed by additional entry on 4H confirmation.
Usage Instructions
Installation: Add the indicator to an IMX/USDT 1H chart on TradingView.
Signal Interpretation:
lime "1H BUY": 1H conditions met, consider initial entry (stop-loss: 3-5% below recent low).
green "FULL BUY": 1H+4H conditions met, confirm trend for additional entry (take-profit: 10% below recent swing high).
Customization: Adjust TF (1H/4H), MACD/Stoch RSI parameters, and HMA usage via the input settings.
Alert Setup: Enable alerts for "ENTRY READY" (1H+4H) or "EARLY PING" (1H only) conditions.
Advantages
Accuracy: Reduces false signals by combining MACD golden cross below 0 with Stoch RSI oversold conditions.
Dual Confirmation: 1H for quick timing and 4H for trend validation, improving risk management.
Visualization: Past BUY points enable easy backtesting and pattern recognition.
Flexibility: 4H confirmation mode adjustable (histogram rise or golden cross).
Limitations
Timeframe Dependency: Optimized for 1H charts; may not work on other timeframes.
Market Conditions: Potential whipsaws in sideways markets; additional filters (e.g., RSI > 50) recommended.
Manual Management: Stop-loss and take-profit require user discretion.
Foxbrady D/G CrossFoxbrady D/G Cross - Golden Cross & Death Cross Indicator**
A clean and simple indicator that identifies Golden Cross and Death Cross events using the classic 50-day and 200-day simple moving averages.
Features:
- Blue line: 50-day SMA (fast moving average)
- Red line: 200-day SMA (slow moving average)
- Green "GC" label appears at the exact crossover point when a Golden Cross occurs (bullish signal)
- Red "DC" label appears at the exact crossover point when a Death Cross occurs (bearish signal)
- Built-in alert conditions for both events
- Customizable MA periods to suit your trading style
How to Use:
The Golden Cross (50 MA crossing above 200 MA) is traditionally viewed as a bullish long-term signal, while the Death Cross (50 MA crossing below 200 MA) is considered a bearish indicator. This indicator makes it easy to spot these events historically and receive alerts when they occur in real-time.
Perfect for swing traders and long-term investors looking to identify major trend changes.
3MA/EMA Alerts指标名称（中文/英文）
中文名：多均线趋势指标（带上穿与金叉提醒）
英文名：Multi MA/EMA Trend Indicator (with Price & Golden Cross Alerts)
指标功能介绍（中文）
多均线趋势指标（带上穿与金叉提醒） 是一个可自定义的均线工具，适用于趋势分析和交易信号提醒。
核心功能：
多均线显示
默认显示 EMA20，EMA80/200 可选择显示
每条均线可独立选择 EMA 或 SMA
自定义颜色和线宽
价格上穿均线提醒
当价格向上突破任意开启的均线时触发提醒
可用于捕捉短线趋势启动点
金叉提醒
当短期均线向上穿过中长期均线时触发提醒
可用于捕捉潜在的趋势反转或加速
中文 UI
参数和提醒信息均为中文，便于快速理解和使用
适用场景
趋势确认
趋势反转捕捉
短线入场和长期持仓参考
Indicator Description (English)
Multi MA/EMA Trend Indicator (with Price & Golden Cross Alerts) is a customizable moving average tool for trend analysis and trading alerts.
Key Features:
Multiple Moving Averages
Default display: EMA20; EMA80/200 optional
Each MA can be set as EMA or SMA individually
Customizable colors and line widths
Price Cross Alerts
Alerts when price crosses above any active MA
Helps identify short-term trend initiation points
Golden Cross Alerts
Alerts when a short-term MA crosses above a mid/long-term MA
Useful for detecting trend acceleration or reversal signals
User-Friendly Interface
Parameters and alerts are labeled in Chinese (can be translated)
Applications
Trend confirmation
Trend reversal detection
Short-term entries and long-term position guidance
MA Pack + Cross Signals (Short vs Long)Overview
A flexible moving average pack that lets you switch between short-term trend detection and long-term trend confirmation .
Short-term mode: plots 5, 10, 20, and 50 MAs with early crossovers (10/50, 20/50).
Long-term mode: plots 50, 100, 200 MAs with Golden Cross and Death Cross signals.
Choice of SMA or EMA .
Alerts included for all crossovers.
Why Use It
Catch early trend shifts in short-term mode.
Confirm institutional trend levels in long-term mode.
Visual signals (triangles + labels) make spotting setups easy.
Alert-ready for automated trade monitoring.
Usage
Add to chart.
In settings, choose Short-term or Long-term .
Watch for markers:
Green triangles = bullish cross
Red triangles = bearish cross
Green label = Golden Cross
Red label = Death Cross
Optional: enable alerts for notifications.
Small Business Economic Conditions - Statistical Analysis ModelThe Small Business Economic Conditions Statistical Analysis Model (SBO-SAM) represents an econometric approach to measuring and analyzing the economic health of small business enterprises through multi-dimensional factor analysis and statistical methodologies. This indicator synthesizes eight fundamental economic components into a composite index that provides real-time assessment of small business operating conditions with statistical rigor. The model employs Z-score standardization, variance-weighted aggregation, higher-order moment analysis, and regime-switching detection to deliver comprehensive insights into small business economic conditions with statistical confidence intervals and multi-language accessibility.
1. Introduction and Theoretical Foundation
The development of quantitative models for assessing small business economic conditions has gained significant importance in contemporary financial analysis, particularly given the critical role small enterprises play in economic development and employment generation. Small businesses, typically defined as enterprises with fewer than 500 employees according to the U.S. Small Business Administration, constitute approximately 99.9% of all businesses in the United States and employ nearly half of the private workforce (U.S. Small Business Administration, 2024).
The theoretical framework underlying the SBO-SAM model draws extensively from established academic research in small business economics and quantitative finance. The foundational understanding of key drivers affecting small business performance builds upon the seminal work of Dunkelberg and Wade (2023) in their analysis of small business economic trends through the National Federation of Independent Business (NFIB) Small Business Economic Trends survey. Their research established the critical importance of optimism, hiring plans, capital expenditure intentions, and credit availability as primary determinants of small business performance.
The model incorporates insights from Federal Reserve Board research, particularly the Senior Loan Officer Opinion Survey (Federal Reserve Board, 2024), which demonstrates the critical importance of credit market conditions in small business operations. This research consistently shows that small businesses face disproportionate challenges during periods of credit tightening, as they typically lack access to capital markets and rely heavily on bank financing.
The statistical methodology employed in this model follows the econometric principles established by Hamilton (1989) in his work on regime-switching models and time series analysis. Hamilton's framework provides the theoretical foundation for identifying different economic regimes and understanding how economic relationships may vary across different market conditions. The variance-weighted aggregation technique draws from modern portfolio theory as developed by Markowitz (1952) and later refined by Sharpe (1964), applying these concepts to economic indicator construction rather than traditional asset allocation.
Additional theoretical support comes from the work of Engle and Granger (1987) on cointegration analysis, which provides the statistical framework for combining multiple time series while maintaining long-term equilibrium relationships. The model also incorporates insights from behavioral economics research by Kahneman and Tversky (1979) on prospect theory, recognizing that small business decision-making may exhibit systematic biases that affect economic outcomes.
2. Model Architecture and Component Structure
The SBO-SAM model employs eight orthogonalized economic factors that collectively capture the multifaceted nature of small business operating conditions. Each component is normalized using Z-score standardization with a rolling 252-day window, representing approximately one business year of trading data. This approach ensures statistical consistency across different market regimes and economic cycles, following the methodology established by Tsay (2010) in his treatment of financial time series analysis.
2.1 Small Cap Relative Performance Component
The first component measures the performance of the Russell 2000 index relative to the S&P 500, capturing the market-based assessment of small business equity valuations. This component reflects investor sentiment toward smaller enterprises and provides a forward-looking perspective on small business prospects. The theoretical justification for this component stems from the efficient market hypothesis as formulated by Fama (1970), which suggests that stock prices incorporate all available information about future prospects.
The calculation employs a 20-day rate of change with exponential smoothing to reduce noise while preserving signal integrity. The mathematical formulation is:
Small_Cap_Performance = (Russell_2000_t / S&P_500_t) / (Russell_2000_{t-20} / S&P_500_{t-20}) - 1
This relative performance measure eliminates market-wide effects and isolates the specific performance differential between small and large capitalization stocks, providing a pure measure of small business market sentiment.
2.2 Credit Market Conditions Component
Credit Market Conditions constitute the second component, incorporating commercial lending volumes and credit spread dynamics. This factor recognizes that small businesses are particularly sensitive to credit availability and borrowing costs, as established in numerous Federal Reserve studies (Bernanke and Gertler, 1995). Small businesses typically face higher borrowing costs and more stringent lending standards compared to larger enterprises, making credit conditions a critical determinant of their operating environment.
The model calculates credit spreads using high-yield bond ETFs relative to Treasury securities, providing a market-based measure of credit risk premiums that directly affect small business borrowing costs. The component also incorporates commercial and industrial loan growth data from the Federal Reserve's H.8 statistical release, which provides direct evidence of lending activity to businesses.
The mathematical specification combines these elements as:
Credit_Conditions = α₁ × (HYG_t / TLT_t) + α₂ × C&I_Loan_Growth_t
where HYG represents high-yield corporate bond ETF prices, TLT represents long-term Treasury ETF prices, and C&I_Loan_Growth represents the rate of change in commercial and industrial loans outstanding.
2.3 Labor Market Dynamics Component
The Labor Market Dynamics component captures employment cost pressures and labor availability metrics through the relationship between job openings and unemployment claims. This factor acknowledges that labor market tightness significantly impacts small business operations, as these enterprises typically have less flexibility in wage negotiations and face greater challenges in attracting and retaining talent during periods of low unemployment.
The theoretical foundation for this component draws from search and matching theory as developed by Mortensen and Pissarides (1994), which explains how labor market frictions affect employment dynamics. Small businesses often face higher search costs and longer hiring processes, making them particularly sensitive to labor market conditions.
The component is calculated as:
Labor_Tightness = Job_Openings_t / (Unemployment_Claims_t × 52)
This ratio provides a measure of labor market tightness, with higher values indicating greater difficulty in finding workers and potential wage pressures.
2.4 Consumer Demand Strength Component
Consumer Demand Strength represents the fourth component, combining consumer sentiment data with retail sales growth rates. Small businesses are disproportionately affected by consumer spending patterns, making this component crucial for assessing their operating environment. The theoretical justification comes from the permanent income hypothesis developed by Friedman (1957), which explains how consumer spending responds to both current conditions and future expectations.
The model weights consumer confidence and actual spending data to provide both forward-looking sentiment and contemporaneous demand indicators. The specification is:
Demand_Strength = β₁ × Consumer_Sentiment_t + β₂ × Retail_Sales_Growth_t
where β₁ and β₂ are determined through principal component analysis to maximize the explanatory power of the combined measure.
2.5 Input Cost Pressures Component
Input Cost Pressures form the fifth component, utilizing producer price index data to capture inflationary pressures on small business operations. This component is inversely weighted, recognizing that rising input costs negatively impact small business profitability and operating conditions. Small businesses typically have limited pricing power and face challenges in passing through cost increases to customers, making them particularly vulnerable to input cost inflation.
The theoretical foundation draws from cost-push inflation theory as described by Gordon (1988), which explains how supply-side price pressures affect business operations. The model employs a 90-day rate of change to capture medium-term cost trends while filtering out short-term volatility:
Cost_Pressure = -1 × (PPI_t / PPI_{t-90} - 1)
The negative weighting reflects the inverse relationship between input costs and business conditions.
2.6 Monetary Policy Impact Component
Monetary Policy Impact represents the sixth component, incorporating federal funds rates and yield curve dynamics. Small businesses are particularly sensitive to interest rate changes due to their higher reliance on variable-rate financing and limited access to capital markets. The theoretical foundation comes from monetary transmission mechanism theory as developed by Bernanke and Blinder (1992), which explains how monetary policy affects different segments of the economy.
The model calculates the absolute deviation of federal funds rates from a neutral 2% level, recognizing that both extremely low and high rates can create operational challenges for small enterprises. The yield curve component captures the shape of the term structure, which affects both borrowing costs and economic expectations:
Monetary_Impact = γ₁ × |Fed_Funds_Rate_t - 2.0| + γ₂ × (10Y_Yield_t - 2Y_Yield_t)
2.7 Currency Valuation Effects Component
Currency Valuation Effects constitute the seventh component, measuring the impact of US Dollar strength on small business competitiveness. A stronger dollar can benefit businesses with significant import components while disadvantaging exporters. The model employs Dollar Index volatility as a proxy for currency-related uncertainty that affects small business planning and operations.
The theoretical foundation draws from international trade theory and the work of Krugman (1987) on exchange rate effects on different business segments. Small businesses often lack hedging capabilities, making them more vulnerable to currency fluctuations:
Currency_Impact = -1 × DXY_Volatility_t
2.8 Regional Banking Health Component
The eighth and final component, Regional Banking Health, assesses the relative performance of regional banks compared to large financial institutions. Regional banks traditionally serve as primary lenders to small businesses, making their health a critical factor in small business credit availability and overall operating conditions.
This component draws from the literature on relationship banking as developed by Boot (2000), which demonstrates the importance of bank-borrower relationships, particularly for small enterprises. The calculation compares regional bank performance to large financial institutions:
Banking_Health = (Regional_Banks_Index_t / Large_Banks_Index_t) - 1
3. Statistical Methodology and Advanced Analytics
The model employs statistical techniques to ensure robustness and reliability. Z-score normalization is applied to each component using rolling 252-day windows, providing standardized measures that remain consistent across different time periods and market conditions. This approach follows the methodology established by Engle and Granger (1987) in their cointegration analysis framework.
3.1 Variance-Weighted Aggregation
The composite index calculation utilizes variance-weighted aggregation, where component weights are determined by the inverse of their historical variance. This approach, derived from modern portfolio theory, ensures that more stable components receive higher weights while reducing the impact of highly volatile factors. The mathematical formulation follows the principle that optimal weights are inversely proportional to variance, maximizing the signal-to-noise ratio of the composite indicator.
The weight for component i is calculated as:
w_i = (1/σᵢ²) / Σⱼ(1/σⱼ²)
where σᵢ² represents the variance of component i over the lookback period.
3.2 Higher-Order Moment Analysis
Higher-order moment analysis extends beyond traditional mean and variance calculations to include skewness and kurtosis measurements. Skewness provides insight into the asymmetry of the sentiment distribution, while kurtosis measures the tail behavior and potential for extreme events. These metrics offer valuable information about the underlying distribution characteristics and potential regime changes.
Skewness is calculated as:
Skewness = E / σ³
Kurtosis is calculated as:
Kurtosis = E / σ⁴ - 3
where μ represents the mean and σ represents the standard deviation of the distribution.
3.3 Regime-Switching Detection
The model incorporates regime-switching detection capabilities based on the Hamilton (1989) framework. This allows for identification of different economic regimes characterized by distinct statistical properties. The regime classification employs percentile-based thresholds:
- Regime 3 (Very High): Percentile rank > 80
- Regime 2 (High): Percentile rank 60-80
- Regime 1 (Moderate High): Percentile rank 50-60
- Regime 0 (Neutral): Percentile rank 40-50
- Regime -1 (Moderate Low): Percentile rank 30-40
- Regime -2 (Low): Percentile rank 20-30
- Regime -3 (Very Low): Percentile rank < 20
3.4 Information Theory Applications
The model incorporates information theory concepts, specifically Shannon entropy measurement, to assess the information content of the sentiment distribution. Shannon entropy, as developed by Shannon (1948), provides a measure of the uncertainty or information content in a probability distribution:
H(X) = -Σᵢ p(xᵢ) log₂ p(xᵢ)
Higher entropy values indicate greater unpredictability and information content in the sentiment series.
3.5 Long-Term Memory Analysis
The Hurst exponent calculation provides insight into the long-term memory characteristics of the sentiment series. Originally developed by Hurst (1951) for analyzing Nile River flow patterns, this measure has found extensive application in financial time series analysis. The Hurst exponent H is calculated using the rescaled range statistic:
H = log(R/S) / log(T)
where R/S represents the rescaled range and T represents the time period. Values of H > 0.5 indicate long-term positive autocorrelation (persistence), while H < 0.5 indicates mean-reverting behavior.
3.6 Structural Break Detection
The model employs Chow test approximation for structural break detection, based on the methodology developed by Chow (1960). This technique identifies potential structural changes in the underlying relationships by comparing the stability of regression parameters across different time periods:
Chow_Statistic = (RSS_restricted - RSS_unrestricted) / RSS_unrestricted × (n-2k)/k
where RSS represents residual sum of squares, n represents sample size, and k represents the number of parameters.
4. Implementation Parameters and Configuration
4.1 Language Selection Parameters
The model provides comprehensive multi-language support across five languages: English, German (Deutsch), Spanish (Español), French (Français), and Japanese (日本語). This feature enhances accessibility for international users and ensures cultural appropriateness in terminology usage. The language selection affects all internal displays, statistical classifications, and alert messages while maintaining consistency in underlying calculations.
4.2 Model Configuration Parameters
Calculation Method: Users can select from four aggregation methodologies:
- Equal-Weighted: All components receive identical weights
- Variance-Weighted: Components weighted inversely to their historical variance
- Principal Component: Weights determined through principal component analysis
- Dynamic: Adaptive weighting based on recent performance
Sector Specification: The model allows for sector-specific calibration:
- General: Broad-based small business assessment
- Retail: Emphasis on consumer demand and seasonal factors
- Manufacturing: Enhanced weighting of input costs and currency effects
- Services: Focus on labor market dynamics and consumer demand
- Construction: Emphasis on credit conditions and monetary policy
Lookback Period: Statistical analysis window ranging from 126 to 504 trading days, with 252 days (one business year) as the optimal default based on academic research.
Smoothing Period: Exponential moving average period from 1 to 21 days, with 5 days providing optimal noise reduction while preserving signal integrity.
4.3 Statistical Threshold Parameters
Upper Statistical Boundary: Configurable threshold between 60-80 (default 70) representing the upper significance level for regime classification.
Lower Statistical Boundary: Configurable threshold between 20-40 (default 30) representing the lower significance level for regime classification.
Statistical Significance Level (α): Alpha level for statistical tests, configurable between 0.01-0.10 with 0.05 as the standard academic default.
4.4 Display and Visualization Parameters
Color Theme Selection: Eight professional color schemes optimized for different user preferences and accessibility requirements:
- Gold: Traditional financial industry colors
- EdgeTools: Professional blue-gray scheme
- Behavioral: Psychology-based color mapping
- Quant: Value-based quantitative color scheme
- Ocean: Blue-green maritime theme
- Fire: Warm red-orange theme
- Matrix: Green-black technology theme
- Arctic: Cool blue-white theme
Dark Mode Optimization: Automatic color adjustment for dark chart backgrounds, ensuring optimal readability across different viewing conditions.
Line Width Configuration: Main index line thickness adjustable from 1-5 pixels for optimal visibility.
Background Intensity: Transparency control for statistical regime backgrounds, adjustable from 90-99% for subtle visual enhancement without distraction.
4.5 Alert System Configuration
Alert Frequency Options: Three frequency settings to match different trading styles:
- Once Per Bar: Single alert per bar formation
- Once Per Bar Close: Alert only on confirmed bar close
- All: Continuous alerts for real-time monitoring
Statistical Extreme Alerts: Notifications when the index reaches 99% confidence levels (Z-score > 2.576 or < -2.576).
Regime Transition Alerts: Notifications when statistical boundaries are crossed, indicating potential regime changes.
5. Practical Application and Interpretation Guidelines
5.1 Index Interpretation Framework
The SBO-SAM index operates on a 0-100 scale with statistical normalization ensuring consistent interpretation across different time periods and market conditions. Values above 70 indicate statistically elevated small business conditions, suggesting favorable operating environment with potential for expansion and growth. Values below 30 indicate statistically reduced conditions, suggesting challenging operating environment with potential constraints on business activity.
The median reference line at 50 represents the long-term equilibrium level, with deviations providing insight into cyclical conditions relative to historical norms. The statistical confidence bands at 95% levels (approximately ±2 standard deviations) help identify when conditions reach statistically significant extremes.
5.2 Regime Classification System
The model employs a seven-level regime classification system based on percentile rankings:
Very High Regime (P80+): Exceptional small business conditions, typically associated with strong economic growth, easy credit availability, and favorable regulatory environment. Historical analysis suggests these periods often precede economic peaks and may warrant caution regarding sustainability.
High Regime (P60-80): Above-average conditions supporting business expansion and investment. These periods typically feature moderate growth, stable credit conditions, and positive consumer sentiment.
Moderate High Regime (P50-60): Slightly above-normal conditions with mixed signals. Careful monitoring of individual components helps identify emerging trends.
Neutral Regime (P40-50): Balanced conditions near long-term equilibrium. These periods often represent transition phases between different economic cycles.
Moderate Low Regime (P30-40): Slightly below-normal conditions with emerging headwinds. Early warning signals may appear in credit conditions or consumer demand.
Low Regime (P20-30): Below-average conditions suggesting challenging operating environment. Businesses may face constraints on growth and expansion.
Very Low Regime (P0-20): Severely constrained conditions, typically associated with economic recessions or financial crises. These periods often present opportunities for contrarian positioning.
5.3 Component Analysis and Diagnostics
Individual component analysis provides valuable diagnostic information about the underlying drivers of overall conditions. Divergences between components can signal emerging trends or structural changes in the economy.
Credit-Labor Divergence: When credit conditions improve while labor markets tighten, this may indicate early-stage economic acceleration with potential wage pressures.
Demand-Cost Divergence: Strong consumer demand coupled with rising input costs suggests inflationary pressures that may constrain small business margins.
Market-Fundamental Divergence: Disconnection between small-cap equity performance and fundamental conditions may indicate market inefficiencies or changing investor sentiment.
5.4 Temporal Analysis and Trend Identification
The model provides multiple temporal perspectives through momentum analysis, rate of change calculations, and trend decomposition. The 20-day momentum indicator helps identify short-term directional changes, while the Hodrick-Prescott filter approximation separates cyclical components from long-term trends.
Acceleration analysis through second-order momentum calculations provides early warning signals for potential trend reversals. Positive acceleration during declining conditions may indicate approaching inflection points, while negative acceleration during improving conditions may suggest momentum loss.
5.5 Statistical Confidence and Uncertainty Quantification
The model provides comprehensive uncertainty quantification through confidence intervals, volatility measures, and regime stability analysis. The 95% confidence bands help users understand the statistical significance of current readings and identify when conditions reach historically extreme levels.
Volatility analysis provides insight into the stability of current conditions, with higher volatility indicating greater uncertainty and potential for rapid changes. The regime stability measure, calculated as the inverse of volatility, helps assess the sustainability of current conditions.
6. Risk Management and Limitations
6.1 Model Limitations and Assumptions
The SBO-SAM model operates under several important assumptions that users must understand for proper interpretation. The model assumes that historical relationships between economic variables remain stable over time, though the regime-switching framework helps accommodate some structural changes. The 252-day lookback period provides reasonable statistical power while maintaining sensitivity to changing conditions, but may not capture longer-term structural shifts.
The model's reliance on publicly available economic data introduces inherent lags in some components, particularly those based on government statistics. Users should consider these timing differences when interpreting real-time conditions. Additionally, the model's focus on quantitative factors may not fully capture qualitative factors such as regulatory changes, geopolitical events, or technological disruptions that could significantly impact small business conditions.
The model's timeframe restrictions ensure statistical validity by preventing application to intraday periods where the underlying economic relationships may be distorted by market microstructure effects, trading noise, and temporal misalignment with the fundamental data sources. Users must utilize daily or longer timeframes to ensure the model's statistical foundations remain valid and interpretable.
6.2 Data Quality and Reliability Considerations
The model's accuracy depends heavily on the quality and availability of underlying economic data. Market-based components such as equity indices and bond prices provide real-time information but may be subject to short-term volatility unrelated to fundamental conditions. Economic statistics provide more stable fundamental information but may be subject to revisions and reporting delays.
Users should be aware that extreme market conditions may temporarily distort some components, particularly those based on financial market data. The model's statistical normalization helps mitigate these effects, but users should exercise additional caution during periods of market stress or unusual volatility.
6.3 Interpretation Caveats and Best Practices
The SBO-SAM model provides statistical analysis and should not be interpreted as investment advice or predictive forecasting. The model's output represents an assessment of current conditions based on historical relationships and may not accurately predict future outcomes. Users should combine the model's insights with other analytical tools and fundamental analysis for comprehensive decision-making.
The model's regime classifications are based on historical percentile rankings and may not fully capture the unique characteristics of current economic conditions. Users should consider the broader economic context and potential structural changes when interpreting regime classifications.
7. Academic References and Bibliography
Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921.
Bernanke, B. S., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48.
Boot, A. W. A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7-25.
Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591-605.
Dunkelberg, W. C., & Wade, H. (2023). NFIB Small Business Economic Trends. National Federation of Independent Business Research Foundation, Washington, D.C.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.
Federal Reserve Board. (2024). Senior Loan Officer Opinion Survey on Bank Lending Practices. Board of Governors of the Federal Reserve System, Washington, D.C.
Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press, Princeton, NJ.
Gordon, R. J. (1988). The Role of Wages in the Inflation Process. American Economic Review, 78(2), 276-283.
Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Hurst, H. E. (1951). Long-term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770-799.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
Krugman, P. (1987). Pricing to Market When the Exchange Rate Changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49-70). MIT Press, Cambridge, MA.
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91.
Mortensen, D. T., & Pissarides, C. A. (1994). Job Creation and Job Destruction in the Theory of Unemployment. Review of Economic Studies, 61(3), 397-415.
Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423.
Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442.
Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons, Hoboken, NJ.
U.S. Small Business Administration. (2024). Small Business Profile. Office of Advocacy, Washington, D.C.
8. Technical Implementation Notes
The SBO-SAM model is implemented in Pine Script version 6 for the TradingView platform, ensuring compatibility with modern charting and analysis tools. The implementation follows best practices for financial indicator development, including proper error handling, data validation, and performance optimization.
The model includes comprehensive timeframe validation to ensure statistical accuracy and reliability. The indicator operates exclusively on daily (1D) timeframes or higher, including weekly (1W), monthly (1M), and longer periods. This restriction ensures that the statistical analysis maintains appropriate temporal resolution for the underlying economic data sources, which are primarily reported on daily or longer intervals.
When users attempt to apply the model to intraday timeframes (such as 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, 6-hour, 8-hour, or 12-hour charts), the system displays a comprehensive error message in the user's selected language and prevents execution. This safeguard protects users from potentially misleading results that could occur when applying daily-based economic analysis to shorter timeframes where the underlying data relationships may not hold.
The model's statistical calculations are performed using vectorized operations where possible to ensure computational efficiency. The multi-language support system employs Unicode character encoding to ensure proper display of international characters across different platforms and devices.
The alert system utilizes TradingView's native alert functionality, providing users with flexible notification options including email, SMS, and webhook integrations. The alert messages include comprehensive statistical information to support informed decision-making.
The model's visualization system employs professional color schemes designed for optimal readability across different chart backgrounds and display devices. The system includes dynamic color transitions based on momentum and volatility, professional glow effects for enhanced line visibility, and transparency controls that allow users to customize the visual intensity to match their preferences and analytical requirements. The clean confidence band implementation provides clear statistical boundaries without visual distractions, maintaining focus on the analytical content.
DynamoSent DynamoSent Pro+ — Professional Listing (Preview)
— Adaptive Macro Sentiment (v6)
— Export, Adaptive Lookback, Confidence, Boxes, Heatmap + Dynamic OB/OS
Preview / Experimental build. I’m actively refining this tool—your feedback is gold.
If you spot edge cases, want new presets, or have market-specific ideas, please comment or DM me on TradingView.
⸻
What it is
DynamoSent Pro+ is an adaptive, non-repainting macro sentiment engine that compresses VIX, DXY and a price-based activity proxy (e.g., SPX/sector ETF/your symbol) into a 0–100 sentiment line. It scales context by volatility (ATR%) and can self-calibrate with rolling quantile OB/OS. On top of that, it adds confidence scoring, a plain-English Context Coach, MTF agreement, exportable sentiment for other indicators, and a clean Light/Dark UI.
Why it’s different
• Adaptive lookback tracks regime changes: when volatility rises, we lengthen context; when it falls, we shorten—less whipsaw, more relevance.
• Dynamic OB/OS (quantiles) self-calibrates to each instrument’s distribution—no arbitrary 30/70 lines.
• MTF agreement + Confidence gate reduce false positives by highlighting alignment across timeframes.
• Exportable output: hidden plot “DynamoSent Export” can be selected as input.source in your other Pine scripts.
• Non-repainting rigor: all request.security() calls use lookahead_off + gaps_on; signals wait for bar close.
Key visuals
• Sentiment line (0–100), OB/OS zones (static or dynamic), optional TF1/TF2 overlays.
• Regime boxes (Overbought / Oversold / Neutral) that update live without repaint.
• Info Panel with confidence heat, regime, trend arrow, MTF readout, and Coach sentence.
• Session heat (Asia/EU/US) to match intraday behavior.
• Light/Dark theme switch in Inputs (auto-contrasted labels & headers).
⸻
How to use (examples & recipes)
1) EURUSD (swing / intraday blend)
• Preset: EURUSD 1H Swing
• Chart: 1H; TF1=1H, TF2=4H (default).
• Proxies: Defaults work (VIX=D, DXY=60, Proxy=D).
• Dynamic OB/OS: ON at 20/80; Confidence ≥ 55–60.
• Playbook:
• When sentiment crosses above 50 + margin with Δ ≥ signalK and MTF agreement ≥ 0.5, treat as trend breakout.
• In Oversold with rising Coach & TF agreement, take fade longs back toward mid-range.
• Alerts: Enable Breakout Long/Short and Fade; keep cooldown 8–12 bars.
2) SPY (daytrading)
• Preset: SPY 15m Daytrade; Chart: 15m.
• VIX (D) matters more; preset weights already favor it.
• Start with static 30/70; later try dynamic 25/75 for adaptive thresholds.
• Use Coach: in US session, when it says “Overbought + MTF agree → sell rallies / chase breakouts”, lean momentum-continuation after pullbacks.
3) BTCUSD (crypto, 24/7)
• Preset: BTCUSD 1H; Chart: 1H.
• DXY and BTC.D inform macro tone; keep Carry-forward ON to bridge sparse ticks.
• Prefer Dynamic OB/OS (15/85) for wider swings.
• Fade signals on weekend chop; Breakout when Confidence > 60 and MTF ≥ 1.0.
4) XAUUSD (gold, macro blend)
• Preset: XAUUSD 4H; Chart: 4H.
• Weights tilt to DXY and US10Y (handled by preset).
• Coach + MTF helps separate trend legs from news pops.
⸻
Best practices
• Theme: Switch Light/Dark in Inputs; the panel adapts contrast automatically.
• Export: In another script → Source → DynamoSent Pro+ → DynamoSent Export. Build your own filters/strategies atop the same sentiment.
• Dynamic vs Static OB/OS:
• Static 30/70: fast, universal baseline.
• Dynamic (quantiles): instrument-aware; use 20/80 (default) or 15/85 for choppy markets.
• Confidence gate: Start at 50–60% to filter noise; raise when you want only A-grade setups.
• Adaptive Lookback: Keep ON. For ultra-liquid indices, you can switch it OFF and set a fixed lookback.
⸻
Non-repainting & safety notes
• All request.security() calls use lookahead=barmerge.lookahead_off and gaps=barmerge.gaps_on.
• No forward references; signals & regime flips are confirmed on bar close.
• History-dependent funcs (ta.change, ta.percentile_linear_interpolation, etc.) are computed each bar (not conditionally).
• Adaptive lookback is clamped ≥ 1 to avoid lowest/highest errors.
• Missing-data warning triggers only when all proxies are NA for a streak; carry-forward can bridge small gaps without repaint.
⸻
Known limits & tips
• If a proxy symbol isn’t available on your plan/exchange, you’ll see the NA warning: choose a different symbol via Symbol Search, or keep Carry-forward ON (it defaults to neutral where needed).
• Intraday VIX is sparse—using Daily is intentional.
• Dynamic OB/OS needs enough history (see dynLenFloor). On short histories it gracefully falls back to static levels.
Thanks for trying the preview. Your comments drive the roadmap—presets, new proxies, extra alerts, and integrations.
Double Median SD Bands | MisinkoMasterThe Double Median SD Bands (DMSDB) is a trend-following tool designed to capture market direction in a way that balances responsiveness and smoothness, filtering out excessive noise without introducing heavy lag.
Think of it like a house:
A jail (too restrictive) makes you miss opportunities.
No house at all (too unsafe) leaves you exposed to false signals.
DMSDB acts like a comfortable house with windows—protecting you from the noise while still letting you see what’s happening in the market.
🔎 Methodology
The script works in the following steps:
Standard Deviation (SD) Calculation
Computes the standard deviation of the selected price source (ohlc4 by default).
The user can choose whether to use biased (sample) or unbiased (population) standard deviation.
Raw Bands Construction
Upper Band = source + (SD × multiplier)
Lower Band = source - (SD × multiplier)
The multiplier can be adjusted for tighter or looser bands.
First Median Smoothing
Applies a median filter over half of the length (len/2) to both bands.
This reduces noise without creating excessive lag.
Second Median Smoothing
Applies another median filter over √len to the already smoothed bands.
This produces a balance:
Cutting the length → maintains responsiveness.
Median smoothing → reduces whipsaws.
The combination creates a fast yet clean band system ideal for trend detection.
📈 Trend Logic
The trend is detected based on price crossing the smoothed bands:
Long / Bullish (Purple) → when price crosses above the upper band.
Short / Bearish (Gold) → when price crosses below the lower band.
Neutral → when price remains between the bands.
🎨 Visualization
Upper and lower bands are plotted as colored lines.
The area between the bands is filled with a transparent zone that reflects the current bias:
Purple shading = Bullish zone.
Golden shading = Bearish zone.
This creates a visual tunnel for trend confirmation, helping traders quickly identify whether price action is trending or consolidating.
⚡ Features
Adjustable Length parameter (len) for dynamic control.
Adjustable Band Multiplier for volatility adaptation.
Choice between biased vs. unbiased standard deviation.
Double median smoothing for clarity + responsiveness.
Works well on cryptocurrencies (e.g., BTCUSD) but is flexible enough for stocks, forex, and indices.
✅ Use Cases
Trend Following → Ride trends by staying on the correct side of the bands.
Entry Timing → Use crossovers above/below bands for entry triggers.
Filter for Other Strategies → Can serve as a directional filter to avoid trading against the trend.
⚠️ Limitations & Notes
This is a trend-following tool, so it will perform best in trending conditions.
In sideways or choppy markets, whipsaws may still occur (although smoothing reduces them significantly).
The indicator is not a standalone buy/sell system. For best results, combine with volume, momentum, or higher-timeframe confluence.
All of this makes for a really unique & original tool, as it removes noise but keeps good responsitivity, using methods from many different principles which make for a smooth a very useful tool
Harmonic Super GuppyHarmonic Super Guppy – Harmonic & Golden Ratio Trend Analysis Framework
Overview
Harmonic Super Guppy is a comprehensive trend analysis and visualization tool that evolves the classic Guppy Multiple Moving Average (GMMA) methodology, pioneered by Daryl Guppy to visualize the interaction between short-term trader behavior and long-term investor trends. into a harmonic and phase-based market framework. By combining harmonic weighting, golden ratio phasing, and multiple moving averages, it provides traders with a deep understanding of market structure, momentum, and trend alignment. Fast and slow line groups visually differentiate short-term trader activity from longer-term investor positioning, while adaptive fills and dynamic coloring clearly illustrate trend coherence, expansion, and contraction in real time.
Traditional GMMA focuses primarily on moving average convergence and divergence. Harmonic Super Guppy extends this concept, integrating frequency-aware harmonic analysis and golden ratio modulation, allowing traders to detect subtle cyclical forces and early trend shifts before conventional moving averages would react. This is particularly valuable for traders seeking to identify early trend continuation setups, preemptive breakout entries, and potential trend exhaustion zones. The indicator provides a multi-dimensional view, making it suitable for scalping, intraday trading, swing setups, and even longer-term position strategies.
The visual structure of Harmonic Super Guppy is intentionally designed to convey trend clarity without oversimplification. Fast lines reflect short-term trader sentiment, slow lines capture longer-term investor alignment, and fills highlight compression or expansion. The adaptive color coding emphasizes trend alignment: strong green for bullish alignment, strong red for bearish, and subtle gray tones for indecision. This allows traders to quickly gauge market conditions while preserving the granularity necessary for sophisticated analysis.
How It Works
Harmonic Super Guppy uses a combination of harmonic averaging, golden ratio phasing, and adaptive weighting to generate its signals.
Harmonic Weighting : Each moving average integrates three layers of harmonics:
Primary harmonic captures the dominant cyclical structure of the market.
Secondary harmonic introduces a complementary frequency for oscillatory nuance.
Tertiary harmonic smooths higher-frequency noise while retaining meaningful trend signals.
Golden Ratio Phase : Phases of each harmonic contribution are adjusted using the golden ratio (default φ = 1.618), ensuring alignment with natural market rhythms. This reduces lag and allows traders to detect trend shifts earlier than conventional moving averages.
Adaptive Trend Detection : Fast SMAs are compared against slow SMAs to identify structural trends:
UpTrend : Fast SMA exceeds slow SMA.
DownTrend : Fast SMA falls below slow SMA.
Frequency Scaling : The wave frequency setting allows traders to modulate responsiveness versus smoothing. Higher frequency emphasizes short-term moves, while lower frequency highlights structural trends. This enables adaptation across asset classes with different volatility characteristics.
Through this combination, Harmonic Super Guppy captures micro and macro market cycles, helping traders distinguish between transient noise and genuine trend development. The multi-harmonic approach amplifies meaningful price action while reducing false signals inherent in standard moving averages.
Interpretation
Harmonic Super Guppy provides a multi-dimensional perspective on market dynamics:
Trend Analysis : Alignment of fast and slow lines reveals trend direction and strength. Expanding harmonics indicate momentum building, while contraction signals weakening conditions or potential reversals.
Momentum & Volatility : Rapid expansion of fast lines versus slow lines reflects short-term bullish or bearish pressure. Compression often precedes breakout scenarios or volatility expansion. Traders can quickly gauge trend vigor and potential turning points.
Market Context : The indicator overlays harmonic and structural insights without dictating entry or exit points. It complements order blocks, liquidity zones, oscillators, and other technical frameworks, providing context for informed decision-making.
Phase Divergence Detection : Subtle divergence between harmonic layers (primary, secondary, tertiary) often signals early exhaustion in trends or hidden strength, offering preemptive insight into potential reversals or sustained continuation.
By observing both structural alignment and harmonic expansion/contraction, traders gain a clear sense of when markets are trending with conviction versus when conditions are consolidating or becoming unpredictable. This allows for proactive trade management, rather than reactive responses to lagging indicators.
Strategy Integration
Harmonic Super Guppy adapts to various trading methodologies with clear, actionable guidance.
Trend Following : Enter positions when fast and slow lines are aligned and harmonics are expanding. The broader the alignment, the stronger the confirmation of trend persistence. For example:
A fast line crossover above slow lines with expanding fills confirms momentum-driven continuation.
Traders can use harmonic amplitude as a filter to reduce entries against prevailing trends.
Breakout Trading : Periods of line compression indicate potential volatility expansion. When fast lines diverge from slow lines after compression, this often precedes breakouts. Traders can combine this visual cue with structural supports/resistances or order flow analysis to improve timing and precision.
Exhaustion and Reversals : Divergences between harmonic components, or contraction of fast lines relative to slow lines, highlight weakening trends. This can indicate liquidity exhaustion, trend fatigue, or corrective phases. For example:
A flattening fast line group above a rising slow line can hint at short-term overextension.
Traders may use these signals to tighten stops, take partial profits, or prepare for contrarian setups.
Multi-Timeframe Analysis : Overlay slow lines from higher timeframes on lower timeframe charts to filter noise and trade in alignment with larger market structures. For example:
A daily bullish alignment combined with a 15-minute breakout pattern increases probability of a successful intraday trade.
Conversely, a higher timeframe divergence can warn against taking counter-trend trades in lower timeframes.
Adaptive Trade Management : Harmonic expansion/contraction can guide dynamic risk management:
Stops may be adjusted according to slow line support/resistance or harmonic contraction zones.
Position sizing can be modulated based on harmonic amplitude and compression levels, optimizing risk-reward without rigid rules.
Technical Implementation Details
Harmonic Super Guppy is powered by a multi-layered harmonic and phase calculation engine:
Harmonic Processing : Primary, secondary, and tertiary harmonics are calculated per period to capture multiple market cycles simultaneously. This reduces noise and amplifies meaningful signals.
Golden Ratio Modulation : Phase adjustments based on φ = 1.618 align harmonic contributions with natural market rhythms, smoothing lag and improving predictive value.
Adaptive Trend Scaling : Fast line expansion reflects short-term momentum; slow lines provide structural trend context. Fills adapt dynamically based on alignment intensity and harmonic amplitude.
Multi-Factor Trend Analysis : Trend strength is determined by alignment of fast and slow lines over multiple bars, expansion/contraction of harmonic amplitudes, divergences between primary, secondary, and tertiary harmonics and phase synchronization with golden ratio cycles.
These computations allow the indicator to be highly responsive yet smooth, providing traders with actionable insights in real time without overloading visual complexity.
Optimal Application Parameters
Asset-Specific Guidance:
Forex Majors : Wave frequency 1.0–2.0, φ = 1.618–1.8
Large-Cap Equities : Wave frequency 0.8–1.5, φ = 1.5–1.618
Cryptocurrency : Wave frequency 1.2–3.0, φ = 1.618–2.0
Index Futures : Wave frequency 0.5–1.5, φ = 1.618
Timeframe Optimization:
Scalping (1–5min) : Emphasize fast lines, higher frequency for micro-move capture.
Day Trading (15min–1hr) : Balance fast/slow interactions for trend confirmation.
Swing Trading (4hr–Daily) : Focus on slow lines for structural guidance, fast lines for entry timing.
Position Trading (Daily–Weekly) : Slow lines dominate; harmonics highlight long-term cycles.
Performance Characteristics
High Effectiveness Conditions:
Clear separation between short-term and long-term trends.
Moderate-to-high volatility environments.
Assets with consistent volume and price rhythm.
Reduced Effectiveness:
Flat or extremely low volatility markets.
Erratic assets with frequent gaps or algorithmic dominance.
Ultra-short timeframes (<1min), where noise dominates.
Integration Guidelines
Signal Confirmation : Confirm alignment of fast and slow lines over multiple bars. Expansion of harmonic amplitude signals trend persistence.
Risk Management : Place stops beyond slow line support/resistance. Adjust sizing based on compression/expansion zones.
Advanced Feature Settings :
Frequency tuning for different volatility environments.
Phase analysis to track divergences across harmonics.
Use fills and amplitude patterns as a guide for dynamic trade management.
Multi-timeframe confirmation to filter noise and align with structural trends.
Disclaimer
Harmonic Super Guppy is a trend analysis and visualization tool, not a guaranteed profit system. Optimal performance requires proper wave frequency, golden ratio phase, and line visibility settings per asset and timeframe. Traders should combine the indicator with other technical frameworks and maintain disciplined risk management practices.
RenKagi Fusion: Aura & SMA Clash IndicatorRenKagi Fusion: Aura & SMA Clash Indicator
Welcome to the RenKagi Fusion Indicator – a powerful, customizable tool that blends the strengths of Renko and Kagi charts to provide noise-filtered trend insights, enhanced with visual Aura effects and SMA (Simple Moving Average) crossover signals. Designed for traders seeking a unique edge in trend detection and reversal identification, this indicator combines traditional charting techniques with modern visualizations to help you navigate markets more effectively. Whether you're trading stocks, forex, or crypto, RenKagi Fusion offers a clean, actionable overview of market dynamics.
Key Features
RenKagi Line (Weighted Fusion of Renko and Kagi): The core of the indicator is the RenKagi line, a weighted average of Renko (brick-based trend filtering) and Kagi (reversal-focused line charts). Users can adjust the weight (default: 60% Renko, 40% Kagi) to prioritize stability or sensitivity. This fusion reduces market noise while highlighting key price movements.
Trend Scoring System: Calculates strength scores for Renko, Kagi, and RenKagi (capped at 20 points, converted to percentages). Scores increase with trend continuation and reset on reversals, giving a quantitative measure of momentum.
Aura Effects (Optional): Visual "glow" around lines based on score percentage – higher scores mean more opaque and thicker auras, adding a dynamic layer to trend visualization.
SMA Clash (Crossover Detection): Monitors daily SMA50, SMA100, and SMA200 for golden/death crosses (SMA50 crossing above/below longer SMAs) and RenKagi-SMA crossovers. These are displayed in a persistent info table for quick reference.
Customizable Visuals: Toggle lines, boxes, shapes, auras, and labels. Background coloring based on selected source (Renko, Kagi, or RenKagi) for intuitive trend bias.
Info Table: A configurable table (position and colors adjustable) summarizing scores, directions, cross states, brick size (with type), Kagi reversal (with type), and weights. No clutter – all in one place.
Alert Conditions: Built-in alerts for direction changes (Renko, Kagi, RenKagi), SMA crossovers, and golden/death crosses – perfect for real-time notifications.
How It Works
Renko Logic: Builds bricks based on user-selected type (Traditional fixed size, ATR dynamic, or Percentage). Scores build as trends persist, resetting on reversals.
Kagi Logic: Line reverses on thresholds (Traditional, ATR, or Percentage), scoring continuous moves.
RenKagi Calculation: Weighted average: (renkoPrice * renkoWeight + kagiLine * (100 - renkoWeight)) / 100. Score is a blend of individual scores.
SMA Integration: Daily timeframe SMAs for reliable long-term signals. Crossovers trigger alerts and update table states persistently until reversed.
Advantages for Traders
Noise Reduction: By fusing Renko's block structure with Kagi's reversal focus, it filters out minor fluctuations, helping identify strong trends early.
Versatility: Fully customizable – adjust weights, types, and visuals to fit any market or timeframe. Ideal for swing trading, trend following, or scalping.
Visual Clarity: Aura and background coloring provide at-a-glance insights, while the table consolidates data without overwhelming the chart.
Actionable Signals: Golden/Death crosses and direction changes offer clear entry/exit points, backed by alerts for timely execution.
Performance Optimization: Limits on lines/labels/boxes (500 each) ensure smooth operation on large datasets.
Usage Tips
Start with default settings for balanced performance.
Use in higher timeframes for trend confirmation or lower for intraday signals.
Combine with your favorite strategies – e.g., buy on RenKagi upward cross with SMA50 and golden cross confirmation.
Test on historical data to optimize weights and thresholds.
Note: This indicator is for educational and informational purposes only. Past performance is not indicative of future results. Always conduct your own analysis and use risk management. No financial advice is provided.
If you find this useful, please like, comment, or share your feedback!
Swing Oracle Stock 2.0- Gradient Enhanced# 🌈 Swing Oracle Pro - Advanced Gradient Trading Indicator
**Transform your technical analysis with stunning gradient visualizations that make market trends instantly recognizable.**
## 🚀 **What Makes This Indicator Special?**
The **Swing Oracle Pro** revolutionizes traditional technical analysis by combining advanced NDOS (Normalized Distance from Origin of Source) calculations with a sophisticated gradient color system. This isn't just another indicator—it's a complete visual trading experience that adapts colors based on market strength, making trend identification effortless and intuitive.
## 🎨 **10 Professional Gradient Themes**
Choose from carefully crafted color schemes designed for optimal visual clarity:
- **🌅 Sunset** - Warm oranges and purples for classic elegance
- **🌊 Ocean** - Cool blues and teals for calm analysis
- **🌲 Forest** - Natural greens and browns for organic feel
- **✨ Aurora** - Ethereal greens and magentas for mystique
- **⚡ Neon** - Vibrant electric colors for high-energy trading
- **🌌 Galaxy** - Deep purples and cosmic hues for night sessions
- **🔥 Fire** - Intense reds and golds for volatile markets
- **❄️ Ice** - Cool whites and blues for clear-headed decisions
- **🌈 Rainbow** - Full spectrum for comprehensive analysis
- **⚫ Monochrome** - Professional grays for focused trading
## 📊 **Core Features**
### **Advanced NDOS System**
- Normalized Distance from Origin of Source calculation with 231-period length
- Smoothed with customizable EMA for reduced noise
- Multi-timeframe confirmation with H1 filter option
- Dynamic gradient coloring based on oscillator position
### **Intelligent Visual Feedback**
- **Primary Gradient Line** - Main NDOS plot with dynamic color transitions
- **Gradient Fill Zones** - Beautiful color-coded areas for bullish, neutral, and bearish regions
- **Smart Transparency** - Colors adjust intensity based on market volatility
- **Dynamic Backgrounds** - Subtle gradient backgrounds that respond to market conditions
### **Enhanced EMA Projection System**
- 75/760 period EMA normalization with 50-period lookback
- Gradient-colored projection line for trend forecasting
- Toggleable display with advanced gradient controls
- Price tracking for precise level identification
### **Multi-Timeframe Analysis Table**
- Real-time trend analysis across 6 timeframes (1m, 3m, 5m, 15m, 1H, 4H)
- Gradient-colored cells showing trend strength
- Customizable table size and position
- Professional emoji indicators (🚀 UP, 📉 DOWN, ➡️ FLAT)
### **Signal System**
- **Gradient Buy Signals** - Triangle up arrows with intensity-based coloring
- **Gradient Sell Signals** - Triangle down arrows with strength indicators
- **Alert Conditions** - Built-in alerts for all signal types
- **7-Day Cycle Tracking** - Tuesday-to-Tuesday weekly cycle visualization
## ⚙️ **Customization Controls**
### **🎨 Gradient Controls**
- **Gradient Intensity** - Adjust color vibrancy (0.1-1.0)
- **Gradient Smoothing** - Control color transition smoothness (1-10 periods)
- **Dynamic Background** - Toggle animated background gradients
- **Advanced Gradients** - Enable/disable EMA projection and enhanced features
### **🛠️ Custom Color System**
- **Bullish Colors** - Define custom start/end colors for bull markets
- **Bearish Colors** - Set personalized bear market gradients
- **Full Theme Override** - Create completely custom color schemes
- **Real-time Preview** - See changes instantly on your chart
## 📈 **How to Use**
1. **Choose Your Theme** - Select from 10 professional gradient themes
2. **Configure Levels** - Adjust high/low levels (default 60/40) for your timeframe
3. **Set Smoothing** - Fine-tune gradient smoothing for your trading style
4. **Enable Features** - Toggle background gradients, candlestick coloring, and advanced EMA projection
5. **Monitor Signals** - Watch for gradient buy/sell arrows and multi-timeframe confirmations
## 🎯 **Trading Applications**
- **Swing Trading** - Perfect for identifying medium-term trend changes
- **Scalping** - Multi-timeframe table provides quick trend confirmation
- **Position Sizing** - Gradient intensity shows signal strength for risk management
- **Market Analysis** - Beautiful visualizations make complex data instantly understandable
- **Education** - Ideal for learning market dynamics through visual feedback
## ⚡ **Performance Optimized**
- **Smart Rendering** - Colors update only on significant changes
- **Efficient Calculations** - Optimized algorithms for smooth performance
- **Memory Management** - Minimal resource usage even with complex gradients
- **Real-time Updates** - Responsive to market changes without lag
## 🚨 **Alert System**
Built-in alert conditions notify you when:
- NDOS crosses above high level (Buy Signal)
- NDOS crosses below low level (Sell Signal)
- Multi-timeframe confirmations align
- Customizable alert messages with emoji indicators
## 🔧 **Technical Specifications**
- **PineScript Version**: v6 (Latest)
- **Overlay**: True (plots on main chart)
- **Calculations**: NDOS, EMA normalization, volatility-based transparency
- **Timeframes**: Compatible with all timeframes
- **Markets**: Stocks, Forex, Crypto, Commodities, Indices
## 💡 **Why Choose Swing Oracle Pro?**
This isn't just another technical indicator—it's a complete visual transformation of your trading experience. The gradient system provides instant visual feedback that traditional indicators simply can't match. Whether you're a beginner learning to read market trends or an experienced trader seeking clearer signals, the Swing Oracle Pro delivers professional-grade analysis with unprecedented visual clarity.
**Experience the future of technical analysis. Your charts will never look the same.**
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*⚠️ Disclaimer: This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always conduct your own research and consider risk management before making trading decisions.*
**🔔 Like this indicator? Please leave a comment and boost! Your feedback helps improve future updates.**
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**📝 Tags:** #GradientTrading #SwingTrading #NDOS #MultiTimeframe #TechnicalAnalysis #VisualTrading #TrendAnalysis #ColorCoded #ProfessionalCharts #TradingToo
