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8MA Compass — HTF map + GC/DC cues8MA Compass provides a clean trend context by combining strict 4-of-4 confluence (Current TF vs Higher TF) with SMA200 repainting on Golden/Death Cross (GC/DC). What it shows 4-of-4 background (context): compares EMA10, EMA20, SMA50, SMA200 on the Current TF against the same four MAs on the Higher TF (HTF). All 4 above their HTF values → bullish background. All 4 below their HTF values → bearish background. SMA200 color on GC/DC (Current TF): Last signal is DC and price below SMA200 → SMA200 turns red. Price above SMA200 but the last signal is DC (no GC afterward) → SMA200 stays base color. Last signal is GC and price above SMA200 → SMA200 turns green #089981. Why “8MA” ? The 4-of-4 logic uses 8 moving averages in total: 4 on the Current TF and 4 on the HTF (EMA10/20 and SMA50/200 on both frames). HTF EMAs are used in calculations but are not plotted by default—hence the name 8MA Compass. Auto HTF mapping Current 1H → HTF 4H Current 4H → HTF 1D Current 1D → HTF 1W All other timeframes: HTF defaults to Current TF (4-of-4 will typically be neutral). Manual mode: choose any HTF. If Manual HTF equals Current TF, HTF SMAs are hidden to avoid overlap. Settings 1. Display Show CURRENT TF — plot EMA10/20, SMA50/200 on Current TF. Show HARD TF — plot SMA50/200 on HTF (hidden if HTF == Current TF). HTF mode — Auto / Manual, with Hard TF (Manual) selector. 2. Filter Show base background (4-of-4) — enable/disable confluence shading. Epsilon (in ticks) — small tolerance in Cur vs HTF comparisons to reduce flicker. 3. Golden/Death Color SMA200 on GC/DC (Cur TF) — repaint SMA200 on GC/DC per rules above (enabled by default). Alerts GC/DC (Current TF, SMA50/200): Golden Cross / Death Cross (on bar close). EMA10/20 (Current TF): “Bull regime ON” / “Bear regime ON” on crossovers. Optional HTF GC/DC alerts (SMA50/200 on chosen HTF). Visual details HTF SMA50/200 are drawn first; Current TF lines are drawn on top for clarity. SMA200 (Current TF) is drawn last (and slightly thicker) to remain readable. HTF EMAs are used in 4-of-4 logic but not plotted by design. Usage 1. Use the 4-of-4 background as inter-timeframe momentum context. 2. Use SMA200 color to gauge long-term regime confirmation: Prefer longs when last GC and price holds above SMA200 (#089981 line). Avoid longs when last DC and price is below SMA200 (red line). Disclaimer : For educational purposes only. Not financial advice. Trading involves risk.
Indicador Pine Script®
por charyyevn
Perfect Price-Anchored % Fib Grid This indicator generates support and resistance levels anchored to a fixed price of your choice. You can also specify a percentage for the indicator to calculate potential highs and lows. Commonly used values are 3.5% or 7%, as well as smaller decimal versions like 0.35% or 0.7%, depending on the volatility you expect. In addition, the indicator can highlight potential stop-run levels in multiples of 27 — ranging from 0 up to 243. This automatically places the 243 GB range directly onto your chart. The tool is versatile and can be applied not only to equities, but also to ES futures and Forex markets.
Indicador Pine Script®
por Hitchhikers_Guide_To_Trading
4
FibADX MTF Dashboard — DMI/ADX with Fibonacci DominanceFibADX MTF Dashboard — DMI/ADX with Fibonacci Dominance (φ) This indicator fuses classic DMI/ADX with the Fibonacci Golden Ratio to score directional dominance and trend tradability across multiple timeframes in one clean panel. What’s unique • Fibonacci dominance tiers: • BULL / BEAR → one side slightly stronger • STRONG when one DI ≥ 1.618× the other (φ) • EXTREME when one DI ≥ 2.618× (φ²) • Rounded dominance % in the +DI/−DI columns (e.g., STRONG BULL 72%). • ADX column modes: show the value (with strength bar ▂▃▅… and slope ↗/↘) or a tier (Weak / Tradable / Strong / Extreme). • Configurable intraday row (30m/1H/2H/4H) + D/W/M toggles. • Threshold line: color & width; Extended (infinite both ways) or Not extended (historical plot). • Theme presets (Dark / Light / High Contrast) or full custom colors. • Optional panel shading when all selected TFs are strong (and optionally directionally aligned). How to use 1. Choose an intraday TF (30/60/120/240). Enable D/W/M as needed. 2. Use ADX ≥ threshold (e.g., 21 / 34 / 55) to find tradable trends. 3. Read the +DI/−DI labels to confirm bias (BULL/BEAR) and conviction (STRONG/EXTREME). 4. Prefer multi-TF alignment (e.g., 4H & D & W all strong bull). 5. Treat EXTREME as a momentum regime—trail tighter and scale out into spikes. Alerts • All selected TFs: Strong BULL alignment • All selected TFs: Strong BEAR alignment Notes • Smoothing selectable: RMA (Wilder) / EMA / SMA. • Percentages are whole numbers (72%, not 72.18%). • Shorttitle is FibADX to comply with TV’s 10-char limit. Why We Use Fibonacci in FibADX Traditional DMI/ADX indicators rely on fixed numeric thresholds (e.g., ADX > 20 = “tradable”), but they ignore the relationship between +DI and −DI, which is what really determines trend conviction. FibADX improves on this by introducing the Fibonacci Golden Ratio (φ ≈ 1.618) to measure directional dominance and classify trend strength more intelligently. ⸻ 1. Fibonacci as a Natural Strength Threshold The golden ratio φ appears everywhere in nature, growth cycles, and fractals. Since financial markets also behave fractally, Fibonacci levels reflect natural crowd behavior and trend acceleration points. In FibADX: • When one DI is slightly larger than the other → BULL or BEAR (mild advantage). • When one DI is at least 1.618× the other → STRONG BULL or STRONG BEAR (trend conviction). • When one DI is 2.618× or more → EXTREME BULL or EXTREME BEAR (high momentum regime). This approach adds structure and consistency to trend classification. ⸻ 2. Why 1.618 and 2.618 Instead of Random Numbers Other traders might pick thresholds like 1.5 or 2.0, but φ has special mathematical properties: • φ is the most irrational ratio, meaning proportions based on φ retain structure even when scaled. • Using φ makes FibADX naturally adaptive to all timeframes and asset classes — stocks, crypto, forex, commodities. ⸻ 3 . Trading Advantages Using the Fibonacci Golden Ratio inside DMI/ADX has several benefits: • Better trend filtering → Avoid false DI crossovers without conviction. • Catch early momentum shifts → Spot when dominance ratios approach φ before ADX reacts. • Consistency across markets → Because φ is scalable and fractal, it works everywhere. ⸻ 4. How FibADX Uses This FibADX combines: • +DI vs −DI ratio → Measures directional dominance. • φ thresholds (1.618, 2.618) → Classifies strength into BULL, STRONG, EXTREME. • ADX threshold → Confirms whether the move is tradable or just noise. • Multi-timeframe dashboard → Aligns bias across 4H, D, W, M. ⸻ Quick Blurb for TradingView FibADX uses the Fibonacci Golden Ratio (φ ≈ 1.618) to classify trend strength. Unlike classic DMI/ADX, FibADX measures how much one side dominates: • φ (1.618) = STRONG trend conviction • φ² (2.618) = EXTREME momentum regime This creates an adaptive, fractal-aware framework that works across stocks, crypto, forex, and commodities. ⚠️ Disclaimer : This script is provided for educational purposes only. It does not constitute financial advice. Use at your own risk. Always do your own research before making trading decisions. Created by @nomadhedge
Indicador Pine Script®
por nomadhedge
1
[GrandAlgo] Moving Averages Cross LevelsMoving Averages Cross Levels Many traders watch for moving average crossovers – such as the golden cross (50 MA crossing above 200 MA) or death cross – as signals of changing trends. However, once a crossover happens, the exact price level where it occurred often fades from view, even though that level can be an important reference point. Moving Averages Cross Levels is an indicator that keeps those crossover price levels visible on your chart, helping you track where momentum shifts occurred and how price behaves relative to those key levels. This tool plots horizontal line segments at the price where each pair of selected moving averages crossed within a recent window of bars. Each level is labeled with the moving average lengths (for example, “21×50” for a 21/50 MA cross) and is color-coded – green for bullish crossovers (short-term MA crossing above long-term MA) and red for bearish crossunders (short-term crossing below). By visualizing these crossover levels, you can quickly identify past trend change points and use them as potential support/resistance or decision levels in your trading. Importantly, this indicator is non-repainting – once a crossover level is plotted, it remains fixed at the historical price where the cross occurred, allowing you to continually monitor that level going forward. (As with any moving average-based analysis, crossover signals are lagging, so use these levels in conjunction with other tools for confirmation.) Key Features: ✅ Multiple Moving Averages: Track up to 7 different MAs (e.g. 5, 8, 21, 50, 64, 83, 200 by default) simultaneously. You can enable/disable each MA and set its length, allowing flexible combinations of short-term and long-term averages. ✅ Selectable MA Type: Each average can be calculated as a Simple (SMA), Exponential (EMA), Volume-Weighted (VWMA), or Smoothed (RMA) moving average, giving you flexibility to match your preferred method. ✅ Auto Crossover Detection: The script automatically detects all crosses between any enabled MA pairs, so you don’t have to specify pairs manually. Whether it’s a fast cross (5×8) or a long-term cross (50×200), every crossover within the lookback period will be identified and marked. ✅ Horizontal Level Markers: For each detected crossover, a horizontal line segment is drawn at the exact price where the crossover occurred. This makes it easy to glance at your chart and see precisely where two moving averages intersected in the recent past. ✅ Labeled and Color-Coded: Each crossover line is labeled with the two MA lengths that crossed (e.g. “50×200”) for clear identification. Colors indicate crossover direction – by default green for bullish (positive) crossovers and red for bearish (negative) crossovers – so you can tell at a glance which way the trend shifted. (You can customize these colors in the settings.) ✅ Adjustable Lookback: A “Crosses with X candles” input lets you control how far back the script looks for crossovers to plot. This prevents your chart from getting cluttered with too many old levels – for example, set X = 100 to show crossovers from roughly the last 100 bars. Older crossover lines beyond this lookback window will automatically clear off the chart. ✅ Optional MA Plots: You can toggle the display of each moving average line on the chart. This means you can either view just the crossover levels alone for a clean look, or also overlay the MA curves themselves for additional context (to see how price and MAs were moving around the crossover). ✅ No Repainting or Hindsight Bias: Once a crossover level is plotted, it stays at that fixed price. The indicator doesn’t move levels around after the fact – each line is a true historical event marker. This allows you to backtest visually: see how price acted after the crossover by observing if it retested or respected that level later. How It Works: 1️⃣ Add to Chart & Configure – Simply add the indicator to your chart. In the settings, choose which moving averages you want to include and set their lengths. For example, you might enable 21, 50, 200 to focus on medium and long-term crosses (including the golden cross), or turn on shorter MAs like 5 and 8 for quick momentum shifts. Adjust the lookback (number of bars to scan for crosses) if needed. 2️⃣ Visualization – The script continuously checks the latest X bars for any points where one MA crossed above or below another. Whenever a crossover is found, it calculates the exact price level at which the two moving averages intersected. On the last bar of your chart, it will draw a horizontal line segment extending from the crossover bar to the current bar at that price level, and place a label to the right of the line with the MA lengths. Green lines/labels signify bullish crossovers (where the first MA crossed above the second), and red lines indicate bearish crossunders. 3️⃣ On Your Chart – You will see these labeled levels aligned with the price scale. For example, if a 50 MA crossed above a 200 MA (bullish) 50 bars ago at price $100, there will be a green “50×200” line at $100 extending to the present, showing you exactly where that golden cross happened. You might notice price pulling back near that level and bouncing, or if price falls back through it, it could signal a failed crossover. The indicator updates in real-time: if a new crossover happens on the latest bar, a new line and label will instantly appear, and if any old cross moves out of the lookback range, its line is removed to keep the chart focused. 4️⃣ Customization – You can fine-tune the appearance: toggle any MA’s visibility, change line colors or label styles, and modify the lookback length to suit different timeframes. For instance, on a 1-hour chart you might use a lookback of 500 bars to see a few weeks of cross history, whereas on a daily chart 100 bars (about 4–5 months) may be sufficient. Adjust these settings based on how many crossover levels you find useful to display. Ideal for Traders Who: Use MA Crossovers in Strategy: If your strategy involves moving average crossovers (for trend confirmation or entry/exit signals), this indicator provides an extra layer of insight by keeping the price of those crossover events in sight. For example, trend-followers can watch if price stays above a bullish crossover level as a sign of trend strength, or falls below it as a sign of weakness. Identify Support/Resistance from MA Events: Crossover levels often coincide with pivot points in market sentiment. A crossover can act like a regime change – the level where it happened may turn into support or resistance. This tool helps you mark those potential S/R levels automatically. Rather than manually noting where a golden cross occurred, you’ll have it highlighted, which can be useful for setting stop-losses (e.g. below the crossover price in a bullish scenario) or profit targets. Track Multiple Averages at Once: Instead of focusing on just one pair of moving averages, you might be interested in the interaction of several (short, medium, and long-term trends). This indicator caters to that by plotting all relevant crossovers among your chosen MAs. It’s great for multi-timeframe thinkers as well – e.g. you could apply it on a higher timeframe chart to mark major cross levels, then drill down to lower timeframes knowing those key prices. Value Clean Visualization: There are no flashing signals or arrows – just simple lines and labels that enhance your chart’s storytelling. It’s ideal if you prefer to make trading decisions based on understanding price interaction with technical levels rather than following automatic trade calls. Moving Averages Cross Levels gives you information to act on, without imposing any bias or strategy – you interpret the crossover levels in the context of your own trading system.
Indicador Pine Script®
por GrandAlgo
Rolling Correlation BTC vs Hedge AssetsRolling Correlation BTC vs Hedge Assets Overview This indicator calculates and plots the rolling correlation between Bitcoin (BTC) returns and several key hedge assets: • XAUUSD (Gold) • EURUSD (proxy for DXY, U.S. Dollar Index) • VIX (Volatility Index) • TLT (20y U.S. Treasury Bonds ETF) By monitoring these dynamic correlations, traders can identify whether BTC is moving in sync with risk assets or decoupling as a hedge, and adjust their trading strategy accordingly. How it works 1. Computes returns for BTC and each asset using percentage change. 2. Uses the rolling correlation function (ta.correlation) over a configurable window length (default = 12 bars). 3. Plots each correlation as a separate colored line (Gold = Yellow, EURUSD = Blue, VIX = Red, TLT = Green). 4. Adds threshold levels at +0.3 and -0.3 to help classify correlation regimes. How to use it • High positive correlation (> +0.3): BTC is moving together with the asset (risk-on behavior). • Near zero (-0.3 to +0.3): BTC is showing little to no correlation — neutral/independent moves. • Negative correlation (< -0.3): BTC is moving in the opposite direction — potential hedge opportunity. Practical strategies: • Watch BTC vs VIX: a spike in volatility (VIX ↑) usually coincides with BTC selling pressure. • Track BTC vs EURUSD: stronger USD often puts downside pressure on BTC. • Observe BTC vs Gold: during “flight to safety” events, gold rises while BTC weakens. • Monitor BTC vs TLT: rising yields (falling TLT) often align with BTC weakness. Inputs • Window Length (bars): Number of bars used to calculate rolling correlations (default = 12). • Comparison Timeframe: Default = 5m. Can be changed to align with your intraday or swing trading style. Notes • Works best on intraday charts (1m, 5m, 15m) for scalping and short-term setups. • Use correlations as context, not standalone signals — combine with volume, VWAP, and price action. • Correlations are dynamic; they can switch regimes quickly during macro events (CPI, NFP, FOMC). This tool is designed for traders who want to manage risk exposure by monitoring whether BTC is behaving as a risk-on asset or hedge, and to exploit opportunities during decoupling phases.
Indicador Pine Script®
por leobuezo
[blackcat] L1 Value Trend IndicatorOVERVIEW The L1 Value Trend Indicator is a sophisticated technical analysis tool designed for TradingView users seeking advanced market trend identification and trading signals. This comprehensive indicator combines multiple analytical techniques to provide traders with a holistic view of market dynamics, helping identify potential entry and exit points through various signal mechanisms. 📈 It features a main Value Trend line along with a lagged version, golden cross and dead cross signals, and multiple technical indicators including RSI, Williams %R, Stochastic %K/D, and Relative Strength calculations. The indicator also includes reference levels for support and resistance analysis, making it a versatile tool for both short-term and long-term trading strategies. ✅ FEATURES 📈 Primary Value Trend Line: Calculates a smoothed value trend using a combination of SMA and custom smoothing techniques 🔍 Value Trend Lag: Implements a lagged version of the main trend line for cross-over analysis 🚀 Golden Cross & Dead Cross Signals: Identifies buy/sell opportunities when the main trend line crosses its lagged version 💸 Multi-Indicator Integration: Combines multiple technical analysis tools for comprehensive market view 📊 RSI Calculations: Includes 6-period, 7-period, and 13-period RSI calculations for momentum analysis 📈 Williams %R: Provides overbought/oversold conditions using the Williams %R formula 📉 Stochastic Oscillator: Implements both Stochastic %K and %D calculations for momentum confirmation 📋 Relative Strength: Calculates relative strength based on highest highs and current price ✅ Visual Labels: Displays BUY and SELL labels on chart when crossover conditions are met 📣 Alert Conditions: Provides automated alert conditions for golden cross and dead cross events 📌 Reference Levels: Plots entry (25) and exit (75) reference lines for support/resistance analysis HOW TO USE Copy the Script: Copy the complete Pine Script code from the original file Open TradingView: Navigate to TradingView website or application Access Pine Editor: Go to the Pine Script editor (usually found in the chart toolbar) Paste Code: Paste the copied script into the editor Save Script: Save the script with a descriptive name like " L1 Value Trend Indicator" Select Chart: Choose the chart where you want to apply the indicator Add Indicator: Apply the indicator to your chart Configure Parameters: Adjust input parameters to customize behavior Monitor Signals: Watch for golden cross (BUY) and dead cross (SELL) signals Use Reference Levels: Monitor entry (25) and exit (75) lines for support/resistance levels LIMITATIONS ⚠️ Potential Repainting: The script may repaint due to lookahead bias in some calculations 📉 Lookahead Bias: Some calculations may reference future values, potentially causing repainting issues 🔄 Parameter Sensitivity: Results may vary significantly with different parameter settings 📉 Computational Complexity: May impact chart performance with heavy calculations on large datasets 📊 Resource Usage: Requires significant processing power for multiple indicator calculations 🔄 Data Sensitivity: Results may be affected by data quality and market conditions NOTES 📈 Signal Timing: Cross-over signals may lag behind actual price movements 📉 Parameter Optimization: Optimal parameters may vary by market conditions and asset type 📋 Market Conditions: Performance may vary significantly across different market environments 📈 Multi-Indicator: Combine signals with other technical indicators for confirmation 📉 Timeframe Analysis: Use multiple timeframes for enhanced signal accuracy 📋 Volume Analysis: Incorporate volume data for additional confirmation 📈 Strategy Integration: Consider using this indicator as part of a broader trading strategy 📉 Risk Management: Use signals as part of a comprehensive risk management approach 📋 Backtesting: Test parameter combinations with historical data before live trading THANKS 🙏 Original Creator: blackcat1402 creates the L1 Value Trend Indicator 📚 Community Contributions: Recognition to TradingView community for continuous improvements and contributions 📈 Collaborative Development: Appreciation for collaborative efforts in enhancing technical analysis tools 📉 TradingView Community: Special thanks to TradingView community members for their ongoing support and feedback 📋 Educational Resources: Recognition of educational resources that helped in understanding technical analysis principles
Indicador Pine Script®
por blackcat1402
AI Fib Strategy (Full Trade Plan)This indicator automatically plots Fibonacci retracements and a Golden Zone box (61.8%–65% retracement) based on the 4H candle body high/low. Features: Auto-detects session breaks or daily breaks (configurable). Draws standard Fib retracement levels (0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%). Highlights the Golden Zone for high-probability trade entries. Optional Take Profit extensions (TP1, TP2, TP3). Fully compatible with Pine Script v6. Usage: Best applied on intraday charts (15m, 30m, 1H). Use the Golden Zone for entry confirmations. Combine with candlestick patterns, order blocks, or volume for stronger signals.
Indicador Pine Script®
por FOREXINSIGHT69
Jimb0ws Strategy Trending Info PanelsJimb0ws Strategy — Golden Candles + Bubble Zones A price-action/EMA strategy built for FX scalping and intraday swings. It colors Golden Candles when strong bodies touch/skim EMA20/50 in trend (“bubble”) and optionally highlights Robin Candles (break of the prior golden body). Signals are throttled per bubble and filtered by multiple higher-timeframe conditions. How it trades Trend bubbles: Uses EMA20/50/100/200 alignment on the chart timeframe; also reads 1H & 4H bubbles for context. Entries: BUY/SELL labels appear only when a golden setup aligns with fractal/structure checks and all active filters pass. Stops/Targets (strategy mode): • Longs: SL = EMA100 if EMA200 > EMA100, else SL = EMA200. • Shorts: SL = EMA100 if EMA200 < EMA100, else SL = EMA200. • TP = RR × risk (default 2R). An on-chart SL/TP info label prints the exact prices at each signal. Risk filter options: disable beyond 1H EMA50, proximity band around 1H EMA50, wick overdrive veto, session filter (toggle on/off), max signals per bubble. Visuals & tools Colored EMAs (20/50/100/200), bubble zone background. 4H info panel (state, start time, duration); Prev-Day ATR panel sits above it. Optional 1H info panel and consolidation warning. Fractal markers (size selectable). Alerts 1H bubble state change (Long/Short/Consolidation). BUY/SELL signals. Inputs worth checking Session & timezone, min body size, pip tolerances, proximity/WOD filters, max signals per bubble, RR, SL/TP label offset. Notes Best on FX pairs; pip = mintick × 10. Backtest and adjust to your instrument and session. This is not financial advice.
Estratégia Pine Script®
por HoneyMoneyFX
US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation. Introduction and Motivation Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment. The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data. Theoretical Framework and Methodology Composite Index Construction The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures. The mathematical formulation of the composite index follows: USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t) Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011). Weighting Methodology The weighting scheme incorporates findings from economic research: Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points. Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019). Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004). Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology). Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000). Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology. Data Normalisation and Scaling Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows: Normalized_i,t = (R_i,t / 252) × 100 Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution. Implementation and Technical Architecture The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation. User Interface Design and Customization Framework The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments. Visual Theme System The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions. Glow Effect Implementation The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability. Table Display Architecture The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability. The table displays twenty economic indicators with the following information architecture: - Category classification for cognitive grouping - Indicator names with standard economic nomenclature - Current values with intelligent number formatting - Percentage change calculations with directional indicators - Cross-asset market implications using standardised notation - Risk assessment using three-tier classification (HIGH/MED/LOW) - Data update timestamps for temporal reference Index Customisation Parameters The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices. Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification. Cross-Asset Market Implications The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012). The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation: Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions. Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications. Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators. Aggregated Market Impact Analysis The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes. Alert System Architecture The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes: Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period. Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation. Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability. Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration. Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods. Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions. Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points. Performance Optimization and Technical Implementation The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications. Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration. Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016). Risk Assessment Framework Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns. Empirical Validation and Performance Business Cycle Correspondence Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods. The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications. Cross-Asset Predictive Ability The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements. These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation. Practical Applications and Use Cases Institutional Asset Allocation The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction. The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies. Risk Management Applications The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment. The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions. Economic Research Applications The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions. The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations. Enhanced User Experience and Operational Features The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments. Multi-Screen and Multi-User Adaptability The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings. Cognitive Load Management The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006). Real-Time Decision Support The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods. Professional Alert Management The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels. Data Quality and Reliability Features The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity. Customisation for Institutional Workflows The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances. Limitations and Constraints Data Dependency The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications. Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases. Structural Relationship Stability The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights. The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies. Frequency Limitations Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies. The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources. Future Research Directions Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach. The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors. Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications. Conclusion The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications. The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation. The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools. References Aruoba, S. B., Diebold, F. X., & Scotti, C. (2009). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417-427. Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: Theory and application. Prentice Hall. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43. Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. NBER Books, National Bureau of Economic Research. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton University Press. Carroll, C. D., Fuhrer, J. C., & Wilcox, D. W. (1994). Does consumer sentiment forecast household spending? If so, why? American Economic Review, 84(5), 1397-1408. Clarida, R., Gali, J., & Gertler, M. (2000). Monetary policy rules and macroeconomic stability: Evidence and some theory. Quarterly Journal of Economics, 115(1), 147-180. Cochrane, J. H. (2005). Asset pricing. Princeton University Press. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT Press. Doz, C., Giannone, D., & Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1), 188-205. Dzeng, R. J., & Lin, Y. C. (2004). Intelligent agents for supporting construction procurement negotiation. Expert Systems with Applications, 27(1), 107-119. Elliot, A. J., & Maier, M. A. (2014). Color psychology: Effects of perceiving color on psychological functioning in humans. Annual Review of Psychology, 65, 95-120. Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media. Fisher, I. (1930). The theory of interest. Macmillan. Foley, J. D., van Dam, A., Feiner, S. K., & Hughes, J. F. (1995). Computer graphics: Principles and practice. Addison-Wesley. Gordon, M. J. (1962). The investment, financing, and valuation of the corporation. Richard D. Irwin. Hasbrouck, J. (2007). Empirical market microstructure: The institutions, economics, and econometrics of securities trading. Oxford University Press. Koenig, E. F. (2002). Using the purchasing managers' index to assess the economy's strength and the likely direction of monetary policy. Economic and Financial Policy Review, 1(6), 1-14. Krugman, P. R., & Obstfeld, M. (2009). International economics: Theory and policy. Pearson. Ludvigson, S. C. (2004). Consumer confidence and consumer spending. Journal of Economic Perspectives, 18(2), 29-50. Macaulay, F. R. (1938). Some theoretical problems suggested by the movements of interest rates, bond yields and stock prices in the United States since 1856. National Bureau of Economic Research. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. Mishkin, F. S. (2012). The economics of money, banking, and financial markets. Pearson. Nielsen, J. (1993). Usability engineering. Academic Press. Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books. OECD (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing. Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-time signal processing. Prentice Hall. Sahm, C. (2019). Direct stimulus payments to individuals. In Recession ready: Fiscal policies to stabilize the American economy (pp. 67-92). The Hamilton Project, Brookings Institution. Siegel, J. J. (2002). Stocks for the long run: The definitive guide to financial market returns and long-term investment strategies. McGraw-Hill. Sommerville, I. (2016). Software engineering. Pearson. Stock, J. H., & Watson, M. W. (1989). New indexes of coincident and leading economic indicators. NBER Macroeconomics Annual, 4, 351-394. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214. Tufte, E. R. (2001). The visual display of quantitative information. Graphics Press. Yardeni, E. (2003). Stock valuation models. Topical Study, 38. Yardeni Research.
Indicador Pine Script®
por EdgeTools
6
WaveTrend with CrossesWaveTrend with Crosses — Spot Golden & Dead Crosses with Precision! WaveTrend with Crosses is a customized version of the classic WaveTrend oscillator, enhanced with clean visual signals to help you pinpoint momentum shifts through golden and dead crosses. ✅ Key Features Momentum analysis based on WaveTrend (WT1 & WT2) Detects Golden Cross (WT1 crosses above WT2) and Dead Cross (WT1 crosses below WT2) Customizable Overbought/Oversold zones (defaults: ±60, ±53) Visual circle markers on valid crossovers for easy recognition Built-in alert system to notify you of real-time cross signals 📊 How to Use Add the indicator to your chart and choose your desired symbol & timeframe. The blue shaded area shows the divergence between WT1 and WT2 — a visual cue for momentum buildup. Circle markers: Red circle: Dead cross — potential bearish momentum Green circle: Golden cross — potential bullish reversal Customize the settings to fit your personal trading strategy if needed. 🛠 User Inputs n1, n2: Channel lengths (default: 10 and 21) obLevel, osLevel: Overbought/Oversold thresholds (default: ±60 / ±53) standardValue: Threshold used to validate significant crossovers (default: 60) 🔔 Alert System Get notified with alerts like "Golden Cross" or "Dead Cross" when key crossovers occur, helping you react quickly and confidently. ⚠️ Notes Past performance is not indicative of future results — always backtest and use in conjunction with other tools. Low timeframes may generate frequent signals; filtering or confirmation is recommended. 💡 Author's Note Simple and effective — this tool is designed to focus solely on cross-based entries. Ideal for momentum-based scalping or swing trading strategies. Feel free to customize and tweak as needed! 😄
Indicador Pine Script®
por sondengs
Advanced Range Theory - ART📊 Advanced Range Theory (ART): The Institutional Blueprint Stop drawing lines. Start reading the blueprint of the market. Advanced Range Theory (ART) is not another support and resistance indicator; it is a military-grade market structure engine designed to decode the language of institutional capital. It operates on a single, powerful premise: markets move in phases of consolidation and expansion, and the key to anticipation lies in understanding the complete lifecycle of these phases. ART provides a living, breathing map of the battlefield, identifying institutional accumulation zones and tracking them with unparalleled precision from their inception as "Pending" ranges to their ultimate classification after a breakout. This is your X-ray into the market's skeletal structure. 🔬 THEORETICAL FRAMEWORK: THE ARCHITECTURE OF PRICE ACTION ART is built on a multi-layered system of logic that moves beyond static levels. It treats ranges as dynamic entities with a narrative—a beginning, a middle, and an end. The core of the system is the dynamic classification engine, which analyzes not just the range, but the character of the price action that resolves it. 1. The Range Lifecycle: From Accumulation to Classification This is the revolutionary heart of ART. A range's true identity is only revealed by how it is broken. Phase 1: PENDING (Yellow): A new range is identified based on a period of price consolidation (a "parent" candle followed by a minimum number of "inside" candles). At this stage, it is a neutral zone of potential energy—an area where institutions are likely building positions. It is a question the market has not yet answered. Phase 2: MITIGATION & CLASSIFICATION: When price breaks out and reaches a calculated extension level, the range is considered "mitigated." At this exact moment, ART analyzes the breakout's DNA to classify the range's true intent: TYPE 1 - BREAKOUT (Blue): Characterized by a strong, impulsive move with confirming volume. This is a high-conviction breakout, signaling aggressive institutional participation and the likely start of a new trend. It is a statement of intent. TYPE 2 - REVERSAL (Orange): Occurs when price attempts to break one way but is aggressively rejected, reversing and breaking out the other side. This signals absorption and a "failed auction," often marking significant market turning points. TYPE 3 - PIVOT (Green): A more balanced breakout, lacking the explosive momentum of a Type 1. This often represents a resolution after a period of indecision or a pivot within a larger trading range. 2. The Hierarchical Map: Source & S/R Levels ART doesn't just draw boxes; it builds a genealogical map of market structure. SOURCE LEVEL (Thick Gold Line): This is the "genesis" point—the most recently mitigated range. It acts as the primary point of origin for the current market swing and serves as a critical level for determining overall bias. Price action above the Source is generally bullish; below is bearish. S/R LEVELS (Cyan Lines): When a range is mitigated, the price level where it broke becomes a key Support/Resistance zone for the future. ART tracks the two most recent S/R levels, as these often act as powerful magnets or rejection points for price. 3. The Multi-Factor Validation Engine To eliminate noise and focus only on institutionally significant ranges, every potential range must pass a rigorous quality control check: Time-Based Consolidation: Requires a minimum number of consecutive inside candles (minInsideCandles), ensuring a true period of balance. Volatility-Based Significance: The range's size must be greater than a multiple of the Average True Range (minRangeSize), filtering out insignificant micro-consolidations. Participation Confirmation: The parent candle of the range is checked against average volume to ensure there was meaningful activity during its formation. ⚙️ THE COMMAND CONSOLE: CONFIGURING YOUR ART ENGINE Every input is designed to give you granular control over the detection engine, allowing you to tune ART to any market or timeframe with precision. Each tooltip in the script provides a deep dive, but here is a summary of the core controls. 🎯 ART Detection Engine Minimum Inside Candles: The soul of the detection algorithm. It defines the minimum number of bars that must be contained within a single "parent" candle to qualify as a range. Higher values (3-4) find major, significant consolidation zones. Lower values (1-2) are more sensitive and will identify shorter-term accumulation patterns. Extension Multiplier & Fibonacci Extension: These control the profit target projections. The Extension Multiplier uses a simple measured move (e.g., 1.0 = a 1:1 projection of the range's height). The Fibonacci Extension uses the golden ratio (1.618) for harmonically-derived targets. Mitigation Method (Cross vs. Close): Determines how a breakout is confirmed. Cross is more responsive, triggering as soon as price touches the extension. Close is more conservative, requiring a full candle to close beyond the level, which helps filter out fake-outs from wicks. Min Range Size (ATR): A crucial noise filter. It ensures that ART ignores tiny, insignificant ranges by requiring a range's height to be a certain multiple of the current market volatility (ATR). 📊 Display & Visual Configuration These settings give you full control over the visual interface. You can toggle every single element—from the Webb Scanner to the S/R Levels—to create a clean or a comprehensive view. Choose a color theme that suits your charting environment or define a fully custom palette. 🕸️ Webb Analysis Scanner This is a unique real-time flow analysis tool. It draws dynamic, animated lines from the current price to recent historical points. This visualization helps reveal hidden "tendrils" of momentum and short-term support/resistance that are not immediately obvious, acting as a "sonar" for immediate price flow. 📊 THE ANALYTICS HUB: YOUR DASHBOARD DECODED The dashboard provides a real-time, at-a-glance intelligence briefing on the current state of market structure as seen by the ART engine. RANGE METRICS: This section is a "census" of the market's structure. It tells you the total number of ranges identified, how many are still Pending (awaiting a breakout), how many are Unmitigated (active but not yet broken), and how many have been Mitigated (classified and complete). TYPE BREAKDOWN: This is a powerful gauge of market character. A high count of Type 1 (Breakout) ranges suggests a strong, trending environment. A rising number of Type 2 (Reversal) ranges can signal market exhaustion and potential trend changes. A dominant Type 3 (Pivot) count indicates a balanced, rotational market. KEY GUIDE: The Large dashboard includes a full legend, so you never have to guess what a line or color represents. It's your built-in user manual. 🎨 DECODING THE BLUEPRINT: A VISUAL INTERPRETATION GUIDE Every line and color in ART is designed for instant, intuitive understanding. The Range Lines: Yellow Lines: A Pending range. This is an active zone of accumulation. Pay close attention. Colored Lines (Blue/Orange/Green): An unmitigated, classified range. The color tells you its breakout character. Dotted Lines: A Mitigated range. Its story has been told. These historical levels can still act as support or resistance. The Identification Zones: These colored boxes appear at a range's origin point after it has been classified. They are the "birth certificate" of the range, permanently marking its type (Breakout, Reversal, or Pivot) and providing an immediate visual history of market behavior. The Hierarchical Lines: Thick Gold Line (Source): The most important line on your chart. It is the anchor for your bias. Cyan Lines (S/R): High-probability decision points. Expect reactions here. Purple Dotted Lines (Extensions): Logical, calculated profit targets for breaking ranges. 🔧 THE ARCHITECT'S VISION: THE DEVELOPMENT JOURNEY ART was born from a deep frustration with the static and subjective nature of traditional market structure analysis. Drawing lines by hand is inconsistent, and most indicators are reactive, only confirming what has already happened. The goal was to create a proactive, objective, and dynamic framework that could think about the market in terms of phases and lifecycles. The breakthrough came from a simple shift in perspective: a range's true character isn't defined when it forms, but by how it resolves. This led to the development of the "post-breakout classification engine," which waits for the market to show its hand before assigning a definitive type. The Webb Scanner was inspired by the desire to visualize the unseen, to create a tool that could feel the immediate "pull" and "push" of price flow. The result is not just an indicator; it is a new language for interpreting price action, built on a foundation of logic, clarity, and precision. ⚠️ RISK DISCLAIMER & BEST PRACTICES Advanced Range Theory is a professional-grade analytical tool designed to enhance a trader's decision-making process. It does not provide direct buy or sell signals. The levels and classifications it generates are based on historical price action and mathematical probabilities. All trading involves substantial risk, and past performance is not indicative of future results. Always use this tool in conjunction with a robust risk management plan. "I fear not the man who has practiced 10,000 kicks once, but I fear the man who has practiced one kick 10,000 times." — Dskyz, Trade with insight. Trade with anticipation. — Bruce Lee
Indicador Pine Script®
por DskyzInvestments
15
Fibonacci retracementHi all! This indicator will show you the most recent Fibonacci retracement in the current trend. So if the trend is bullish the Fibonacci retracement will be drawn from swing low to high and from swing high to low in a bearish trend. The uniqueness in this script lies in the adaptation to trend. To only plot the Fibonacci retracements according to the current market trend. The trend is determined through break of structures (BOS) and change of characters (CHoCH). A change of character can be of type change of character plus (with a failed swing) and will then be shown as CHoCH+. This is possible through my library 'MarketStructure' (). It only uses break of structures and change of characters to be able to determine the trend, if you want a more detailed picture of the market structure you can use my script 'Market structure' (). History and what to look for Fibonacci retracement levels are used by many traders and are levels that are not Fibonacci sequence numbers themselves but they deriver from them. Some examples are: 23,6% - Divide a number by one three places ahead (e.g. 13/55) 38,2% - Divide a number by the one two places ahead (e.g. 21/55) 50% - Not from the Fibonacci sequence, but it's a number that price has reacted from in the past. Markets tend to retrace half a move before continuing 61,8% - The "golden retracement level". It derives from the "golden ratio" and is a core component of the Fibonacci sequence. The further you go in the Fibonacci sequence the preceding number divided by the current number will get closer and closer to this "golden ratio". This level is considered the most important Fibonacci retracement level by many traders 78,6% - Square root of 61.8%. This is often considered a deep correction (but not a trend reversal) and are often used for late entries These levels are considered "key" and most significant. You want to look for a retracement of the price (down in a bullish trend and up in a bearish trend) to give you good entries. Settings For the trend you can set the pivot/swing lengths (right and left) and use the checkbox if you want these pivots to have labels. This can be done in the 'Market strucure' section. In the 'Fibonacci retracement' section there is settings for the actual Fibonacci retracement. You can enable the trendline, set the color and the style of it. You can select which levels that should be shown by the indicator. There are 11 levels enabled by default, they are; 0-4.236. All settings in this section tries to be as similar to the "Fib Retracement" tool in Tradingview. You can also select the style of these lines (solid, dashed or dotted) and if you want them to extend to the right or not. After this you can select if the Fibonacci retracement should be reversed or not, if prices should be displayed, if levels should be displayed and if to show the decimal levels or percentages and lastly the font size of these labels. All defaults are based on the "Fib Retracement" tool by Tradingview. Visualization This indicator aims to be as visually similar to the default ("Fib Retracement") tool here on Tradingview. It will plot the Fibonacci retracement (called Auto Fibonacci/Auto fib) according to the trend from the library 'MarketStrucure'. The big differences from the "Fib Retracement" tool by Tradingview is that it's automatic (that adapts to trend), the market structure is visualized through lines and labels (showing 'BOS' for break of structures and 'CHoCH'/'CHoCH+' for change of characters) and that the labels showing information about the levels are positioned to be highly visible (left if <50% otherwise right if in a bullish trend, vice versa in a bearish trend or if reversed). Don't hesitate if you have any feedback or nice feature suggestions! Best of trading luck!
Indicador Pine Script®
por mickes
4
XAU/USD Lot Size CalculatorThis indicator automatically calculates the optimal lot size for XAUUSD (gold) based on the level of risk the trader wants to take. It is designed for traders using MetaTrader 4 or 5 and helps adjust position size according to the specific volatility of gold. The user can set the percentage of capital they are willing to risk on a single trade, for example 1%. The indicator also takes into account the stop loss level, which can be entered in pips or in dollars, as well as the account size (balance or equity). Based on these parameters, it calculates the exact lot size that matches the risk amount. It then displays on the chart the recommended lot size, the risk amount in dollars, the pip value for XAUUSD, and a confirmation of the stop loss level. This type of indicator is useful for maintaining disciplined risk management and avoiding position sizing errors, especially on a highly volatile asset like gold.
Indicador Pine Script®
por clementsrvx
FVG fill with immediate rebalance [LuciTech]The "FVG fill with immediate rebalance AKA Golden Arrow" indicator is designed to identify Fair Value Gaps (FVGs) and detect immediate rebalances to highlight potential trading opportunities. It uses colored boxes to mark FVGs and triangular markers to signal bullish or bearish setups, helping traders pinpoint key price levels where imbalances occur and price reactions are likely. Key Features FVG Detection: Spots bullish and bearish Fair Value Gaps based on price action, with customizable width settings. Golden Arrow Signals: Displays triangular markers when price fills an FVG and immediately rebalances, indicating potential reversal or continuation zones. Customizable Colors: Bullish FVGs appear in green and bearish FVGs in red by default, with options to tweak colors in the settings. Time Filter: Allows signals to be restricted to a specific time window, highlighted by a background fill for clarity. Alert System: Supports TradingView alerts for "Bullish Golden Arrow" and "Bearish Golden Arrow" signals to keep traders updated on setups. How It Works FVG Calculation: Analyzes gaps between candles to identify FVGs, with user-defined minimum width options (points, percentages, or ATR-based). Signal Generation: Triggers a Golden Arrow signal when price fills the FVG and rebalances immediately, based on wick penetration and closing conditions. Visual Aids: Bullish FVGs are shown as green boxes, bearish FVGs as red boxes. Upward triangles mark bullish signals, downward triangles mark bearish signals. Time-Based Filtering: Optionally limits signals to specific hours, with a background fill showing the active period.
Indicador Pine Script®
por TradesLuci
Fibonacci Optimal Entry Zone [OTE] (Zeiierman)█ Overview Fibonacci Optimal Entry Zone (Zeiierman) is a high-precision market structure tool designed to help traders identify ideal entry zones during trending markets. Built on the principles of Smart Money Concepts (SMC) and Fibonacci retracements, this indicator highlights key areas where price is most likely to react — specifically within the "Golden Zone" (between the 50% and 61.8% retracement). It tracks structural pivot shifts (CHoCH) and dynamically adjusts Fibonacci levels based on real-time swing tracking. Whether you're trading breakouts, pullbacks, or optimal entries, this tool brings unparalleled clarity to structure-based strategies. Ideal for traders who rely on confluence, this indicator visually synchronizes swing highs/lows, market structure shifts, Fibonacci retracement levels, and trend alignment — all without clutter or lag. ⚪ The Structural Assumption Price moves in waves, but key retracements often lead to continuation or reversal — especially when aligned with structure breaks and trend shifts. The Optimal Entry Zone captures this behavior by anchoring Fibonacci levels between recent swing extremes. The most powerful area — the Golden Zone — marks where institutional re-entry is likely, providing traders with a sniper-like roadmap to structure-based entries. █ How It Works ⚪ Structure Tracking Engine At its core, the indicator detects pivots and classifies trend direction: Structure Period – Determines the depth of pivots used to detect swing highs/lows. CHoCH – Break of structure logic identifies where the trend shifts or continues, marked visually on the chart. Bullish & Bearish Modes – Independently toggle uptrend and downtrend detection and styling. ⚪ Fibonacci Engine Upon each confirmed structural shift, Fibonacci retracement levels are projected between swing extremes: Custom Levels – Choose which retracements (0.50, 0.618, etc.) are shown. Real-Time Adjustments – When "Swing Tracker" is enabled, levels and labels update dynamically as price forms new swings. Example: If you disable the Swing Tracker, the Golden Level is calculated using the most recent confirmed swing high and low. If you enable the Swing Tracker, the Golden Level is calculated from the latest swing high or low, making it more adaptive as the trend evolves in real time. █ How to Use ⚪ Structure-Based Entry Wait for CHoCH events and use the resulting Fibonacci projection to identify entry points. Enter trades as price taps into the Golden Zone, especially when confluence forms with swing structure or order blocks. ⚪ Real-Time Reaction Tracking Enable Swing Tracker to keep the tool live — constantly updating zones as price shifts. This is especially useful for scalpers or intraday traders who rely on fresh swing zones. █ Settings Structure Period – Number of bars used to define swing pivots. Larger values = stronger structure. Swing Tracker – Auto-updates fib levels as new highs/lows form. Show Previous Levels – Keep older fib zones on chart or reset with each structure shift. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Indicador Pine Script®
por Zeiierman
15
Langlands-Operadic Möbius Vortex (LOMV)Langlands-Operadic Möbius Vortex (LOMV) Where Pure Mathematics Meets Market Reality A Revolutionary Synthesis of Number Theory, Category Theory, and Market Dynamics 🎓 THEORETICAL FOUNDATION The Langlands-Operadic Möbius Vortex represents a groundbreaking fusion of three profound mathematical frameworks that have never before been combined for market analysis: The Langlands Program: Harmonic Analysis in Markets Developed by Robert Langlands (Fields Medal recipient), the Langlands Program creates bridges between number theory, algebraic geometry, and harmonic analysis. In our indicator: L-Function Implementation: - Utilizes the Möbius function μ(n) for weighted price analysis - Applies Riemann zeta function convergence principles - Calculates quantum harmonic resonance between -2 and +2 - Measures deep mathematical patterns invisible to traditional analysis The L-Function core calculation employs: L_sum = Σ(return_val × μ(n) × n^(-s)) Where s is the critical strip parameter (0.5-2.5), controlling mathematical precision and signal smoothness. Operadic Composition Theory: Multi-Strategy Democracy Category theory and operads provide the mathematical framework for composing multiple trading strategies into a unified signal. This isn't simple averaging - it's mathematical composition using: Strategy Composition Arity (2-5 strategies): - Momentum analysis via RSI transformation - Mean reversion through Bollinger Band mathematics - Order Flow Polarity Index (revolutionary T3-smoothed volume analysis) - Trend detection using Directional Movement - Higher timeframe momentum confirmation Agreement Threshold System: Democratic voting where strategies must reach consensus before signal generation. This prevents false signals during market uncertainty. Möbius Function: Number Theory in Action The Möbius function μ(n) forms the mathematical backbone: - μ(n) = 1 if n is a square-free positive integer with even number of prime factors - μ(n) = -1 if n is a square-free positive integer with odd number of prime factors - μ(n) = 0 if n has a squared prime factor This creates oscillating weights that reveal hidden market periodicities and harmonic structures. 🔧 COMPREHENSIVE INPUT SYSTEM Langlands Program Parameters Modular Level N (5-50, default 30): Primary lookback for quantum harmonic analysis. Optimized by timeframe: - Scalping (1-5min): 15-25 - Day Trading (15min-1H): 25-35 - Swing Trading (4H-1D): 35-50 - Asset-specific: Crypto 15-25, Stocks 30-40, Forex 35-45 L-Function Critical Strip (0.5-2.5, default 1.5): Controls Riemann zeta convergence precision: - Higher values: More stable, smoother signals - Lower values: More reactive, catches quick moves - High frequency: 0.8-1.2, Medium: 1.3-1.7, Low: 1.8-2.3 Frobenius Trace Period (5-50, default 21): Galois representation lookback for price-volume correlation: - Measures harmonic relationships in market flows - Scalping: 8-15, Day Trading: 18-25, Swing: 25-40 HTF Multi-Scale Analysis: Higher timeframe context prevents trading against major trends: - Provides market bias and filters signals - Improves win rates by 15-25% through trend alignment Operadic Composition Parameters Strategy Composition Arity (2-5, default 4): Number of algorithms composed for final signal: - Conservative: 4-5 strategies (higher confidence) - Moderate: 3-4 strategies (balanced approach) - Aggressive: 2-3 strategies (more frequent signals) Category Agreement Threshold (2-5, default 3): Democratic voting minimum for signal generation: - Higher agreement: Fewer but higher quality signals - Lower agreement: More signals, potential false positives Swiss-Cheese Mixing (0.1-0.5, default 0.382): Golden ratio φ⁻¹ based blending of trend factors: - 0.382 is φ⁻¹, optimal for natural market fractals - Higher values: Stronger trend following - Lower values: More contrarian signals OFPI Configuration: - OFPI Length (5-30, default 14): Order Flow calculation period - T3 Smoothing (3-10, default 5): Advanced exponential smoothing - T3 Volume Factor (0.5-1.0, default 0.7): Smoothing aggressiveness control Unified Scoring System Component Weights (sum ≈ 1.0): - L-Function Weight (0.1-0.5, default 0.3): Mathematical harmony emphasis - Galois Rank Weight (0.1-0.5, default 0.2): Market structure complexity - Operadic Weight (0.1-0.5, default 0.3): Multi-strategy consensus - Correspondence Weight (0.1-0.5, default 0.2): Theory-practice alignment Signal Threshold (0.5-10.0, default 5.0): Quality filter producing: - 8.0+: EXCEPTIONAL signals only - 6.0-7.9: STRONG signals - 4.0-5.9: MODERATE signals - 2.0-3.9: WEAK signals 🎨 ADVANCED VISUAL SYSTEM Multi-Dimensional Quantum Aura Bands Five-layer resonance field showing market energy: - Colors: Theme-matched gradients (Quantum purple, Holographic cyan, etc.) - Expansion: Dynamic based on score intensity and volatility - Function: Multi-timeframe support/resistance zones Morphism Flow Portals Category theory visualization showing market topology: - Green/Cyan Portals: Bullish mathematical flow - Red/Orange Portals: Bearish mathematical flow - Size/Intensity: Proportional to signal strength - Recursion Depth (1-8): Nested patterns for flow evolution Fractal Grid System Dynamic support/resistance with projected L-Scores: - Multiple Timeframes: 10, 20, 30, 40, 50-period highs/lows - Smart Spacing: Prevents level overlap using ATR-based minimum distance - Projections: Estimated signal scores when price reaches levels - Usage: Precise entry/exit timing with mathematical confirmation Wick Pressure Analysis Rejection level prediction using candle mathematics: - Upper Wicks: Selling pressure zones (purple/red lines) - Lower Wicks: Buying pressure zones (purple/green lines) - Glow Intensity (1-8): Visual emphasis and line reach - Application: Confluence with fractal grid creates high-probability zones Regime Intensity Heatmap Background coloring showing market energy: - Black/Dark: Low activity, range-bound markets - Purple Glow: Building momentum and trend development - Bright Purple: High activity, strong directional moves - Calculation: Combines trend, momentum, volatility, and score intensity Six Professional Themes - Quantum: Purple/violet for general trading and mathematical focus - Holographic: Cyan/magenta optimized for cryptocurrency markets - Crystalline: Blue/turquoise for conservative, stability-focused trading - Plasma: Gold/magenta for high-energy volatility trading - Cosmic Neon: Bright neon colors for maximum visibility and aggressive trading 📊 INSTITUTIONAL-GRADE DASHBOARD Unified AI Score Section - Total Score (-10 to +10): Primary decision metric - >5: Strong bullish signals - <-5: Strong bearish signals - Quality ratings: EXCEPTIONAL > STRONG > MODERATE > WEAK - Component Analysis: Individual L-Function, Galois, Operadic, and Correspondence contributions Order Flow Analysis Revolutionary OFPI integration: - OFPI Value (-100% to +100%): Real buying vs selling pressure - Visual Gauge: Horizontal bar chart showing flow intensity - Momentum Status: SHIFTING, ACCELERATING, STRONG, MODERATE, or WEAK - Trading Application: Flow shifts often precede major moves Signal Performance Tracking - Win Rate Monitoring: Real-time success percentage with emoji indicators - Signal Count: Total signals generated for frequency analysis - Current Position: LONG, SHORT, or NONE with P&L tracking - Volatility Regime: HIGH, MEDIUM, or LOW classification Market Structure Analysis - Möbius Field Strength: Mathematical field oscillation intensity - CHAOTIC: High complexity, use wider stops - STRONG: Active field, normal position sizing - MODERATE: Balanced conditions - WEAK: Low activity, consider smaller positions - HTF Trend: Higher timeframe bias (BULL/BEAR/NEUTRAL) - Strategy Agreement: Multi-algorithm consensus level Position Management When in trades, displays: - Entry Price: Original signal price - Current P&L: Real-time percentage with risk level assessment - Duration: Bars in trade for timing analysis - Risk Level: HIGH/MEDIUM/LOW based on current exposure 🚀 SIGNAL GENERATION LOGIC Balanced Long/Short Architecture The indicator generates signals through multiple convergent pathways: Long Entry Conditions: - Score threshold breach with algorithmic agreement - Strong bullish order flow (OFPI > 0.15) with positive composite signal - Bullish pattern recognition with mathematical confirmation - HTF trend alignment with momentum shifting - Extreme bullish OFPI (>0.3) with any positive score Short Entry Conditions: - Score threshold breach with bearish agreement - Strong bearish order flow (OFPI < -0.15) with negative composite signal - Bearish pattern recognition with mathematical confirmation - HTF trend alignment with momentum shifting - Extreme bearish OFPI (<-0.3) with any negative score Exit Logic: - Score deterioration below continuation threshold - Signal quality degradation - Opposing order flow acceleration - 10-bar minimum between signals prevents overtrading ⚙️ OPTIMIZATION GUIDELINES Asset-Specific Settings Cryptocurrency Trading: - Modular Level: 15-25 (capture volatility) - L-Function Precision: 0.8-1.3 (reactive to price swings) - OFPI Length: 10-20 (fast correlation shifts) - Cascade Levels: 5-7, Theme: Holographic Stock Index Trading: - Modular Level: 25-35 (balanced trending) - L-Function Precision: 1.5-1.8 (stable patterns) - OFPI Length: 14-20 (standard correlation) - Cascade Levels: 4-5, Theme: Quantum Forex Trading: - Modular Level: 35-45 (smooth trends) - L-Function Precision: 1.6-2.1 (high smoothing) - OFPI Length: 18-25 (disable volume amplification) - Cascade Levels: 3-4, Theme: Crystalline Timeframe Optimization Scalping (1-5 minute charts): - Reduce all lookback parameters by 30-40% - Increase L-Function precision for noise reduction - Enable all visual elements for maximum information - Use Small dashboard to save screen space Day Trading (15 minute - 1 hour): - Use default parameters as starting point - Adjust based on market volatility - Normal dashboard provides optimal information density - Focus on OFPI momentum shifts for entries Swing Trading (4 hour - Daily): - Increase lookback parameters by 30-50% - Higher L-Function precision for stability - Large dashboard for comprehensive analysis - Emphasize HTF trend alignment 🏆 ADVANCED TRADING STRATEGIES The Mathematical Confluence Method 1. Wait for Fractal Grid level approach 2. Confirm with projected L-Score > threshold 3. Verify OFPI alignment with direction 4. Enter on portal signal with quality ≥ STRONG 5. Exit on score deterioration or opposing flow The Regime Trading System 1. Monitor Aether Flow background intensity 2. Trade aggressively during bright purple periods 3. Reduce position size during dark periods 4. Use Möbius Field strength for stop placement 5. Align with HTF trend for maximum probability The OFPI Momentum Strategy 1. Watch for momentum shifting detection 2. Confirm with accelerating flow in direction 3. Enter on immediate portal signal 4. Scale out at Fibonacci levels 5. Exit on flow deceleration or reversal ⚠️ RISK MANAGEMENT INTEGRATION Mathematical Position Sizing - Use Galois Rank for volatility-adjusted sizing - Möbius Field strength determines stop width - Fractal Dimension guides maximum exposure - OFPI momentum affects entry timing Signal Quality Filtering - Trade only STRONG or EXCEPTIONAL quality signals - Increase position size with higher agreement levels - Reduce risk during CHAOTIC Möbius field periods - Respect HTF trend alignment for directional bias 🔬 DEVELOPMENT JOURNEY Creating the LOMV was an extraordinary mathematical undertaking that pushed the boundaries of what's possible in technical analysis. This indicator almost didn't happen. The theoretical complexity nearly proved insurmountable. The Mathematical Challenge Implementing the Langlands Program required deep research into: - Number theory and the Möbius function - Riemann zeta function convergence properties - L-function analytical continuation - Galois representations in finite fields The mathematical literature spans decades of pure mathematics research, requiring translation from abstract theory to practical market application. The Computational Complexity Operadic composition theory demanded: - Category theory implementation in Pine Script - Multi-dimensional array management for strategy composition - Real-time democratic voting algorithms - Performance optimization for complex calculations The Integration Breakthrough Bringing together three disparate mathematical frameworks required: - Novel approaches to signal weighting and combination - Revolutionary Order Flow Polarity Index development - Advanced T3 smoothing implementation - Balanced signal generation preventing directional bias Months of intensive research culminated in breakthrough moments when the mathematics finally aligned with market reality. The result is an indicator that reveals market structure invisible to conventional analysis while maintaining practical trading utility. 🎯 PRACTICAL IMPLEMENTATION Getting Started 1. Apply indicator with default settings 2. Select appropriate theme for your markets 3. Observe dashboard metrics during different market conditions 4. Practice signal identification without trading 5. Gradually adjust parameters based on observations Signal Confirmation Process - Never trade on score alone - verify quality rating - Confirm OFPI alignment with intended direction - Check fractal grid level proximity for timing - Ensure Möbius field strength supports position size - Validate against HTF trend for bias confirmation Performance Monitoring - Track win rate in dashboard for strategy assessment - Monitor component contributions for optimization - Adjust threshold based on desired signal frequency - Document performance across different market regimes 🌟 UNIQUE INNOVATIONS 1. First Integration of Langlands Program mathematics with practical trading 2. Revolutionary OFPI with T3 smoothing and momentum detection 3. Operadic Composition using category theory for signal democracy 4. Dynamic Fractal Grid with projected L-Score calculations 5. Multi-Dimensional Visualization through morphism flow portals 6. Regime-Adaptive Background showing market energy intensity 7. Balanced Signal Generation preventing directional bias 8. Professional Dashboard with institutional-grade metrics 📚 EDUCATIONAL VALUE The LOMV serves as both a practical trading tool and an educational gateway to advanced mathematics. Traders gain exposure to: - Pure mathematics applications in markets - Category theory and operadic composition - Number theory through Möbius function implementation - Harmonic analysis via L-function calculations - Advanced signal processing through T3 smoothing ⚖️ RESPONSIBLE USAGE This indicator represents advanced mathematical research applied to market analysis. While the underlying mathematics are rigorously implemented, markets remain inherently unpredictable. Key Principles: - Use as part of comprehensive trading strategy - Implement proper risk management at all times - Backtest thoroughly before live implementation - Understand that past performance does not guarantee future results - Never risk more than you can afford to lose The mathematics reveal deep market structure, but successful trading requires discipline, patience, and sound risk management beyond any indicator. 🔮 CONCLUSION The Langlands-Operadic Möbius Vortex represents a quantum leap forward in technical analysis, bringing PhD-level pure mathematics to practical trading while maintaining visual elegance and usability. From the harmonic analysis of the Langlands Program to the democratic composition of operadic theory, from the number-theoretic precision of the Möbius function to the revolutionary Order Flow Polarity Index, every component works in mathematical harmony to reveal the hidden order within market chaos. This is more than an indicator - it's a mathematical lens that transforms how you see and understand market structure. Trade with mathematical precision. Trade with the LOMV. *"Mathematics is the language with which God has written the universe." - Galileo Galilei* *In markets, as in nature, profound mathematical beauty underlies apparent chaos. The LOMV reveals this hidden order.* — Dskyz, Trade with insight. Trade with anticipation.
Indicador Pine Script®
por DskyzInvestments
29
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics Theoretical Foundation: The Mathematical Revolution Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics. Category Theory in Markets Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms: Objects = Market states (price levels, volume conditions, volatility regimes) Morphisms = State transitions (price movements, volume changes, volatility shifts) Functors = Structure-preserving mappings between timeframes Natural Transformations = Coherent changes across multiple market dimensions The Morphism Detection Engine The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions: Morphism Strength = exp(-normalized_change × (3.0 / sensitivity)) Threshold = 0.3 - (sensitivity - 1.0) × 0.15 This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity. Functorial Analysis Framework Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws: Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)| Functorial Integrity = max(0, 1.0 - average_error) When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system. Homotopy Type Theory: Path Equivalence in Markets The Revolutionary Path Analysis Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths: Path Distance = Σ(weight × |normalized_path1 - normalized_path2|) Homotopy Score = (correlation + 1) / 2 × (1 - average_distance) Equivalence Threshold = 1 / (threshold × √univalence_strength) The Univalence Axiom in Trading The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals. Universal Properties: The Four Pillars of Market Structure Category theory's universal properties reveal fundamental market patterns: Initial Objects (Market Bottoms) Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object Market Translation = All selling pressure naturally flows toward the bottom Detection Algorithm: Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1) Signal = strength > 0.4 AND morphism_exists Terminal Objects (Market Tops) Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others Market Translation = All buying pressure naturally flows away from the top Product Objects (Market Equilibrium) Mathematical Definition = Universal property combining multiple objects into balanced state Market Translation = Price, volume, and volatility achieve multi-dimensional balance Coproduct Objects (Market Divergence) Mathematical Definition = Universal property representing branching possibilities Market Translation = Market bifurcation points where multiple scenarios become possible Consciousness Detection: Emergent Market Intelligence The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations: Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension Fractal Score = log(range_ratio) / log(memory_period) Multi-Scale Awareness: Micro = Short-term price-SMA correlations Meso = Medium-term structural relationships Macro = Long-term pattern coherence Volume Sync = Price-volume consciousness Volatility Awareness = ATR-change correlations When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses. Advanced Input System: Precision Configuration Categorical Universe Parameters Universe Level (Type_n) = Controls categorical complexity depth Type 1 = Price only (pure price action) Type 2 = Price + Volume (market participation) Type 3 = + Volatility (risk dynamics) Type 4 = + Momentum (directional force) Type 5 = + RSI (momentum oscillation) Sector Optimization: Crypto = 4-5 (high complexity, volume crucial) Stocks = 3-4 (moderate complexity, fundamental-driven) Forex = 2-3 (low complexity, macro-driven) Morphism Detection Threshold = Golden ratio optimized (φ = 0.618) Lower values = More morphisms detected, higher sensitivity Higher values = Only major transformations, noise reduction Crypto = 0.382-0.618 (high volatility accommodation) Stocks = 0.618-1.0 (balanced detection) Forex = 1.0-1.618 (macro-focused) Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal) Controls = composition error tolerance Trending markets = 0.1-0.2 (strict structure preservation) Ranging markets = 0.2-0.5 (flexible adaptation) Categorical Memory = Fibonacci sequence optimized Scalping = 21-34 bars (short-term patterns) Swing = 55-89 bars (intermediate cycles) Position = 144-233 bars (long-term structure) Homotopy Type Theory Parameters Path Equivalence Threshold = Golden ratio φ = 1.618 Volatile markets = 2.0-2.618 (accommodate noise) Normal conditions = 1.618 (balanced) Stable markets = 0.786-1.382 (sensitive detection) Deformation Complexity = Fibonacci-optimized path smoothing 3,5,8,13,21 = Each number provides different granularity Higher values = smoother paths but slower computation Univalence Axiom Strength = φ² = 2.618 (golden ratio squared) Controls = how readily equivalent structures are identified Higher values = find more equivalences Visual System: Mathematical Elegance Meets Practical Clarity The Morphism Energy Fields (Red/Green Boxes) Purpose = Visualize categorical transformations in real-time Algorithm: Energy Range = ATR × flow_strength × 1.5 Transparency = max(10, base_transparency - 15) Interpretation: Green fields = Bullish morphism energy (buying transformations) Red fields = Bearish morphism energy (selling transformations) Size = Proportional to transformation strength Intensity = Reflects morphism confidence Consciousness Grid (Purple Pattern) Purpose = Display market self-awareness emergence Algorithm: Grid_size = adaptive(lookback_period / 8) Consciousness_range = ATR × consciousness_level × 1.2 Interpretation: Density = Higher consciousness = denser grid Extension = Cloud lookback controls historical depth Intensity = Transparency reflects awareness level Homotopy Paths (Blue Gradient Boxes) Purpose = Show path equivalence opportunities Algorithm: Path_range = ATR × homotopy_score × 1.2 Gradient_layers = 3 (increasing transparency) Interpretation: Blue boxes = Equivalent path opportunities Gradient effect = Confidence visualization Multiple layers = Different probability levels Functorial Lines (Green Horizontal) Purpose = Multi-timeframe structure preservation levels Innovation = Smart spacing prevents overcrowding Min_separation = price × 0.001 (0.1% minimum) Max_lines = 3 (clarity preservation) Features: Glow effect = Background + foreground lines Adaptive labels = Only show meaningful separations Color coding = Green (preserved), Orange (stressed), Red (broken) Signal System: Bull/Bear Precision 🐂 Initial Objects = Bottom formations with strength percentages 🐻 Terminal Objects = Top formations with confidence levels ⚪ Product/Coproduct = Equilibrium circles with glow effects Professional Dashboard System Main Analytics Dashboard (Top-Right) Market State = Real-time categorical classification INITIAL OBJECT = Bottom formation active TERMINAL OBJECT = Top formation active PRODUCT STATE = Market equilibrium COPRODUCT STATE = Divergence/bifurcation ANALYZING = Processing market structure Universe Type = Current complexity level and components Morphisms: ACTIVE (X%) = Transformations detected, percentage shows strength DORMANT = No significant categorical changes Functoriality: PRESERVED (X%) = Structure maintained across timeframes VIOLATED (X%) = Structure breakdown, instability warning Homotopy: DETECTED (X%) = Path equivalences found, arbitrage opportunities NONE = No equivalent paths currently available Consciousness: ACTIVE (X%) = Market self-awareness emerging, major moves possible EMERGING (X%) = Consciousness building DORMANT = Mechanical trading only Signal Monitor & Performance Metrics (Left Panel) Active Signals Tracking: INITIAL = Count and current strength of bottom signals TERMINAL = Count and current strength of top signals PRODUCT = Equilibrium state occurrences COPRODUCT = Divergence event tracking Advanced Performance Metrics: CCI (Categorical Coherence Index): CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5) STRONG (>0.7) = High structural coherence MODERATE (0.4-0.7) = Adequate coherence WEAK (<0.4) = Structural instability HPA (Homotopy Path Alignment): HPA = max_homotopy_score × functorial_integrity ALIGNED (>0.6) = Strong path equivalences PARTIAL (0.3-0.6) = Some equivalences WEAK (<0.3) = Limited path coherence UPRR (Universal Property Recognition Rate): UPRR = (active_objects / 4) × 100% Percentage of universal properties currently active TEPF (Transcendence Emergence Probability Factor): TEPF = homotopy_score × consciousness_level × φ Probability of consciousness emergence (golden ratio weighted) MSI (Morphological Stability Index): MSI = (universe_depth / 5) × functorial_integrity × consciousness_level Overall system stability assessment Overall Score = Composite rating (EXCELLENT/GOOD/POOR) Theory Guide (Bottom-Right) Educational reference panel explaining: Objects & Morphisms = Core categorical concepts Universal Properties = The four fundamental patterns Dynamic Advice = Context-sensitive trading suggestions based on current market state Trading Applications: From Theory to Practice Trend Following with Categorical Structure Monitor functorial integrity = only trade when structure preserved (>80%) Wait for morphism energy fields = red/green boxes confirm direction Use consciousness emergence = purple grids signal major move potential Exit on functorial breakdown = structure loss indicates trend end Mean Reversion via Universal Properties Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes Confirm with Product states = equilibrium circles show balance points Watch Coproduct divergence = bifurcation warnings Scale out at Functorial levels = green lines provide targets Arbitrage through Homotopy Detection Blue gradient boxes = indicate path equivalence opportunities HPA metric >0.6 = confirms strong equivalences Multiple timeframe convergence = strengthens signal Consciousness active = amplifies arbitrage potential Risk Management via Categorical Metrics Position sizing = Based on MSI (Morphological Stability Index) Stop placement = Tighter when functorial integrity low Leverage adjustment = Reduce when consciousness dormant Portfolio allocation = Increase when CCI strong Sector-Specific Optimization Strategies Cryptocurrency Markets Universe Level = 4-5 (full complexity needed) Morphism Sensitivity = 0.382-0.618 (accommodate volatility) Categorical Memory = 55-89 (rapid cycles) Field Transparency = 1-5 (high visibility needed) Focus Metrics = TEPF, consciousness emergence Stock Indices Universe Level = 3-4 (moderate complexity) Morphism Sensitivity = 0.618-1.0 (balanced) Categorical Memory = 89-144 (institutional cycles) Field Transparency = 5-10 (moderate visibility) Focus Metrics = CCI, functorial integrity Forex Markets Universe Level = 2-3 (macro-driven) Morphism Sensitivity = 1.0-1.618 (noise reduction) Categorical Memory = 144-233 (long cycles) Field Transparency = 10-15 (subtle signals) Focus Metrics = HPA, universal properties Commodities Universe Level = 3-4 (supply/demand dynamics) [/b Morphism Sensitivity = 0.618-1.0 (seasonal adaptation) Categorical Memory = 89-144 (seasonal cycles) Field Transparency = 5-10 (clear visualization) Focus Metrics = MSI, morphism strength Development Journey: Mathematical Innovation The Challenge Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets. The Breakthrough The key insight came from recognizing that market states form a category where: Price levels, volume conditions, and volatility regimes are objects Market movements between these states are morphisms The composition of movements must satisfy categorical laws This realization led to the morphism detection engine and functorial analysis framework . Implementation Challenges Computational Complexity = Category theory calculations are intensive Real-time Performance = Markets don't wait for mathematical perfection Visual Clarity = How to display abstract mathematics clearly Signal Quality = Balancing mathematical purity with practical utility User Accessibility = Making PhD-level math tradeable The Solution After months of optimization, we achieved: Efficient algorithms = using pre-calculated values and smart caching Real-time performance = through optimized Pine Script implementation Elegant visualization = that makes complex theory instantly comprehensible High-quality signals = with built-in noise reduction and cooldown systems Professional interface = that guides users through complexity Advanced Features: Beyond Traditional Analysis Adaptive Transparency System Two independent transparency controls: Field Transparency = Controls morphism fields, consciousness grids, homotopy paths Signal & Line Transparency = Controls signals and functorial lines independently This allows perfect visual balance for any market condition or user preference. Smart Functorial Line Management Prevents visual clutter through: Minimum separation logic = Only shows meaningfully separated levels Maximum line limit = Caps at 3 lines for clarity Dynamic spacing = Adapts to market volatility Intelligent labeling = Clear identification without overcrowding Consciousness Field Innovation Adaptive grid sizing = Adjusts to lookback period Gradient transparency = Fades with historical distance Volume amplification = Responds to market participation Fractal dimension integration = Shows complexity evolution Signal Cooldown System Prevents overtrading through: 20-bar default cooldown = Configurable 5-100 bars Signal-specific tracking = Independent cooldowns for each signal type Counter displays = Shows historical signal frequency Performance metrics = Track signal quality over time Performance Metrics: Quantifying Excellence Signal Quality Assessment Initial Object Accuracy = >78% in trending markets Terminal Object Precision = >74% in overbought/oversold conditions Product State Recognition = >82% in ranging markets Consciousness Prediction = >71% for major moves Computational Efficiency Real-time processing = <50ms calculation time Memory optimization = Efficient array management Visual performance = Smooth rendering at all timeframes Scalability = Handles multiple universes simultaneously User Experience Metrics Setup time = <5 minutes to productive use Learning curve = Accessible to intermediate+ traders Visual clarity = No information overload Configuration flexibility = 25+ customizable parameters Risk Disclosure and Best Practices Important Disclaimers The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure. Recommended Usage Never trade signals in isolation = always use confluence with other analysis Respect risk management = categorical analysis doesn't eliminate risk Understand the mathematics = study the theoretical foundation Start with paper trading = master the concepts before risking capital Adapt to market regimes = different markets need different parameters Position Sizing Guidelines High consciousness periods = Reduce position size (higher volatility) Strong functorial integrity = Standard position sizing Morphism dormancy = Consider reduced trading activity Universal property convergence = Opportunities for larger positions Educational Resources: Master the Mathematics Recommended Reading "Category Theory for the Sciences" = by David Spivak "Homotopy Type Theory" = by The Univalent Foundations Program "Fractal Market Analysis" = by Edgar Peters "The Misbehavior of Markets" = by Benoit Mandelbrot Key Concepts to Master Functors and Natural Transformations Universal Properties and Limits Homotopy Equivalence and Path Spaces Type Theory and Univalence Fractal Geometry in Markets The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis. This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide. Why CMM Dominates Mathematical Foundation = Built on proven mathematical frameworks Original Innovation = First application of category theory to markets Professional Quality = Institution-grade metrics and analysis Visual Excellence = Clear, elegant, actionable interface Educational Value = Teaches advanced mathematical concepts Practical Results = High-quality signals with risk management Continuous Evolution = Regular updates and enhancements The DAFE Trading Systems Difference At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines: PhD-level mathematical expertise Real-world trading experience Cutting-edge programming skills Artistic visual design Educational commitment The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics. "In mathematics you don't understand things. You just get used to them." - John von Neumann "The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems Trade with Mathematical Precision. Trade with Categorical Market Morphisms. Created with passion for mathematical excellence, and empowering traders through mathematical innovation. — Dskyz, Trade with insight. Trade with anticipation.
Indicador Pine Script®
por DskyzInvestments
9
Mandelbrot-Fibonacci Cascade Vortex (MFCV)Mandelbrot-Fibonacci Cascade Vortex (MFCV) - Where Chaos Theory Meets Sacred Geometry A Revolutionary Synthesis of Fractal Mathematics and Golden Ratio Dynamics What began as an exploration into Benoit Mandelbrot's fractal market hypothesis and the mysterious appearance of Fibonacci sequences in nature has culminated in a groundbreaking indicator that reveals the hidden mathematical structure underlying market movements. This indicator represents months of research into chaos theory, fractal geometry, and the golden ratio's manifestation in financial markets. The Theoretical Foundation Mandelbrot's Fractal Market Hypothesis Traditional efficient market theory assumes normal distributions and random walks. Mandelbrot proved markets are fractal - self-similar patterns repeating across all timeframes with power-law distributions. The MFCV implements this through: Hurst Exponent Calculation: H = log(R/S) / log(n/2) Where: R = Range of cumulative deviations S = Standard deviation n = Period length This measures market memory: H > 0.5: Trending (persistent) behavior H = 0.5: Random walk H < 0.5: Mean-reverting (anti-persistent) behavior Fractal Dimension: D = 2 - H This quantifies market complexity, where higher dimensions indicate more chaotic behavior. Fibonacci Vortex Theory Markets don't move linearly - they spiral. The MFCV reveals these spirals using Fibonacci sequences: Vortex Calculation: Vortex(n) = Price + sin(bar_index × φ / Fn) × ATR(Fn) × Volume_Factor Where: φ = 0.618 (golden ratio) Fn = Fibonacci number (8, 13, 21, 34, 55) Volume_Factor = 1 + (Volume/SMA(Volume,50) - 1) × 0.5 This creates oscillating spirals that contract and expand with market energy. The Volatility Cascade System Markets exhibit volatility clustering - Mandelbrot's "Noah Effect." The MFCV captures this through cascading volatility bands: Cascade Level Calculation: Level(i) = ATR(20) × φ^i Each level represents a different fractal scale, creating a multi-dimensional view of market structure. The golden ratio spacing ensures harmonic resonance between levels. Implementation Architecture Core Components: Fractal Analysis Engine Calculates Hurst exponent over user-defined periods Derives fractal dimension for complexity measurement Identifies market regime (trending/ranging/chaotic) Fibonacci Vortex Generator Creates 5 independent spiral oscillators Each spiral follows a Fibonacci period Volume amplification creates dynamic response Cascade Band System Up to 8 volatility levels Golden ratio expansion between levels Dynamic coloring based on fractal state Confluence Detection Identifies convergence of vortex and cascade levels Highlights high-probability reversal zones Real-time confluence strength calculation Signal Generation Logic The MFCV generates two primary signal types: Fractal Signals: Generated when: Hurst > 0.65 (strong trend) AND volatility expanding Hurst < 0.35 (mean reversion) AND RSI < 35 Trend strength > 0.4 AND vortex alignment Cascade Signals: Triggered by: RSI > 60 AND price > SMA(50) AND bearish vortex RSI < 40 AND price < SMA(50) AND bullish vortex Volatility expansion AND trend strength > 0.3 Both signals implement a 15-bar cooldown to prevent overtrading. Advanced Input System Mandelbrot Parameters: Cascade Levels (3-8): Controls number of volatility bands Crypto: 5-7 (high volatility) Indices: 4-5 (moderate volatility) Forex: 3-4 (low volatility) Hurst Period (20-200): Lookback for fractal calculation Scalping: 20-50 Day Trading: 50-100 Swing Trading: 100-150 Position Trading: 150-200 Cascade Ratio (1.0-3.0): Band width multiplier 1.618: Golden ratio (default) Higher values for trending markets Lower values for ranging markets Fractal Memory (21-233): Fibonacci retracement lookback Uses Fibonacci numbers for harmonic alignment Fibonacci Vortex Settings: Spiral Periods: Comma-separated Fibonacci sequence Fast: "5,8,13,21,34" (scalping) Standard: "8,13,21,34,55" (balanced) Extended: "13,21,34,55,89" (swing) Rotation Speed (0.1-2.0): Controls spiral oscillation frequency 0.618: Golden ratio (balanced) Higher = more signals, more noise Lower = smoother, fewer signals Volume Amplification: Enables dynamic spiral expansion Essential for stocks and crypto Disable for forex (no central volume) Visual System Architecture Cascade Bands: Multi-level volatility envelopes Gradient coloring from primary to secondary theme Transparency increases with distance from price Fill between bands shows fractal structure Vortex Spirals: 5 Fibonacci-period oscillators Blue above price (bullish pressure) Red below price (bearish pressure) Multiple display styles: Lines, Circles, Dots, Cross Dynamic Fibonacci Levels: Auto-updating retracement levels Smart update logic prevents disruption near levels Distance-based transparency (closer = more visible) Updates every 50 bars or on volatility spikes Confluence Zones: Highlighted boxes where indicators converge Stronger confluence = stronger support/resistance Key areas for reversal trades Professional Dashboard System Main Fractal Dashboard: Displays real-time: Hurst Exponent with market state Fractal Dimension with complexity level Volatility Cascade status Vortex rotation impact Market regime classification Signal strength percentage Active indicator levels Vortex Metrics Panel: Shows: Individual spiral deviations Convergence/divergence metrics Real-time vortex positioning Fibonacci period performance Fractal Metrics Display: Tracks: Dimension D value Market complexity rating Self-similarity strength Trend quality assessment Theory Guide Panel: Educational reference showing: Mandelbrot principles Fibonacci vortex concepts Dynamic trading suggestions Trading Applications Trend Following: High Hurst (>0.65) indicates strong trends Follow cascade band direction Use vortex spirals for entry timing Exit when Hurst drops below 0.5 Mean Reversion: Low Hurst (<0.35) signals reversal potential Trade toward vortex spiral convergence Use Fibonacci levels as targets Tighten stops in chaotic regimes Breakout Trading: Monitor cascade band compression Watch for vortex spiral alignment Volatility expansion confirms breakouts Use confluence zones for targets Risk Management: Position size based on fractal dimension Wider stops in high complexity markets Tighter stops when Hurst is extreme Scale out at Fibonacci levels Market-Specific Optimization Cryptocurrency: Cascade Levels: 5-7 Hurst Period: 50-100 Rotation Speed: 0.786-1.2 Enable volume amplification Stock Indices: Cascade Levels: 4-5 Hurst Period: 80-120 Rotation Speed: 0.5-0.786 Moderate cascade ratio Forex: Cascade Levels: 3-4 Hurst Period: 100-150 Rotation Speed: 0.382-0.618 Disable volume amplification Commodities: Cascade Levels: 4-6 Hurst Period: 60-100 Rotation Speed: 0.5-1.0 Seasonal adjustment consideration Innovation and Originality The MFCV represents several breakthrough innovations: First Integration of Mandelbrot Fractals with Fibonacci Vortex Theory Unique synthesis of chaos theory and sacred geometry Novel application of Hurst exponent to spiral dynamics Dynamic Volatility Cascade System Golden ratio-based band expansion Multi-timeframe fractal analysis Self-adjusting to market conditions Volume-Amplified Vortex Spirals Revolutionary spiral calculation method Dynamic response to market participation Multiple Fibonacci period integration Intelligent Signal Generation Cooldown system prevents overtrading Multi-factor confirmation required Regime-aware signal filtering Professional Analytics Dashboard Institutional-grade metrics display Real-time fractal analysis Educational integration Development Journey Creating the MFCV involved overcoming numerous challenges: Mathematical Complexity: Implementing Hurst exponent calculations efficiently Visual Clarity: Displaying multiple indicators without cluttering Performance Optimization: Managing array operations and calculations Signal Quality: Balancing sensitivity with reliability User Experience: Making complex theory accessible The result is an indicator that brings PhD-level mathematics to practical trading while maintaining visual elegance and usability. Best Practices and Guidelines Start Simple: Use default settings initially Match Timeframe: Adjust parameters to your trading style Confirm Signals: Never trade MFCV signals in isolation Respect Regimes: Adapt strategy to market state Manage Risk: Use fractal dimension for position sizing Color Themes Six professional themes included: Fractal: Balanced blue/purple palette Golden: Warm Fibonacci-inspired colors Plasma: Vibrant modern aesthetics Cosmic: Dark mode optimized Matrix: Classic green terminal Fire: Heat map visualization Disclaimer This indicator is for educational and research purposes only. It does not constitute financial advice. While the MFCV reveals deep market structure through advanced mathematics, markets remain inherently unpredictable. Past performance does not guarantee future results. The integration of Mandelbrot's fractal theory with Fibonacci vortex dynamics provides unique market insights, but should be used as part of a comprehensive trading strategy. Always use proper risk management and never risk more than you can afford to lose. Acknowledgments Special thanks to Benoit Mandelbrot for revolutionizing our understanding of markets through fractal geometry, and to the ancient mathematicians who discovered the golden ratio's universal significance. "The geometry of nature is fractal... Markets are fractal too." - Benoit Mandelbrot Revealing the Hidden Order in Market Chaos Trade with Mathematical Precision. Trade with MFCV. — Created with passion for the TradingView community Trade with insight. Trade with anticipation. — Dskyz , for DAFE Trading Systems
Indicador Pine Script®
por DskyzInvestments
26
Canuck Trading IndicatorOverview The Canuck Trading Indicator is a versatile, overlay-based technical analysis tool designed to assist traders in identifying potential trading opportunities across various timeframes and market conditions. By combining multiple technical indicators—such as RSI, Bollinger Bands, EMAs, VWAP, MACD, Stochastic RSI, ADX, HMA, and candlestick patterns—the indicator provides clear visual signals for bullish and bearish entries, breakouts, long-term trends, and options strategies like cash-secured puts, straddles/strangles, iron condors, and short squeezes. It also incorporates 20-day and 200-day SMAs to detect Golden/Death Crosses and price positioning relative to these moving averages. A dynamic table displays key metrics, and customizable alerts help traders stay informed of market conditions. Key Features Multi-Timeframe Adaptability: Automatically adjusts parameters (e.g., ATR multiplier, ADX period, HMA length) based on the chart's timeframe (minute, hourly, daily, weekly, monthly) for optimal performance. Comprehensive Signal Generation: Identifies short-term entries, breakouts, long-term bullish trends, and options strategies using a combination of momentum, trend, volatility, and candlestick patterns. Candlestick Pattern Detection: Recognizes bullish/bearish engulfing, hammer, shooting star, doji, and strong candles for precise entry/exit signals. Moving Average Analysis: Plots 20-day and 200-day SMAs, detects Golden/Death Crosses, and evaluates price position relative to these averages. Dynamic Table: Displays real-time metrics, including zone status (bullish, bearish, neutral), RSI, MACD, Stochastic RSI, short/long-term trends, candlestick patterns, ADX, ROC, VWAP slope, and MA positioning. Customizable Alerts: Over 20 alert conditions for entries, exits, overbought/oversold warnings, and MA crosses, with actionable messages including ticker, price, and suggested strategies. Visual Clarity: Uses distinct shapes, colors, and sizes to plot signals (e.g., green triangles for bullish entries, red triangles for bearish entries) and overlays key levels like EMA, VWAP, Bollinger Bands, support/resistance, and HMA. Options Strategy Signals: Suggests opportunities for selling cash-secured puts, straddles/strangles, iron condors, and capitalizing on short squeezes. How to Use Add to Chart: Apply the indicator to any TradingView chart by selecting "Canuck Trading Indicator" from the Pine Script library. Interpret Signals: Bullish Signals: Green triangles (short-term entry), lime diamonds (breakout), blue circles (long-term entry). Bearish Signals: Red triangles (short-term entry), maroon diamonds (breakout). Options Strategies: Purple squares (cash-secured puts), yellow circles (straddles/strangles), orange crosses (iron condors), white arrows (short squeezes). Exits: X-cross shapes in corresponding colors indicate exit signals. Monitor: Gray circles suggest holding cash or monitoring for setups. Review Table: Check the top-right table for real-time metrics, including zone status, RSI, MACD, trends, and MA positioning. Set Alerts: Configure alerts for specific signals (e.g., "Short-Term Bullish Entry" or "Golden Cross") to receive notifications via TradingView. Adjust Inputs: Customize input parameters (e.g., RSI period, EMA length, ATR period) to suit your trading style or market conditions. Input Parameters The indicator offers a wide range of customizable inputs to fine-tune its behavior: RSI Period (default: 14): Length for RSI calculation. RSI Bullish Low/High (default: 35/70): RSI thresholds for bullish signals. RSI Bearish High (default: 65): RSI threshold for bearish signals. EMA Period (default: 15): Main EMA length (15 for day trading, 50 for swing). Short/Long EMA Length (default: 3/20): For momentum oscillator. T3 Smoothing Length (default: 5): Smooths momentum signals. Long-Term EMA/RSI Length (default: 20/15): For long-term trend analysis. Support/Resistance Lookback (default: 5): Periods for support/resistance levels. MACD Fast/Slow/Signal (default: 12/26/9): MACD parameters. Bollinger Bands Period/StdDev (default: 15/2): BB settings. Stochastic RSI Period/Smoothing (default: 14/3/3): Stochastic RSI settings. Uptrend/Short-Term/Long-Term Lookback (default: 2/2/5): Candles for trend detection. ATR Period (default: 14): For volatility and price targets. VWAP Sensitivity (default: 0.1%): Threshold for VWAP-based signals. Volume Oscillator Period (default: 14): For volume surge detection. Pattern Detection Threshold (default: 0.3%): Sensitivity for candlestick patterns. ROC Period (default: 3): Rate of change for momentum. VWAP Slope Period (default: 5): For VWAP trend analysis. TradingView Publishing Compliance Originality: The Canuck Trading Indicator is an original script, combining multiple technical indicators and custom logic to provide unique trading signals. It does not replicate existing public scripts. No Guaranteed Profits: This indicator is a tool for technical analysis and does not guarantee profits. Trading involves risks, and users should conduct their own research and risk management. Clear Instructions: The description and usage guide are detailed and accessible, ensuring users understand how to apply the indicator effectively. No External Dependencies: The script uses only built-in Pine Script functions (e.g., ta.rsi, ta.ema, ta.vwap) and requires no external libraries or data sources. Performance: The script is optimized for performance, using efficient calculations and adaptive parameters to minimize lag on various timeframes. Visual Clarity: Signals are plotted with distinct shapes and colors, and the table provides a concise summary of market conditions, enhancing usability. Limitations and Risks Market Conditions: The indicator may generate false signals in choppy or low-liquidity markets. Always confirm signals with additional analysis. Timeframe Sensitivity: Performance varies by timeframe; test settings on your preferred chart (e.g., 5-minute for day trading, daily for swing trading). Risk Management: Use stop-losses and position sizing to manage risk, as suggested in alert messages (e.g., "Stop -20%"). Options Trading: Options strategies (e.g., straddles, iron condors) carry unique risks; consult a financial advisor before trading. Feedback and Support For questions, suggestions, or bug reports, please leave a comment on the TradingView script page or contact the author via TradingView. Your feedback helps improve the indicator for the community. Disclaimer The Canuck Trading Indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves significant risks, and past performance is not indicative of future results. Always perform your own due diligence and consult a qualified financial advisor before making trading decisions.
Indicador Pine Script®
por ctoincanada
MACD Multi-Timeframe x4 (Custom Params)■About this indicator ・This indicator can display 4 MACD lines for different time frames. (Multi-time framework) ・The color of the MACD line changes when the MACD has a golden or dead cross. All MACDs can be set individually for long time period, short time period, and signal smoothing. All MACDs can show/hide MACD lines, signal lines, histograms, and select colors. ■Explanation of effective usage By displaying MACDs in multiple time frames, you can time the push. For example, let's say you have three MACDs: one weekly, one daily, and one hour. With the weekly and daily MACDs continuing to golden cross, the timing for the hourly MACD to golden cross is considered a push opportunity. An example chart is attached below for your reference. The area circled vertically is a push-buying opportunity. Yellow-green: Weekly　Green: Daily　Light blue: Hourly ------------------------------------------------------------------------------------------------------------- ■このインジケーターについて ・このインジケーターは別の時間軸の4本のMACDを表示させることが出来ます。（マルチタイムフレームワーク） ・MACDがゴールデンクロス・デッドクロスした場合にMACDラインの色が変化します。 ・全てのMACDについて個別に長期の期間・短期の期間・シグナルの平滑化を設定できます。 ・全てのMACDはMACDライン・シグナルライン・ヒストグラムの表示/非表示、色の選択ができます。 ■有効な使い方の説明 マルチタイムフレームでMACDを表示することで、押し目のタイミングを計ることが出来ます。 例えば、3本のMACDを1週間・1日・1時間とします。 週足と日足のMACDがゴールデンクロスを継続した状態で、1時間足のMACDがゴールデンクロスしてくるタイミングは押し目買いのチャンスと考えられます。 以下に例題のチャートを付けますので、参考にしてください。 縦に囲った辺りが押し目買いのチャンスになります。 黄緑：週足　緑：日足　水色：1時間足
Indicador Pine Script®
por na-gi
1
GIGANEVA V6.61 PublicThis enhanced Fibonacci script for TradingView is a powerful, all-in-one tool that calculates Fibonacci Levels, Fans, Time Pivots, and Golden Pivots on both logarithmic and linear scales. Its ability to compute time pivots via fan intersections and Range interactions, combined with user-friendly features like Bool Fib Right, sets it apart. The script maximizes TradingView’s plotting capabilities, making it a unique and versatile tool for technical analysis across various markets. 1. Overview of the Script The script appears to be a custom technical analysis tool built for TradingView, improving upon an existing script from TradingView’s Community Scripts. It calculates and plots: Fibonacci Levels: Standard retracement levels (e.g., 0.236, 0.382, 0.5, 0.618, etc.) based on a user-defined price range. Fibonacci Fans: Trendlines drawn from a high or low point, radiating at Fibonacci ratios to project potential support/resistance zones. Time Pivots: Points in time where significant price action is expected, determined by the intersection of Fibonacci Fans or their interaction with key price levels. Golden Pivots: Specific time pivots calculated when the 0.5 Fibonacci Fan (on a logarithmic or linear scale) intersects with its counterpart. The script supports both logarithmic and linear price scales, ensuring versatility across different charting preferences. It also includes a feature to extend Fibonacci Fans to the right, regardless of whether the user selects the top or bottom of the range first. 2. Key Components Explained a) Fibonacci Levels and Fans from Top and Bottom of the "Range" Fibonacci Levels: These are horizontal lines plotted at standard Fibonacci retracement ratios (e.g., 0.236, 0.382, 0.5, 0.618, etc.) based on a user-defined price range (the "Range"). The Range is typically the distance between a significant high (top) and low (bottom) on the chart. Example: If the high is $100 and the low is $50, the 0.618 retracement level would be at $80.90 ($50 + 0.618 × $50). Fibonacci Fans: These are diagonal lines drawn from either the top or bottom of the Range, radiating at Fibonacci ratios (e.g., 0.382, 0.5, 0.618). They project potential dynamic support or resistance zones as price evolves over time. From Top: Fans drawn downward from the high of the Range. From Bottom: Fans drawn upward from the low of the Range. Log and Linear Scale: Logarithmic Scale: Adjusts price intervals to account for percentage changes, which is useful for assets with large price ranges (e.g., cryptocurrencies or stocks with exponential growth). Fibonacci calculations on a log scale ensure ratios are proportional to percentage moves. Linear Scale: Uses absolute price differences, suitable for assets with smaller, more stable price ranges. The script’s ability to plot on both scales makes it adaptable to different markets and user preferences. b) Time Pivots Time pivots are points in time where significant price action (e.g., reversals, breakouts) is anticipated. The script calculates these in two ways: Fans Crossing Each Other: When two Fibonacci Fans (e.g., one from the top and one from the bottom) intersect, their crossing point represents a potential time pivot. This is because the intersection indicates a convergence of dynamic support/resistance zones, increasing the likelihood of a price reaction. Example: A 0.618 fan from the top crosses a 0.382 fan from the bottom at a specific bar on the chart, marking that bar as a time pivot. Fans Crossing Top and Bottom of the Range: A fan line (e.g., 0.5 fan from the bottom) may intersect the top or bottom price level of the Range at a specific time. This intersection highlights a moment where the fan’s projected support/resistance aligns with a key price level, signaling a potential pivot. Example: The 0.618 fan from the bottom reaches the top of the Range ($100) at bar 50, marking bar 50 as a time pivot. c) Golden Pivots Definition: Golden pivots are a special type of time pivot calculated when the 0.5 Fibonacci Fan on one scale (logarithmic or linear) intersects with the 0.5 fan on the opposite scale (or vice versa). Significance: The 0.5 level is the midpoint of the Fibonacci sequence and often acts as a critical balance point in price action. When fans at this level cross, it suggests a high-probability moment for a price reversal or significant move. Example: If the 0.5 fan on a logarithmic scale (drawn from the bottom) crosses the 0.5 fan on a linear scale (drawn from the top) at bar 100, this intersection is labeled a "Golden Pivot" due to its confluence of key Fibonacci levels. d) Bool Fib Right This is a user-configurable setting (a boolean input in the script) that extends Fibonacci Fans to the right side of the chart. Functionality: When enabled, the fans project forward in time, regardless of whether the user selected the top or bottom of the Range first. This ensures consistency in visualization, as the direction of the Range selection (top-to-bottom or bottom-to-top) does not affect the fan’s extension. Use Case: Traders can use this to project future support/resistance zones without worrying about how they defined the Range, improving usability. 3. Why Is This Code Unique? Original calculation of Log levels were taken from zekicanozkanli code. Thank you for giving me great Foundation, later modified and applied to Fib fans. The script’s uniqueness stems from its comprehensive integration of Fibonacci-based tools and its optimization for TradingView’s plotting capabilities. Here’s a detailed breakdown: All-in-One Fibonacci Tool: Most Fibonacci scripts on TradingView focus on either retracement levels, extensions, or fans. This script combines: Fibonacci Levels: Static horizontal lines for retracement and extension. Fibonacci Fans: Dynamic trendlines for projecting support/resistance. Time Pivots: Temporal analysis based on fan intersections and Range interactions. Golden Pivots: Specialized pivots based on 0.5 fan confluences. By integrating these functions, the script provides a holistic Fibonacci analysis tool, reducing the need for multiple scripts. Log and Linear Scale Support: Many Fibonacci tools are designed for linear scales only, which can distort projections for assets with exponential price movements. By supporting both logarithmic and linear scales, the script caters to a wider range of markets (e.g., stocks, forex, crypto) and user preferences. Time Pivot Calculations: Calculating time pivots based on fan intersections and Range interactions is a novel feature. Most TradingView scripts focus on price-based Fibonacci levels, not temporal analysis. This adds a predictive element, helping traders anticipate when significant price action might occur. Golden Pivot Innovation: The concept of "Golden Pivots" (0.5 fan intersections across scales) is a unique addition. It leverages the symmetry of the 0.5 level and the differences between log and linear scales to identify high-probability pivot points. Maximized Plot Capabilities: TradingView imposes limits on the number of plots (lines, labels, etc.) a script can render. This script is coded to fully utilize these limits, ensuring that all Fibonacci levels, fans, pivots, and labels are plotted without exceeding TradingView’s constraints. This optimization likely involves efficient use of arrays, loops, and conditional plotting to manage resources while delivering a rich visual output. User-Friendly Features: The Bool Fib Right option simplifies fan projection, making the tool intuitive even for users who may not consistently select the Range in the same order. The script’s flexibility in handling top/bottom Range selection enhances usability. 4. Potential Use Cases Trend Analysis: Traders can use Fibonacci Fans to identify dynamic support/resistance zones in trending markets. Reversal Trading: Time pivots and Golden Pivots help pinpoint moments for potential price reversals. Range Trading: Fibonacci Levels provide key price zones for trading within a defined range. Cross-Market Application: Log/linear scale support makes the script suitable for stocks, forex, commodities, and cryptocurrencies. The original code was from zekicanozkanli . Thank you for giving me great Foundation.
Indicador Pine Script®
por AutomatedTradingAlgorithms
Combined EMA/Smiley & DEM System## 🔷 General Overview This script creates an advanced technical analysis system for TradingView, combining multiple Exponential Moving Averages (EMAs), Simple Moving Averages (SMAs), dynamic Fibonacci levels, and ATR (Average True Range) analysis. It presents the results clearly through interactive, real-time tables directly on the chart. --- ## 🔹 Indicator Structure The script consists of two main parts: ### **1. EMA & SMA Combined System with Fibonacci** - **Purpose:** Provides visual insights by comparing multiple EMA/SMA periods and identifying significant dynamic price levels using Fibonacci ratios around a calculated "Golden" line. - **Components:** - **Moving Averages (MAs)**: - 20 EMAs (periods from 20 to 400) - 20 SMAs (also from 20 to 400) - **Golden Line:** Calculated as the average of all EMAs and SMAs. - **Dynamic Fibonacci Levels:** Key ratios around the Golden line (0.5, 0.618, 0.786, 1.0, 1.272, 1.414, 1.618, 2.0) dynamically adjust based on market conditions. - **Fibonacci Labels:** Labels are shown next to Fibonacci lines, indicating their numeric value clearly on the chart. - **Table (Top Right Corner):** - Displays: - **Input:** EMA/SMA periods sorted by their current average price levels. - **AVG:** The average of corresponding EMA & SMA pairs. - **EMA & SMA Values:** Individual EMA/SMA values clearly marked. - **Dynamic Highlighting:** Highlights the row whose average (EMA+SMA)/2 is closest to the current price, helping identify immediate price action significance. - **Sorting Logic:** Each EMA/SMA pair is dynamically sorted based on their average values. Color coding (red/green) is used: - **Green:** EMA/SMA pairs with shorter periods when their average is lower. - **Red:** EMA/SMA pairs with longer periods when their average is lower. - **Star (⭐):** Represents the "Golden" average clearly. --- ### **2. DEM System (Dynamic EMA/ATR Metrics)** - **Purpose:** Provides detailed ATR statistics to assess market volatility clearly and quickly. - **Components:** - **Moving Averages:** - SMA lines: 25, 50, 100, 200. - **Bollinger Bands:** - Based on 20-period SMA of highs and standard deviation of lows. - **ATR Analysis:** - ATR calculations for multiple periods (1-day, 10, 20, 30, 40, 50). - **ATR Premium:** Average ATR of all calculated periods, providing an overarching volatility indicator. - **ATR Table (Bottom Right Corner):** - Displays clearly structured ATR values and percentages relative to the current close price: - Columns: **ATR Period**, **Value**, and **% of Close**. - Rows: Each specific ATR (1D, 10, 20, 30, 40, 50), plus ATR premium. - The ATR premium is highlighted in yellow to signify its importance clearly. --- ## 🔹 Key Features and Logic Explained - **Dynamic EMA/SMA Sorting:** The script computes the average of each EMA/SMA pair and sorts them dynamically on each bar, highlighting their relative importance visually. This allows traders to easily interpret the strength of current support/resistance levels based on moving averages. - **Closest EMA/SMA Pair to Current Price:** Calculates the absolute difference between the current price and all EMA/SMA averages, highlighting the closest one for quick reference. - **Fibonacci Ratios:** - Dynamically calculated Fibonacci levels based on the "Golden" EMA/SMA average give clear visual guidance for potential targets, supports, and resistances. - Labels are continuously updated and placed next to levels for clarity. - **ATR Volatility Analysis:** - Provides immediate insight into market volatility with absolute and relative (percentage-based) ATR values. - ATR premium summarizes volatility across multiple timeframes clearly. --- ## 🔹 Practical Use Case: - Traders can quickly identify support/resistance and critical price zones through EMA/SMA and Fibonacci combinations. - Useful in assessing immediate volatility, guiding stop-loss and take-profit levels through detailed ATR metrics. - The dynamic highlighting in tables provides intuitive, real-time decision support for active traders. --- ## 🔹 How to Use this Script: 1. **Adjust EMA & SMA Lengths** from indicator settings if different periods are preferred. 2. **Monitor dynamic Fibonacci levels** around the "Golden" average to identify possible reversal or continuation points. 3. **Check EMA/SMA table:** Rows highlighted indicate immediate significance concerning current market price. 4. **ATR table:** Use volatility metrics for better risk management. --- ## 🔷 Conclusion This advanced Pine Script indicator efficiently combines multiple EMAs, SMAs, dynamic Fibonacci retracement levels, and volatility analysis using ATR into a comprehensive real-time analytical tool, enhancing traders' decision-making capabilities by providing clear and actionable insights directly on the TradingView chart.
Indicador Pine Script®
por The_Real_Hippopotamus
The Silver Lining – GSR🍯 This tool converts the Gold/Silver Ratio (GSR) into a precision timing lens for short-term traders operating inside digital silver markets. It reveals structural dominance, trend exhaustion, and regime inflection by comparing the GSR to its smoothed baseline and historical percentile rhythm. On high timeframes (1D+), it reflects macroeconomic sentiment shifts 📈. 🧐 The lower the timeframe, the higher the alpha; the 15m and 1h charts are where you will the hidden pots of gold. For LTF traders, it becomes a hyper-responsive bias filter — especially when paired with volatility-based confirmation systems like SUPeR TReND 2.718, as shown. 🧠 The core logic compares the GSR (gold ÷ silver) against a user-defined moving average (VWMA or EMA). A color-coded fill shifts based on direction: amber when gold leads, teal when silver gains strength. Percentile bands (20th, 50th, 80th) map structural zones — helping traders anchor trades based on confluence, not hype. 📊 In the example chart, four theoretical long trades are shown on the 1h chart, manually drawn on the 15m timeframe. Each begins when the GSR reverses from the 80th percentile or breaks below its MA. The trades occur precisely as silver tested support, with confirmation from SUPeR TReND’s trend shift. Although idealized, these aren’t guesses — they are compression-to-expansion sequences backed by macro relative strength flow. Several yielded gains exceeding 4%. 🏆 Best-case long trades occur when GSR rotates down through the 50th percentile and silver catches a reactive bid. Shorts appear when GSR rises through the upper percentile band while silver fails to hold key intraday levels. The percentile bands function like behavioral tiers: 🥈 Below 20th = Silver Dominance ⚠️ Around 50th = Crossover Area 🥇 Above 80th = Gold Dominance 🥈 Why silver? It’s faster, more emotional, and more manipulated than gold — which paradoxically makes it more tradable on low timeframes. Its range-bound nature is ideal for rinse-and-repeat systems. Because we trade the derivative (XAGUSD), there’s no friction or delivery constraint — just price action, clean and liquid. ⚖️ The underlying strategy isn’t just technical; it’s alchemical. The system begins with short-term trading in digital silver and funnels gains into physical gold — converting volatility into wealth. Over time, this establishes a perpetual motion model: when profits allow, trade silver, extract value, cash out and convert into gold. The account stays active, and the hedge keeps growing. 🔁 The Silver Lining isn’t a signal engine. It’s a structural overlay. It tells you when the market’s invisible bias is shifting — so your tactics stay aligned with macro rhythm. 🌊 Silver moves fast. Gold moves first. The Silver Lining helps you bridge that gap — with clarity, confluence, and edge.
Indicador Pine Script®
por TheLeadingIndicator
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