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Neural Network Buy and Sell SignalsTrend Architect Suite Lite - Neural Network Buy and Sell Signals Advanced AI-Powered Signal Scoring This indicator provides neural network market analysis on buy and sell signals designed for scalpers and day traders who use 30s to 5m charts. Signals are generated based on an ATR system and then filtered and scored using an advanced AI-driven system. Features Neural Network Signal Engine 5-Layer Deep Learning analysis combining market structure, momentum, and market state detection AI-based Letter Grade Scoring (A+ through F) for instant signal quality assessment Normalized Input Processing with Z-score standardization and outlier clipping Real-time Signal Evaluation using 5 market dimensions Advanced Candle Types Standard Candlesticks - Raw price action Heikin Ashi - Trend smoothing and noise reduction Linear Regression - Mathematical trend visualization Independent Signal vs Display - Calculate signals on one type, display another Key Settings Signal Configuration - Signal Trigger Sensitivity (Default: 1.7) - Controls signal frequency vs quality - Stop Loss ATR Multiplier (Default: 1.5) - Risk management sizing - Signal Candle Type (Default: Candlesticks) - Data source for signal calculations - Display Candle Type (Default: Linear Regression) - Visual candle display Display Options - Signal Distance (Default: 1.35 ATR) - Label positioning from price - Label Size (Default: Medium) - Optimal readability Trading Applications Scalping - Fast pace signal detection with quality filtering - ATR-based stop management prevents signal overlap - Neural network attempts to reduces false signals in choppy markets Day Trading - Multi-timeframe compatible with adaptation settings - Clear trend visualization with Linear Regression candles - Support/resistance integration for better entries/exits Signal Filtering - Use A+/A grades for highest probability setups - B grades for confirmation in trending markets - C-F grades help identify market uncertainty Why Choose Trend Architect Lite? No Lag - Real-time neural network processing No Repainting - Signals appear and stay fixed Clean Charts - Focus on price action, not indicators Smart Filtering - AI reduces noise and false signals Flexible and customizable - Works across all timeframes and instruments Compatibility - All Timeframes - 1m to Monthly charts - All Instruments - Forex, Crypto, Stocks, Futures, Indices Risk Disclaimer This indicator is a tool for technical analysis and should not be used as the sole basis for trading decisions. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
Pine Script® indicator
by B3AR_Trades
13
DrFx Algo MA💎 (V.3.2)🚀 Introducing DrFx Algo MA💎 (V 3.2) – The All-in-One Trading Assistant! Turn your charts into a powerhouse of precision and clarity. 🔍 What makes DrFx Algo unique? ✅ Smart Candle Coloring – Instantly visualize market sentiment with vibrant, real-time candle colors: 🟢 Green for bullish trends 🔴 Red for bearish movements 💜 Violet & Rose candles highlight EMA proximity for potential trend shifts 📊 Advanced Trend Detection – Dual EMA overlays (20 & 50) clearly define the market's direction. Blue for uptrend, Red for downtrend. Simple, visual, and effective. 📈 Zero Lag EMA (ZLEMA) – Capture fast movements before the crowd. React quicker to market momentum with less delay than traditional EMAs. 🎯 Built-in RSI Alerts – Automatic triangle markers for overbought and oversold levels. Catch early reversal signals without watching RSI manually. 🔔 Smart Alerts System – Get notified instantly with bullish and bearish triggers, right when it matters. 📉 Support & Resistance Auto-Zones – Automatically plots dynamic levels using pivot detection. No more guessing key price zones. 📋 Customizable Trade Dashboard – Input your entries, SL/TP, and trade type. Stay organized with a clean, modern on-screen table. Fully editable. 🧪 Perfect for: Trend traders Momentum scalpers EMA/RSi strategy followers Visual learners 💼 Whether you’re a day trader or swing trader, DrFx Algo MA💎 gives you the clarity, speed, and edge you need in the market. 💬 Need help with access, backtesting, or have any questions about our indicators? Our support team is available 24/7 on Telegram. Just reach out through the link below: 👉https://t.me/Drfxai “You can reach out by searching this username Drfxai”
Pine Script® indicator
by DrFXAi
2
Cryptokazancev Strategy PackCryptokazancev Strategy Pack Комплексный инструмент для анализа рыночной структуры / Comprehensive Market Structure Analysis Tool 🇷🇺 Описание на русском Cryptokazancev Strategy Pack by ZeeZeeMon - это мощный набор инструментов для технического анализа, включающий: • Ордерблоки (Order Blocks) с настройкой количества и цветов • Пивоты (Pivot Points) различных таймфреймов • Рыночную структуру с зонами Фибоначчи (0.618, 0.786) • Разворотные конструкции (пинбары и поглощения) • Зоны интереса на основе скопления свингов 📊 Основные функции: 1. Ордерблоки - Автоматическое определение бычьих/медвежьих OB - Настройка максимального количества блоков (до 30) - Кастомизация цветов 2. Пивоты - Поддержка таймфреймов: Дневные/Недельные/Месячные/Квартальные/Годовые - Уровни Camarilla (P, R1-R4, S1-S4) 3. Рыночная структура - Четкое определение тренда (UP/DOWN) - Ключевые уровни Фибо (0.618 и 0.786) - Настройка глубины анализа (10-1000 баров) 4. Разворотные конструкции - Обнаружение пинбаров - Обнаружение поглощений - Настройка чувствительности 5. Зоны интереса - Алгоритм кластеризации свингов - Настройка через ATR-мультипликатор - Лимит отображаемых зон 🇬🇧 English Description ZeeZeeMon Pack is a comprehensive market analysis toolkit featuring: • Order Blocks with customizable count and colors • Pivot Points for multiple timeframes • Market Structure with Fibonacci zones • Reversal patterns (pinbars and engulfings) • Interest Zones based on swing clustering 📊 Key Features: 1. Order Blocks - Auto-detection of bullish/bearish OB - Configurable max blocks (up to 30) - Custom color schemes 2. Pivot Points - Supports: Daily/Weekly/Monthly/Quarterly/Yearly - Camarilla levels (P, R1-R4, S1-S4) 3. Market Structure - Clear trend detection (UP/DOWN) - Key Fibonacci levels (0.618 & 0.786) - Adjustable analysis depth (10-1000 bars) 4. Reversal Patterns - Smart pinbar detection - ATR-based engulfing filter - Sensitivity adjustment 5. Interest Zones - Swing clustering algorithm - ATR-multiplier configuration - Display limit (up to 10 zones) ⚙️ Technical Highlights: • Built with Pine Script v5 • Performance-optimized • Well-commented code • Flexible settings system ⚠️ Важно / Important: Индикатор в бета-версии. Тестируйте перед использованием в реальной торговле. This is BETA version. Please test before live trading. 💬 Поддержка / Support: Комментарии к скрипту / Script comments section
Pine Script® indicator
by cryptokazancev
Hann Window FIR Filter Ribbon [BigBeluga]🔵 OVERVIEW The Hann Window FIR Filter Ribbon is a trend-following visualization tool based on a family of FIR filters using the Hann window function. It plots a smooth and dynamic ribbon formed by six Hann filters of progressively increasing length. Gradient coloring and filled bands reveal trend direction and compression/expansion behavior. When short-term trend shifts occur (via filter crossover), it automatically anchors visual support/resistance zones at the nearest swing highs or lows. 🔵 CONCEPTS Hann FIR Filter: A finite impulse response filter that uses a Hann (cosine-based) window for weighting past price values, resulting in a non-lag, ultra-smooth output. hannFilter(length)=> var float hann = na // Final filter output float filt = 0 float coef = 0 for i = 1 to length weight = 1 - math.cos(2 * math.pi * i / (length + 1)) filt += price * weight coef += weight hann := coef != 0 ? filt / coef : na Ribbon Stack: The indicator plots 6 Hann FIR filters with increasing lengths, creating a smooth "ribbon" that adapts to price shifts and visually encodes volatility. Gradient Coloring: Line colors and fill opacity between layers are dynamically adjusted based on the distance between the filters, showing momentum expansion or contraction. Dynamic Swing Zones: When the shortest filter crosses its nearest neighbor, a swing high/low is located, and a triangle-style level is anchored and projected to the right. Self-Extending Levels: These dynamic levels persist and extend until invalidated or replaced by a new opposite trend break. 🔵 FEATURES Plots 6 Hann FIR filters with increasing lengths (controlled by Ribbon Size input). Automatically colors each filter and the fill between them with smooth gradient transitions. Detects trend shifts via filter crossover and anchors visual resistance (red) or support (green) zones. Support/resistance zones are triangle-style bands built around recent swing highs/lows. Levels auto-extend right and adapt in real time until invalidated by price action. Ribbon responds smoothly to price and shows contraction or expansion behavior clearly. No lag in crossover detection thanks to FIR architecture. Adjustable sensitivity via Length and Ribbon Size inputs. 🔵 HOW TO USE Use the ribbon gradient as a visual trend strength and smooth direction cue. Watch for crossover of shortest filters as early trend change signals. Monitor support/resistance zones as potential high-probability reaction points. Combine with other tools like momentum or volume to confirm trend breaks. Adjust ribbon thickness and length to suit your trading timeframe and volatility preference. 🔵 CONCLUSION Hann Window FIR Filter Ribbon blends digital signal processing with trading logic to deliver a visually refined, non-lagging trend tool. The adaptive ribbon offers insight into momentum compression and release, while swing-based levels give structure to potential reversals. Ideal for traders who seek smooth trend detection with intelligent, auto-adaptive zone plotting.
Pine Script® indicator
by BigBeluga
1
Trend Strength Index [Alpha Extract]The Trend Strength Index leverages Volume Weighted Moving Average (VWMA) and Average True Range (ATR) to quantify trend intensity in cryptocurrency markets, particularly Bitcoin. The combination of VWMA and ATR is particularly powerful because VWMA provides a more accurate representation of the market's true average price by weighting periods of higher trading volume more heavily—capturing genuine momentum driven by increased participation rather than treating all price action equally, which is crucial in volatile assets like Bitcoin where volume spikes often signal institutional interest or market shifts. Meanwhile, ATR normalizes this measurement for volatility, ensuring that trend strength readings remain comparable across different market conditions; without ATR's adjustment, raw price deviations from the mean could appear artificially inflated during high-volatility periods (like during news events or liquidations) or understated in low-volatility sideways markets, leading to misleading signals. Together, they create a volatility-adjusted, volume-sensitive metric that reliably distinguishes between meaningful trend developments and noise. This indicator measures the normalized distance between price and its volume-weighted mean, providing a clear visualization of trend strength while accounting for market volatility. It helps traders identify periods of strong directional movement versus consolidation, with color-coded gradients for intuitive interpretation. 🔶 CALCULATION The indicator processes price data through these analytical stages: Volume Weighted Moving Average: Computes a smoothed average weighted by trading volume Volatility Normalization: Uses ATR to account for market volatility Distance Measurement: Calculates absolute deviation between current price and VWMA Strength Normalization: Divides price deviation by ATR for a volatility-adjusted metric Formula: VWMA = Volume-Weighted Moving Average of Close over specified length ATR = Average True Range over specified length Price Distance = |Close - VWMA| Trend Strength = Price Distance / ATR 🔶 DETAILS Visual Features: VWMA Line: Blue line overlay on the price chart representing the volume-weighted mean Trend Strength Area: Histogram-style area plot with dynamic color gradient (red for weak trends, transitioning through orange and yellow to green for strong trends) Threshold Line: Horizontal red line at the customizable Trend Enter level Background Highlight: Subtle green background when trend strength exceeds the enter threshold for strong trend visualization Alert System: Triggers notifications for strong trend detection Interpretation: 0-Weak (Red): Minimal trend strength, potential consolidation or ranging market Mid-Range (Orange/Yellow): Building momentum, watch for breakout potential At/Above Enter Threshold (Green): Strong trend conditions, potential for continued directional moves Threshold Crossing: Trend strength crossing above the enter level signals increasing conviction in the current direction Color Transitions: Gradual shifts from warm (red/orange) to cool (green) tones indicate strengthening trends 🔶 EXAMPLES Strong Trend Entry: When trend strength crosses above the enter threshold (e.g., 1.2), it identifies the onset of a powerful move where price deviates significantly from the mean. Example: During a rally, trend strength rising from yellow (around 1.0) to green (1.2+) often precedes sustained upward momentum, providing entry opportunities for trend followers. Consolidation Detection: Low trend strength values in red shades (below 0.5) highlight periods of low volatility and mean reversion potential. Example: After a sharp sell-off, persistent red values signal a likely sideways phase, allowing traders to avoid whipsaws and wait for orange/yellow transitions as a precursor to recovery. Volatility-Adjusted Pullbacks: In volatile markets, the ATR component ensures trend strength remains accurate; a dip back to yellow from green during minor corrections can indicate healthy pullbacks within a strong trend. Example: Trend strength briefly falling to yellow levels (e.g., 0.8-1.1) after hitting green provides profit-taking signals without invalidating the overall bullish bias if the VWMA holds as support. Threshold Alert Integration: The alert condition combines strength value with the enter threshold for timely notifications. Example: Receiving a "Strong Trend Detected" alert when the area plot turns green helps confirm Bitcoin's breakout from consolidation, aligning with increased volume for higher-probability trades. 🔶 SETTINGS Customization Options: Lengths: VWMA length (default 14), ATR length (default 14) Thresholds: Trend enter (default 1.2, step 0.1), trend exit (default 1.15, for potential future signal enhancements) Visuals: Automatic color scaling with red at 0, transitioning to green at/above enter threshold Alert Conditions: Strong trend detection (when strength > enter) The Trend Strength Index equips traders with a robust, easy-to-interpret tool for gauging trend intensity in volatile markets like Bitcoin. By normalizing price deviations against volatility, it delivers reliable signals for identifying high-momentum opportunities while the gradient coloring and alerts facilitate quick assessments in both trending and choppy conditions.
Pine Script® indicator
by AlphaExtract
4
Advanced Forex Currency Strength Meter # Advanced Forex Currency Strength Meter 🚀 The Ultimate Currency Strength Analysis Tool for Forex Traders This sophisticated indicator measures and compares the relative strength of major currencies (EUR, GBP, USD, JPY, CHF, CAD, AUD, NZD) to help you identify the strongest and weakest currencies in real-time, providing clear trading signals based on currency strength differentials. ## 📊 What This Indicator Does The Advanced Forex Currency Strength Meter analyzes currency relationships across 28+ major forex pairs and 8 currency indices to determine which currencies are gaining or losing strength. Instead of relying on individual pair analysis, this tool gives you a bird's-eye view of the entire forex market, helping you: Identify the strongest and weakest currencies at any given time Find high-probability trading opportunities by pairing strong vs weak currencies Avoid ranging markets by detecting when currencies have similar strength Get clear LONG/SHORT/NEUTRAL signals for your current trading pair Optimize your trading strategy based on your preferred timeframe and holding period ## ⚙️ How The Indicator Works ### Dual Calculation Method The indicator uses a sophisticated dual approach for maximum accuracy: Pairs-Based Analysis: Calculates currency strength from 28+ major forex pairs (EURUSD, GBPUSD, USDJPY, etc.) Index-Based Analysis: Incorporates official currency indices (DXY, EXY, BXY, JXY, CXY, AXY, SXY, ZXY) Weighted Combination: Blends both methods using smart weighting for enhanced accuracy ### Smart Auto-Optimization System The indicator automatically adjusts its parameters based on your chart timeframe and intended holding period: The system recognizes that scalping requires different sensitivity than swing trading, automatically optimizing lookback periods, analysis timeframes, signal thresholds, and index weights. ### Strength Calculation Process Fetches price data from multiple timeframes using optimized tuple requests Calculates percentage change over the specified lookback period Optionally normalizes by ATR (Average True Range) to account for volatility differences Combines pair-based and index-based calculations using dynamic weighting Generates relative strength by comparing base currency vs quote currency Produces clear trading signals when strength differential exceeds threshold ## 🎯 How To Use The Indicator ### Quick Start Add the indicator to any forex pair chart Enable 🧠 Smart Auto-Optimization (recommended for beginners) Watch for LONG 🚀 signals when the relative strength line is green and above threshold Watch for SHORT 🐻 signals when the relative strength line is red and below threshold Avoid trading during NEUTRAL ⚪ periods when currencies have similar strength Note: This is highly recommended to couple this indicator with fundamental analysis and use it as an extra signal. ### 📋 Parameters Reference #### 🤖 Smart Settings 🧠 Smart Auto-Optimization: (Default: Enabled) Automatically optimizes all parameters based on chart timeframe and trading style #### ⚙️ Manual Override These settings are only active when Smart Auto-Optimization is disabled: Manual Lookback Period: (Default: 14) Number of periods to analyze for strength calculation Manual ATR Period: (Default: 14) Period for ATR normalization calculation Manual Analysis Timeframe: (Default: 240) Higher timeframe for strength analysis Manual Index Weight: (Default: 0.5) Weight given to currency indices vs pairs (0.0 = pairs only, 1.0 = indices only) Manual Signal Threshold: (Default: 0.5) Minimum strength differential required for trading signals #### 📊 Display Show Signal Markers: (Default: Enabled) Display triangle markers when signals change Show Info Label: (Default: Enabled) Show comprehensive information label with current analysis #### 🔍 Analysis Use ATR Normalization: (Default: Enabled) Normalize strength calculations by volatility for fairer comparison #### 💰 Currency Indices 💰 Use Currency Indices: (Default: Enabled) Include all 8 currency indices in strength calculation for enhanced accuracy #### 🎨 Colors Strong Currency Color: (Default: Green) Color for positive/strong signals Weak Currency Color: (Default: Red) Color for negative/weak signals Neutral Color: (Default: Gray) Color for neutral conditions Strong/Weak Backgrounds: Background colors for clear signal visualization ### 🧠 Smart Optimization Profiles The indicator automatically selects optimal parameters based on your chart timeframe: #### ⚡ Scalping Profile (1M-5M Charts) For positions held for a few minutes: Lookback: 5 periods (fast/sensitive) Analysis Timeframe: 15 minutes Index Weight: 20% (favor pairs for speed) Signal Threshold: 0.3% (sensitive triggers) #### 📈 Intraday Profile (10M-1H Charts) For positions held for a few hours: Lookback: 12 periods (balanced sensitivity) Analysis Timeframe: 4 hours Index Weight: 40% (balanced approach) Signal Threshold: 0.4% (moderate sensitivity) #### 📊 Swing Profile (4H-Daily Charts) For positions held for a few days: Lookback: 21 periods (stable analysis) Analysis Timeframe: Daily Index Weight: 60% (favor indices for stability) Signal Threshold: 0.5% (conservative triggers) #### 📆 Position Profile (Weekly+ Charts) For positions held for a few weeks: Lookback: 30 periods (long-term view) Analysis Timeframe: Weekly Index Weight: 70% (heavily favor indices) Signal Threshold: 0.6% (very conservative) ### Entry Timing Wait for clear LONG 🚀 or SHORT 🐻 signals Avoid trading during NEUTRAL ⚪ periods Look for signal confirmations on multiple timeframes ### Risk Management Stronger signals (higher relative strength values) suggest higher probability trades Use appropriate position sizing based on signal strength Consider the trading style profile when setting stop losses and take profits 💡 Pro Tip: The indicator works best when combined with your existing technical analysis. Use currency strength to identify which pairs to trade, then use your favorite technical indicators to determine when to enter and exit. ## 🔧 Key Features 28+ Forex Pairs Analysis: Comprehensive coverage of major currency relationships 8 Currency Indices Integration: DXY, EXY, BXY, JXY, CXY, AXY, SXY, ZXY for enhanced accuracy Smart Auto-Optimization: Automatically adapts to your trading style and timeframe ATR Normalization: Fair comparison across different currency pairs and volatility levels Real-Time Signals: Clear LONG/SHORT/NEUTRAL signals with visual markers Performance Optimized: Efficient tuple-based data requests minimize external calls User-Friendly Interface: Simplified settings with comprehensive tooltips Multi-Timeframe Support: Works on any timeframe from 1-minute to monthly charts Transform your forex trading with the power of currency strength analysis! 🚀
Pine Script® indicator
by igaudette
AshishBediSPLAshishBediSPL: Dynamic Premium Analysis with Integrated Signals This indicator provides a comprehensive view of combined options premiums by aggregating data from Call and Put contracts for a selected index and expiry. It integrates multiple popular technical indicators like EMA Crossover, Supertrend, VWAP, RSI, and SMA, allowing users to select their preferred tools for generating dynamic buy and sell signals directly on the premium chart. AshishBediSPL" is a powerful TradingView indicator designed to analyze options premiums. It calculates a real-time combined premium for a chosen index (NIFTY, BANKNIFTY, FINNIFTY, etc.) and specific expiry date. You have the flexibility to visualize the premium of Call options, Put options, or a combined premium of both. The indicator then overlays several popular technical analysis tools, which you can selectively enable: EMA Crossover: Identify trend changes with configurable fast and slow Exponential Moving Averages. Supertrend: Detect trend direction and potential reversal points. VWAP (Volume Weighted Average Price): Understand the average price of the premium considering trading volume. RSI (Relative Strength Index): Gauge momentum and identify overbought/oversold conditions. SMA (Simple Moving Average): Analyze price smoothing and trend identification. Based on your selected indicators, the tool generates clear "Buy" and "Sell" signals directly on the chart, helping you identify potential entry and exit points. Customizable alerts are also available to keep you informed. Unlock a new perspective on options trading with "AshishBediSPL." This indicator focuses on the combined value of options premiums, giving you a consolidated view of market sentiment for a chosen index and expiry. Instead of just looking at individual option prices, "AshishBediSPL" blends the Call and Put premiums (or focuses on one, based on your preference) and empowers you with a suite of built-in technical indicators: EMA, Supertrend, VWAP, RSI, and SMA. Pick the indicators that resonate with your strategy, and let the tool generate actionable buy and sell signals right on your chart. With customizable alerts, you'll never miss a crucial market move. Gain deeper insights and make more informed trading decisions with "AshishBediSPL. Combined options premium: This accurately describes what your indicator calculates. Selected index and expiry: Essential inputs for the indicator. Call/Put options or combined: Explains the flexibility in data display. Multiple technical indicators (EMA Crossover, Supertrend, VWAP, RSI, SMA): Lists the analysis tools included. Buy/Sell signals: The primary output of the indicator. Customizable alerts: A valuable feature for users.
Pine Script® indicator
by shivanshbedi2
RSI-Stochastic Combined Oscillator(Mastersinnifty)Description The RSI-Stochastic Combined Oscillator blends the strengths of RSI and Stochastic indicators to offer a refined view of market momentum. This custom oscillator highlights high-probability turning points using both value crossovers and directional momentum filters. Enhanced signal logic distinguishes between strong and weak trade setups. How It Works Calculates RSI and Stochastic %K using user-defined lengths. Generates a combined oscillator by averaging RSI and Stochastic %K. Smoothes the output with configurable MA for clarity. Generates bullish/bearish signals based on crossover logic and momentum strength. Includes overbought/oversold zones and background color warnings. Optional signal table displays real-time values for RSI, Stochastic, Combo, and Signal Line. Inputs RSI Length – Period for RSI calculation. Stochastic %K/%D Length – Periods for Stochastic values. Combined Oscillator Smoothing – Moving average smoothing period. Overbought/Oversold Levels – Thresholds for signal filtering and background alerts. Use Case Ideal for traders looking to: Confirm entries using dual momentum logic. Filter out noise with smoothed oscillators. Identify high-conviction reversal zones. Receive alerts based on strong and weak momentum shifts. Disclaimer This indicator is designed for educational purposes only and does not constitute financial advice. Always conduct your own analysis before making trading decisions.
Pine Script® indicator
by Mastersinnifty
Ichimoku Trend CycleIchimoku Trend Cycle A precision dual-trend system combining Ichimoku-based ATR Supertrends — engineered for clarity, reliability, and smart trend detection. 🔷 What is Ichimoku Trend Cycle? The Ichimoku Trend Cycle harnesses the power of traditional Ichimoku analysis with modern ATR-based supertrend technology: Alpha Trend: Primary trend detection using Ichimoku conversion & baseline logic Beta Trend: Secondary confirmation trend with independent ATR calculations Dual Confirmation Engine : Both trends must align for signal generation This powerful combination delivers clean, non-repainting Buy/Sell signals while filtering out market noise and false breakouts. 🔍 How It Works Blue Alpha + Red Beta Trends Align Bullish → BUY Signal Both Trends Turn Bearish → SELL Signal You get ONE signal per trend change — no spam, no noise, just crystal-clear direction changes. ⚙️ Core Features ✅ Ichimoku-Enhanced Supertrends Traditional Ichimoku conversion/baseline logic powering modern ATR bands. ✅ Dual-Trend Confirmation Alpha and Beta trends must agree — eliminates false signals. ✅ One Alert Per Trend Shift Clean entries, zero noise, no repeated signals. ✅ Visual Excellence Color-coded trend lines with high-contrast BUY/SELL labels. ✅ Fully Customizable Independent settings for both trend systems plus smoothing options. 🎯 Perfect For Swing Traders wanting confirmed trend changes Position Traders seeking major trend shifts Anyone who values clean charts with sharp decision points 🛠 Settings Breakdown Alpha Trend Settings: Primary trend with conversion/baseline periods + ATR multiplier Beta Trend Settings: Secondary confirmation trend with independent parameters Smoothing MA Settings: Optional MA smoothing with Bollinger Bands support Alert Settings: Customize signal confirmation periods Candle Color Settings: Fully customizable trend and candle color schemes ✅ Built-in Smart Alerts — Never miss a trend change again ⚡ Zero-lag Performance — Works flawlessly across all timeframes 📈 Strategy-Ready Code — Professional-grade, non-repainting signals Transform your trading with the precision of Ichimoku and the reliability of dual-trend confirmation.
Pine Script® indicator
by Velu1012
1
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.
Pine Script® indicator
by EdgeTools
Previous Levels by HAZEDPrevious Day/Week/Month High/Low Levels with 50% Equilibrium 🎯 Key Features: - Previous Period Levels: Automatically plots previous Day, Week, and Month highs and lows - 50% Equilibrium Zones: Shows the midpoint between each period's high and low - Precise Line Placement: Lines start from the exact bar where the high/low occurred (not period beginning) - Clean Visual Design: Solid lines for key levels, semi-transparent for equilibrium zones - Customizable Display: Toggle each timeframe independently with custom colors and styles 📊 How It Works: The indicator identifies the previous period's high and low points, then draws horizontal lines starting from the exact time those levels were created. The 50% equilibrium levels mark the midpoint between each period's range, providing additional support/resistance reference points. ⚙️ Settings: - Timeframe Controls: Enable/disable Daily, Weekly, Monthly levels - Line Styles: Choose between solid, dashed, or dotted lines - Color Customization: Set individual colors for each timeframe - Label Options: Show/hide price values, adjust label size - 50% Levels: Toggle equilibrium zones with semi-transparent styling 💡 Trading Applications: - Support & Resistance: Previous highs/lows act as key S/R levels - Breakout Trading: Monitor price action around these critical levels - Mean Reversion: 50% equilibrium zones often act as magnet levels - Multi-Timeframe Analysis: See how different timeframe levels interact 🔧 Technical Notes: - Lines extend to the right for future reference - Only shows levels when chart timeframe is equal or lower than the level timeframe - Uses precise historical data to ensure accurate line placement - Optimized for performance with clean code structure Perfect for swing traders, day traders, and anyone using support/resistance analysis! Feel free to leave feedback and suggestions for future updates!
Pine Script® indicator
by hazedoptions
Time-Decaying Percentile Oscillator [BackQuant]Time-Decaying Percentile Oscillator 1. Big-picture idea Traditional percentile or stochastic oscillators treat every bar in the look-back window as equally important. That is fine when markets are slow, but if volatility regime changes quickly yesterday’s print should matter more than last month’s. The Time-Decaying Percentile Oscillator attempts to fix that blind spot by assigning an adjustable weight to every past price before it is ranked. The result is a percentile score that “breathes” with market tempo much faster to flag new extremes yet still smooth enough to ignore random noise. 2. What the script actually does Build a weight curve • You pick a look-back length (default 28 bars). • You decide whether weights fall Linearly , Exponentially , by Power-law or Logarithmically . • A decay factor (lower = faster fade) shapes how quickly the oldest price loses influence. • The array is normalised so all weights still sum to 1. Rank prices by weighted mass • Every close in the window is paired with its weight. • The pairs are sorted from low to high. • The cumulative weight is walked until it equals your chosen percentile level (default 50 = median). • That price becomes the Time-Decayed Percentile . Find dispersion with robust statistics • Instead of a fragile standard deviation the script measures weighted Median-Absolute-Deviation about the new percentile. • You multiply that deviation by the Deviation Multiplier slider (default 1.0) to get a non-parametric volatility band. Build an adaptive channel • Upper band = percentile + (multiplier × deviation) • Lower band = percentile – (multiplier × deviation) Normalise into a 0-100 oscillator • The current close is mapped inside that band: 0 = lower band, 50 = centre, 100 = upper band. • If the channel squeezes, tiny moves still travel the full scale; if volatility explodes, it automatically widens. Optional smoothing • A second-stage moving average (EMA, SMA, DEMA, TEMA, etc.) tames the jitter. • Length 22 EMA by default—change it to tune reaction speed. Threshold logic • Upper Threshold 70 and Lower Threshold 30 separate standard overbought/oversold states. • Extreme bands 85 and 15 paint background heat when aggressive fade or breakout trades might trigger. Divergence engine • Looks back twenty bars. • Flags Bullish divergence when price makes a lower low but oscillator refuses to confirm (value < 40). • Flags Bearish divergence when price prints a higher high but oscillator stalls (value > 60). 3. Component walk-through • Source – Any price series. Close by default, switch to typical price or custom OHLC4 for futures spreads. • Look-back Period – How many bars to rank. Short = faster, long = slower. • Base Percentile Level – 50 shows relative position around the median; set to 25 / 75 for quartile tracking or 90 / 10 for extreme tails. • Deviation Multiplier – Higher values widen the dynamic channel, lowering whipsaw but delaying signals. • Decay Settings – Type decides the curve shape. Exponential (default 1.16) mimics EMA logic. – Factor < 1 shrinks influence faster; > 1 spreads influence flatter. – Toggle Enable Time Decay off to compare with classic equal-weight stochastic. • Smoothing Block – Choose one of seven MA flavours plus length. • Thresholds – Overbought / Oversold / Extreme levels. Push them out when working on very mean-reverting assets like FX; pull them in for trend monsters like crypto. • Display toggles – Show or hide threshold lines, extreme filler zones, bar colouring, divergence labels. • Colours – Bullish green, bearish red, neutral grey. Every gradient step is automatically blended to generate a heat map across the 0-100 range. 4. How to read the chart • Oscillator creeping above 70 = market auctioning near the top of its adaptive range. • Fast poke above 85 with no follow-through = exhaustion fade candidate. • Slow grind that lives above 70 for many bars = valid bullish trend, not a fade. • Cross back through 50 shows balance has shifted; treat it like a micro trend change. • Divergence arrows add extra confidence when you already see two-bar reversal candles at range extremes. • Background shading (semi-transparent red / green) warns of extreme states and throttles your position size. 5. Practical trading playbook Mean-reversion scalps  1. Wait for oscillator to reach your desired OB/ OS levels  2. Check the slope of the smoothing MA—if it is flattening the squeeze is mature.  3. Look for a one- or two-bar reversal pattern.  4. Enter against the move; first target = midline 50, second target = opposite threshold.  5. Stop loss just beyond the extreme band. Trend continuation pullbacks  1. Identify a clean directional trend on the price chart.  2. During the trend, TDP will oscillate between midline and extreme of that side.  3. Buy dips when oscillator hits OS levels, and the same for OB levels & shorting  4. Exit when oscillator re-tags the same-side extreme or prints divergence. Volatility regime filter  • Use the Enable Time Decay switch as a regime test.  • If equal-weight oscillator and decayed oscillator diverge widely, market is entering a new volatility regime—tighten stops and trade smaller. Divergence confirmation for other indicators  • Pair TDP divergence arrows with MACD histogram or RSI to filter false positives.  • The weighted nature means TDP often spots divergence a bar or two earlier than standard RSI. Swing breakout strategy  1. During consolidation, band width compresses and oscillator oscillates around 50.  2. Watch for sudden expansion where oscillator blasts through extreme bands and stays pinned.  3. Enter with momentum in breakout direction; trail stop behind upper or lower band as it re-expands. 6. Customising decay mathematics Linear – Each older bar loses the same fixed amount of influence. Intuitive and stable; good for slow swing charts. Exponential – Influence halves every “decay factor” steps. Mirrors EMA thinking and is fastest to react. Power-law – Mid-history bars keep more authority than exponential but oldest data still fades. Handy for commodities where seasonality matters. Logarithmic – The gentlest curve; weight drops sharply at first then levels off. Mimics how traders remember dramatic moves for weeks but forget ordinary noise quickly. Turn decay off to verify the tool’s added value; most users never switch back. 7. Alert catalogue • TD Overbought / TD Oversold – Cross of regular thresholds. • TD Extreme OB / OS – Breach of danger zones. • TD Bullish / Bearish Divergence – High-probability reversal watch. • TD Midline Cross – Momentum shift that often precedes a window where trend-following systems perform. 8. Visual hygiene tips • If you already plot price on a dark background pick Bullish Color and Bearish Color default; change to pastel tones for light themes. • Hide threshold lines after you memorise the zones to declutter scalping layouts. • Overlay mode set to false so the oscillator lives in its own panel; keep height about 30 % of screen for best resolution. 9. Final notes Time-Decaying Percentile Oscillator marries robust statistical ranking, adaptive dispersion and decay-aware weighting into a simple oscillator. It respects both recent order-flow shocks and historical context, offers granular control over responsiveness and ships with divergence and alert plumbing out of the box. Bolt it onto your price action framework, trend-following system or volatility mean-reversion playbook and see how much sooner it recognises genuine extremes compared to legacy oscillators. Backtest thoroughly, experiment with decay curves on each asset class and remember: in trading, timing beats timidity but patience beats impulse. May this tool help you find that edge.
Pine Script® indicator
by BackQuant
BERLIN-MAX 1V.5BERLIN-MAX 1V.5 is a comprehensive trading indicator designed for TradingView that combines multiple advanced strategies and tools. It integrates EMA crossover signals, UT Bot logic with ATR-based trailing stops, customizable stop-loss and target multipliers per timeframe, Hull Moving Averages with color-coded trends, linear regression channels for support and resistance, and a multi-timeframe RSI and volume signal table. This script aims to provide clear entry and exit signals for scalping and swing trading, enhancing decision-making across different market conditions.
Pine Script® indicator
by alnaimi515
2
NightWatch 24/5 [theUltimator5]NightWatch 24/5 is a comprehensive indicator designed to seamlessly display both regular and overnight trading (BOATS exchange) into a single chart. Current TV limitations don't allow both overnight trading and regular exchanges to appear on the same chart due to timeframe visibility settings. We can either select between RTH (Regular Trading Hours) or ETH (Extended Trading Hours). There is no option to show 24 hour charts when looking at a stock. This indicator attempts to solve this issue. Please read the entire description thoroughly because this indicator takes a little bit of setup to work properly! ---IMPORTANT-- - This indicator MUST be used over a liquid cryptocurrency chart, like Bitcoin. It requires access to something that trades 24/7 and has volume data for all periods. Bitcoin on Coinbase is the best option. Please select Bitcoin as your main ticker before adding this indicator to the chart. ------------------- This indicator combines the price of both the regular trading hours and the overnight trading to create a single price line and volume candles. You can select view settings to either overlay the price on the chart, or have it below the chart. Volume can be toggled on or off as well. Default settings: Ticker = GME Overlay Candles on Main Chart = true Display Data = Both Price and Volume Show Status Table = true Here is an explanation for each of these settings: Ticker - Type in the ticker you want to track overnight and intraday data for Overlay Candles on Main chart - This will push the price candles onto the main chart area instead of below it. Volume candles will remain in their own separate pane below. This is useful if you want to track both price and volume without adding the indicator twice. Display Data - This determines what data to show. Volume, price, or both volume and price. Show Status Table - This toggles on or off the table that shows the ticker name, current session, and the price (change) of the ticker since the most recent daily close. If you overlay the price onto the chart, the price of the stock you are looking at will likely be a VERY different price than the crypto it is overlaying against. There are a couple workarounds. You can either zoom into the chart around the price of the stock you are looking at (time consuming), or you can go into your object tree and drag the indicator up into the main chart area. This will overlay the price onto the crypto while maintaining it's own unique y-axis. After you move the indicator up, you can add the indicator back a second time, then change the settings to only show the volume candles. You can then toggle off the table on one of the two so you don't see duplicate tables. This is the setting I am showing in my chart above. The indicator is added twice with the price being pulled up into the same window as Bitcoin, then a second instance below showing just volume. --LIMITATIONS-- Since the indicator requires the use of a 24 hour market ticker like Bitcoin, it DOES NOT display extended hours data. The price and volume data STOPS at 16:00 EST then resumes back up at 20:00 EST when BOATS opens. At 04:00, the price and volume then stops until 09:30, when the regular trading hours begin. This causes a flat line in the price during those periods. Unfortunately, there is no current workaround to this issue. If Bitcoin becomes illiquid (or whatever crypto you choose), it will only populate data for the ticker you want if there is data available for that crypto at the same time period. A gap in Bitcoin volume will show a gap in trade activity for your ticker.
Pine Script® indicator
by TheUltimator5
ICT Concepts [LuxAlgo//@version=5 indicator("Full Entry: RSI + EMA + CHoCH + FVG + TP/SL", overlay=true) rsiPeriod = 14 emaPeriod = 50 tpPerc = 1.5 slPerc = 1.0 is15min = timeframe.period == "15" // يشتغل فقط على فريم 15 دقيقة rsi = ta.rsi(close, rsiPeriod) ema = ta.ema(close, emaPeriod) // قمة وقاع سابقة ph = ta.highest(high, 20) pl = ta.lowest(low, 20) // CHoCH bullCHoCH = close > ph and close > ema bearCHoCH = close < pl and close < ema // FVG حساب fvgUp = low > high fvgDn = high < low fvgHigh = high fvgLow = low touchFVGUp = fvgUp and low <= fvgHigh and low >= fvgLow touchFVGDown = fvgDn and high >= fvgLow and high <= fvgHigh // دخول فقط بفريم 15 دقيقة + لمس FVG longEntry = is15min and bullCHoCH and rsi < 35 and touchFVGUp shortEntry = is15min and bearCHoCH and rsi > 65 and touchFVGDown // TP/SL longTP = close * (1 + tpPerc / 100) longSL = close * (1 - slPerc / 100) shortTP = close * (1 - tpPerc / 100) shortSL = close * (1 + slPerc / 100) // إشارات دخول plotshape(longEntry, location=location.belowbar, color=color.green, style=shape.labelup, text="BUY") plotshape(shortEntry, location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL") // TP / SL plot(longEntry ? longTP : na, style=plot.style_cross, color=color.green) plot(longEntry ? longSL : na, style=plot.style_cross, color=color.red) plot(shortEntry ? shortTP : na, style=plot.style_cross, color=color.red) plot(shortEntry ? shortSL : na, style=plot.style_cross, color=color.green) // EMA plot(ema, title="EMA 50", color=color.orange) // FVG رسم boxFVG = fvgUp or fvgDn var box fvgBox = na if boxFVG fvgBox := box.new(left=bar_index, top=fvgHigh, right=bar_index + 3, bottom=fvgLow, border_color=color.gray, bgcolor=color.new(color.gray, 85)) // تنبيهات alertcondition(longEntry, title="Buy Signal", message="Buy: CHoCH + RSI + EMA + FVG") alertcondition(shortEntry, title="Sell Signal", message="Sell: CHoCH + RSI + EMA + FVG")
Pine Script® indicator
by Rawe_hosari
INDI HẰNG //@version=5 indicator("CM SlingShot System (Customizable)", overlay=true, shorttitle="CM_SSS") // ==== 📌 INPUT SETTINGS ==== group1 = "Entry Settings" sae = input.bool(true, title="📍 Show Aggressive Entry (pullback)?", group=group1) sce = input.bool(true, title="📍 Show Conservative Entry (confirmation)?", group=group1) group2 = "Visual Settings" st = input.bool(true, title="🔼 Show Trend Arrows (top/bottom)?", group=group2) sl = input.bool(false, title="🅱🆂 Show 'B' & 'S' Letters Instead of Arrows", group=group2) pa = input.bool(true, title="🡹🡻 Show Entry Arrows", group=group2) group3 = "MA Settings" fastLength = input.int(38, title="Fast EMA Period", group=group3) slowLength = input.int(62, title="Slow EMA Period", group=group3) timeframe = input.timeframe("D", title="Timeframe for EMAs", group=group3) // ==== 📈 EMA CALCULATIONS ==== emaFast = request.security(syminfo.tickerid, timeframe, ta.ema(close, fastLength)) emaSlow = request.security(syminfo.tickerid, timeframe, ta.ema(close, slowLength)) col = emaFast > emaSlow ? color.lime : emaFast < emaSlow ? color.red : color.gray // ==== ✅ SIGNAL CONDITIONS ==== pullbackUp = emaFast > emaSlow and close < emaFast pullbackDn = emaFast < emaSlow and close > emaFast entryUp = emaFast > emaSlow and close < emaFast and close > emaFast entryDn = emaFast < emaSlow and close > emaFast and close < emaFast // ==== 🌈 CHART PLOTS ==== plot(emaFast, title="Fast EMA", color=color.new(col, 0), linewidth=2) plot(emaSlow, title="Slow EMA", color=color.new(col, 0), linewidth=4) fill(plot(emaSlow, title="", color=color.new(col, 0)), plot(emaFast, title="", color=color.new(col, 0)), color=color.silver, transp=70) // Highlight bars barcolor(sae and (pullbackUp or pullbackDn) ? color.yellow : na) barcolor(sce and (entryUp or entryDn) ? color.aqua : na) // Trend arrows upTrend = emaFast >= emaSlow downTrend = emaFast < emaSlow plotshape(st and upTrend, title="UpTrend", style=shape.triangleup, location=location.belowbar, color=color.green) plotshape(st and downTrend, title="DownTrend", style=shape.triangledown, location=location.abovebar, color=color.red) // Entry indicators plotarrow(pa and entryUp ? 1 : na, colorup=color.green, offset=-1) plotarrow(pa and entryDn ? -1 : na, colordown=color.red, offset=-1) plotchar(sl and entryUp ? low - ta.tr : na, char="B", location=location.absolute, color=color.green) plotchar(sl and entryDn ? high + ta.tr : na, char="S", location=location.absolute, color=color.red)
Pine Script® indicator
by MsLEO89
SwingSignal RSI Overlay AdvancedSwingSignal RSI Overlay Advanced By BFAS This advanced indicator leverages the Relative Strength Index (RSI) to pinpoint critical market reversal points by highlighting key swing levels with intuitive visual markers. Key Features: Detects overbought and oversold levels with customizable RSI period and threshold settings. Visually marks swing points: Red star (HH) for Higher Highs. Yellow star (LH) for Lower Highs. Blue star (HL) for Higher Lows. Green star (LL) for Lower Lows. Connects swings with lines, aiding in the analysis of market structure. Optimized for use on the main chart (overlay), tracking candles in real time. This indicator provides robust visual support for traders aiming to identify price patterns related to RSI momentum, facilitating entry and exit decisions based on clear swing signals.
Pine Script® indicator
by BFAS76