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אינדיקטורים ואסטרטגיות

ADX Forecast Colorful [DiFlip]ADX Forecast Colorful Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis. ⯁ Real-Time ADX Forecasting For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions. ⯁ Highly Customizable The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system. ⯁ Innovative and Science-Based This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator. ⯁ Scientific Foundation: Linear Regression Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is: y = β₀ + β₁x + ε Where: y  = predicted value (e.g., future ADX) x  = explanatory variable (e.g., bar index or time) β₀ = intercept β₁ = slope (rate of change) ε   = random error term The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends. ⯁ Least Squares Estimation To minimize the error, the regression coefficients are calculated as: β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Where: Σ = summation x̄ and ȳ = means of x and y i ranges from 1 to n (number of data points) These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity. ⯁ Linear Regression in Machine Learning Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool. ⯁ Visual Interpretation Imagine an ADX time series like this: Time → ADX  → The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals. ⯁ Summary of Scientific Concepts Linear Regression: Models variable relationships with a straight line. Least Squares: Minimizes prediction errors for best fit. Time-Series Forecasting: Predicts future values using historical data. Supervised Learning: Trains models to predict outcomes from inputs. Statistical Smoothing: Reduces noise and highlights underlying trends. ⯁ Why This Indicator Is Revolutionary Scientifically grounded: Based on rigorous statistical theory. Unprecedented: First public ADX using least-squares forecast modeling. Smart: Uses machine learning logic. Forward-Looking: Generates predictive, not just reactive, signals. Customizable: Flexible for any strategy or timeframe. ⯁ Conclusion By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies. ⯁ Example of simple linear regression with one independent variable This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables. ⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x) This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component. ⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models. ⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model. ⯁ Result of fitting a set of data points with a quadratic function This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line. ⯁ What is the ADX? The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down. The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength. ⯁ How to use the ADX? The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones: Strong Trend: When the ADX is above 25, indicating a strong trend. Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend. Neutral Zone: Between 20 and 25, where the trend strength is unclear. ⯁ Entry Conditions Each condition below is fully configurable and can be combined to build precise trading logic. 📈 BUY 🅰️  Signal Validity: The signal will remain valid for X bars . 🅰️  Signal Sequence: Configurable as AND or OR . 🅰️ +DI > -DI 🅰️ +DI < -DI 🅰️ +DI > ADX 🅰️ +DI < ADX 🅰️ -DI > ADX 🅰️ -DI < ADX 🅰️ ADX > Threshold 🅰️ ADX < Threshold 🅰️ +DI > Threshold 🅰️ +DI < Threshold 🅰️ -DI > Threshold 🅰️ -DI < Threshold 🅰️ +DI (Crossover) -DI 🅰️ +DI (Crossunder) -DI 🅰️ +DI (Crossover) ADX 🅰️ +DI (Crossunder) ADX 🅰️ +DI (Crossover) Threshold 🅰️ +DI (Crossunder) Threshold 🅰️ -DI (Crossover) ADX 🅰️ -DI (Crossunder) ADX 🅰️ -DI (Crossover) Threshold 🅰️ -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast 📉 SELL 🅰️  Signal Validity: The signal will remain valid for X bars . 🅰️  Signal Sequence: Configurable as AND or OR . 🅰️ +DI > -DI 🅰️ +DI < -DI 🅰️ +DI > ADX 🅰️ +DI < ADX 🅰️ -DI > ADX 🅰️ -DI < ADX 🅰️ ADX > Threshold 🅰️ ADX < Threshold 🅰️ +DI > Threshold 🅰️ +DI < Threshold 🅰️ -DI > Threshold 🅰️ -DI < Threshold 🅰️ +DI (Crossover) -DI 🅰️ +DI (Crossunder) -DI 🅰️ +DI (Crossover) ADX 🅰️ +DI (Crossunder) ADX 🅰️ +DI (Crossover) Threshold 🅰️ +DI (Crossunder) Threshold 🅰️ -DI (Crossover) ADX 🅰️ -DI (Crossunder) ADX 🅰️ -DI (Crossover) Threshold 🅰️ -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast 🤖 Automation All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems. ⯁ Unique Features Linear Regression: (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Background Colors: "bgcolor" Background Colors: "fill" Linear Regression (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Table of Conditions: BUY/SELL Conditions Label: BUY/SELL Plot Labels in the graph above: BUY/SELL Automate & Monitor Signals/Alerts: BUY/SELL Background Colors: "bgcolor" Background Colors: "fill"
אינדיקטור Pine Script®
מאת ‎DiFlip‎
MSS + Multi FVG TrackerMSS + Multi FVG Tracker Description An advanced institutional trading tool that combines Market Structure Shift (MSS) detection with multi-level Fair Value Gap (FVG) tracking. This indicator identifies breakouts of previous swing highs/lows on higher timeframes, then systematically tracks and validates multiple FVGs within each trend direction, generating precise entry signals when price respects the gap structure. How It Works Higher Timeframe Trend Detection The indicator analyzes a higher timeframe (default 15-minute) to determine the overall bias, displaying background colors that show bullish or bearish directionality. This ensures you only trade with institutional trend direction. Market Structure Shift (MSS/BOS) When price closes above a previous swing high (in uptrends) or below a previous swing low (in downtrends), a BOS (Break of Structure) is marked with a line and label. This signals that the institutional structure has shifted and a new trend impulse is beginning. Multi-Level FVG Tracking Once an MSS occurs: The indicator begins scanning for Fair Value Gaps (gaps between candles where no trading occurred) Bullish FVGs: Gaps above the closing price of a bearish candle (low > high ) Bearish FVGs: Gaps below the closing price of a bullish candle (high < low ) Multiple FVGs are tracked simultaneously (up to 5 configurable) across the same impulse Intelligent FVG Validation Each FVG is continuously monitored: Invalidated: If price closes through the gap (below a bullish FVG or above a bearish FVG), it's automatically deleted Touched: If price enters the gap zone, it's marked as "touched" Signal Generated: When a touched FVG shows strong directional confirmation (bullish candle closing above the FVG top, or bearish candle closing below the FVG bottom), a LONG or SHORT signal is triggered Key Features HTF Trend Confirmation: Only trades aligned with higher timeframe bias (eliminates counter-trend noise) Multi-FVG Architecture: Tracks up to 5 gaps per trend impulse simultaneously Automatic Gap Invalidation: Removes FVGs that break below/above, keeping only valid levels Smart Signal Generation: Entry signals require both FVG respect + directional confirmation Color-Coded Structure: Bullish signals in green, bearish in red with instant visual clarity Background Trend Visualization: Subtle background shading shows HTF bias at all times Customizable Parameters: Adjust swing period, HTF timeframe, and max FVGs to track Ideal For ICT Smart Money traders using FVG + MSS methodologies Institutional order flow analysts trading market structure Multi-timeframe traders looking for confluence-based entries Scalpers to swing traders on 5-minute to 1-hour charts Anyone seeking high-probability setups with clear invalidation rules Trading Applications Scalp FVG reversals: Enter when price respects a touched FVG with confirmation Trade impulses with structure: Follow MSS with FVG confluence for institutional-grade entries Identify pullback opportunities: Track multiple FVGs during retracements for re-entry zones Confirm breakout validity: Only take breaks when aligned with HTF trend + FVG structure Avoid false breakouts: Invalidated FVGs signal that the move is losing structure How to Use Wait for the MSS: Background color shift + BOS line confirms market structure break Monitor FVG Creation: Boxes appear as gaps form within the new impulse Watch for Invalidation: Red boxes disappear if price breaks the gap—signal invalid Wait for Touch + Confirmation: FVG must be touched AND show strong directional candle Take the Signal: Triangle entry markers appear with audio/visual alerts Clear Risk Management: Use the invalidated FVG level as your stop loss Signal Strength Indicators Strongest Setup: Multiple FVGs created + one respects while others invalidate (shows structure) Medium Setup: Single FVG touched and confirmed Weaker Setup: Quick touch with weak confirmation candle (wait for better structure) Customization Options HTF Timeframe: Change from 15-min to 5, 30, 60 min or higher for different trading styles Swing Period: Adjust from 10 bars for faster detection to 20+ for structural shifts Max FVGs: Track 1-5 simultaneous gaps (lower = cleaner, higher = more opportunities) Colors: Customize bullish/bearish colors to match your chart theme Default Settings Optimized For NASDAQ futures and liquid forex pairs 5-minute to 1-hour timeframe trading Smart Money / ICT methodology High-probability impulse + gap trading Pro Tips The cleaner your chart (fewer invalidated FVGs), the stronger the structural move Multiple valid FVGs in one impulse suggest institutional accumulation/distribution HTF background color changes are early warnings of trend structure shift Best setups occur when 2-3 FVGs exist and one shows clear confirmation
אינדיקטור Pine Script®
מאת ‎julioperez75955‎
AlphaRank MA Lens – Multi-Timeframe Moving Average MapAlphaRank MA Lens – Multi-Timeframe Moving Average Map AlphaRank MA Lens is a clean, open-source moving-average overlay that turns price action into an easy-to-read trend map. It focuses on structure and context only — no signals, no backtest, no hype — just a clear view of where price sits relative to key moving averages. The script plots the 10 / 20 / 50 / 100 / 150 / 200 / 730 moving averages with full color control and a single “MA Type” switch, so you can flip the whole stack between SMA and EMA in one click. Instead of loading multiple separate MA indicators, this puts the full trend stack in one tool. An optional background highlight lets you choose a reference MA (for example the 200 MA) and softly shade the chart: Green when price is above that MA Red when price is below it This makes trend regime changes easy to see at a glance. How traders typically use it (education only): 10/20/50 MAs → short-term trend and momentum. 100/150/200/730 MAs → bigger structural trend and “where price lives” in the long-term range. Many traders consider conditions healthier when price and the short MAs are stacked above the longer MAs, and weaker when price trades below them. Follow my work: AlphaRank This script is for educational and analytical purposes only and does not provide trading advice or performance promises. Always combine it with your own judgment, testing, and risk management.
אינדיקטור Pine Script®
מאת ‎AlphaRank‎
Visible RangeOverview This is a precision tool designed for quantitative traders and engineers who need exact control over their chart's visual scope. Unlike standard time calculations that fail in markets with trading breaks (like A-Shares, Futures, or Stocks), this indicator uses a loop-back mechanism to count the actual number of visible bars, ensuring your indicators (e.g., MA60, MA200) have sufficient sample data. Why use this? If you use multi-timeframe layouts (e.g., Daily/Hourly/15s), it is critical to know exactly how much data is visible. The Problem: In markets like the Chinese A-Share market (T+1, 4-hour trading day), calculating Time Range / Timeframe results in massive errors because it includes closed market hours (lunch breaks, nights, weekends). The Solution: This script iterates through the visible range to count the true bar_index, providing 100% accurate data density metrics. Key Features True Bar Counting: Uses a for loop to count actual candles, ignoring market breaks. perfect for non-24/7 markets. Integer Precision: Displays time ranges (Days, Hours, Mins, Secs) in clean integers. No messy decimals. Compact UI: Displays information in a single line (e.g., View: 30 Days (120 Bars)), default to the Top Right corner to save screen space. Fully Customizable: Adjustable position, text size, and colors to fit any dark/light theme. Performance Optimized: Includes max_bars_back limits to prevent browser lag on deep history lookups. Settings Position: Default Top Right (can be moved to any corner). Max Bar Count: Default 5000 (Safety limit for loop calculation).
אינדיקטור Pine Script®
מאת ‎sanpingz‎
SPY EMA + VWAP Day Trading Strategy (Market Hours Only)//@version=5 indicator("SPY EMA + VWAP Day Trading Strategy (Market Hours Only)", overlay=true) // === Market Hours Filter (EST / New York Time) === nySession = input.session("0930-1600", "Market Session (NY Time)") inSession = time(timeframe.period, "America/New_York") >= time(nySession, "America/New_York") // EMAs ema9 = ta.ema(close, 9) ema21 = ta.ema(close, 21) // VWAP vwap = ta.vwap(close) // Plot EMAs & VWAP plot(ema9, "EMA 9", color=color.green, linewidth=2) plot(ema21, "EMA 21", color=color.orange, linewidth=2) plot(vwap, "VWAP", color=color.blue, linewidth=2) // ----------- Signals ----------- long_raw = close > ema9 and ema9 > ema21 and close > vwap and ta.crossover(ema9, ema21) short_raw = close < ema9 and ema9 < ema21 and close < vwap and ta.crossunder(ema9, ema21) // Apply Market Hours Filter long_signal = long_raw and inSession short_signal = short_raw and inSession // Plot Signals plotshape(long_signal, title="BUY", style=shape.labelup, location=location.belowbar, color=color.green, size=size.small, text="BUY") plotshape(short_signal, title="SELL", style=shape.labeldown, location=location.abovebar, color=color.red, size=size.small, text="SELL") // Alerts alertcondition(long_signal, title="BUY Alert", message="BUY Signal (Market Hours Only)") alertcondition(short_signal, title="SELL Alert", message="SELL Signal (Market Hours Only)")
אינדיקטור Pine Script®
מאת ‎hungryPiex3L‎
NQUSB Sector Industry Stocks Strength A Comprehensive Multi-Industry Performance Comparison Tool The complete Pine Script code and supporting Python automation scripts are available on GitHub: GitHub Repository: github.com Original idea from by www.tradingview.com ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ═══ WHAT'S NEW ═══ 4-Level Hierarchical Navigation: Primary: All 11 NQUSB sectors (NQUSB10, NQUSB15, NQUSB20, etc.) Secondary (Default): Broad sectors like Technology, Energy Tertiary: Industry groups within sectors Quaternary: Individual stocks within industries (37 semiconductors) Enhanced Stock Coverage: 1,176 total stocks across 129 industries 37 semiconductor stocks Market-cap weighted selection: 60% tech / 35% others Range: 1-37 stocks per industry ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ═══ CORE FEATURES ═══ 1. Drill-Down/Drill-Up Navigation View NVDA at different granularity levels: Quaternary: ● NVDA ranks #3 of 37 semiconductors Tertiary: ✓ Semiconductors at 85% (strongest in tech hardware) Secondary: ✓ Tech Hardware at 82% (stronger than software) Primary: ✓ Technology at 78% (#1 sector overall) Insight: One indicator, one stock, four perspectives - instantly see if strength is stock-specific, industry-specific, or sector-wide. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2. Visual Current Stock Identification Violet Markers - Instant Recognition: ● (dot) marker when current stock is in top N performers ✕ (cross) marker when current stock is below top N Violet color (#9C27B0) on both symbol and value labels Example: "NVDA ● ranks #3 of 37" ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3. Rank Display in Title Dynamic title shows performance context: "Semiconductors (RS Rating - 3 Months) | NVDA ranks #3 of 37" #1 = Best performer, higher number = lower rank Total adjusts if current stock auto-added ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4. Auto-Add Current Stock Always Included: Current stock automatically added if not in predefined list Example: Viewing PRSO → "PRSO ranks #37 of 39 ✕" Works for any stock - from NVDA to obscure small-caps Violet markers ensure visibility even when ranked low ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ═══ DUAL PERFORMANCE METRICS ═══ RS Rating (Relative Strength): Normalized strength score 1-99 Compare stocks across different price ranges Default benchmark: SPX % Return: Simple percentage price change Direct performance comparison 11 Time Periods: 1 Week, 2 Weeks, 1 Month, 2 Months, 3 Months (Default) , 6 Months, 1 Year, YTD, MTD, QTD, Custom (1-500 days) Result: 22 analytical combinations (2 metrics × 11 periods) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ═══ USE CASES ═══ Sector Rotation Analysis: Is NVDA's strength semiconductors-specific or tech-wide? Drill through all 4 levels to find answer Identify which industry groups are leading/lagging Finding Hidden Gems: JPM ranks #3 of 13 in Major Banks But Financials sector weak overall (68%) = Relative strength play in weak sector Cross-Industry Comparison: 129 industries covered Market-wide scan capability Find strongest performers across all sectors ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ═══ TECHNICAL SPECIFICATIONS ═══ V32 Stats: Total Industries: 129 Total Stocks: 1,176 File Size: 82,032 bytes (80.1 KB) Request Limit: 39 max (Semiconductors), 10-16 typical Granularity Levels: 4 (Primary → Quaternary) Smart Stock Allocation: Technology industries: 60% coverage Other industries: 35% coverage Market-cap weighted selection Formula: MIN(39, MAX(5, CEILING(total × percentage))) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ═══ KEY ADVANTAGES ═══ vs. Single Industry Tools: ✓ 129 industries vs 1 ✓ Market-wide perspective ✓ Hierarchical navigation ✓ Sector rotation detection vs. Manual Comparison: ✓ No ETF research needed ✓ Instant visual markers ✓ Automatic ranking ✓ One-click drill-down ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ For complete documentation, Python automation scripts, and CSV data files: github.com Version: V32 Last Updated: 2025-11-30 Pine Script Version: v5
אינדיקטור Pine Script®
מאת ‎ama0000‎
Sector Rotation - Risk Preference Indicator# Sector Rotation - Risk Preference Indicator ## Overview This indicator measures market risk appetite by comparing the relative strength between **Aggressive** and **Defensive** sectors. It provides a clean, single-line visualization to help traders identify market sentiment shifts and potential trend reversals. ## How It Works The indicator calculates a **Bullish/Bearish Ratio** by dividing the average price of aggressive sector ETFs by defensive sector ETFs, then normalizing to a baseline of 100. **Formula:** - Ratio = (Aggressive Sectors Average / Defensive Sectors Average) × 100 **Interpretation:** - **Ratio > 100**: Risk-on sentiment (Aggressive sectors outperforming Defensive) - **Ratio < 100**: Risk-off sentiment (Defensive sectors outperforming Aggressive) - **Ratio ≈ 100**: Neutral (Both sector groups performing equally) ## Default Sectors **Defensive Sectors** (Safe havens during uncertainty): - XLP - Consumer Staples Select Sector SPDR Fund - XLU - Utilities Select Sector SPDR Fund - XLV - Health Care Select Sector SPDR Fund **Aggressive Sectors** (Growth-oriented, higher risk): - XLK - Technology Select Sector SPDR Fund - XBI - SPDR S&P Biotech ETF - XRT - SPDR S&P Retail ETF ## Features ✅ **Fully Customizable Sectors** - Choose any ETFs/tickers for each sector group ✅ **Smoothing Control** - Adjustable SMA period to reduce noise (default: 2) ✅ **Clean Visualization** - Single blue line for easy interpretation ✅ **Multi-timeframe Support** - Works on any timeframe ✅ **Lightweight** - Minimal calculations for fast performance ## Settings ### Defensive Sectors Group - **Defensive Sector 1**: First defensive ETF ticker (default: XLP) - **Defensive Sector 2**: Second defensive ETF ticker (default: XLU) - **Defensive Sector 3**: Third defensive ETF ticker (default: XLV) ### Aggressive Sectors Group - **Aggressive Sector 1**: First aggressive ETF ticker (default: XLK) - **Aggressive Sector 2**: Second aggressive ETF ticker (default: XBI) - **Aggressive Sector 3**: Third aggressive ETF ticker (default: XRT) ### Display Settings - **Smoothing Length**: SMA period for ratio smoothing (default: 2, range: 1-50) - Lower values = More responsive but noisier - Higher values = Smoother but more lagging ## Use Cases ### 1. Market Regime Identification - **Rising Ratio (trending up)** → Bull market / Risk-on environment - Aggressive sectors leading, investors chasing growth - Favorable for long positions in tech, growth stocks - **Falling Ratio (trending down)** → Bear market / Risk-off environment - Defensive sectors leading, investors seeking safety - Consider defensive positioning or short opportunities ### 2. Divergence Analysis - **Bullish Divergence**: Price makes new lows but ratio rises - Suggests underlying strength returning - Potential market bottom forming - **Bearish Divergence**: Price makes new highs but ratio falls - Suggests weakening momentum - Potential market top forming ### 3. Trend Confirmation - **Strong uptrend + Rising ratio** → Confirmed bullish trend - **Strong downtrend + Falling ratio** → Confirmed bearish trend - **Uptrend + Falling ratio** → Weakening trend, watch for reversal - **Downtrend + Rising ratio** → Potential trend exhaustion ## Best Practices ⚠️ **Timeframe Selection** - Recommended: Daily, 4H, 1H for cleaner signals - Lower timeframes (15m, 5m) may produce noisy signals ⚠️ **Complementary Analysis** - Use alongside price action and volume analysis - Combine with support/resistance levels - Not designed as a standalone trading system ⚠️ **Market Conditions** - Most effective in trending markets - Less reliable during ranging/consolidation periods - Works best in liquid, well-traded sectors ⚠️ **Customization Tips** - Can substitute with international sectors (EWU, EWZ, etc.) - Can use crypto sectors (DeFi vs Layer1, etc.) - Adjust smoothing based on trading style (day trading = 2-5, swing = 10-20) ## Display Options ### Default View (overlay=false) - Shows in separate pane below chart - Dedicated scale for ratio values ### Alternative View - Can be moved to main chart pane (drag indicator) I typically overlay this indicator on the SPY daily chart to observe divergences. I don’t focus on specific values but rather on the direction of the trend. The author is not responsible for any trading losses incurred using this indicator. ## Support & Feedback For questions, feature requests, or bug reports: - Comment below - Send a private message - Check for updates regularly If you find this indicator useful, please: - ⭐ Leave a like/favorite - 💬 Share your experience in comments - 📊 Share charts showing interesting patterns
אינדיקטור Pine Script®
מאת ‎gary186‎
NQ-VIX Expected Move LevelsNQ -VIX Daily Price Bands This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets. Formulas: Upper Band = Daily Open + (NQ Price × VIX ÷ √252 ÷ 100) Lower Band = Daily Open - (NQ Price × VIX ÷ √252 ÷ 100) The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open. Features: Real-time band calculation that updates throughout the trading session Upper band (green) extends from the current day's open Lower band (red) contracts from the current day's open Inner upper band (green) at 50% of expected move Inner lower band (red) at 50% of expected move Middle Inner upper band (green) at 80% of expected move Middle Inner lower band (red) at 80% of expected move Information table displaying: Current NQ price and VIX level Daily Open Expected move
אינדיקטור Pine Script®
מאת ‎alanjeremiebarbe‎
MTF Trading Helper & Multi AlertsHi dear fellows, I´m using this indicator for my trading, so every then and when I will publish updates on this one. This indicator should help to identify the right trading setup. I´m using it to trade index futures and stocks. MTF Trading Helper & Multi Alerts Overview This indicator provides a clear visual representation of trend direction across three timeframes. It helps traders identify trend alignment, potential reversals, and optimal entry/exit points by analyzing the relationship between different smoothed timeframes. You can set up multiple alerts (as one alert in Tradingview) How It Works The indicator displays three colored circles representing the smoothed candle direction on three different timeframes: Bottom plot represents the overall trend direction, the plot in the middle shows intermediate momentum, and the one on top captures short-term price action. When a color change occurs, the circle appears in a darker shade to highlight the transition. 🟢 Green = Bullish - 🔴 Red = Bearish This change can also trigger multiple alerts. Timeframe Settings - important Choose between two trading setups, either for: Intraday 1-minute candles or 1h for swing trading. Set up your chart accordingly to that timeframe. Intraday | 1Min chart candles Swing | 1 hour chart candles Plots TF3 represents the overall trend direction (bottom), TF2 shows intermediate momentum (middle), and TF1 captures short-term price action (top). Interpretation & Strategy Alerts 1. Trend Bullish (TF3 turns Green) The higher timeframe has shifted bullish - a potential new uptrend is forming. Example: You're watching ES-mini on the Intraday setting. TF3 turns green after being red for several days. This signals the broader trend may be shifting bullish - consider looking for long opportunities. 2. Trend Bearish (TF3 turns Red) The higher timeframe has shifted bearish - consider protecting profits or exiting long positions. Example: You hold a long position in Es-mini. TF3 turns red, indicating the macro trend is weakening. This is your signal to take profits or tighten stop-losses. 3. Possible Accumulation (TF3 Red + TF2 turns Green) While the overall trend is still bearish, the medium timeframe shows buying pressure. Smart money may be accumulating - watch closely for a potential trend reversal. Example: Es-mini has been in a downtrend (TF3 red). Suddenly TF2 turns green while TF3 remains red. This could indicate institutional buying before a reversal. Don't buy yet, but add it to your watchlist and wait for confirmation. 4. Trend Continuation (TF3 Green + TF2 turns Green) The medium timeframe realigns with the bullish macro trend - a potential buying opportunity as momentum returns to the uptrend. Example: Es-mini is in an uptrend (TF3 green). After a pullback, TF2 was red but now turns green again. The pullback appears to be over - this is a trend continuation signal and a potential entry point. 5. Buy the Dip (TF3 + TF2 Green + TF1 turns Green) All timeframes are now aligned bullish. The short-term pullback is complete and price is resuming the uptrend - optimal entry for short-term trades. Example: Es-mini is trending up (TF3 + TF2 green). A small dip caused TF1 to turn red briefly. When TF1 turns green again, all three timeframes are aligned - this is your "Buy the Dip" signal with strong confirmation. 6. Sell the Dip (TF3 + TF2 Green + TF1 turns Red) Short-term weakness within an uptrend. This can be used to take partial profits, wait for a better entry, or trail stops tighter. Example: You're long on ES-mini with TF3 and TF2 green. TF1 turns red, indicating short-term selling pressure. Consider taking partial profits here and wait for TF1 to turn green again (Buy the Dip) to add back to your position. How to Use Choose your scenario: Select "Intraday" 1min-chart for day trading or "Swing" 1h-chart for swingtrading Enable alerts: Turn on the strategy alerts you want to receive in the settings Wait for signals: Let the indicator notify you when conditions align Confirm with price action: Always use additional confirmation before entering trades Best Practices ✅ Use TF3 as your trend filter - only take longs when TF3 turns green and hold them :) ✅ Use TF2 for timing - wait for TF2 to align with TF3 for swings. ✅ Use TF2 for early entries (accumulation phase) when TF3 is still red. Watch out! ✅ Use TF1 for entries when TF3 and TF2 are green. Only buy if TF1 is red. Keep it short and sweet. ✅ Combine with support/resistance levels for better entries ✅ Use proper risk management - no indicator is 100% accurate Disclaimer This indicator is for educational purposes only. Past performance does not guarantee future results. Always do your own research and use proper risk management. Never risk more than you can afford to lose.
אינדיקטור Pine Script®
מאת ‎xriswart‎
NQ-VIX Expected Move LTF LevelsNQ -VIX LTF Price Bands This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets. Formulas: Upper Band = (Input TF Open) + (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100 Lower Band = Daily Open - (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100 The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open. Features: Real-time band calculation that updates throughout the trading session Upper band (green) extends from the current TF's open Lower band (red) contracts from the current TF's open Inner upper band (green) at 50% of expected move Inner lower band (red) at 50% of expected move Middle Inner upper band (green) at 80% of expected move Middle Inner lower band (red) at 80% of expected move Information table displaying: Current input TF Current NQ price and VIX level Current input TF Open Expected move
אינדיקטור Pine Script®
מאת ‎alanjeremiebarbe‎
ICT Fair Value Gap (FVG) Detector │ Auto-Mitigated │ 2025Accurate ICT / Smart Money Concepts Fair Value Gap (FVG) detector Features: • Detects both Bullish (-FVG) and Bearish (+FVG) using strict 3-candle rule • Boxes automatically extend right until price mitigates them • Boxes auto-delete when price closes inside the gap (true mitigation) • No repainting – 100% reliable • Clean, lightweight, and works on all markets & timeframes • Fully customizable colors and transparency How to use: – Bullish FVG (green) = potential support / buy zone in uptrend – Bearish FVG (red) = potential resistance / sell zone in downtrend Exactly matches The Inner Circle Trader (ICT) methodology used by thousands of SMC traders in 2024–2025. Enjoy and trade safe!
אינדיקטור Pine Script®
מאת ‎keerthanamurugesan‎
Fast Autocorrelation Estimator█ Overview: The Fast ACF and PACF Estimation indicator efficiently calculates the autocorrelation function (ACF) and partial autocorrelation function (PACF) using an online implementation. It helps traders identify patterns and relationships in financial time series data, enabling them to optimize their trading strategies and make better-informed decisions in the markets. █ Concepts: Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay. This indicator displays autocorrelation based on lag number. The autocorrelation is not displayed based over time on the x-axis. It's based on the lag number which ranges from 1 to 30. The calculations can be done with "Log Returns", "Absolute Log Returns" or "Original Source" (the price of the asset displayed on the chart). When calculating autocorrelation, the resulting value will range from +1 to -1, in line with the traditional correlation statistic. An autocorrelation of +1 represents a perfect correlation (an increase seen in one time series leads to a proportionate increase in the other time series). An autocorrelation of -1, on the other hand, represents a perfect inverse correlation (an increase seen in one time series results in a proportionate decrease in the other time series). Lag number indicates which historical data point is autocorrelated. For example, if lag 3 shows significant autocorrelation, it means current data is influenced by the data three bars ago. The Fast Online Estimation of ACF and PACF Indicator is a powerful tool for analyzing the linear relationship between a time series and its lagged values in TradingView. The indicator implements an online estimation of the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF) up to 30 lags, providing a real-time assessment of the underlying dependencies in your time series data. The Autocorrelation Function (ACF) measures the linear relationship between a time series and its lagged values, capturing both direct and indirect dependencies. The Partial Autocorrelation Function (PACF) isolates the direct dependency between the time series and a specific lag while removing the effect of any indirect dependencies. This distinction is crucial in understanding the underlying relationships in time series data and making more informed decisions based on those relationships. For example, let's consider a time series with three variables: A, B, and C. Suppose that A has a direct relationship with B, B has a direct relationship with C, but A and C do not have a direct relationship. The ACF between A and C will capture the indirect relationship between them through B, while the PACF will show no significant relationship between A and C, as it accounts for the indirect dependency through B. Meaning that when ACF is significant at for lag 5, the dependency detected could be caused by an observation that came in between, and PACF accounts for that. This indicator leverages the Fast Moments algorithm to efficiently calculate autocorrelations, making it ideal for analyzing large datasets or real-time data streams. By using the Fast Moments algorithm, the indicator can quickly update ACF and PACF values as new data points arrive, reducing the computational load and ensuring timely analysis. The PACF is derived from the ACF using the Durbin-Levinson algorithm, which helps in isolating the direct dependency between a time series and its lagged values, excluding the influence of other intermediate lags. █ How to Use the Indicator: Interpreting autocorrelation values can provide valuable insights into the market behavior and potential trading strategies. When applying autocorrelation to log returns, and a specific lag shows a high positive autocorrelation, it suggests that the time series tends to move in the same direction over that lag period. In this case, a trader might consider using a momentum-based strategy to capitalize on the continuation of the current trend. On the other hand, if a specific lag shows a high negative autocorrelation, it indicates that the time series tends to reverse its direction over that lag period. In this situation, a trader might consider using a mean-reversion strategy to take advantage of the expected reversal in the market. ACF of log returns: Absolute returns are often used to as a measure of volatility. There is usually significant positive autocorrelation in absolute returns. We will often see an exponential decay of autocorrelation in volatility. This means that current volatility is dependent on historical volatility and the effect slowly dies off as the lag increases. This effect shows the property of "volatility clustering". Which means large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes. ACF of absolute log returns: Autocorrelation in price is always significantly positive and has an exponential decay. This predictably positive and relatively large value makes the autocorrelation of price (not returns) generally less useful. ACF of price: █ Significance: The significance of a correlation metric tells us whether we should pay attention to it. In this script, we use 95% confidence interval bands that adjust to the size of the sample. If the observed correlation at a specific lag falls within the confidence interval, we consider it not significant and the data to be random or IID (identically and independently distributed). This means that we can't confidently say that the correlation reflects a real relationship, rather than just random chance. However, if the correlation is outside of the confidence interval, we can state with 95% confidence that there is an association between the lagged values. In other words, the correlation is likely to reflect a meaningful relationship between the variables, rather than a coincidence. A significant difference in either ACF or PACF can provide insights into the underlying structure of the time series data and suggest potential strategies for traders. By understanding these complex patterns, traders can better tailor their strategies to capitalize on the observed dependencies in the data, which can lead to improved decision-making in the financial markets. Significant ACF but not significant PACF: This might indicate the presence of a moving average (MA) component in the time series. A moving average component is a pattern where the current value of the time series is influenced by a weighted average of past values. In this case, the ACF would show significant correlations over several lags, while the PACF would show significance only at the first few lags and then quickly decay. Significant PACF but not significant ACF: This might indicate the presence of an autoregressive (AR) component in the time series. An autoregressive component is a pattern where the current value of the time series is influenced by a linear combination of past values at specific lags. Often we find both significant ACF and PACF, in that scenario simply and AR or MA model might not be sufficient and a more complex model such as ARMA or ARIMA can be used. █ Features: Source selection: User can choose either 'Log Returns' , 'Absolute Returns' or 'Original Source' for the input data. Autocorrelation Selection: User can choose either 'ACF' or 'PACF' for the plot selection. Plot Selection: User can choose either 'Autocorrelarrogram' or 'Historical Autocorrelation' for plotting the historical autocorrelation at a specified lag. Max Lag: User can select the maximum number of lags to plot. Precision: User can set the number of decimal points to display in the plot.
אינדיקטור Pine Script®
מאת ‎Motgench‎
Relative Volume EMA (RVOL)Relative Volume EMA (RVOL) measures the current bar’s volume relative to its typical volume over a selected lookback period. It helps traders identify whether a price move is supported by real participation or if it’s occurring on weak, low-quality volume. This version uses: RVOL = Current Volume ÷ Volume EMA Volume EMA Length: adjustable Signal Threshold: a customizable horizontal line (default = 1.2) How to Use 1. RVOL > 1.2 → High-Quality Momentum A value above 1.2 indicates that the current bar has at least 20% more volume than normal, suggesting: Strong conviction Algorithmic activity Momentum-backed breakout or breakdown Higher probability trend continuation These bars are ideal for confirming entries after a technical setup (e.g., pullback, engulfing pattern, Ichimoku trend confirmation, etc.). 2. RVOL < 1.0 → Weak or Low-Quality Move When RVOL is below 1.0: Volume is below average Moves are more likely to fail or reverse Breakouts are unreliable Triggers lack institutional participation These bars are best avoided for trade entries. Why This Indicator Is Useful In many strategies, price alone is not enough. RVOL acts as a filter to ensure that your signals occur during times when the market is actually active and committed. Typical use cases: Confirm trend-following entries Validate pullbacks and breakout candles Filter out low-volume chop Identify session-based volume surges Improve risk-to-reward quality by entering only during true momentum Recommended Settings EMA Length: 20 Threshold Line: 1.2 Works well on Forex, Crypto, and Indices Best used on 15m, 30m, 1H, and 4H charts
אינדיקטור Pine Script®
מאת ‎FXLoungeIN‎
️Omega RatioThe Omega Ratio is a risk-return performance measure of an investment asset, portfolio, or strategy. It is defined as the probability-weighted ratio, of gains versus losses for some threshold return target. The ratio is an alternative for the widely used Sharpe ratio and is based on information the Sharpe ratio discards. █ OVERVIEW As we have mentioned many times, stock market returns are usually not normally distributed. Therefore the models that assume a normal distribution of returns may provide us with misleading information. The Omega Ratio improves upon the common normality assumption among other risk-return ratios by taking into account the distribution as a whole. █ CONCEPTS Two distributions with the same mean and variance, would according to the most commonly used Sharpe Ratio suggest that the underlying assets of the distribution offer the same risk-return ratio. But as we have mentioned in our Moments indicator, variance and standard deviation are not a sufficient measure of risk in the stock market since other shape features of a distribution like skewness and excess kurtosis come into play. Omega Ratio tackles this problem by employing all four Moments of the distribution and therefore taking into account the differences in the shape features of the distributions. Another important feature of the Omega Ratio is that it does not require any estimation but is rather calculated directly from the observed data. This gives it an advantage over standard statistical estimators that require estimation of parameters and are therefore sampling uncertainty in its calculations. █ WAYS TO USE THIS INDICATOR Omega calculates a probability-adjusted ratio of gains to losses, relative to the Minimum Acceptable Return (MAR). This means that at a given MAR using the simple rule of preferring more to less, an asset with a higher value of Omega is preferable to one with a lower value. The indicator displays the values of Omega at increasing levels of MARs and creating the so-called Omega Curve. Knowing this one can compare Omega Curves of different assets and decide which is preferable given the MAR of your strategy. The indicator plots two Omega Curves. One for the on chart symbol and another for the off chart symbol that u can use for comparison. When comparing curves of different assets make sure their trading days are the same in order to ensure the same period for the Omega calculations. Value interpretation: Omega<1 will indicate that the risk outweighs the reward and therefore there are more excess negative returns than positive. Omega>1 will indicate that the reward outweighs the risk and that there are more excess positive returns than negative. Omega=1 will indicate that the minimum acceptable return equals the mean return of an asset. And that the probability of gain is equal to the probability of loss. █ FEATURES • "Low-Risk security" lets you select the security that you want to use as a benchmark for Omega calculations. • "Omega Period" is the size of the sample that is used for the calculations. • “Increments” is the number of Minimal Acceptable Return levels the calculation is carried on. • “Other Symbol” lets you select the source of the second curve. • “Color Settings” you can set the color for each curve.
אינדיקטור Pine Script®
מאת ‎Motgench‎