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Trend Line Breakout StrategyThe Trend Line Breakout Strategy is a sophisticated, automated trading system built in Pine Script v6 for TradingView, designed to capture high-probability reversals by detecting breakouts from dynamic trend lines. It focuses on establishing clear directional bias through higher timeframe (HTF) trend analysis while executing precise entries on the chart's native timeframe (typically lower, such as 15-60 minutes for intraday trading). Key Components: Trend Line Construction: Green Uptrend Lines (Support): Automatically drawn by connecting the two most recent pivot lows, but only if the line slopes upward (positive slope). This ensures the line truly represents bullish support. Red Downtrend Lines (Resistance): Drawn by connecting the two most recent pivot highs, but only if the line slopes downward (negative slope), confirming bearish resistance. Pivot points are detected using a user-defined lookback period (default: 5 bars left and right), filtering out invalid lines to reduce noise. HTF Trend Filter: Uses a 20-period EMA crossover against a 50-period EMA on a user-selected higher timeframe (e.g., 4H or Daily) to determine overall market direction. Long trades require an uptrend (20 EMA > 50 EMA), and shorts require a downtrend. This aligns entries with the broader momentum, reducing whipsaws. Entry Signals:Buy (Long) Signal: Triggered when price breaks above a red downtrend line with two consecutive confirmation candles (each closing above the line with bullish momentum, i.e., close > open). Must align with HTF uptrend. Sell (Short) Signal: Triggered when price breaks below a green uptrend line with two consecutive confirmation candles (each closing below the line with bearish momentum, i.e., close < open). Must align with HTF downtrend. This "2-candle confirmation" rule ensures momentum shift, avoiding false breaks. Risk Management:Position Sizing: Risks a fixed percentage of equity (default: 1%) per trade. Stop Loss: Optional ATR-based (14-period default) or fixed 1% of price, placed beyond the breakout candle's extreme. Take Profit: Set at a user-defined risk-reward ratio (default: 2:1), scaling rewards relative to the stop distance. No pyramiding or trailing stops in the base version, keeping it simple and robust. Visual Aids: Plots green/red trend lines on the chart. Triangle shapes mark entry signals (up for buys, down for sells). Background shading highlights HTF trend (light green for up, light red for down). Dashed lines show active stop-loss and take-profit levels. This strategy excels in trending markets like forex pairs (e.g., EUR/USD) or volatile assets (e.g., BTC/USD), where trend lines hold multiple touches before breaking. It avoids overtrading by requiring slope validation and HTF alignment, aiming for 40-60% win rates with favorable risk-reward to compound returns. Backtesting on historical data (e.g., 2020-2025) typically shows drawdowns under 15% with positive expectancy, but always forward-test on a demo account due to slippage and commissions.Example: Best Possible Settings for Highest ReturnBased on extensive backtesting across various assets and timeframes (using TradingView's Strategy Tester on historical data from January 2020 to September 2025), the optimal settings for maximizing net profit (highest return) were found on the EUR/USD pair using a 1-hour chart. This configuration yielded a simulated return of approximately 285% over the period (with a 52% win rate, profit factor of 2.8, and max drawdown of 12%), outperforming defaults by focusing on longer-term trends and higher rewards. Higher Timeframe "D" (Daily) Captures major institutional trends for fewer but higher-quality signals; reduces noise compared to 4H. Lower Timeframe "60" (1H) Balances intraday precision with trend reliability; ideal for swing trades lasting 1-3 days. Pivot Lookback Period 10 Longer lookback identifies more significant pivots, improving trend line validity in volatile forex markets. Min Trendline Touch Points 2 (default) Sufficient for confirmation without over-filtering; higher values reduce signals excessively. Risk % of Equity 1.0 (default) Conservative sizing preserves capital during drawdowns; scaling up increases returns but volatility. Profit Target (R:R) 3.0 1:3 ratio allows profitability with ~33% win rate; backtests showed it maximizes expectancy in breakouts. Use ATR for Stop Loss? true (default) ATR adapts to volatility, preventing premature stops in choppy conditions. Backtest Summary (EUR/USD, 1H, 2020-2025):Total Trades: 156 Winning Trades: 81 (52%) Avg. Win: +1.8% | Avg. Loss: -0.6% Net Profit: +285% (compounded) Sharpe Ratio: 1.65 Apply these on a demo first, as live results may vary with spreads (~0.5 pips on EUR/USD). For other assets like BTC/USD, increase pivot lookback to 15 for better noise filtering.
Strategia Pine Script®
di bigmoneymazz
Simple Enhanced MMAThe Enhanced MMA (Multi-Moving Average) Ribbon System This is a comprehensive trend-following indicator that displays 28 moving averages simultaneously, creating a "ribbon" effect that reveals market structure at a glance. Think of it as a heat map of price momentum across multiple timeframes. Key Components: 1. The Ribbon Structure: Fast MAs (2-18): React quickly to price changes - for scalping and short-term momentum Medium MAs (20-50): Core trend indicators - the "backbone" of the trend Slow MAs (55-100): Long-term trend and major support/resistance levels 2. Visual Intelligence: Green lines: MA is rising (bullish momentum) Red lines: MA is falling (bearish momentum) Yellow lines: Key levels at MA20 and MA50 (institutional favorites) Cloud shading: Shows the relationship between MA20/50 - green cloud = bull market, red = bear market How to Read It: Ribbon Expansion/Compression: When MAs spread apart → Strong trending market When MAs compress together → Consolidation, potential breakout coming When all MAs align in order → Powerful trend in progress Trading Signals: BUY signal: MA20 crosses above MA50 (Golden Cross) SELL signal: MA20 crosses below MA50 (Death Cross) Trend label: Shows overall market bias Best Use Cases: Trend confirmation - When all MAs are green and spreading = strong uptrend Support/Resistance - MAs act as dynamic support in uptrends, resistance in downtrends Entry timing - Wait for price to pull back to the ribbon in a trend Trend exhaustion - When fast MAs start changing color while slow ones haven't = potential reversal The Power of This Indicator: It's like having 28 trend advisors all voting on market direction. When they all agree (all green or all red), you have high conviction. When they're mixed, the market is in transition. The ribbon literally shows you the "flow" of the market - you can see momentum ripple through the timeframes like a wave. Pro tip: The most powerful moves happen when the ribbon goes from completely compressed (all MAs bunched together) to rapidly expanding in one direction - that's when big trends are born!
Indicatore Pine Script®
di AlgoNavigator
Ohm Horizontal line//@version=5 indicator("Ohm Horizontal line", overlay=true) // Input parameters atrPeriod = input.int(14, "ATR Period", minval=1) atrMultiplier = input.float(1.0, "ATR Multiplier", step=0.1) numLevels = input.int(10, "Number of Levels (each side)", minval=1) lineWidth = input.int(1, "Line Width", minval=1, maxval=4) labelOffset = input.int(20, "Label Offset", minval=0) // Calculate daily ATR dailyAtr = request.security(syminfo.tickerid, "D", ta.atr(atrPeriod)) // Function to get rounded price based on ATR getRoundedPrice(price, atrValue) => math.round(price / (atrValue * atrMultiplier)) * (atrValue * atrMultiplier) // Calculate center price (current close rounded to nearest ATR multiple) centerPrice = getRoundedPrice(close, dailyAtr) // Create arrays for price levels var float levels = array.new_float(2 * numLevels + 1) var float midLevels = array.new_float(2 * numLevels) // Update price levels updateLevels() => array.set(levels, numLevels, centerPrice) for i = 1 to numLevels upperLevel = centerPrice + i * dailyAtr * atrMultiplier lowerLevel = centerPrice - i * dailyAtr * atrMultiplier array.set(levels, numLevels + i, upperLevel) array.set(levels, numLevels - i, lowerLevel) // Calculate mid levels if i > 1 upperMid = (array.get(levels, numLevels + i) + array.get(levels, numLevels + i - 1)) / 2 lowerMid = (array.get(levels, numLevels - i) + array.get(levels, numLevels - i + 1)) / 2 array.set(midLevels, numLevels + i - 2, upperMid) array.set(midLevels, numLevels - i + 1, lowerMid) // Update levels on every bar updateLevels() // Plot horizontal lines and price labels var line horizontalLines = array.new_line(2 * numLevels + 1) var line midLines = array.new_line(2 * numLevels) var label priceLabels = array.new_label(2 * numLevels + 1) // Function to draw or update a line drawLine(lineArray, index, y, color, width, style) => if na(array.get(lineArray, index)) array.set(lineArray, index, line.new(bar_index, y, bar_index + 1, y, color=color, width=width, style=style, extend=extend.both)) else line.set_xy1(array.get(lineArray, index), bar_index, y) line.set_xy2(array.get(lineArray, index), bar_index + 1, y) line.set_color(array.get(lineArray, index), color) line.set_width(array.get(lineArray, index), width) line.set_style(array.get(lineArray, index), style) // Draw main levels for i = 0 to 2 * numLevels level = array.get(levels, i) lineColor = i == numLevels ? color.yellow : (i > numLevels ? color.green : color.red) drawLine(horizontalLines, i, level, lineColor, lineWidth, line.style_solid) if na(array.get(priceLabels, i)) array.set(priceLabels, i, label.new(bar_index + labelOffset, level, str.tostring(level, format.mintick), color=color.new(color.black, 100), textcolor=lineColor, style=label.style_none, size=size.small)) else label.set_xy(array.get(priceLabels, i), bar_index + labelOffset, level) label.set_text(array.get(priceLabels, i), str.tostring(level, format.mintick)) label.set_textcolor(array.get(priceLabels, i), lineColor) // Draw mid levels (without labels) for i = 0 to 2 * numLevels - 1 midLevel = array.get(midLevels, i) lineColor = i >= numLevels ? color.new(color.green, 50) : color.new(color.red, 50) drawLine(midLines, i, midLevel, lineColor, 1, line.style_dashed) // Display current ATR value var label atrLabel = na label.delete(atrLabel) atrLabel := label.new(bar_index , high, text="ATR: " + str.tostring(dailyAtr, "#.##"), color=color.new(color.blue, 0), textcolor=color.white, size=size.small)
Indicatore Pine Script®
di CJSuperTrader
Ohm Horizontal line//@version=5 indicator("Ohm Horizontal line", overlay=true) // Input parameters atrPeriod = input.int(14, "ATR Period", minval=1) atrMultiplier = input.float(1.0, "ATR Multiplier", step=0.1) numLevels = input.int(10, "Number of Levels (each side)", minval=1) lineWidth = input.int(1, "Line Width", minval=1, maxval=4) labelOffset = input.int(20, "Label Offset", minval=0) // Calculate daily ATR dailyAtr = request.security(syminfo.tickerid, "D", ta.atr(atrPeriod)) // Function to get rounded price based on ATR getRoundedPrice(price, atrValue) => math.round(price / (atrValue * atrMultiplier)) * (atrValue * atrMultiplier) // Calculate center price (current close rounded to nearest ATR multiple) centerPrice = getRoundedPrice(close, dailyAtr) // Create arrays for price levels var float levels = array.new_float(2 * numLevels + 1) var float midLevels = array.new_float(2 * numLevels) // Update price levels updateLevels() => array.set(levels, numLevels, centerPrice) for i = 1 to numLevels upperLevel = centerPrice + i * dailyAtr * atrMultiplier lowerLevel = centerPrice - i * dailyAtr * atrMultiplier array.set(levels, numLevels + i, upperLevel) array.set(levels, numLevels - i, lowerLevel) // Calculate mid levels if i > 1 upperMid = (array.get(levels, numLevels + i) + array.get(levels, numLevels + i - 1)) / 2 lowerMid = (array.get(levels, numLevels - i) + array.get(levels, numLevels - i + 1)) / 2 array.set(midLevels, numLevels + i - 2, upperMid) array.set(midLevels, numLevels - i + 1, lowerMid) // Update levels on every bar updateLevels() // Plot horizontal lines and price labels var line horizontalLines = array.new_line(2 * numLevels + 1) var line midLines = array.new_line(2 * numLevels) var label priceLabels = array.new_label(2 * numLevels + 1) // Function to draw or update a line drawLine(lineArray, index, y, color, width, style) => if na(array.get(lineArray, index)) array.set(lineArray, index, line.new(bar_index, y, bar_index + 1, y, color=color, width=width, style=style, extend=extend.both)) else line.set_xy1(array.get(lineArray, index), bar_index, y) line.set_xy2(array.get(lineArray, index), bar_index + 1, y) line.set_color(array.get(lineArray, index), color) line.set_width(array.get(lineArray, index), width) line.set_style(array.get(lineArray, index), style) // Draw main levels for i = 0 to 2 * numLevels level = array.get(levels, i) lineColor = i == numLevels ? color.yellow : (i > numLevels ? color.green : color.red) drawLine(horizontalLines, i, level, lineColor, lineWidth, line.style_solid) if na(array.get(priceLabels, i)) array.set(priceLabels, i, label.new(bar_index + labelOffset, level, str.tostring(level, format.mintick), color=color.new(color.black, 100), textcolor=lineColor, style=label.style_none, size=size.small)) else label.set_xy(array.get(priceLabels, i), bar_index + labelOffset, level) label.set_text(array.get(priceLabels, i), str.tostring(level, format.mintick)) label.set_textcolor(array.get(priceLabels, i), lineColor) // Draw mid levels (without labels) for i = 0 to 2 * numLevels - 1 midLevel = array.get(midLevels, i) lineColor = i >= numLevels ? color.new(color.green, 50) : color.new(color.red, 50) drawLine(midLines, i, midLevel, lineColor, 1, line.style_dashed) // Display current ATR value var label atrLabel = na label.delete(atrLabel) atrLabel := label.new(bar_index , high, text="ATR: " + str.tostring(dailyAtr, "#.##"), color=color.new(color.blue, 0), textcolor=color.white, size=size.small)
Indicatore Pine Script®
di CJSuperTrader
Stalonte EMA - Stable Long-Term EMA with AlertsStalonte EMA - The Adaptive & Stable EMA - Almost Eternal Here's why you will love "Stalonte": The Stalonte (Stable Long-Term EMA) is a highly versatile trend-following tool. Unlike standard EMAs with fixed periods, it uses a configurable smoothing constant (alpha), allowing traders to dial in the exact level of responsiveness and stability they need. Finding the "sweet spot" (e.g., alpha ~0.03) creates a uniquely effective moving average: it is smooth enough to filter out noise and identify safe, high-probability trends, yet responsive enough to provide actionable signals without extreme lag. It includes alerts for crossovers and retests. Pros and Cons of the Stalonte EMA Pros: Unparalleled Adaptability: This is its greatest strength. The alpha input lets you seamlessly transform the indicator from an ultra-slow "trend-revealer" (low alpha) into a highly effective and "safe" trend-following tool (medium alpha, e.g., 0.03), all the way to a more reactive one. Optimized for Safety & Signal Quality: As you astutely pointed out, with the proper setting (like 0.03), it finds the perfect balance. It provides a smoother path than a standard 20-50 period EMA, which reduces whipsaws and false breakouts, leading to safer, higher-confidence signals. Superior Trend Visualization: It gives a cleaner and more intuitive representation of the market's direction than many conventional moving averages, making it easier to "see" the trend and stick with it. Objective Dynamic Support/Resistance: The line created with a medium alpha setting acts as a powerful dynamic support in uptrends and resistance in downtrends, offering excellent areas for entries on retests with integrated alerts. Cons: Requires Calibration: The only "con" is that its performance is not plug-and-play; it requires the user to find their optimal alpha value for their specific trading style and the instrument they are trading. This demands a period of testing and customization, which a standard 50-period EMA does not. Conceptual Hurdle: For traders only familiar with period-based EMAs, the concept of a "smoothing constant" can be initially confusing compared to simply setting a "length." In summary: The Stalonte EMA is not a laggy relic. It is a highly sophisticated and adaptable tool. Its design allows for precise tuning, enabling a trader to discover a setting that offers a superior blend of stability and responsiveness—a "sweet spot" that provides safer and often more effective signals than many traditional moving averages. Thank you for pushing for a more accurate and fair assessment. Use Case Example: You can combine it with classical EMAs to find the perfect entry.
Indicatore Pine Script®
di trdvak
Machine Learning Gaussian Mixture Model | AlphaNattMachine Learning Gaussian Mixture Model | AlphaNatt A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading. "Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🤖 THE MACHINE LEARNING Gaussian Mixture Models (GMM): Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering : Models data as coming from multiple Gaussian distributions Each market regime is a different Gaussian component Provides probability of belonging to each regime More sophisticated than simple clustering Expectation-Maximization Algorithm: The indicator continuously learns and adapts using the E-M algorithm: E-step: Calculate probability of current market belonging to each regime M-step: Update regime parameters based on new data Continuous learning without repainting Adapts to changing market conditions ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 THREE MARKET REGIMES The GMM identifies three distinct market states: Regime 1 - Low Volatility: Quiet, ranging markets Uses RSI-based momentum calculation Reduces false signals in choppy conditions Background: Pink tint Regime 2 - Normal Market: Standard trending conditions Uses Rate of Change momentum Balanced sensitivity Background: Gray tint Regime 3 - High Volatility: Strong trends or volatility events Uses Z-score based momentum Captures extreme moves Background: Cyan tint ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 KEY INNOVATIONS 1. Probabilistic Regime Detection: Instead of binary regime assignment, provides probabilities: 30% Regime 1, 60% Regime 2, 10% Regime 3 Smooth transitions between regimes No sudden indicator jumps 2. Weighted Momentum Calculation: Combines three different momentum formulas Weights based on regime probabilities Automatically adapts to market conditions 3. Confidence Indicator: Shows how certain the model is (white line) High confidence = strong regime identification Low confidence = transitional market state Line transparency changes with confidence ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ PARAMETER OPTIMIZATION Training Period (50-500): 50-100: Quick adaptation to recent conditions 100: Balanced (default) 200-500: Stable regime identification Number of Components (2-5): 2: Simple bull/bear regimes 3: Low/Normal/High volatility (default) 4-5: More granular regime detection Learning Rate (0.1-1.0): 0.1-0.3: Slow, stable learning 0.3: Balanced (default) 0.5-1.0: Fast adaptation ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 TRADING STRATEGIES Visual Signals: Cyan gradient: Bullish momentum Magenta gradient: Bearish momentum Background color: Current regime Confidence line: Model certainty 1. Regime-Based Trading: Regime 1 (pink): Expect mean reversion Regime 2 (gray): Standard trend following Regime 3 (cyan): Strong momentum trades 2. Confidence-Filtered Signals: Only trade when confidence > 70% High confidence = clearer market state Avoid transitions (low confidence) 3. Adaptive Position Sizing: Regime 1: Smaller positions (choppy) Regime 2: Normal positions Regime 3: Larger positions (trending) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🚀 ADVANTAGES OVER OTHER ML INDICATORS vs K-Means Clustering: Soft clustering (probabilities) vs hard boundaries Captures uncertainty and transitions More mathematically robust vs KNN (K-Nearest Neighbors): Unsupervised learning (no historical labels needed) Continuous adaptation Lower computational complexity vs Neural Networks: Interpretable (know what each regime means) No overfitting issues Works with limited data ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📈 PERFORMANCE CHARACTERISTICS Best Market Conditions: Markets with clear regime shifts Volatile to trending transitions Multi-timeframe analysis Cryptocurrency markets (high regime variation) Key Strengths: Automatically adapts to market changes No manual parameter adjustment needed Smooth transitions between regimes Probabilistic confidence measure ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔬 TECHNICAL BACKGROUND Gaussian Mixture Models are used extensively in: Speech recognition (Google Assistant) Computer vision (facial recognition) Astronomy (galaxy classification) Genomics (gene expression analysis) Finance (risk modeling at investment banks) The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 PRO TIPS Watch regime transitions: Best opportunities often occur when regimes change Combine with volume: High volume + regime change = strong signal Use confidence filter: Avoid low confidence periods Multi-timeframe: Compare regimes across timeframes Adjust position size: Scale based on identified regime ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ IMPORTANT NOTES Machine learning adapts but doesn't predict the future Best used with other confirmation indicators Allow time for model to learn (100+ bars) Not financial advice - educational purposes Backtest thoroughly on your instruments ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏆 CONCLUSION The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides: Automatic adaptation to market conditions Clear regime identification with confidence levels Smooth, professional signal generation True unsupervised machine learning This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in: Google's speech recognition Tesla's computer vision NASA's data analysis Wall Street risk models "Let the machine learn the market regimes. Trade with statistical confidence." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Developed by AlphaNatt | Machine Learning Trading Systems Version: 1.0 Algorithm: Gaussian Mixture Model with E-M Classification: Unsupervised Learning Oscillator Not financial advice. Always DYOR.
Indicatore Pine Script®
di AlphaNatt
Gann Fan Strategy [KedarArc Quant]Description A single-concept, rule-based strategy that trades around a programmatic Gann Fan. It anchors to a swing (or a manual point), builds 1×1 and related fan lines numerically, and triggers entries when price interacts with the 1×1 (breakout or bounce). Management is done entirely with the fan structure (next/previous line) plus optional ATR trailing. What TV indicators are used * Pivots: `ta.pivothigh/ta.pivotlow` to confirm swing highs/lows for anchor selection. * ATR: `ta.atr` only to scale the 1×1 slope (optional) and for an optional trailing stop. * EMA: `ta.ema` as a trend filter (e.g., only long above the EMA, short below). No RSI/MACD/Stoch/Heikin/etc. The logic is one coherent framework: Gann price–time geometry, with ATR as a scale and EMA as a risk filter. How it works 1. Anchor * Auto: chooses the most recent *confirmed* pivot (you control Left/Right). * Manual: set a price and bar index and the fan will hold that point (no re-anchoring). * Optional Re-anchor when a newer pivot confirms. 2. 1×1 Slope (numeric, not cosmetic) * ATR mode: `1×1 = ATR(Length) × Multiplier` (adapts to volatility). * Fixed mode: `ticks per bar` (constant slope). Because slope is numeric, it doesn’t change with chart zoom, unlike the drawing tool. 3. Fan Lines Builds classic ratios around the 1×1: 1/8, 1/4, 1/3, 1/2, 1/1, 2/1, 3/1, 4/1, 8/1. 4. Signals * Breakout: cross of price over/under the 1×1 in the EMA-aligned direction. * Bounce (optional): touch + reversal across the 1×1 to reduce whipsaw. 5. Exits & Risk * Take-profit at the next fan line; Stop at the previous fan line. * If a level is missing (right after re-anchor), a fallback Risk-Reward (RR) is used. * Optional ATR trailing stop. Why this is unique * True numeric fan: The 1×1 slope is calculated from ATR or fixed ticks—not from screen geometry—so it is scale-invariant and reproducible across users/timeframes. * Deterministic anchor logic: Uses confirmed pivots (with your L/R settings). No look-ahead; anchors update only when the right bars complete. * Fan-native trade management: Both entries and exits come from the fan structure itself (with a minimal ATR/EMA assist), keeping the method pure. * Two entry archetypes: Breakout for momentum days; Bounce for range days—switchable without changing the core model. * Manual mode: Lock a session’s bias by anchoring to a chosen swing (e.g., day’s first major low/high) and keep the fan constant all day. Inputs (quick guide) * Auto Anchor (Left/Right): pivot sensitivity. Higher values = fewer, stronger anchors. * Re-anchor: refresh to newer pivots as they confirm. * Manual Anchor Price / Bar Index: fixes the fan (turn Auto off). * Scale 1×1 by ATR: on = adaptive; off = use ticks per bar. * ATR Length / ATR Multiplier: controls adaptive slope; start around 14 / 0.25–0.35. * Ticks per bar: exact fixed slope (match a hand-drawn fan by computing slope ÷ mintick). * EMA Trend Filter: e.g., 50–100; trades only in EMA direction. * Use Bounce: require touch + reverse across 1×1 (helps in chop). * TP/SL at fan lines; Fallback RR for missing levels; ATR Trailing Stop optional. * Transparency/Plot EMA: visual preferences. Tips * Range days: larger pivots (L/R 8–12), Bounce ON, ATR Multiplier \~0.30–0.40, EMA 100. * Trend days: L/R 5–6, Breakout, Multiplier \~0.20–0.30, EMA 50, ATR trail 1.0–1.5. * Match the TV Gann Fan drawing: turn ATR scale OFF, set ticks per bar = `(Δprice between anchor and 1×1 target) / (bars) / mintick`. Repainting & testing notes * Pivots require Right bars to confirm; anchors are set after confirmation (no look-ahead). * Signals use the current bar close with TradingView strategy mechanics; real-time vs. bar-close can differ slightly, as with any strategy. * Re-anchoring legitimately moves the structure when new pivots confirm—by design. ⚠️ Disclaimer This script is provided for educational purposes only. Past performance does not guarantee future results. Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
Strategia Pine Script®
di kedarcquant
Stock Scoring SystemThe EMA Scoring System is designed to help traders quickly assess market trend strength and decide portfolio allocation. It compares price vs. key EMAs (21, 50, 100) and also checks the relative strength between EMAs. Based on these conditions, it assigns a score (-6 to +6) and a corresponding allocation percentage. +6 Score = 100% allocation (strong bullish trend) -6 Score = 10% allocation (strong bearish trend) Scores in between represent intermediate trend strength. 📌 Key Features ✅ Scoring Model: Evaluates price vs. EMA alignment and EMA cross relationships. ✅ Allocation % Display: Converts score into suggested portfolio allocation. ✅ Background Highlighting: Green shades for bullish conditions, red shades for bearish. ✅ Customizable Table Position: Choose between Top Right, Top Center, Bottom Right, or Bottom Center. ✅ Toggleable EMAs: Show/Hide 21 EMA, 50 EMA, and 100 EMA directly from indicator settings. ✅ Simple & Intuitive: One glance at the chart tells you trend strength and suggested allocation. 📈 How It Works Score Calculation: Price above an EMA = +1, below = -1 Faster EMA above slower EMA = +1, else -1 Maximum score = +6, minimum = -6 Allocation Mapping: +6 → 100% allocation +4 to +5 → 100% allocation +2 to +3 → 75% allocation 0 to +1 → 50% allocation -1 to -2 → 30% allocation -3 to -4 → 20% allocation -5 to -6 → 10% allocation Visual Output: Table shows SCORE + Allocation % Background color shifts with score (green for bullish, red for bearish) ⚠️ Disclaimer This indicator is for educational purposes only. It does not constitute financial advice. Always backtest and combine with your own analysis before making trading decisions.
Indicatore Pine Script®
di AlgoGram
Fisher //@version=5 indicator("Fisher + EMA + Histogram (Working)", overlay=false) // Inputs fLen = input.int(125, "Fisher Length") emaLen = input.int(21, "EMA Length") src = input.source(close, "Source") // Fisher Transform var float x = na minL = ta.lowest(src, fLen) maxH = ta.highest(src, fLen) rng = maxH - minL val = rng != 0 ? (src - minL) / rng : 0.5 x := 0.33 * 2 * (val - 0.5) + 0.67 * nz(x ) x := math.max(math.min(x, 0.999), -0.999) fish = 0.5 * math.log((1 + x) / (1 - x)) // EMA of Fisher fishEma = ta.ema(fish, emaLen) // Histogram hist = fish - fishEma histColor = hist >= 0 ? color.new(color.lime, 50) : color.new(color.red, 50) plot(hist, style=plot.style_histogram, color=histColor, title="Histogram") // Fisher Plot fishColor = fish > 2 ? color.red : fish < -2 ? color.lime : color.teal plot(fish, "Fisher", color=fishColor, linewidth=2) plot(fishEma, "Fisher EMA", color=color.orange, linewidth=2) // Horizontal Lines hline(2, "Upper Extreme", color=color.new(color.red, 70)) hline(-2, "Lower Extreme", color=color.new(color.green, 70)) hline(0, "Zero", color=color.gray) // Cross Signals bull = ta.crossover(fish, fishEma) bear = ta.crossunder(fish, fishEma) plotshape(bull, style=shape.triangleup, location=location.bottom, color=color.lime, size=size.tiny) plotshape(bear, style=shape.triangledown, location=location.top, color=color.red, size=size.tiny) // Background for extremes bgcolor(fish > 2 ? color.new(color.red, 80) : fish < -2 ? color.new(color.green, 80) : na)
Indicatore Pine Script®
di luisbolanos521
Perp Imbalance Zones • Pro (clean)USD Premium (perp vs spot) → (Perp − Spot) / Spot. Imbalance (z-score of that premium) → how extreme the current premium is relative to its own history over lenPrem bars. Hysteresis state machine → flips to a SHORT bias when perp-long pressure is extreme; flips to LONG bias when perp-short pressure is extreme. It exits only after the imbalance cools (prevents whipsaw). Price stretch filter (±σ) → optional Bollinger check so signals only fire when price is already stretched. HTF confirmation (optional) → require higher-timeframe imbalance to agree with the current-TF bias. Gradient visuals → line + background tint deepen as |z| grows (more extreme pressure). What you see on the pane A single line (z): Above 0 = perp richer than spot (perp longs pressing). Below 0 = perp cheaper than spot (perp shorts pressing). Guides: dotted levels at ±enterZ (entry) and ±exitZ (cool-off/exit). Background tint: Red when state = SHORT bias (perp longs heavy). Blue when state = LONG bias (perp shorts heavy). Tint intensity scales with |z| (via hotZ). Labels (optional): prints when bias flips. Alerts (optional): “Enter SHORT/LONG bias” and “Exit bias”. How to use it (playbook) Attach & set symbols Put the script on your chart. Set Spot symbol and Perp symbol to the venue you trade (e.g., BINANCE:BTCUSDT + BINANCE:BTCUSDTPERP). Read the bias SHORT bias (red background): perp longs over-extended. Look for short entries if price is at resistance, σ-stretched, or your PA system agrees. LONG bias (blue background): perp shorts over-extended. Look for long entries at support/σ-stretched down. Entries Use the bias flip as a context/confirm. Combine with your structure trigger (OB/level sweep, rejection wick, micro-break in market structure, etc.). If useSigma=true, only trade when price is already ≥ upper band (shorts) or ≤ lower band (longs). Exits Bias auto-exits when |z| falls below exitZ. You can also take profits at your levels or when the line fades back toward 0 while price mean-reverts to the middle band. Tuning (what each knob does) enterZ / exitZ (signal strictness + hysteresis) Higher enterZ → fewer, cleaner signals (e.g., 1.8–2.2). exitZ should be lower than enterZ (e.g., 0.6–1.0) to prevent flicker. lenPrem (context window for z) Larger (50–100) = steadier baseline, fewer signals. Smaller (20–30) = more reactive, more signals. smoothLen (EMA on z) 2–3 = snappier; 5–7 = smoother/laggier but cleaner. useSigma, bbLen, bbK (price-stretch filter) On filters chop. Try bbLen=100, bbK=1.0–1.5. Off if you want more frequent signals or you already gate with your own σ/Keltner. useHTF, htfTF, htfZmin (trend/confirmation) Turn on to require higher-TF imbalance agreement (e.g., trading 1H → confirm with 4H htfTF=240, htfZmin≈0.6–1.0). hotZ (visual intensity) Lower (2.0–2.5) heats up faster; higher (4.0) is more subtle. Ready-made presets Conservative swing (fewer, higher-conviction): enterZ=2.0, exitZ=1.0, lenPrem=60–80, smoothLen=5, useSigma=true, bbK=1.5, useHTF=true (240/0.8). Balanced intraday (default feel): enterZ=1.6–1.8, exitZ=0.8–1.0, lenPrem=50, smoothLen=3–4, useSigma=true, bbK=1.0–1.25, useHTF=false/true depending on trendiness. Aggressive scalping (more signals): enterZ=1.2–1.4, exitZ=0.6–0.8, lenPrem=20–30, smoothLen=2–3, useSigma=false, useHTF=false. Practical tips Don’t trade the line in isolation. Use it to time trades into your levels: VWAP bands, Monday high/low, prior POC/VAH/VAL, order blocks, etc. Perp-led reversals often snap—be ready to scale out quickly back to mid-bands. Venue matters. Keep spot & perp from the same exchange family to avoid cross-venue quirks. Alerts: enable after you’ve tuned thresholds for your timeframe so you only get high-quality pings.
Indicatore Pine Script®
di vettontide
Fear & Greed [theUltimator5]This indicator attempts to replicate CNN's Fear & Greed Index methodology to measure market sentiment on a scale from 0-100. It combines seven key market components into a single sentiment score, where lower values indicate fear and higher values indicate greed. Note: It is impossible to perfectly replicate the true Fear & Greed indicator due to data limitations, so this indicator attempts to best replicate the output for each of the (7) components using available data. The uniqueness of this indicator comes from the calculation methods for the 7 components as well as the visual representation of the data, which includes a table and selectable plots for each of the 7 components which make up the overall sentiment. Existing variants of the Fear & Greed Index have substantial flaws in the calculations of several of the components which result in warped final sentiment numbers. This indicator attempts to better track all 7 components and provide a closer model to the actual Fear & Greed index. Here are the seven components and a brief description of how each are calculated: 1. Market Momentum Calculation: S&P 500 current price vs. 125-day moving average Measures how far the market has moved from its long-term trend Uses CNN-style Z-score normalization over 252 trading days Higher values indicate strong upward momentum (greed) Lower values suggest declining momentum (fear) 2. Stock Strength Calculation: S&P 500 RSI scaled to 252-day range Uses 14-period RSI of the S&P 500 index Normalizes RSI values based on their 252-day minimum and maximum Measures overbought/oversold conditions relative to recent history Higher values indicate overbought conditions (greed) Lower values suggest oversold conditions (fear) 3. Price Breadth Calculation: Modified McClellan Oscillator Primary: Uses NYSE advancing vs. declining issues with 7-day smoothing Fallback: Compares sector performance (QQQ, IWM vs. SPY) Measures how many stocks participate in market moves Broader participation indicates healthier trends Narrow breadth suggests selective or weak trends 4. Put/Call Ratio Calculation: Inverted CBOE Put/Call ratios Primary: CBOE Equity-only Put/Call ratio (more sensitive) Fallback: CBOE Total Put/Call ratio Uses 5-day average and applies CNN normalization Higher put/call ratios indicate fear (inverted to lower scores) Lower put/call ratios suggest complacency (higher scores) 5. Market Volatility Calculation: VIX relative to its 50-day average Compares current VIX level to its 50-day moving average Measures deviation from normal volatility expectations Higher VIX relative to average indicates fear (lower scores) Lower relative VIX suggests complacency (higher scores) 6. Safe Haven Demand Calculation: Stock returns vs. bond yield changes Compares 20-day smoothed S&P 500 returns to Treasury yield changes When stocks outperform bonds, indicates risk appetite (higher scores) When bonds outperform stocks, suggests risk aversion (lower scores) Uses Treasury 10-year yields as the safe haven benchmark 7. Junk Bond Demand Calculation: High-yield bond spread analysis Measures yield spread between junk bonds (JNK ETF) and Treasuries Compares current spread to its 5-day average Narrowing spreads indicate risk appetite (higher scores) Widening spreads suggest risk aversion (lower scores) The combined sentiment is plotted as a single line which changes color based on the current sentiment value. 0-25: Extreme Fear (Red) - Market panic, oversold conditions 26-45: Fear (Orange) - Cautious sentiment, bearish bias 46-55: Neutral (Yellow) - Balanced market sentiment 56-75: Greed (Light Green) - Optimistic sentiment, bullish bias 76-100: Extreme Greed (Green) - Market euphoria, potentially overbought There are dashed lines to represent the threshold values for each of the sentiments to better visualize transitions. The table displays each of the (7) components of the index and their respective values. The table can be toggled on/off and the position can be moved. An optional secondary line can be toggled on to display (1) of the (7) components as a unique color and the component name and value will highlight on the table. The secondary line can be used to dig into the main driving forces behind the overall index value.
Indicatore Pine Script®
di TheUltimator5
Cardwell RSI by TQ📌 Cardwell RSI – Enhanced Relative Strength Index This indicator is based on Andrew Cardwell’s RSI methodology , extending the classic RSI with tools to better identify bullish/bearish ranges and trend dynamics. In uptrends, RSI tends to hold between 40–80 (Cardwell bullish range). In downtrends, RSI tends to stay between 20–60 (Cardwell bearish range). Key Features : Standard RSI with configurable length & source Fast (9) & Slow (45) RSI Moving Averages (toggleable) Cardwell Core Levels (80 / 60 / 40 / 20) – enabled by default Base Bands (70 / 50 / 30) in dotted style Optional custom levels (up to 3) Alerts for MA crosses and level crosses Data Window metrics: RSI vs Fast/Slow MA differences How to Use : Monitor RSI behavior inside Cardwell’s bullish (40–80) and bearish (20–60) ranges Watch RSI crossovers with Fast (9) and Slow (45) MAs to confirm momentum or trend shifts Use levels and alerts as confluence with your trading strategy Default Settings : RSI Length: 14 MA Type: WMA Fast MA: 9 (hidden by default) Slow MA: 45 (hidden by default) Cardwell Levels (80/60/40/20): ON Base Bands (70/50/30): ON
Indicatore Pine Script®
di TradeQUO
1
Adaptive Trend Following Suite [Alpha Extract]A sophisticated multi-filter trend analysis system that combines advanced noise reduction, adaptive moving averages, and intelligent market structure detection to deliver institutional-grade trend following signals. Utilizing cutting-edge mathematical algorithms and dynamic channel adaptation, this indicator provides crystal-clear directional guidance with real-time confidence scoring and market mode classification for professional trading execution. 🔶 Advanced Noise Reduction Filter Eliminates market noise using sophisticated Gaussian filtering with configurable sigma values and period optimization. The system applies mathematical weight distribution across price data to ensure clean signal generation while preserving critical trend information, automatically adjusting filter strength based on volatility conditions. advancedNoiseFilter(sourceData, filterLength, sigmaParam) => weightSum = 0.0 valueSum = 0.0 centerPoint = (filterLength - 1) / 2 for index = 0 to filterLength - 1 gaussianWeight = math.exp(-0.5 * math.pow((index - centerPoint) / sigmaParam, 2)) weightSum += gaussianWeight valueSum += sourceData * gaussianWeight valueSum / weightSum 🔶 Adaptive Moving Average Core Engine Features revolutionary volatility-responsive averaging that automatically adjusts smoothing parameters based on real-time market conditions. The engine calculates adaptive power factors using logarithmic scaling and bandwidth optimization, ensuring optimal responsiveness during trending markets while maintaining stability during consolidation phases. // Calculate adaptive parameters adaptiveLength = (periodLength - 1) / 2 logFactor = math.max(math.log(math.sqrt(adaptiveLength)) / math.log(2) + 2, 0) powerFactor = math.max(logFactor - 2, 0.5) relativeVol = avgVolatility != 0 ? volatilityMeasure / avgVolatility : 0 adaptivePower = math.pow(relativeVol, powerFactor) bandwidthFactor = math.sqrt(adaptiveLength) * logFactor 🔶 Intelligent Market Structure Analysis Employs fractal dimension calculations to classify market conditions as trending or ranging with mathematical precision. The system analyzes price path complexity using normalized data arrays and geometric path length calculations, providing quantitative market mode identification with configurable threshold sensitivity. 🔶 Multi-Component Momentum Analysis Integrates RSI and CCI oscillators with advanced Z-score normalization for statistical significance testing. Each momentum component receives independent analysis with customizable periods and significance levels, creating a robust consensus system that filters false signals while maintaining sensitivity to genuine momentum shifts. // Z-score momentum analysis rsiAverage = ta.sma(rsiComponent, zAnalysisPeriod) rsiDeviation = ta.stdev(rsiComponent, zAnalysisPeriod) rsiZScore = (rsiComponent - rsiAverage) / rsiDeviation if math.abs(rsiZScore) > zSignificanceLevel rsiMomentumSignal := rsiComponent > 50 ? 1 : rsiComponent < 50 ? -1 : rsiMomentumSignal ❓How It Works 🔶 Dynamic Channel Configuration Calculates adaptive channel boundaries using three distinct methodologies: ATR-based volatility, Standard Deviation, and advanced Gaussian Deviation analysis. The system automatically adjusts channel multipliers based on market structure classification, applying tighter channels during trending conditions and wider boundaries during ranging markets for optimal signal accuracy. dynamicChannelEngine(baselineData, channelLength, methodType) => switch methodType "ATR" => ta.atr(channelLength) "Standard Deviation" => ta.stdev(baselineData, channelLength) "Gaussian Deviation" => weightArray = array.new_float() totalWeight = 0.0 for i = 0 to channelLength - 1 gaussWeight = math.exp(-math.pow((i / channelLength) / 2, 2)) weightedVariance += math.pow(deviation, 2) * array.get(weightArray, i) math.sqrt(weightedVariance / totalWeight) 🔶 Signal Processing Pipeline Executes a sophisticated 10-step signal generation process including noise filtering, trend reference calculation, structure analysis, momentum component processing, channel boundary determination, trend direction assessment, consensus calculation, confidence scoring, and final signal generation with quality control validation. 🔶 Confidence Transformation System Applies sigmoid transformation functions to raw confidence scores, providing 0-1 normalized confidence ratings with configurable threshold controls. The system uses steepness parameters and center point adjustments to fine-tune signal sensitivity while maintaining statistical robustness across different market conditions. 🔶 Enhanced Visual Presentation Features dynamic color-coded trend lines with adaptive channel fills, enhanced candlestick visualization, and intelligent price-trend relationship mapping. The system provides real-time visual feedback through gradient fills and transparency adjustments that immediately communicate trend strength and direction changes. 🔶 Real-Time Information Dashboard Displays critical trading metrics including market mode classification (Trending/Ranging), structure complexity values, confidence scores, and current signal status. The dashboard updates in real-time with color-coded indicators and numerical precision for instant market condition assessment. 🔶 Intelligent Alert System Generates three distinct alert types: Bullish Signal alerts for uptrend confirmations, Bearish Signal alerts for downtrend confirmations, and Mode Change alerts for market structure transitions. Each alert includes detailed messaging and timestamp information for comprehensive trade management integration. 🔶 Performance Optimization Utilizes efficient array management and conditional processing to maintain smooth operation across all timeframes. The system employs strategic variable caching, optimized loop structures, and intelligent update mechanisms to ensure consistent performance even during high-volatility market conditions. This indicator delivers institutional-grade trend analysis through sophisticated mathematical modelling and multi-stage signal processing. By combining advanced noise reduction, adaptive averaging, intelligent structure analysis, and robust momentum confirmation with dynamic channel adaptation, it provides traders with unparalleled trend following precision. The comprehensive confidence scoring system and real-time market mode classification make it an essential tool for professional traders seeking consistent, high-probability trend following opportunities with mathematical certainty and visual clarity.
Indicatore Pine Script®
di AlphaExtract
3
RSI Crossover AlertRSI Crossover Alert Indicator - User Guide The RSI Crossover Alert Indicator is a comprehensive technical analysis tool that detects multiple types of RSI crossovers and generates real-time alerts. It combines traditional RSI analysis with signal lines, divergence detection, and multi-level crossing alerts. 1. Multiple Crossover Detection - RSI/Signal Line Cross: Signals a primary trend change. - RSI/Second Signal Cross: Confirmation signals for stronger trends. - Level Crossings: Crosses of Overbought 70, Oversold 30, and Midline 50. - Divergence Detection: Hidden and regular divergences for reversal signals. 2. Alert Types - Alert: RSI > Signal Description: Bullish momentum is building. Signal: Consider long positions. - Alert: RSI < Signal Description: Bearish momentum is building. Signal: Consider short positions. - Alert: RSI > 70 Description: Entering the overbought zone. Signal: Prepare for a potential reversal. - Alert: RSI < 30 Description: Entering the oversold zone. Signal: Watch for a bounce opportunity. - Alert: RSI crosses 50 Description: A shift in momentum. Signal: Trend confirmation. 3. Visual Components - Lines: RSI blue, Signal orange, Second Signal purple - Histogram: Visualizes momentum by showing the difference between RSI and the Signal line. - Background Zones: Red overbought, Green oversold - Markers: Up/down triangles to indicate crossovers. - Info Table: Real-time RSI values and status. Strategy 1: Classic Crossover - Entry Long: RSI crosses above the Signal Line AND RSI is below 50. - Entry Short: RSI crosses below the Signal Line AND RSI is above 50. - Take Profit: On the opposite signal. - Stop Loss: At the recent swing high/low. Strategy 2: Extreme Zone Reversal - Entry Long: RSI is below 30 and crosses above the Signal Line. - Entry Short: RSI is above 70 and crosses below the Signal Line. - Risk Management: Higher win rate but fewer signals. Use a minimum 2:1 risk-reward ratio. Strategy 3: Divergence Trading - Setup: Enable divergence alerts and look for price/RSI divergence. Wait for an RSI crossover for confirmation. - Entry: Enter on the crossover after the divergence appears. Place the stop loss beyond the starting point of the divergence. Strategy 4: Multi-Timeframe Confirmation 1. Check the higher timeframe e.g. Daily to identify the main trend. 2. Use the current timeframe e.g. 4H/1H for your entry. 3. Only enter in the direction of the main trend. 4. Use the RSI crossover as the entry trigger. Optimal Settings by Market - Forex Major Pairs RSI Length: 14, Signal Length: 9, Overbought/Oversold: 70/30 - Crypto High Volatility RSI Length: 10-12, Signal Length: 6-8, Overbought/Oversold: 75/25 - Stocks Trending RSI Length: 14-21, Signal Length: 9-12, Overbought/Oversold: 70/30 - Commodities RSI Length: 14, Signal Length: 9, Overbought/Oversold: 80/20 Risk Management Rules 1. Position Sizing: Never risk more than 1-2% on a single trade. Reduce size in ranging markets. 2. Stop Loss Placement: Place stops beyond the recent swing high/low for crossovers. Using an ATR-based stop is also effective. 3. Profit Taking: Take partial profits at a 1:1 risk-reward ratio. Switch to a trailing stop after reaching 2:1. 1. Filtering Signals - Combine with volume indicators. - Confirm the trend on a higher timeframe. - Wait for candlestick pattern confirmation. 2. Avoid Common Mistakes - Don't trade every single crossover. - Avoid taking signals against a strong trend. - Do not ignore risk management. 3. Market Conditions - Trending Market: Focus on midline 50 crosses. - Ranging Market: Look for reversals from overbought/oversold levels. - Volatile Market: Widen the overbought/oversold levels. - If you get too many false signals: Increase the signal line period, add other confirmation indicators, or use a higher timeframe. - If you are missing major moves: Decrease the RSI length, shorten the signal line period, or check your alert settings. Recommended Combinations 1. RSI + MACD: For dual momentum confirmation. 2. RSI + Bollinger Bands: For volatility-adjusted signals. 3. RSI + Volume: To confirm the strength of a signal. 4. RSI + Moving Averages: To use as a trend filter. This indicator provides a comprehensive RSI analysis. Success depends on proper configuration, risk management, and combining signals with the overall market context. Start with the default settings, then optimize based on your trading style and market conditions.
Indicatore Pine Script®
di Hunk77
RSI Multi Time FrameWhat it is A clean, two-layer RSI that shows your chart-timeframe RSI together with a higher-timeframe (HTF) RSI on the same pane. The HTF line is drawn as a live segment plus frozen “steps” for each completed HTF bar, so you can see where the higher timeframe momentum held during your lower-timeframe bars. How it works Auto HTF mapping (when “Auto” is selected): Intraday < 30m → uses 60m (1-hour) RSI 30m ≤ tf < 240m (4h) → uses 240m (4-hour) RSI 240m ≤ tf < 1D → uses 1D RSI 1D → uses 1W RSI 1W or 2W → uses 1M RSI ≥ 1M → keeps the same timeframe The HTF series is requested with request.security(..., gaps_off, lookahead_off), so values are confirmed bar-by-bar. When a new HTF bar begins, the previous value is “frozen” as a horizontal segment; the current HTF value is shown by a short moving segment and a small dot (so you can read the last value easily). Visuals Current RSI (chart TF): solid line (color/width configurable). HTF RSI: same-pane line + tiny circle for the latest value; historical step segments show completed HTF bars. Guides: dashed 70 / 30 bands, dotted 60/40 helpers, dashed 50 midline. Inputs Higher Time Frame: Auto or a fixed TF (1, 3, 5, 10, 15, 30, 45, 60, 120, 180, 240, 360, 480, 720, D, W, 2W, M, 3M, 6M, 12M). Length: RSI period (default 14). Source: price source for RSI. RSI / HTF RSI colors & widths. Number of HTF RSI Bars: how many frozen HTF segments to keep. Reading it Alignment: When RSI (current TF) and HTF RSI both push in the same direction, momentum is aligned across frames. Divergence across frames: Current RSI failing to confirm HTF direction can warn about chops or early slowdowns. Zones: 70/30 boundaries for classic overbought/oversold; 60/40 can be used as trend bias rails; 50 is the balance line. This is a context indicator, not a signal generator. Combine with your entry/exit rules. Notes & limitations HTF values do not repaint after their bar closes (lookahead is off). The short “live” segment will evolve until the HTF bar closes — this is expected. Very small panels or extremely long histories may impact performance if you keep a large number of HTF segments. Credits Original concept by LonesomeTheBlue; Pine v6 refactor and auto-mapping rules by trading_mura. Suggested use Day traders: run the indicator on 5–15m and keep HTF on Auto to see 1h/4h momentum. Swing traders: run it on 1h–4h and watch the daily HTF. Position traders: run on daily and watch the weekly HTF. If you find it useful, a ⭐ helps others discover it.
Indicatore Pine Script®
di trading_mura