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Relative Performance Spread**Relative Performance Spread Indicator – Overview** This indicator compares the **relative performance between two stocks** by normalizing their prices and calculating the **spread**, **area under the curve (AUC)**, or **normalized price ratio**. ### **How It Works** * **Input**: Select a second stock (`ticker2`) and a moving average window. * **Normalization**: Each stock is normalized by its own moving average → `norm = close / MA`. * **Spread**: The difference `spread = norm1 - norm2` reflects which stock is outperforming. * **AUC**: Cumulative spread over time shows prolonged dominance or underperformance. * **Bounds**: Bollinger-style bands are drawn around the spread to assess deviation extremes. ### **Usage** * **Plot Type Options**: * `"Spread"`: Spot outperformance; crossing bands may signal rotation. * `"AUC"`: Track long-term relative trend dominance. * `"Normalized"`: Directly compare scaled price movements. Use this tool for **pair trading**, **relative momentum**, or **rotation strategies**. It adapts well across assets with different price scales.
Chỉ báo Pine Script®
của gunnjac
The VoVix Experiment The VoVix Experiment The VoVix Experiment is a next-generation, regime-aware, volatility-adaptive trading strategy for futures, indices, and more. It combines a proprietary VoVix (volatility-of-volatility) anomaly detector with price structure clustering and critical point logic, only trading when multiple independent signals align. The system is designed for robustness, transparency, and real-world execution. Logic: VoVix Regime Engine: Detects pre-move volatility anomalies using a fast/slow ATR ratio, normalized by Z-score. Only trades when a true regime spike is detected, not just random volatility. Cluster & Critical Point Filters: Price structure and volatility clustering must confirm the VoVix signal, reducing false positives and whipsaws. Adaptive Sizing: Position size scales up for “super-spikes” and down for normal events, always within user-defined min/max. Session Control: Trades only during user-defined hours and days, avoiding illiquid or high-risk periods. Visuals: Aurora Flux Bands (From another Original of Mine (Options Flux Flow): glow and change color on signals, with a live dashboard, regime heatmap, and VoVix progression bar for instant insight. Backtest Settings Initial capital: $10,000 Commission: Conservative, realistic roundtrip cost: 15–20 per contract (including slippage per side) I set this to $25 Slippage: 3 ticks per trade Symbol: CME_MINI:NQ1! Timeframe: 15 min (but works on all timeframes) Order size: Adaptive, 1–2 contracts Session: 5:00–15:00 America/Chicago (default, fully adjustable) Why these settings? These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for MNQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions. Forward Testing: (This is no guarantee. I've provided these results to show that executions perform as intended. Test were done on Tradovate) ALL TRADES Gross P/L: $12,907.50 # of Trades: 64 # of Contracts: 186 Avg. Trade Time: 1h 55min 52sec Longest Trade Time: 55h 46min 53sec % Profitable Trades: 59.38% Expectancy: $201.68 Trade Fees & Comm.: $(330.95) Total P/L: $12,576.55 Winning Trades: 59.38% Breakeven Trades: 3.12% Losing Trades: 37.50% Link: www.dropbox.com Inputs & Tooltips VoVix Regime Execution: Enable/disable the core VoVix anomaly detector. Volatility Clustering: Require price/volatility clusters to confirm VoVix signals. Critical Point Detector: Require price to be at a statistically significant distance from the mean (regime break). VoVix Fast ATR Length: Short ATR for fast volatility detection (lower = more sensitive). VoVix Slow ATR Length: Long ATR for baseline regime (higher = more stable). VoVix Z-Score Window: Lookback for Z-score normalization (higher = smoother, lower = more reactive). VoVix Entry Z-Score: Minimum Z-score for a VoVix spike to trigger a trade. VoVix Exit Z-Score: Z-score below which the regime is considered decayed (exit). VoVix Local Max Window: Bars to check for local maximum in VoVix (higher = stricter). VoVix Super-Spike Z-Score: Z-score for “super” regime events (scales up position size). Min/Max Contracts: Adaptive position sizing range. Session Start/End Hour: Only trade between these hours (exchange time). Allow Weekend Trading: Enable/disable trading on weekends. Session Timezone: Timezone for session filter (e.g., America/Chicago for CME). Show Trade Labels: Show/hide entry/exit labels on chart. Flux Glow Opacity: Opacity of Aurora Flux Bands (0–100). Flux Band EMA Length: EMA period for band center. Flux Band ATR Multiplier: Width of bands (higher = wider). Compliance & Transparency * No hidden logic, no repainting, no pyramiding. * All signals, sizing, and exits are fully explained and visible. * Backtest settings are stricter than most real accounts. * All visuals are directly tied to the strategy logic. * This is not a mashup or cosmetic overlay; every component is original and justified. Disclaimer Trading is risky. This script is for educational and research purposes only. Do not trade with money you cannot afford to lose. Past performance is not indicative of future results. Always test in simulation before live trading. Proprietary Logic & Originality Statement This script, “The VoVix Experiment,” is the result of original research and development. All core logic, algorithms, and visualizations—including the VoVix regime detection engine, adaptive execution, volatility/divergence bands, and dashboard—are proprietary and unique to this project. 1. VoVix Regime Logic The concept of “volatility of volatility” (VoVix) is an original quant idea, not a standard indicator. The implementation here (fast/slow ATR ratio, Z-score normalization, local max logic, super-spike scaling) is custom and not found in public TradingView scripts. 2. Cluster & Critical Point Logic Volatility clustering and “critical point” detection (using price distance from a rolling mean and standard deviation) are general quant concepts, but the way they are combined and filtered here is unique to this script. The specific logic for “clustered chop” and “critical point” is not a copy of any public indicator. 3. Adaptive Sizing The adaptive sizing logic (scaling contracts based on regime strength) is custom and not a standard TradingView feature or public script. 4. Time Block/Session Control The session filter is a common feature in many strategies, but the implementation here (with timezone and weekend control) is written from scratch. 5. Aurora Flux Bands (From another Original of Mine (Options Flux Flow) The “glowing” bands are inspired by the idea of volatility bands (like Bollinger Bands or Keltner Channels), but the visual effect, color logic, and integration with regime signals are original to this script. 6. Dashboard, Watermark, and Metrics The dashboard, real-time Sharpe/Sortino, and VoVix progression bar are all custom code, not copied from any public script. What is “standard” or “common quant practice”? Using ATR, EMA, and Z-score are standard quant tools, but the way they are combined, filtered, and visualized here is unique. The structure and logic of this script are original and not a mashup of public code. This script is 100% original work. All logic, visuals, and execution are custom-coded for this project. No code or logic is directly copied from any public or private script. Use with discipline. Trade your edge. — Dskyz, for DAFE Trading Systems
Chiến lược Pine Script®
của DskyzInvestments
9
Dskyz (DAFE) GENESIS Dskyz (DAFE) GENESIS: Adaptive Quant, Real Regime Power Let’s be honest: Most published strategies on TradingView look nearly identical—copy-paste “open-source quant,” generic “adaptive” buzzwords, the same shallow explanations. I’ve even fallen into this trap with my own previously posted strategies. Not this time. What Makes This Unique GENESIS is not a black-box mashup or a pre-built template. It’s the culmination of DAFE’s own adaptive, multi-factor, regime-aware quant engine—built to outperform, survive, and visualize live edge in anything from NQ/MNQ to stocks and crypto. True multi-factor core: Volume/price imbalances, trend shifts, volatility compression/expansion, and RSI all interlock for signal creation. Adaptive regime logic: Trades only in healthy, actionable conditions—no “one-size-fits-all” signals. Momentum normalization: Uses rolling, percentile-based fast/slow EMA differentials, ALWAYS normalized, ALWAYS relevant—no “is it working?” ambiguity. Position sizing that adapts: Not fixed-lot, not naive—not a loophole for revenge trading. No hidden DCA or pyramiding—what you see is what you trade. Dashboard and visual system: Directly connected to internal logic. If it’s shown, it’s used—and nothing cosmetic is presented on your chart that isn’t quantifiable. 📊 Inputs and What They Mean (Read Carefully) Maximum Raw Score: How many distinct factors can contribute to regime/trade confidence (default 4). If you extend the quant logic, increase this. RSI Length / Min RSI for Shorts / Max RSI for Longs: Fine-tunes how “overbought/oversold” matters; increase the length for smoother swings, tighten floors/ceilings for more extreme signals. ⚡ Regime & Momentum Gates Min Normed Momentum/Score (Conf): Raise to demand only the strongest trends—your filter to avoid algorithmic chop. 🕒 Volatility & Session ATR Lookback, ATR Low/High Percentile: These control your system’s awareness of when the market is dead or ultra-volatile. All sizing and filter logic adapts in real time. Trading Session (hours): Easy filter for when entries are allowed; default is regular trading hours—no surprise overnight fills. 📊 Sizing & Risk Max Dollar Risk / Base-Max Contracts: All sizing is adaptive, based on live regime and volatility state—never static or “just 1 contract.” Control your max exposures and real $ risk. ATR will effect losses in high volatility times. 🔄 Exits & Scaling Stop/Trail/Scale multipliers: You choose how dynamic/flexible risk controls and profit-taking need to be. ATR-based, so everything auto-adjusts to the current market mode. Visuals That Actually Matter Dashboard (Top Right): Shows only live, relevant stats: scoring, status, position size, win %, win streak, total wins—all from actual trade engine state (not “simulated”). Watermark (Bottom Right): Momentum bar visual is always-on, regime-aware, reflecting live regime confidence and momentum normalization. If the bar is empty, you’re truly in no-momentum. If it glows lime, you’re riding the strongest possible edge. *No cosmetics, no hidden code distractions. Backtest Settings Initial capital: $10,000 Commission: Conservative, realistic roundtrip cost: 15–20 per contract (including slippage per side) I set this to $25 Slippage: 3 ticks per trade Symbol: CME_MINI:NQ1! Timeframe: 1 min (but works on all timeframes) Order size: Adaptive, 1–3 contracts No pyramiding, no hidden DCA Why these settings? These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for NQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions. Why It Wins While others put out “AI-powered” strategies with little logic or soul, GENESIS is ruthlessly practical. It is built around what keeps traders alive: - Context-aware signals, not just patterns - Tight, transparent risk - Inputs that adapt, not confuse - Visuals that clarify, not distract - Code that runs clean, efficient, and with minimal overfitting risk (try it on QQQ, AMD, SOL, etc. out of the box) Disclaimer (for TradingView compliance): Trading is risky. Futures, stocks, and crypto can result in significant losses. Do not trade with funds you cannot afford to lose. This is for educational and informational purposes only. Use in simulation/backtest mode before live trading. No past performance is indicative of future results. Always understand your risk and ownership of your trades. This will not be my last—my goal is to keep raising the bar until DAFE is a brand or I’m forced to take this private. Use with discipline, use with clarity, and always trade smarter. — Dskyz , powered by DAFE Trading Systems.
Chiến lược Pine Script®
của DskyzInvestments
6
REVELATIONS (VoVix - PoC) REVELATIONS (VoVix - POC): True Regime Detection Before the Move Let’s not sugarcoat it: Most strategies on TradingView are recycled—RSI, MACD, OBV, CCI, Stochastics. They all lag. No matter how many overlays you stack, every one of these “standard” indicators fires after the move is underway. The retail crowd almost always gets in late. That’s never been enough for my team, for DAFE, or for anyone who’s traded enough to know the real edge vanishes by the time the masses react. How is this different? REVELATIONS (VoVix - POC) was engineered from raw principle, structured to detect pre-move regime change—before standard technicals even light up. We built, tested, and refined VoVix to answer one hard question: What if you could see the spike before the trend? Here’s what sets this system apart, line-by-line: o True volatility-of-volatility mathematics: It’s not just "ATR of ATR" or noise smoothing. VoVix uses normalized, multi-timeframe v-vol spikes, instantly detecting orderbook stress and "outlier" market events—before the chart shows them as trends. o Purist regime clustering: Every trade is enabled only during coordinated, multi-filter regime stress. No more signals in meaningless chop. o Nonlinear entry logic: No trade is ever sent just for a “good enough” condition. Every entry fires only if every requirement is aligned—local extremes, super-spike threshold, regime index, higher timeframe, all must trigger in sync. o Adaptive position size: Your contracts scale up with event strength. Tiny size during nominal moves, max leverage during true regime breaks—never guesswork, never static exposure. o All exits governed by regime decay logic: Trades are closed not just on price targets but at the precise moment the market regime exhausts—the hardest part of systemic trading, now solved. How this destroys the lag: Standard indicators (RSI, MACD, OBV, CCI, and even most “momentum” overlays) simply tell you what already happened. VoVix triggers as price structure transitions—anyone running these generic scripts will trade behind the move while VoVix gets in as stress emerges. Real alpha comes from anticipation, not confirmation. The visuals only show what matters: Top right, you get a live, live quant dashboard—regime index, current position size, real-time performance (Sharpe, Sortino, win rate, and wins). Bottom right: a VoVix "engine bar" that adapts live with regime stress. Everything you see is a direct function of logic driving this edge—no cosmetics, no fake momentum. Inputs/Signals—explained carefully for clarity: o ATR Fast Length & ATR Slow Length: These are the heart of VoVix’s regime sensing. Fast ATR reacts to sharp volatility; Slow ATR is stability baseline. Lower Fast = reacts to every twitch; higher Slow = requires more persistent, “real” regime shifts. Tip: If you want more signals or faster markets, lower ATR Fast. To eliminate noise, raise ATR Slow. o ATR StdDev Window: Smoothing for volatility-of-volatility normalization. Lower = more jumpy, higher = only the cleanest spikes trigger. Tip: Shorten for “jumpy” assets, raise for indices/futures. o Base Spike Threshold: Think of this as your “minimum event strength.” If the current move isn’t volatile enough (normalized), no signal. Tip: Higher = only biggest moves matter. Lower for more signals but more potential noise. o Super Spike Multiplier: The “are you sure?” test—entry only when the current spike is this multiple above local average. Tip: Raise for ultra-selective/swing-trading; lower for more active style. Regime & MultiTF: o Regime Window (Bars): How many bars to scan for regime cluster “events.” Short for turbo markets, long for big swings/trends only. o Regime Event Count: Only trade when this many spikes occur within the Regime Window—filters for real stress, not isolated ticks. Tip: Raise to only ever trade during true breakouts/crashes. o Local Window for Extremes: How many bars to check that a spike is a local max. Tip: Raise to demand only true, “clearest” local regime events; lower for early triggers. o HTF Confirm: Higher timeframe regime confirmation (like 45m on an intraday chart). Ensures any event you act on is visible in the broader context. Tip: Use higher timeframes for only major moves; lower for scalping or fast regimes. Adaptive Sizing: o Max Contracts (Adaptive): The largest size your system will ever scale to, even on extreme event. Tip: Lower for small accounts/conservative risk; raise on big accounts or when you're willing to go big only on outlier events. o Min Contracts (Adaptive): The “toe-in-the-water.” Smallest possible trade. Tip: Set as low as your broker/exchange allows for safety, or higher if you want to always have meaningful skin in the game. Trade Management: o Stop %: Tightness of your stop-loss relative to entry. Lower for tighter/safer, higher for more breathing room at cost of greater drawdown. o Take Profit %: How much you'll hold out for on a win. Lower = more scalps. Higher = only run with the best. o Decay Exit Sensitivity Buffer: Regime index must dip this far below the trading threshold before you exit for “regime decay.” Tip: 0 = exit as soon as stress fails, higher = exits only on stronger confirmation regime is over. o Bars Decay Must Persist to Exit: How long must decay be present before system closes—set higher to avoid quick fades and whipsaws. Backtest Settings Initial capital: $10,000 Commission: Conservative, realistic roundtrip cost: 15–20 per contract (including slippage per side) I set this to $25 Slippage: 3 ticks per trade Symbol: CME_MINI:NQ1! Timeframe: 1 min (but works on all timeframes) Order size: Adaptive, 1–3 contracts No pyramiding, no hidden DCA Why these settings? These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for NQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions. Tip: Set to 1 for instant regime exit; raise for extra confirmation (less whipsaw risk, exits held longer). ________________________________________ Bottom line: Tune the sensitivity, selectivity, and risk of REVELATIONS by these inputs. Raise thresholds and windows for only the best, most powerful signals (institutional style); lower for activity (scalpers, fast cryptos, signals in constant motion). Sizing is always adaptive—never static or martingale. Exits are always based on both price and regime health. Every input is there for your control, not to sell “complexity.” Use with discipline, and make it your own. This strategy is not just a technical achievement: It’s a statement about trading smarter, not just more. * I went back through the code to make sure no the strategy would not suffer from repainting, forward looking, or any frowned upon loopholes. Disclaimer: Trading is risky and carries the risk of substantial loss. Do not use funds you aren’t prepared to lose. This is for research and informational purposes only, not financial advice. Backtest, paper trade, and know your risk before going live. Past performance is not a guarantee of future results. Expect more: We’ll keep pushing the standard, keep evolving the bar until “quant” actually means something in the public code space. Use with clarity, use with discipline, and always trade your edge. — Dskyz , for DAFE Trading Systems
Chiến lược Pine Script®
của DskyzInvestments
8
US Growth Momentum Index (UGMI)US Growth Momentum Index (UGMI) is a macroeconomic indicator designed to reflect the current economic health of the United States. The index combines four key economic factors—Industrial Production (INDPRO), Retail Sales (RSAFS), Unemployment Rate (UNRATE), and the 10Y-2Y Yield Curve Spread—and aggregates them into a single line representing overall growth momentum. The UGMI is based on Z-Score calculations for each of these components, helping to identify whether the U.S. economy is in an expansionary growth phase or a contractionary recession phase. How to Read the UGMI: Core Growth Line (Blue): This is the main line representing the aggregated growth momentum. It is calculated using the Z-scores of the key economic indicators. 0 Line: The 0 line represents the neutral zone. If the UGMI is above the 0 line, it suggests that the economy is expanding. Below the 0 line indicates contraction or a potential recession. Zones Above 1: When the UGMI is above 1, it represents a strong expansionary phase or boom, possibly signaling the top of a bull market or local peak. Zones Below -1: When the UGMI falls below -1, it indicates a severe recession or significant contraction in the economy. Color Coding: Green: When the UGMI is above the 1 line, the background turns green, indicating a strong growth phase. Red: When the UGMI is below -1, the background turns red, representing a recession or significant economic downturn. This indicator helps traders and investors to understand whether the economy is growing or contracting, and is especially useful for identifying potential market tops or turning points.
Chỉ báo Pine Script®
của simonweisser_business
Market Warning Dashboard Enhanced📊 Market Warning Dashboard Enhanced A powerful macro risk dashboard that tracks and visualizes early signs of market instability across multiple key indicators—presented in a clean, professional layout with a real-time thermometer-style danger gauge. 🔍 Included Macro Signals: Yield Curve Inversion: 10Y-2Y and 10Y-3M spreads Credit Spreads: High-yield (HYG) vs Investment Grade (LQD) Volatility Structure: VIX/VXV ratio Breadth Estimate: SPY vs 50-day MA (as a proxy) 🔥 Features: Real-time Danger Score: 0 (Safe) to 100 (Extreme Risk) Descriptive warnings for each signal Color-coded thermometer gauge Alert conditions for each macro risk Background shifts on rising systemic risk ⚠️ This dashboard can save your portfolio by alerting you to macro trouble before it hits the headlines—ideal for swing traders, long-term investors, and anyone who doesn’t want to get blindsided by systemic risk.
Chỉ báo Pine Script®
của StonkJuicer
Higher Timeframe TrendMap [BigBeluga]🔵HTF TrendMap A powerful visual overlay that brings higher timeframe market structure directly onto your intraday chart. This tool maps directional bias, trend strength, and dynamic range boundaries from a user-selected HTF (like Daily or 4H), offering a real-time confluence layer for scalpers, day traders, and swing traders. By plotting the evolving average (HL2), it acts as a volatility-weighted trend anchor, allowing you to align lower timeframe entries with higher timeframe intent. Technical Overview: At the close of each higher timeframe (HTF) candle, the indicator stores the high, low, and calculates the HL2 midpoint. These values are then referenced on the lower timeframe chart to plot trend direction and price boundaries. 🔵 KEY FEATURES Maps the selected higher timeframe (HTF) (e.g., Daily) onto your current chart. At the close of each HTF candle , it starts to calculate and store the highest, lowest, and average (HL2) price levels . The average (HL2) value is treated as the HTF trend baseline —plotted in orange for uptrend , blue for downtrend . Visual curve thickens and fades to show progress through the HTF period (stronger color = fresher data). Horizontal dashed lines show HTF high and low levels that persist until the next period closes. On every HTF close, two price labels are printed for the high and low levels. Vertical separators visually mark the start of each HTF candle for easy structural recognition. A real-time dashboard shows selected HTF, current trend direction (🢁/🢃), and updates dynamically. 🔵 HOW TO USE Use the HTF average line as a bias filter —only long when the trend is up (orange), short when down (blue). HTF high/low labels help identify key breakout or rejection zones . Combine with intraday systems or reversal tools for multi-timeframe confluence setups . Ideal for scalpers and swing traders who rely on HTF momentum shifts . 🔵 CONCLUSION HTF TrendMap provides a clean, data-rich layer of higher timeframe context to any chart. With adaptive trend coloring, volatility mapping, and real-time data labeling, it enables traders to stay in sync with macro structure while executing on the micro.
Chỉ báo Pine Script®
của BigBeluga
11
Bollinger + EMA Strategy with StatsThis strategy is a mean-reversion trading model that combines Bollinger Band deviation entries with EMA-based exits. It enters a long position when the price drops significantly below the lower Bollinger Band by a user-defined multiple of standard deviation (x), and a short position when the price exceeds the upper band by the same logic. To manage risk, it uses a wider Bollinger Band threshold (y standard deviations) as a stop loss, while take profit occurs when the price reverts to the n-period EMA, indicating mean reversion. The strategy maintains only one active position at a time—either long or short—and allocates a fixed percentage of capital per trade. Performance metrics such as equity curve, drawdown, win rate, and total trades are tracked and displayed for backtesting evaluation.
Chiến lược Pine Script®
của Trader_dake
1
ETF Builder & Backtest System [TradeDots]Create, analyze, and monitor your own custom “ETF-like” portfolio directly on TradingView. This script merges up to 10 different assets with user-defined weightings into a single composite chart, allowing you to see how your personalized portfolio would have performed historically. It is an original tool designed to help traders and investors quickly gauge risk and return profiles without leaving the TradingView platform. 📝 HOW IT WORKS 1. Custom Portfolio Construction Multiple Assets : Combine up to 10 different stocks, ETFs, cryptocurrencies, or other symbols. User-Defined Weights : Allocate each asset a percentage weight (e.g., 15% in AAPL, 10% in MSFT, etc.). Single Composite Value : The script calculates a weighted “ETF-style” price, effectively simulating a merged portfolio curve on your chart. 2. Performance Tracking & Return Analysis Automatic History Capture : The indicator records each asset’s starting price when it first appears in your chosen date range. Rolling Updates : As time progresses, all asset prices are continually evaluated and the portfolio value is updated in real time. Buy & Hold Returns : See how each asset—and the overall portfolio—performed from the “start” date to the most recent bar. Annualized Return : Automatically calculates CAGR (Compound Annual Growth Rate) to help visualize performance over varying timescales. 3. Table & Visual Output Performance Table : A comprehensive table displays individual asset returns, annualized returns, and portfolio totals. Normalized Chart Plot : The composite ETF value is scaled to 100 at the start date, making it easy to compare relative growth or decline. Optional Time Filter : You can define a specific date range (Start/End Dates) to focus on a particular period or to limit historical data. ⚙️ KEY FEATURES 1. Flexible Asset Selection Choose any symbols from multiple asset classes. The script will only run calculations when data is available—no need to worry about missing quotes. 2. Dynamic Table Reporting Start Price for each asset Percentage Weight in the portfolio Total Return (%) and Annualized Return (%) 3. Simple Backtesting Logic This script takes a straightforward Buy & Hold perspective. Once the start date is reached, the portfolio remains static until the end date, so you can quickly assess hypothetical growth. 4. Plot Customization Toggle the main “ETF” plot on/off. Alter the visual style for tables and text. Adjust the time filter to limit or extend your performance measurement window. 🚀 HOW TO USE IT 1. Add the Script Search for “ETF Builder & Backtest System ” in the Indicators & Strategies tab or manually add it to your chart after saving it in your Pine Editor. 2. Configure Inputs Enable Time Filter : Choose whether to restrict the analysis to a particular date range. Start & End Date : Define the period you want to measure performance over (e.g., from 2019-12-31 to 2025-01-01). Assets & Weights : Enter each symbol and specify a percentage weight (up to 10 assets). Display Options : Pick where you want the Table to appear and choose background/text colors. 3. Interpret the Table & Plots Asset Rows : Each asset’s ticker, weighting, start price, and performance metrics. ETF Total Row : Summarizes total weighting, composite starting value, and overall returns. Normalized Plot : Tracks growth/decline of the combined portfolio, starting at 100 on the chart. 4. Refine Your Strategy Compare how different weights or a new mix of assets would have performed over the same period. Assess if certain assets contribute disproportionately to your returns or volatility. Use the results to guide allocations in your real trading or paper trading accounts. ❗️LIMITATIONS 1. Buy & Hold Only This script does not handle rebalancing or partial divestments. Once the portfolio starts, weights remain fixed throughout the chosen timeframe. 2. No Reinvestment Tracking Dividends or other distributions are not factored into performance. 3. Data Availability If historical data for a particular asset is unavailable on TradingView, related results may display as “N/A.” 4. Market Regimes & Volatility Past performance does not guarantee similar future behavior. Markets can change rapidly, which may render historical backtests less predictive over time. ⚠️ RISK DISCLAIMER Trading and investing carry significant risk and can result in financial loss. The “ETF Builder & Backtest System ” is provided for informational and educational purposes only. It does not constitute financial advice. Always conduct your own research. Use proper risk management and position sizing. Past performance does not guarantee future results. This script is an original creation by TradeDots, published under the Mozilla Public License 2.0. Use this indicator as part of a broader trading or investment approach—consider fundamental and technical factors, overall market context, and personal risk tolerance. No trading tool can assure profits; exercise caution and responsibility in all financial decisions.
Chỉ báo Pine Script®
của tradedots
1
Mongoose Capital: FlowWave + Conviction Strip🟩 Indicator Name Mongoose Capital: FlowWave + Conviction Strip 📜 Short Description Smoothed Money Flow Oscillator with conviction scoring columns to assess flow strength. 🧠 Description (Long Form) The Mongoose Capital: FlowWave + Conviction Strip is a refined visualization of money flow dynamics designed to identify shifts in volume pressure and trend strength. This dual-panel indicator includes: • FlowWave Line — A smoothed momentum curve built from normalized money flow data, filtered through dual EMAs. Green (positive) and purple (negative) segments help traders quickly assess bias shifts. • Conviction Score Columns — A histogram below the zero line shows strength of flow deltas (momentum of volume pressure). Green/red bars appear when strength exceeds a critical threshold, while gray bars indicate low conviction. • Background Zone Coloring — Optional dark red/green gradient to enhance visibility of positive/negative phases. ✅ Designed for traders who value clarity and minimal noise ✅ Pairs well with macro trend filters or breakout strategies ✅ Built and published by Mongoose Capital 🔧 Default Settings Money Flow Length: 14 Signal Cooldown: 5 bars Source: HLC3 EMA Wave Filter: 3 Strength Threshold: 20 🧪 Suggested Use Confirm entries/exits in trend continuation setups Identify divergences between price and money flow Filter low-conviction trades using the histogram's gray zone Spot early accumulation or distribution through wave crossovers 📢 Author Published by: TheRealMongoose Powered by: Mongoose Capital Feel free to tag us in your setups.
Chỉ báo Pine Script®
của TheRealMongoose
1
Cumulative Ease of MovementThis indicator visualizes Cumulative Ease of Movement (EOM) to assess the efficiency of price movement relative to volume and volatility. Instead of analysing individual bars, this tool accumulates EOM values over time, helping you identify persistent buying or selling pressure. The original Ease of Movement created by Richard Arms is an oscillator: EOM = ((Midpoint Move) / Box Ratio) Midpoint Move = ((High + Low)/2) - ((High + Low )/2) Box Ratio = Volume / (High - Low) this indicator simply accumulates those values over time. It incorporates a full Guppy Multiple Moving Average (GMMA) framework applied to the cumulative EOM curve, highlighting trend strength, transition zones, and slow/fast compression. Optional bar colouring and candle overlays reflect EOM direction and allow visual clarity during trending or congested conditions. To facilitate the interpretation of the cumulative EOM, the GMMA and the Heikin Ashi smooths the signals, but the EOM itself is not smoothed. Features - Cumulative EOM plot (toggleable) - GMMA overlays: short, medium, long-term - Optional bar colouring based on EOM trend - Heikin Ashi-style smoothing option - EOM rendered as candle plot for momentum interpretation Use Cases - Identify trend strength and consistency through EOM slope + GMMA expansion - Spot absorption and exhaustion zones when price pushes but cumulative EOM diverges - Detect transitions when GMMA layers begin to compress and flip - Validate breakout efficiency with strong EOM continuation The Cumulative Ease of Movement (EOM) is designed to help traders follow the path of least resistance in the market by tracking whether price is moving efficiently with or against volume over time. Rather than focusing on isolated bar-by-bar effort, it accumulates directional bias, allowing you to see whether buying or selling pressure is sustained and aligned with ease — or whether market moves are inefficient and likely to fade. If you do not wish to use candles, or heikin ashi, you can enable the Cumulative EOM line and disable the candles.
Chỉ báo Pine Script®
của theodorewakefield
5
Intraday Uncertainty [PhenLabs]📊 Intraday Uncertainty Version: PineScript™ v6 📌 Description The Intraday Uncertainty indicator offers traders a visual representation of market certainty/uncertainty during trading sessions. By comparing each price bar’s range to the Average True Range (ATR), it provides an intuitive way to gauge market conviction through a color gradient system. This tool helps traders identify periods of high certainty (potentially trending markets) versus high uncertainty (potentially choppy or volatile markets) without complex calculations or multiple indicators. The color-coded bars create an immediate visual cue to support decision-making in varying market conditions. 🚀 Points of Innovation Automated range-to-ATR ratio calculation that adapts to changing market volatility Dynamic color gradient system that visually distinguishes between certain and uncertain price action Customizable gradient clamping to fine-tune sensitivity to market conditions Integrated dashboard that provides clear interpretation guidance Position-flexible legend that accommodates different chart layouts Highly optimized for performance with minimal calculation overhead 🔧 Core Components ATR Calculation: Measures market volatility using a configurable lookback period Range-to-ATR Ratio: Compares current bar’s high-low range against average volatility Gradient Mapping System: Converts numerical uncertainty values into an intuitive color scale Dashboard Legend: Provides clear interpretation guidance with customizable positioning 🔥 Key Features Bar Coloring: Instantly identifies market certainty levels through intuitive color gradients Customizable ATR Period: Adjust sensitivity to historical volatility based on trading style Gradient Clamping: Fine-tune the color sensitivity using the Range/ATR multiplier Color Customization: Personalize the color scheme to match your chart aesthetics Informative Dashboard: Quickly interpret color meanings with the optional on-chart legend Flexible Display Options: Customize dashboard position and text size for your chart layout 🎨 Visualization Color Gradient: Bars colored on a spectrum from green (high certainty) to red (high uncertainty) Dashboard Legend: Optional on-chart guide explaining the color interpretation Color Intensity: Stronger colors indicate more extreme certainty/uncertainty levels At-a-glance Interpretation: Quickly identify market conviction without analyzing numbers 📖 Usage Guidelines Calculation Settings ATR Period Default: 14 Range: 1+ Description: Controls the lookback period for ATR calculation. Lower values increase sensitivity to recent volatility, while higher values provide more stability. Gradient Clamp (Range/ATR Multiplier) Default: 2.0 Range: 0.1+ Description: Sets the maximum Range/ATR ratio for gradient scaling. Ranges above this value display the end color (high uncertainty). Color Settings Gradient Start Color (High Certainty) Default: Green Description: Color representing high market certainty (low Range/ATR ratio) Gradient End Color (Low Certainty) Default: Red Description: Color representing low market certainty (high Range/ATR ratio) Dashboard Settings Show Dashboard Legend Default: True Description: Toggles the visibility of the on-chart interpretation guide Dashboard Position Options: top_right, top_left, bottom_right, bottom_left, middle_right, middle_left Default: bottom_right Description: Controls the placement of the dashboard on your chart Dashboard Text Size Options: tiny, small, normal, large, huge Default: normal Description: Adjusts the text size of the dashboard for readability ✅ Best Use Cases Identifying potential trend shifts when certainty levels change dramatically Confirming trend strength through consistent certainty levels Detecting choppy/sideways markets with persistent high uncertainty Filtering trading signals from other indicators based on certainty levels Gauging market conviction behind price breakouts or pullbacks Optimizing entry/exit timing based on certainty/uncertainty transitions ⚠️ Limitations Does not predict future price direction, only measures current bar certainty May provide false signals during news events or unexpected volatility spikes Requires context within the broader market environment for optimal interpretation Color interpretation is relative rather than absolute across different securities ATR-based calculation means sensitivity varies across different timeframes 💡 What Makes This Unique Simplicity: Single visual indicator that doesn’t require multiple technical tools Adaptability: Automatically adjusts to changing market volatility conditions Contextual Analysis: Provides market conviction context beyond just price movement Intuitive Design: Color-based system that requires minimal learning curve Efficiency: Lightweight calculation that doesn’t impact chart performance 🔬 How It Works 1. ATR Calculation: Calculates the Average True Range using the specified period Establishes a baseline for normal market volatility 2. Range Analysis: Measures each bar’s high-low range Compares this range to the current ATR value to create a ratio 3. Gradient Mapping: Converts the Range/ATR ratio to a normalized value between 0 and 1 Maps this value onto a color gradient between the start and end colors Applies the resulting color to the price bar 4. Dashboard Creation: Constructs an information panel on the last visible bar Populates it with color samples and interpretation guidance 💡 Note: This indicator works best when used in conjunction with other technical analysis tools rather than in isolation. The certainty/uncertainty measure provides context for your trading decisions but should not be the sole basis for entries and exits. Consider using higher certainty periods for trend-following strategies and exercise caution during periods of high uncertainty.
Chỉ báo Pine Script®
của PhenLabs
Dskyz (DAFE) MAtrix with ATR-Powered Precision Dskyz (DAFE) MAtrix with ATR-Powered Precision This cutting‐edge futures trading strategy built to thrive in rapidly changing market conditions. Developed for high-frequency futures trading on instruments such as the CME Mini MNQ, this strategy leverages a matrix of sophisticated moving averages combined with ATR-based filters to pinpoint high-probability entries and exits. Its unique combination of adaptable technical indicators and multi-timeframe trend filtering sets it apart from standard strategies, providing enhanced precision and dynamic responsiveness. imgur.com Core Functional Components 1. Advanced Moving Averages A distinguishing feature of the DAFE strategy is its robust, multi-choice moving averages (MAs). Clients can choose from a wide array of MAs—each with specific strengths—in order to fine-tune their trading signals. The code includes user-defined functions for the following MAs: imgur.com Hull Moving Average (HMA): The hma(src, len) function calculates the HMA by using weighted moving averages (WMAs) to reduce lag considerably while smoothing price data. This function computes an intermediate WMA of half the specified length, then a full-length WMA, and finally applies a further WMA over the square root of the length. This design allows for rapid adaptation to price changes without the typical delays of traditional moving averages. Triple Exponential Moving Average (TEMA): Implemented via tema(src, len), TEMA uses three consecutive exponential moving averages (EMAs) to effectively cancel out lag and capture price momentum. The final formula—3 * (ema1 - ema2) + ema3—produces a highly responsive indicator that filters out short-term noise. Double Exponential Moving Average (DEMA): Through the dema(src, len) function, DEMA calculates an EMA and then a second EMA on top of it. Its simplified formula of 2 * ema1 - ema2 provides a smoother curve than a single EMA while maintaining enhanced responsiveness. Volume Weighted Moving Average (VWMA): With vwma(src, len), this MA accounts for trading volume by weighting the price, thereby offering a more contextual picture of market activity. This is crucial when volume spikes indicate significant moves. Zero Lag EMA (ZLEMA): The zlema(src, len) function applies a correction to reduce the inherent lag found in EMAs. By subtracting a calculated lag (based on half the moving average window), ZLEMA is exceptionally attuned to recent price movements. Arnaud Legoux Moving Average (ALMA): The alma(src, len, offset, sigma) function introduces ALMA—a type of moving average designed to be less affected by outliers. With parameters for offset and sigma, it allows customization of the degree to which the MA reacts to market noise. Kaufman Adaptive Moving Average (KAMA): The custom kama(src, len) function is noteworthy for its adaptive nature. It computes an efficiency ratio by comparing price change against volatility, then dynamically adjusts its smoothing constant. This results in an MA that quickly responds during trending periods while remaining smoothed during consolidation. Each of these functions—integrated into the strategy—is selectable by the trader (via the fastMAType and slowMAType inputs). This flexibility permits the tailored application of the MA most suited to current market dynamics and individual risk management preferences. 2. ATR-Based Filters and Risk Controls ATR Calculation and Volatility Filter: The strategy computes the Average True Range (ATR) over a user-defined period (atrPeriod). ATR is then used to derive both: Volatility Assessment: Expressed as a ratio of ATR to closing price, ensuring that trades are taken only when volatility remains within a safe, predefined threshold (volatilityThreshold). ATR-Based Entry Filters: Implemented as atrFilterLong and atrFilterShort, these conditions ensure that for long entries the price is sufficiently above the slow MA and vice versa for shorts. This acts as an additional confirmation filter. Dynamic Exit Management: The exit logic employs a dual approach: Fixed Stop and Profit Target: Stops and targets are set at multiples of ATR (fixedStopMultiplier and profitTargetATRMult), helping manage risk in volatile markets. Trailing Stop Adjustments: A trailing stop is calculated using the ATR multiplied by a user-defined offset (trailOffset), which captures additional profits as the trade moves favorably while protecting against reversals. 3. Multi-Timeframe Trend Filtering The strategy enhances its signal reliability by leveraging a secondary, higher timeframe analysis: 15-Minute Trend Analysis: By retrieving 15-minute moving averages (fastMA15m and slowMA15m) via request.security, the strategy determines the broader market trend. This secondary filter (enabled or disabled through useTrendFilter) ensures that entries are aligned with the prevailing market direction, thereby reducing the incidence of false signals. 4. Signal and Execution Logic Combined MA Alignment: The entry conditions are based primarily on the alignment of the fast and slow MAs. A long condition is triggered when the current price is above both MAs and the fast MA is above the slow MA—complemented by the ATR filter and volume conditions. The reverse applies for a short condition. Volume and Time Window Validation: Trades are permitted only if the current volume exceeds a minimum (minVolume) and the current hour falls within the predefined trading window (tradingStartHour to tradingEndHour). An additional volume spike check (comparing current volume to a moving average of past volumes) further filters for optimal market conditions. Comprehensive Order Execution: The strategy utilizes flexible order execution functions that allow pyramiding (up to 10 positions), ensuring that it can scale into positions as favorable conditions persist. The use of both market entries and automated exits (with profit targets, stop-losses, and trailing stops) ensures that risk is managed at every step. 5. Integrated Dashboard and Metrics For transparency and real-time analysis, the strategy includes: On-Chart Visualizations: Both fast and slow MAs are plotted on the chart, making it easy to see the market’s technical foundation. Dynamic Metrics Dashboard: A built-in table displays crucial performance statistics—including current profit/loss, equity, ATR (both raw and as a percentage), and the percentage gap between the moving averages. These metrics offer immediate insight into the health and performance of the strategy. Input Parameters: Detailed Breakdown Every input is meticulously designed to offer granular control: Fast & Slow Lengths: Determine the window size for the fast and slow moving averages. Smaller values yield more sensitivity, while larger values provide a smoother, delayed response. Fast/Slow MA Types: Choose the type of moving average for fast and slow signals. The versatility—from basic SMA and EMA to more complex ones like HMA, TEMA, ZLEMA, ALMA, and KAMA—allows customization to fit different market scenarios. ATR Parameters: atrPeriod and atrMultiplier shape the volatility assessment, directly affecting entry filters and risk management through stop-loss and profit target levels. Trend and Volume Filters: Inputs such as useTrendFilter, minVolume, and the volume spike condition help confirm that a trade occurs in active, trending markets rather than during periods of low liquidity or market noise. Trading Hours: Restricting trade execution to specific hours (tradingStartHour and tradingEndHour) helps avoid illiquid or choppy markets outside of prime trading sessions. Exit Strategies: Parameters like trailOffset, profitTargetATRMult, and fixedStopMultiplier provide multiple layers of risk management and profit protection by tailoring how exits are generated relative to current market conditions. Pyramiding and Fixed Trade Quantity: The strategy supports multiple entries within a trend (up to 10 positions) and sets a predefined trade quantity (fixedQuantity) to maintain consistent exposure and risk per trade. Dashboard Controls: The resetDashboard input allows for on-the-fly resetting of performance metrics, keeping the strategy’s performance dashboard accurate and up-to-date. Why This Strategy is Truly Exceptional Multi-Faceted Adaptability: The ability to switch seamlessly between various moving average types—each suited to particular market conditions—enables the strategy to adapt dynamically. This is a testament to the high level of coding sophistication and market insight infused within the system. Robust Risk Management: The integration of ATR-based stops, profit targets, and trailing stops ensures that every trade is executed with well-defined risk parameters. The system is designed to mitigate unexpected market swings while optimizing profit capture. Comprehensive Market Filtering: By combining moving average crossovers with volume analysis, volatility thresholds, and multi-timeframe trend filters, the strategy only enters trades under the most favorable conditions. This multi-layered filtering reduces noise and enhances signal quality. -Final Thoughts- The Dskyz Adaptive Futures Elite (DAFE) MAtrix with ATR-Powered Precision strategy is not just another trading algorithm—it is a multi-dimensional, fully customizable system built on advanced technical principles and sophisticated risk management techniques. Every function and input parameter has been carefully engineered to provide traders with a system that is both powerful and transparent. For clients seeking a state-of-the-art trading solution that adapts dynamically to market conditions while maintaining strict discipline in risk management, this strategy truly stands in a class of its own. ****Please show support if you enjoyed this strategy. I'll have more coming out in the near future!! -Dskyz Caution DAFE is experimental, not a profit guarantee. Futures trading risks significant losses due to leverage. Backtest, simulate, and monitor actively before live use. All trading decisions are your responsibility.
Chiến lược Pine Script®
của DskyzInvestments
16
Delta Volume[integral]Delta Volume – Visualizing Accumulated Candle Dominance This indicator measures and accumulates the net difference between bullish and bearish candle volumes over a user-defined range of bars. It integrates the volume dominance over time, offering traders a unique view into how buying or selling pressure has been distributed. 🔍 Concept & Logic Delta Volume Calculation For each bar, the script looks x to y bars back in time (e.g., from 10 bars ago to 5 bars ago) and: Adds volume for bullish candles (close > open) Subtracts volume for bearish candles (close < open) This gives us a snapshot of volume dominance for that range. What is Integration in This Context? Integration, in this script, refers to the accumulation (summation) of these dominance differences over a period. Much like integrating a function in calculus (i.e., area under the curve), here we are integrating the "net advantage" of buyers vs. sellers. Over time, this builds a cumulative picture of directional pressure, showing whether buyers (positive integration) or sellers (negative integration) are in control. Why It Matters Unlike simple volume charts, this tool filters noise by focusing on who is dominating the market—buyers or sellers—and tracks that dominance over time. It gives a macro-level view of pressure buildup, which can precede major breakouts or reversals. 📊 Visual Features Buy Volume (green columns): Sum of volumes from bullish candles. Sell Volume (red columns): Sum of volumes from bearish candles. Candle Difference (white line): Net dominance difference (Buy - Sell). Integrated Dominance Difference: Cumulative label showing the total buyer-seller dominance over the defined integration period. Zero Line (dashed): Balance point. 🧠 Use Case Detect divergences between price and cumulative volume pressure. Confirm trend strength when integrated delta volume aligns with price movement. Spot accumulation or distribution phases invisible on price action alone. ⚠️ If you're applying this to symbols with no volume data (e.g., certain Forex or indices), the script will stop with an error message.
Chỉ báo Pine Script®
của kdhchgo
DEGA RMA | QuantEdgeB🧠 Introducing DEGA RMA (DGR ) by QuantEdgeB 🛠️ Overview DEGA RMA (DGR) is a precision-engineered trend-following system that merges DEMA, Gaussian kernel smoothing, and ATR-based envelopes into a single, seamless overlay indicator. Its mission: to filter out market noise while accurately capturing directional bias using a layered volatility-sensitive trend core. DGR excels at identifying valid breakouts, sustained momentum conditions, and trend-defining price behavior without falling into the trap of frequent signal reversals. 🔍 How It Works 1️⃣ Double Exponential Moving Average (DEMA) The system begins by applying a DEMA to the selected price source. DEMA responds faster than a traditional EMA, making it ideal for capturing transitions in momentum. 2️⃣ Gaussian Filtering A custom Gaussian kernel is used to smooth the DEMA signal. The Gaussian function applies symmetrical weights, centered around the most recent bar, effectively softening sharp price oscillations while preserving the underlying trend structure. 3️⃣ Recursive Moving Average (RMA) Core The filtered Gaussian output is then processed through an RMA to generate a stable dynamic baseline. This baseline becomes the foundation for the final trend logic. 4️⃣ ATR-Scaled Breakout Zones Upper and lower trend envelopes are calculated using a custom ATR filter built on DEMA-smoothed volatility. • ✅ Long Signal when price closes above the upper envelope • ❌ Short Signal when price closes below the lower envelope • ➖ Neutral when inside the band (no signal noise) ✨ Key Features 🔹 Multi-Layer Trend Model DEMA → Gaussian → RMA creates a signal structure that is both responsive and robust. 🔹 Volatility-Aware Entry System Adaptive ATR bands adjust in real-time, expanding during high volatility and contracting during calm periods. 🔹 Noise-Reducing Gaussian Kernel Sigma-adjustable kernel ensures signal smoothness without introducing excessive lag. 🔹 Clean Visual System Candle coloring and band fills make trend state easy to read and act on at a glance. ⚙️ Custom Settings • DEMA Source – Input source for trend core (default: close) • DEMA Length – Length for initial smoothing (default: 30) • Gaussian Filter Length – Determines smoothing depth (default: 4) • Gaussian Sigma – Sharpness of Gaussian curve (default: 2.0) • RMA Length – Core baseline smoothing (default: 12) • ATR Length – Volatility detection period (default: 40) • ATR Mult Up/Down – Controls the upper/lower threshold range for signals (default: 1.7) 📌 How to Use 1️⃣ Trend-Following Mode • Go Long when price closes above the upper ATR band • Go Short when price closes below the lower ATR band • Remain neutral otherwise 2️⃣ Breakout Confirmation Tool DGR’s ATR-based zone logic helps validate price breakouts and filter out false signals that occur inside compressed ranges. 3️⃣ Volatility Monitoring Watch the ATR envelope width — a narrowing band often precedes expansion and potential directional shifts. 📌 Conclusion DEGA RMA (DGR) is a thoughtfully constructed trend-following framework that goes beyond basic moving averages. Its Gaussian smoothing, adaptive ATR thresholds, and layered filtering logic provide a versatile solution for traders looking for cleaner signals, less noise, and real-time trend awareness. Whether you're trading crypto, forex, or equities — DGR adapts to volatility while keeping your chart clean and actionable. 🔹 Summary • ✅ Advanced Smoothing → DEMA + Gaussian + RMA = ultra-smooth trend core • ✅ Volatility-Adjusted Zones → ATR envelope scaling removes whipsaws • ✅ Fully Customizable → Tailor to any asset or timeframe • ✅ Quant-Inspired Structure → Built for clarity, consistency, and confidence 📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets. 📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Chỉ báo Pine Script®
của BaseTrustCapital
2
Kernel Weighted DMI | QuantEdgeB📊 Introducing Kernel Weighted DMI (K-DMI) by QuantEdgeB 🛠️ Overview K-DMI is a next-gen momentum indicator that combines the traditional Directional Movement Index (DMI) with advanced kernel smoothing techniques to produce a highly adaptive, noise-resistant trend signal. Unlike standard DMI that can be overly reactive or choppy in consolidation phases, K-DMI applies kernel-weighted filtering (Linear, Exponential, or Gaussian) to stabilize directional movement readings and extract a more reliable momentum signal. ✨ Key Features 🔹 Kernel Smoothing Engine Smooths DMI using your choice of kernel (Linear, Exponential, Gaussian) for flexible noise reduction and clarity. 🔹 Dynamic Trend Signal Generates real-time long/short trend bias based on signal crossing upper or lower thresholds (defaults: ±1). 🔹 Visual Encoding Includes directional gradient fills, candle coloring, and momentum-based overlays for instant signal comprehension. 🔹 Multi-Mode Plotting Optional moving average overlays visualize structure and compression/expansion within price action. 📐 How It Works 1️⃣ Directional Movement Index (DMI) Calculates the traditional +DI and -DI differential to derive directional bias. 2️⃣ Kernel-Based Smoothing Applies a custom-weighted average across historical DMI values using one of three smoothing methods: • Linear → Simple tapering weights • Exponential → Decay curve for recent emphasis • Gaussian → Bell-shaped weight for centered precision 3️⃣ Signal Generation • ✅ Long → Signal > Long Threshold (default: +1) • ❌ Short → Signal < Short Threshold (default: -1) Additional overlays signal potential compression zones or trend resumption using gradient and line fills. ⚙️ Custom Settings • DMI Length: Default = 7 • Kernel Type: Options → Linear, Exponential, Gaussian (Def:Linear) • Kernel Length: Default = 25 • Long Threshold: Default = 1 • Short Threshold: Default = -1 • Color Mode: Strategy, Solar, Warm, Cool, Classic, Magic • Show Labels: Optional entry signal labels (Long/Short) • Enable Extra Plots: Toggle MA overlays and dynamic bands 👥 Who Is It For? ✅ Trend Traders → Identify sustained directional bias with smoother signal lines ✅ Quant Analysts → Leverage advanced smoothing models to enhance data clarity ✅ Discretionary Swing Traders → Visualize clean breakouts or fades within choppy zones ✅ MA Compression Traders → Use overlay MAs to detect expansion opportunities 📌 Conclusion Kernel Weighted DMI is the evolution of classic momentum tracking—merging traditional DMI logic with adaptable kernel filters. It provides a refined lens for trend detection, while optional visual overlays support price structure analysis. 🔹 Key Takeaways: 1️⃣ Smoothed and stabilized DMI for reliable trend signal generation 2️⃣ Optional Gaussian/exponential weighting for adaptive responsiveness 3️⃣ Custom gradient fills, dynamic MAs, and candle coloring to support visual clarity 📌 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets. 📌 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Chỉ báo Pine Script®
của BaseTrustCapital
1
Strategy Stats [presentTrading]Hello! it's another weekend. This tool is a strategy performance analysis tool. Looking at the TradingView community, it seems few creators focus on this aspect. I've intentionally created a shared version. Welcome to share your idea or question on this. █ Introduction and How it is Different Strategy Stats is a comprehensive performance analytics framework designed specifically for trading strategies. Unlike standard strategy backtesting tools that simply show cumulative profits, this analytics suite provides real-time, multi-timeframe statistical analysis of your trading performance. Multi-timeframe analysis: Automatically tracks performance metrics across the most recent time periods (last 7 days, 30 days, 90 days, 1 year, and 4 years) Advanced statistical measures: Goes beyond basic metrics to include Information Coefficient (IC) and Sortino Ratio Real-time feedback: Updates performance statistics with each new trade Visual analytics: Color-coded performance table provides instant visual feedback on strategy health Integrated risk management: Implements sophisticated take profit mechanisms with 3-step ATR and percentage-based exits BTCUSD Performance The table in the upper right corner is a comprehensive performance dashboard showing trading strategy statistics. Note: While this presentation uses Vegas SuperTrend as the underlying strategy, this is merely an example. The Stats framework can be applied to any trading strategy. The Vegas SuperTrend implementation is included solely to demonstrate how the analytics module integrates with a trading strategy. ⚠️ Timeframe Limitations Important: TradingView's backtesting engine has a maximum storage limit of 10,000 bars. When using this strategy stats framework on smaller timeframes such as 1-hour or 2-hour charts, you may encounter errors if your backtesting period is too long. Recommended Timeframe Usage: Ideal for: 4H, 6H, 8H, Daily charts and above May cause errors on: 1H, 2H charts spanning multiple years Not recommended for: Timeframes below 1H with long history █ Strategy, How it Works: Detailed Explanation The Strategy Stats framework consists of three primary components: statistical data collection, performance analysis, and visualization. 🔶 Statistical Data Collection The system maintains several critical data arrays: equityHistory: Tracks equity curve over time tradeHistory: Records profit/loss of each trade predictionSignals: Stores trade direction signals (1 for long, -1 for short) actualReturns: Records corresponding actual returns from each trade For each closed trade, the system captures: float tradePnL = strategy.closedtrades.profit(tradeIndex) float tradeReturn = strategy.closedtrades.profit_percent(tradeIndex) int tradeType = entryPrice < exitPrice ? 1 : -1 // Direction 🔶 Performance Metrics Calculation The framework calculates several key performance metrics: Information Coefficient (IC): The correlation between prediction signals and actual returns, measuring forecast skill. IC = Correlation(predictionSignals, actualReturns) Where Correlation is the Pearson correlation coefficient: Correlation(X,Y) = (nΣXY - ΣXY) / √ Sortino Ratio: Measures risk-adjusted return focusing only on downside risk: Sortino = (Avg_Return - Risk_Free_Rate) / Downside_Deviation Where Downside Deviation is: Downside_Deviation = √ R_i represents individual returns, T is the target return (typically the risk-free rate), and n is the number of observations. Maximum Drawdown: Tracks the largest percentage drop from peak to trough: DD = (Peak_Equity - Trough_Equity) / Peak_Equity * 100 🔶 Time Period Calculation The system automatically determines the appropriate number of bars to analyze for each timeframe based on the current chart timeframe: bars_7d = math.max(1, math.round(7 * barsPerDay)) bars_30d = math.max(1, math.round(30 * barsPerDay)) bars_90d = math.max(1, math.round(90 * barsPerDay)) bars_365d = math.max(1, math.round(365 * barsPerDay)) bars_4y = math.max(1, math.round(365 * 4 * barsPerDay)) Where barsPerDay is calculated based on the chart timeframe: barsPerDay = timeframe.isintraday ? 24 * 60 / math.max(1, (timeframe.in_seconds() / 60)) : timeframe.isdaily ? 1 : timeframe.isweekly ? 1/7 : timeframe.ismonthly ? 1/30 : 0.01 🔶 Visual Representation The system presents performance data in a color-coded table with intuitive visual indicators: Green: Excellent performance Lime: Good performance Gray: Neutral performance Orange: Mediocre performance Red: Poor performance █ Trade Direction The Strategy Stats framework supports three trading directions: Long Only: Only takes long positions when entry conditions are met Short Only: Only takes short positions when entry conditions are met Both: Takes both long and short positions depending on market conditions █ Usage To effectively use the Strategy Stats framework: Apply to existing strategies: Add the performance tracking code to any strategy to gain advanced analytics Monitor multiple timeframes: Use the multi-timeframe analysis to identify performance trends Evaluate strategy health: Review IC and Sortino ratios to assess predictive power and risk-adjusted returns Optimize parameters: Use performance data to refine strategy parameters Compare strategies: Apply the framework to multiple strategies to identify the most effective approach For best results, allow the strategy to generate sufficient trade history for meaningful statistical analysis (at least 20-30 trades). █ Default Settings The default settings have been carefully calibrated for cryptocurrency markets: Performance Tracking: Time periods: 7D, 30D, 90D, 1Y, 4Y Statistical measures: Return, Win%, MaxDD, IC, Sortino Ratio IC color thresholds: >0.3 (green), >0.1 (lime), <-0.1 (orange), <-0.3 (red) Sortino color thresholds: >1.0 (green), >0.5 (lime), <0 (red) Multi-Step Take Profit: ATR multipliers: 2.618, 5.0, 10.0 Percentage levels: 3%, 8%, 17% Short multiplier: 1.5x (makes short take profits more aggressive) Stop loss: 20%
Chiến lược Pine Script®
của PresentTrading
2
[blackcat] L3 Volatility Ehlers Stochastic CGOOVERVIEW This advanced indicator integrates the Center of Gravity Oscillator (CGO) with an Ehlers-Stochastic framework and an Adaptive Local Minimum-Maximum Average (ALMA) smoothing algorithm. Designed for non-overlaid charts, it identifies market momentum shifts by analyzing price action through multi-layer volatility analysis. FEATURES • Dual-line system: ✓ Stochastic CGO: Core oscillating line derived from weighted OHLC price calculations ✓ ALMA Lagging Line: Smoothing component using customizable offset/sigma parameters • Dynamic color scheme: ✓ Green/red trend differentiation via crossover comparison ✓ Optional fill areas between lines (toggleable) • Clear trade signals: ✓ Buy/Sell labels triggered by mathematically defined crossovers ✓ Zero-reference baseline marker (#0ebb23) • Customizable parameters: Fast Length (9 default) controls CGO sensitivity Slow Length (5 default) governs ALMA responsiveness ALMA Offset/Sigma allow adaptive curve optimization HOW TO USE Configure core parameters: • Adjust Fast Length (CGO timeframe window) • Set Slow Length, ALMA Offset, and Sigma for smoother/laggier response Interpret visuals: • Bullish trend = green shaded zone (when primary line above lagging line) • Bearish trend = red shaded zone (primary line below lagging line) Analyze signals: • Buy triggers occur when rising CGO crosses above ALMA while below zero • Sell triggers activate when falling CGO breaks below ALMA after exceeding zero base Optimize display: ✓ Enable/disable fill area via Fill Between Lines LIMITATIONS • Relies heavily on lookback periods - rapid market changes may reduce predictive accuracy • Signal frequency increases during high-volatility environments • Requires additional confirmation methods due to occasional premature crossovers • Default parameter settings may lack universality across asset classes NOTES • Best paired with volume-based confirmations for stronger signals • Reducing ALMA Sigma sharpens line responsiveness at cost of noise susceptibility • Increasing Fast Length extends calculation horizon while reducing peak sensitivity • Weighted OHLC source formula prioritizes closing prices for swing direction assessment
Chỉ báo Pine Script®
của blackcat1402
5
Multi-Oscillator Adaptive Kernel | AlphaAlgosMulti-Oscillator Adaptive Kernel | AlphaAlgos Overview The Multi-Oscillator Adaptive Kernel (MOAK) is an advanced technical analysis tool that combines multiple oscillators through sophisticated kernel-based smoothing algorithms. This indicator is designed to provide clearer trend signals while filtering out market noise, offering traders a comprehensive view of market momentum across multiple timeframes. Key Features • Fusion of multiple technical oscillators (RSI, Stochastic, MFI, CCI) • Advanced kernel smoothing technology with three distinct mathematical models • Customizable sensitivity and lookback periods • Clear visual signals for trend shifts and reversals • Overbought/oversold zones for precise entry and exit timing • Adaptive signal that responds to varying market conditions Technical Components The MOAK indicator utilizes a multi-layer approach to signal generation: 1. Oscillator Fusion The core of the indicator combines normalized readings from up to four popular oscillators: • RSI (Relative Strength Index) - Measures the speed and change of price movements • Stochastic - Compares the closing price to the price range over a specific period • MFI (Money Flow Index) - Volume-weighted RSI that includes trading volume • CCI (Commodity Channel Index) - Measures current price level relative to an average price 2. Kernel Smoothing The combined oscillator data is processed through one of three kernel functions: • Exponential Kernel - Provides stronger weighting to recent data with exponential decay • Linear Kernel - Applies a linear weighting from most recent to oldest data points • Gaussian Kernel - Uses a bell curve distribution that helps filter out extreme values 3. Dual Signal Lines • Fast Signal Line - Responds quickly to price changes • Slow Signal Line - Provides confirmation and shows the underlying trend direction Configuration Options Oscillator Selection: • Enable/disable each oscillator (RSI, Stochastic, MFI, CCI) • Customize individual lookback periods for each oscillator Kernel Settings: • Kernel Type - Choose between Exponential, Linear, or Gaussian mathematical models • Kernel Length - Adjust the smoothing period (higher values = smoother line) • Sensitivity - Fine-tune the indicator's responsiveness (higher values = more responsive) Display Options: • Color Bars - Toggle price bar coloring based on indicator direction How to Interpret the Indicator Signal Line Direction: • Upward movement (teal) indicates bullish momentum • Downward movement (magenta) indicates bearish momentum Trend Shifts: • Small circles mark the beginning of new uptrends • X-marks indicate the start of new downtrends Overbought/Oversold Conditions: • Values above +50 suggest overbought conditions (potential reversal or pullback) • Values below -50 suggest oversold conditions (potential reversal or bounce) Trading Strategies Trend Following: • Enter long positions when the signal line turns teal and shows an uptrend • Enter short positions when the signal line turns magenta and shows a downtrend • Use the slow signal line (area fill) as confirmation of the underlying trend Counter-Trend Trading: • Look for divergences between price and the indicator • Consider profit-taking when the indicator reaches overbought/oversold areas • Wait for trend shift signals before entering counter-trend positions Multiple Timeframe Analysis: • Use the indicator across different timeframes for confirmation • Higher timeframe signals carry more weight than lower timeframe signals Best Practices • Experiment with different kernel types for various market conditions • Gaussian kernels often work well in ranging markets • Exponential kernels can provide earlier signals in trending markets • Combine with volume analysis for higher probability trades • Use appropriate stop-loss levels as the indicator does not guarantee price movements This indicator is provided as-is with no guarantees of profit. Always use proper risk management when trading with any technical indicator. Nothing is financial advise.
Chỉ báo Pine Script®
của AlphaNatt
3
Schaff Trend Cycle (STC)The STC (Schaff Trend Cycle) indicator is a momentum oscillator that combines elements of MACD and stochastic indicators to identify market cycles and potential trend reversals. Key features of the STC indicator: Oscillates between 0 and 100, similar to a stochastic oscillator Values above 75 generally indicate overbought conditions Values below 25 generally indicate oversold conditions Signal line crossovers (above 75 or below 25) can suggest potential entry/exit points Faster and more responsive than traditional MACD Designed to filter out market noise and identify cyclical trends Traders typically use the STC indicator to: Identify potential trend reversals Confirm existing trends Generate buy/sell signals when combined with other technical indicators Filter out false signals in choppy market conditions This STC implementation includes multiple smoothing options that act as filters: None: Raw STC values without additional smoothing, which provides the most responsive but potentially noisier signals. EMA Smoothing: Applies a 3-period Exponential Moving Average to reduce noise while maintaining reasonable responsiveness (default). Sigmoid Smoothing: Transforms the STC values using a sigmoid (S-curve) function, creating more gradual transitions between signals and potentially reducing whipsaw trades. Digital (Schmitt Trigger) Smoothing: Creates a binary output (0 or 100) with built-in hysteresis to prevent rapid switching. The STC indicator uses dynamic color coding to visually represent momentum: Green: When the STC value is above its 5-period EMA, indicating positive momentum Red: When the STC value is below its 5-period EMA, indicating negative momentum The neutral zone (25-75) is highlighted with a light gray fill to clearly distinguish between normal and extreme readings. Alerts: Bullish Signal Alert: The STC has been falling It bottoms below the 25 level It begins to rise again This pattern helps confirm potential uptrend starts with higher reliability. Bearish Signal Alert: The STC has been rising It peaks above the 75 level It begins to decline This pattern helps identify potential downtrend starts.
Chỉ báo Pine Script®
của mihakralj
Elastic Volume-Weighted Student-T TensionOverview The Elastic Volume-Weighted Student-T Tension Bands indicator dynamically adapts to market conditions using an advanced statistical model based on the Student-T distribution. Unlike traditional Bollinger Bands or Keltner Channels, this indicator leverages elastic volume-weighted averaging to compute real-time dispersion and location parameters, making it highly responsive to volatility changes while maintaining robustness against price fluctuations. This methodology is inspired by incremental calculation techniques for weighted mean and variance, as outlined in the paper by Tony Finch: 📄 "Incremental Calculation of Weighted Mean and Variance" . Key Features ✅ Adaptive Volatility Estimation – Uses an exponentially weighted Student-T model to dynamically adjust band width. ✅ Volume-Weighted Mean & Dispersion – Incorporates real-time volume weighting, ensuring a more accurate representation of market sentiment. ✅ High-Timeframe Volume Normalization – Provides an option to smooth volume impact by referencing a higher timeframe’s cumulative volume, reducing noise from high-variability bars. ✅ Customizable Tension Parameters – Configurable standard deviation multipliers (σ) allow for fine-tuned volatility sensitivity. ✅ %B-Like Oscillator for Relative Price Positioning – The main indicator is in form of a dedicated oscillator pane that normalizes price position within the sigma ranges, helping identify overbought/oversold conditions and potential momentum shifts. ✅ Robust Statistical Foundation – Utilizes kurtosis-based degree-of-freedom estimation, enhancing responsiveness across different market conditions. How It Works Volume-Weighted Elastic Mean (eμ) – Computes a dynamic mean price using an elastic weighted moving average approach, influenced by trade volume, if not volume detected in series, study takes true range as replacement. Dispersion (eσ) via Student-T Distribution – Instead of assuming a fixed normal distribution, the bands adapt to heavy-tailed distributions using kurtosis-driven degrees of freedom. Incremental Calculation of Variance – The indicator applies Tony Finch’s incremental method for computing weighted variance instead of arithmetic sum's of fixed bar window or arrays, improving efficiency and numerical stability. Tension Calculation – There are 2 dispersion custom "zones" that are computed based on the weighted mean and dynamically adjusted standard student-t deviation. %B-Like Oscillator Calculation – The oscillator normalizes the price within the band structure, with values between 0 and 1: * 0.00 → Price is at the lower band (-2σ). * 0.50 → Price is at the volume-weighted mean (eμ). * 1.00 → Price is at the upper band (+2σ). * Readings above 1.00 or below 0.00 suggest extreme movements or possible breakouts. Recommended Usage For scalping in lower timeframes, it is recommended to use the fixed α Decay Factor, it is in raw format for better control, but you can easily make a like of transformation to N-bar size window like in EMA-1 bar dividing 2 / decayFactor or like an RMA dividing 1 / decayFactor. The HTF selector catch quite well Higher Time Frame analysis, for example using a Daily chart and using as HTF the 200-day timeframe, weekly or monthly. Suitable for trend confirmation, breakout detection, and mean reversion plays. The %B-like oscillator helps gauge momentum strength and detect divergences in price action if user prefer a clean chart without bands, this thanks to pineScript v6 force overlay feature. Ideal for markets with volume-driven momentum shifts (e.g., futures, forex, crypto). Customization Parameters Fixed α Decay Factor – Controls the rate of volume weighting influence for an approximation EWMA approach instead of using sum of series or arrays, making the code lightweight & computing fast O(1). HTF Volume Smoothing – Instead of a fixed denominator for computing α , a volume sum of the last 2 higher timeframe closed candles are used as denominator for our α weight factor. This is useful to review mayor trends like in daily, weekly, monthly. Tension Multipliers (±σ) – Adjusts sensitivity to dispersion sigma parameter (volatility). Oscillator Zone Fills – Visual cues for price positioning within the cloud range. Posible Interpretations As market within indicators relay on each individual edge, this are just some key ideas to glimpse how the indicator could be interpreted by the user: 📌 Price inside bands – Market is considered somehow "stable"; price is like resting from tension or "charging batteries" for volume spike moves. 📌 Price breaking outer bands – Potential breakout or extreme movement; watch for reversals or continuation from strong moves. Market is already in tension or generating it. 📌 Narrowing Bands – Decreasing volatility; expect contraction before expansion. 📌 Widening Bands – Increased volatility; prepare for high probability pull-back moves, specially to the center location of the bands (the mean) or the other side of them. 📌 Oscillator is just the interpretation of the price normalized across the Student-T distribution fitting "curve" using the location parameter, our Elastic Volume weighted mean (eμ) fixed at 0.5 value. Final Thoughts The Elastic Volume-Weighted Student-T Tension indicator provides a powerful, volume-sensitive alternative to traditional volatility bands. By integrating real-time volume analysis with an adaptive statistical model, incremental variance computation, in a relative price oscillator that can be overlayed in the chart as bands, it offers traders an edge in identifying momentum shifts, trend strength, and breakout potential. Think of the distribution as a relative "tension" rubber band in which price never leave so far alone. DISCLAIMER: The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets. The following indicator was made for NON LUCRATIVE ACTIVITIES and must remain as is, following TradingView's regulations. Use of indicator and their code are published for work and knowledge sharing. All access granted over it, their use, copy or re-use should mention authorship(s) and origin(s). WARNING NOTICE! THE INCLUDED FUNCTION MUST BE CONSIDERED FOR TESTING. The models included in the indicator have been taken from open sources on the web and some of them has been modified by the author, problems could occur at diverse data sceneries, compiler version, or any other externality.
Chỉ báo Pine Script®
của xel_arjona
26
ALMA 20, 50, 200The ALMA (Arnaud Legoux Moving Average) crossover strategy uses two ALMA lines (fast and slow) to generate buy/sell signals, aiming to reduce lag and noise compared to traditional moving averages, and is often combined with volume filters for improved accuracy. Here's a more detailed explanation: What it is: The ALMA indicator is a moving average (MA) variant designed to reduce lag and improve responsiveness while maintaining a smooth curve, using a Gaussian filter. How it works: ALMA calculates two moving averages, one from left to right and one from right to left, and then processes the output through a customizable formula for increased smoothness or responsiveness. Crossover Strategy: A common ALMA strategy involves using two ALMA lines with different lengths (fast and slow). A buy signal is generated when the fast ALMA crosses above the slow ALMA, and a sell signal when the fast ALMA crosses below the slow ALMA. Benefits: ALMA offers advantages like reduced lag, smoothness, and filtering capabilities, making it useful for identifying trends and potential reversals. Potential Risks: Like any indicator, ALMA can produce false signals, so it's crucial to combine it with other indicators and analyze price action. Parameters: ALMA has parameters like "Length" (number of periods), "Sigma" (filter's range, affecting responsiveness), and "Offset" (for accessing data of different candles). Other uses: ALMA can also be used for trend identification, dynamic support and resistance, and combined with other indicators to enhance trading strategies.
Chỉ báo Pine Script®
của subhranami1968
Adaptive Trend FinderAdaptive Trend Finder - The Ultimate Trend Detection Tool Introducing Adaptive Trend Finder, the next evolution of trend analysis on TradingView. This powerful indicator is an enhanced and refined version of Adaptive Trend Finder (Log), designed to offer even greater flexibility, accuracy, and ease of use. What’s New? Unlike the previous version, Adaptive Trend Finder allows users to fully configure and adjust settings directly within the indicator menu, eliminating the need to modify chart settings manually. A major improvement is that users no longer need to adjust the chart's logarithmic scale manually in the chart settings; this can now be done directly within the indicator options, ensuring a smoother and more efficient experience. This makes it easier to switch between linear and logarithmic scaling without disrupting the analysis. This provides a seamless user experience where traders can instantly adapt the indicator to their needs without extra steps. One of the most significant improvements is the complete code overhaul, which now enables simultaneous visualization of both long-term and short-term trend channels without needing to add the indicator twice. This not only improves workflow efficiency but also enhances chart readability by allowing traders to monitor multiple trend perspectives at once. The interface has been entirely redesigned for a more intuitive user experience. Menus are now clearer, better structured, and offer more customization options, making it easier than ever to fine-tune the indicator to fit any trading strategy. Key Features & Benefits Automatic Trend Period Selection: The indicator dynamically identifies and applies the strongest trend period, ensuring optimal trend detection with no manual adjustments required. By analyzing historical price correlations, it selects the most statistically relevant trend duration automatically. Dual Channel Display: Traders can view both long-term and short-term trend channels simultaneously, offering a broader perspective of market movements. This feature eliminates the need to apply the indicator twice, reducing screen clutter and improving efficiency. Fully Adjustable Settings: Users can customize trend detection parameters directly within the indicator settings. No more switching chart settings – everything is accessible in one place. Trend Strength & Confidence Metrics: The indicator calculates and displays a confidence score for each detected trend using Pearson correlation values. This helps traders gauge the reliability of a given trend before making decisions. Midline & Channel Transparency Options: Users can fine-tune the visibility of trend channels, adjusting transparency levels to fit their personal charting style without overwhelming the price chart. Annualized Return Calculation: For daily and weekly timeframes, the indicator provides an estimate of the trend’s performance over a year, helping traders evaluate potential long-term profitability. Logarithmic Adjustment Support: Adaptive Trend Finder is compatible with both logarithmic and linear charts. Traders who analyze assets like cryptocurrencies, where log scaling is common, can enable this feature to refine trend calculations. Intuitive & User-Friendly Interface: The updated menu structure is designed for ease of use, allowing quick and efficient modifications to settings, reducing the learning curve for new users. Why is this the Best Trend Indicator? Adaptive Trend Finder stands out as one of the most advanced trend analysis tools available on TradingView. Unlike conventional trend indicators, which rely on fixed parameters or lagging signals, Adaptive Trend Finder dynamically adjusts its settings based on real-time market conditions. By combining automatic trend detection, dual-channel visualization, real-time performance metrics, and an intuitive user interface, this indicator offers an unparalleled edge in trend identification and trading decision-making. Traders no longer have to rely on guesswork or manually tweak settings to identify trends. Adaptive Trend Finder does the heavy lifting, ensuring that users are always working with the strongest and most reliable trends. The ability to simultaneously display both short-term and long-term trends allows for a more comprehensive market overview, making it ideal for scalpers, swing traders, and long-term investors alike. With its state-of-the-art algorithms, fully customizable interface, and professional-grade accuracy, Adaptive Trend Finder is undoubtedly one of the most powerful trend indicators available. Try it today and experience the future of trend analysis. This indicator is a technical analysis tool designed to assist traders in identifying trends. It does not guarantee future performance or profitability. Users should conduct their own research and apply proper risk management before making trading decisions. // Created by Julien Eche - @Julien_Eche
Chỉ báo Pine Script®
của Julien_Exe
26