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波动止损(VS)

ATR Ticks with MultiplierThis is a simple label "indicator" that allows you to easily view the current ATR(x) and get a multiplier of the ATR for stop loss and take profit target. This allows you to quickly adjust your brackets based on high or low volatility conditions. For example, if the ATR(5) is 10 points on the NQ, your multiplier stop loss is 1.25, your multiplier for take profit is 2.5 (2R), the ticks show will be 50 for the stop loss and 100 for the take profit.
Pine Script®指标
由marketsurvivalist提供
Ultimate PA Confirm + Auto TP/SL (M5/M15) by TURBO TRADERKey Features M5 / M15 Price Action Detection: Automatically scans and detects high-probability Bullish/Bearish Engulfing and Pin Bar patterns on lower timeframes. 1-Bar Confirmation Logic (No Repaint): Signals are strictly calculated and printed on the open of the next candle after the confirmation bar closes. This guarantees 100% fixed, non-repainting signals. Dynamic Auto TP1 / TP2 Levels: Profit targets are entirely volatility-based, calculated dynamically using the actual measured range (High to Low) of the trigger Price Action candle. SL Buffer (+300 Pips/Points): Automatically places the Stop Loss 300 pips/points below the low (for Buys) or above the high (for Sells) of the PA candle. This structural buffer protects your trades from market noise and stop hunts. Visuals on the Chart Once applied, the indicator projects 3 dynamic, color-coded levels on your chart: 🔴 Red Line = Stop Loss (SL with buffer) 🟢 Green Line = Take Profit 1 (TP1) 🔵 Blue Line = Take Profit 2 (TP2) All levels are recalculated and locked instantly whenever a fresh "BUY CONFIRM" or "SELL CONFIRM" printed signal occurs. Fully Customizable Settings Simply double-click the indicator on your chart to open the Settings menu. From there, you can fine-tune: Candle Proportions: Adjust body-to-wick ratios to make the Price Action scanner more strict or aggressive. Risk-to-Reward Multipliers: Modify TP1 and TP2 targets. SL Buffer Size: Scale the 300-pip default buffer up or down depending on current market volatility or the asset's spread. Built to handle high-velocity markets—fast execution, zero lag!
Pine Script®指标
由TurboTHTrader提供
Pvt Fibo S&R to Target 12Pvt Fibo S&R to Target 12 is an institutional-grade breakout and automated target management engine built completely on classical Daily/Weekly/Monthly Pivot Points and Fibonacci Support/Resistance (S/R) levels. Instead of chasing lagging signals, this script monitors the heavy institutional liquidity pools yoked tightly to major horizontal key levels. By processing live price action through three selectable algorithmic breakout frameworks simultaneously, it provides non-repainting, highly optimized breakout signals coupled with an automated 2-tier take-profit sequence. 🎯 The 3 Multi-Mode Algorithmic Breakout Engines To adapt to varying market environments, the script operates 3 distinct breakout styles (all active by default to maximize strategic flexibility): Mode 1 (Volume & Candle Anatomy Expansion): Triggers only when the main pivot line is violated by a candle with significant volume (surpassing its 20-period SMA) and a tightly packed, dominant body ratio (Marubozu style). This mode filters out low-volume institutional retail traps (fakeouts). Mode 2 (Direct Raw Momentum Breakout): Fires immediately upon a raw crossover/crossunder of the key horizontal line. It completely strips away filters to capitalize on sudden, news-driven, hyper-aggressive market expansions where speed is paramount. Mode 3 (Safe-Zone Hold Confirmation): A strict trend-verification system designed for conservative execution. Rather than reacting to the initial breach, it tracks market absorption by requiring the price to successfully close and hold on the breakout side for X consecutive bars without slipping back across the line. 💰 Automated Target Lifecycle Management (Target 1-2) The script is natively engineered with a stateful order sequence controller optimized for webhook routers like WunderTrading. It eliminates manual intervention by actively trailing the position's lifecycle: The Entry: When any selected Mode condition is met, an entry signal (L1/L2/L3 or S1/S2/S3) prints visually on the chart and triggers the entry webhook message. Target 1 (Partial Take-Profit): The exact moment the live price breaches the first major Fibonacci extension line (R1 for Longs, S1 for Shorts), a TARGET-1 alert fires. This allows automated bots to scale out partially or move stops to break-even. Target 2 (Take-Profit & Position Liquidation): When the price strikes the primary institutional target line (R2 for Longs, S2 for Shorts), a TARGET-2 alert fires. Simultaneously, the internal position script state completely resets to zero, terminating the trade lifecycle securely without needing to wait for a lagging opposite signal. 🛠️ Key UI Parameters for Fine-Tuning Pivot Calculation Period: Shift smoothly between D (Daily), W (Weekly), or M (Monthly) horizontal levels. Daily/Weekly lines are highly recommended for intraday timeframes. Volume Multiplier: Increase this value (e.g., 1.8 or 2.0) to restrict Mode 1 signals only to massive, institutional-sized volume anomalies. Min Candle Body Ratio (%): Filter out indecisive dojis or long-wick candles by demanding a solid, determined candle structure during breakout tests. Safe-Zone Bar Count (X): Adjust the number of consecutive candle closes required for Mode 3 validation before entering. ⚡ Best Testing Environments & Timeframes Top Performing Timeframes: * 15-Minute (15m) and 1-Hour (1h) charts provide the ultimate balance between asset noise reduction and massive intra-week trend capture. 5-Minute (5m) charts work excellent for scalpers, provided you raise the Volume Multiplier to counter micro-fakeouts. Optimal Trading Assets: * Cryptocurrencies: High-momentum majors (BTC, ETH) that experience explosive expansion out of tight sideways consolidation ranges. Indices & Commodities: NASDAQ (NAS100), S&P500, and GOLD (XAU/USD) due to their aggressive trend continuity when major daily key levels break. Forex Major Pairs: EUR/USD, GBP/USD, and USD/JPY during high-liquidity New York and London session overlaps.
Pine Script®指标
由lisabyco提供
SuperTrend LiteClassic SuperTrend overlay with trend-based coloring and BUY/SELL entry labels. ▲ BUY = SuperTrend flipped bullish (go long) ▼ SELL = SuperTrend flipped bearish (go short / exit) Green fill = bullish zone. Red fill = bearish zone. Fixed settings: ATR(10), multiplier 3.0 — the most commonly used SuperTrend parameters. This is the Lite version. The Pro version adds: • ATR Trailing Stop with ratchet mechanism — dynamic stop-loss that never moves against you • Stop-hit & re-entry detection — know when your stop is taken and when to get back in • Real-time info table (trend direction, stop distance %, ATR value) • Phone push alerts — 4 alert conditions (entry, stop-hit, re-entry, trend change) • Adjustable ATR periods, multipliers, line styles, display modes • 2-bar confirmation filter to reduce whipsaws → Read the full review at theindicatorlab.com/reviews/supertrend-atr-trailing-stop/
Pine Script®指标
由theindicatorlab提供
Apex Volatility Flow [Pineify]Apex Volatility Flow ATR Chandelier Oscillator Apex Volatility Flow converts ATR-based Chandelier behavior into a normalized 0-100 oscillator. It tracks flow above or below 50, and marks ATR contraction when volatility falls below a longer baseline. Key Features Chandelier-style direction logic using extremes and ATR distance. Bullish/bearish oscillator coloring around the 50-line bias filter. Squeeze dots and alerts for compression and flow crosses. How It Works The script begins with Average True Range . A long reference uses recent highs minus ATR times the multiplier, while a short reference uses recent lows plus ATR times the multiplier. The anchors blend close-based and wick extremes to reduce one-bar distortion. A close above the short reference is bullish; a close below the long reference is bearish. The active level is tracked, then its range resets when direction changes. The active level is normalized, smoothed with an SMA, and compared with 50. A squeeze appears when ATR is below 80% of a longer ATR baseline. How the Components Work Together The Chandelier logic supplies trend context, while the oscillator scale helps compare charts. Squeeze dots stay separate because compression can break either way; read them with flow crosses, 50-line retests, and price structure. Trading Ideas and Insights Bullish continuation may be worth studying when flow holds above 50 and compression releases. Bearish crosses below 50 can whipsaw in strong uptrends; follow-through matters. During extended squeezes, confirm direction with structure, volume, or higher-timeframe trend. Unique Aspects It turns Chandelier-style stops into a compact oscillator instead of a price overlay. Normalization resets on direction changes, avoiding stale values from the prior move. How to Use Add the indicator to a liquid market and timeframe. Use 50 as the main bullish/bearish flow reference. Treat squeeze dots as compression context, not standalone entries. Use the built-in alert conditions for crosses or squeeze events. Customization Volatility Length (default: 22) - Controls ATR and anchors. Higher values smooth but react later. ATR Multiplier (default: 3.0) - Sets stop distance. Higher values reduce flips but delay regime changes. Oscillator Smoothing (default: 4) - Smooths the output. Higher values reduce noise but add lag. Colors - Adjust bullish, bearish, and squeeze marker colors. Conclusion Apex Volatility Flow is for traders who want ATR and Chandelier-style context in one oscillator pane. It can help organize flow bias and compression, but should be combined with structure and risk management. It uses no higher-timeframe security calls; live-bar values may still change before close.
Pine Script®指标
由Pineify提供
X-BreakX-Break — London Breakout Indicator X-Break is a London Session Breakout indicator built on the Asian Range. It automatically calculates the Asian session high and low (03:00–10:00 UTC+3), draws the range box, and waits for a strong breakout candle after the London open (11:00 UTC+3). How it works: Builds the Asian Range box every day automatically Looks for breakout signals only after London open Confirms signals on candle close only — no repaint Filters weak breakouts using candle body strength Signal conditions: BUY: Close above Asia High with strong bullish body SELL: Close below Asia Low with strong bearish body Built-in filters: Min body % of bar: 60% Min body vs Asia Range: 30% Optional EMA trend filter Optional ATR body filter Risk Management: TP and SL calculated automatically Multiple SL modes: AsiaRange · Asia Edge · ATR · % of Price · Points Supports single R:R or split TP/SL mode Best timeframe: 15 minutes Best markets: US100 · US30 · US500 · XAUUSD · BTCUSD Alerts: Built-in BUY and SELL alerts — set to Once Per Bar Close
Pine Script®指标
由Mohdsal提供
Strategy Sensitivity MatrixThe Strategy Sensitivity Matrix is an institutional-grade backtesting tool designed to evaluate the robustness and parameter sensitivity of trend-following strategies. It enables users to compare the historical performance of a broad range of parameter combinations across multiple metrics to assess the overall stability of the selected strategy. The model displays the complete backtest landscape in a structured, color-coded matrix that allows investors to quickly identify robust parameter regions and evaluate historical performance stability across parameter combinations. At its core, the matrix systematically evaluates a wide range of parameter combinations, where every individual cell represents the backtest result for one unique parameter configuration. Users can switch between volatility-based strategies and moving-average strategies. In volatility mode, the matrix rows represent volatility lengths and the matrix columns represent volatility factors. In crossover mode, the rows represent fast moving-average lengths and the columns represent slow moving-average lengths. Supported volatility types include the Average True Range (ATR), Standard Deviation (SD), and Mean Absolute Deviation (MAD). Supported moving-average types include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), and Weighted Moving Average (WMA). Supported display metrics include: CAGR = Compounded Annual Growth Rate. Sharpe = CAGR per unit of standard deviation. Sortino = CAGR per unit of downside deviation. Martin = CAGR relative to the Ulcer Index (UI). Calmar = CAGR relative to maximum drawdown. Max DD = Largest peak-to-trough decline in value. Alpha (α) = Excess annualized risk-adjusted returns. Expectancy = Average expected return per trade. Profit Factor = Total gross profit per unit of losses. Win Rate = Ratio of profitable trades to total trades. Trades/Year = Average number of trades per year. The matrix follows an intuitive percentile-based coloring framework that dynamically compares the relative performance and stability of all parameter combinations. Stronger values above or equal to the matrix median are highlighted in green, with bright green representing the top 10% of all parameter combinations. Weaker values below the matrix median are highlighted in orange, while red represents objectively weak performance based on the selected metric. Broad clusters of consistently strong results generally suggest lower parameter sensitivity and potentially greater robustness, while isolated peaks generally suggest elevated parameter sensitivity. The summary table displayed above the matrix provides a broader distribution-level statistical overview of results across all parameter combinations. This structure allows investors to evaluate whether strong historical performance appears statistically widespread or narrowly concentrated across the parameter landscape. Stable parameter landscapes generally exhibit lower standard deviation, similar median and average values, and smaller performance gaps between the best and top 10% parameter combinations. The summary table includes the following sections: Start = Start month and year of the selected backtest period. End = End month and year of the selected backtest period. Metric = Performance metric currently displayed in the matrix. B&H = Buy-and-hold performance for the selected metric. Best = Best-performing parameter combination in the matrix. Top 10% = Average value of the top 10% parameter combinations. Median = Median value across all parameter combinations. Average = Average value across all parameter combinations. Std Dev = Standard deviation of all parameter combinations. ≥ B&H = Percentage of combinations equal or better than B&H. In summary, the Strategy Sensitivity Matrix is a powerful robustness analysis tool designed to help investors make data-driven decisions when evaluating parameter combinations across trend-following strategies. By evaluating the full parameter landscape, investors can quickly determine whether strong historical performance appears broadly distributed across stable parameter regions or narrowly concentrated within isolated parameter combinations. While historical robustness can provide valuable insight into past market behavior over the selected backtest period, users should remain mindful that market structures evolve over time and that historically stable parameter regions may not necessarily persist in future market conditions.
Pine Script®指标
由QuantitativeAlpha提供
NIFTY Expected Range (VIX based)NIFTY VIX Expected Range This indicator calculates the expected monthly trading range for the NIFTY50 index using INDIA VIX volatility data. The script uses the following formula to estimate the implied percentage move: INDIAVIX / (Sqrt(12)) ​ The calculated value represents the expected percentage range for NIFTY based on current market volatility. How It Works Takes the current day’s NIFTY opening price Fetches live INDIA VIX data Calculates the expected volatility range Plots: Upper expected range Lower expected range Opening price reference line Highlights price action moving outside the implied range using a light red background Example If: NIFTY Open = 24000 INDIA VIX = 16 Then: 16 / (sqrt(12) ≈ 4.62% Expected range becomes: Upper Range = 25,108.8 Lower Range = 22,891.2 Features Real-time INDIA VIX integration Automatic daily range recalculation Visual upper/lower boundary levels Volatility-based market context Clean overlay directly on chart Use Cases Identifying statistically stretched price moves Monitoring volatility expansion Setting directional targets Risk management and position sizing Option selling and volatility trading strategies Notes This indicator is based on implied volatility expectations, not guaranteed support/resistance levels. Best used as a probabilistic framework alongside price action and market structure. Perfect for traders who want a simple visual representation of volatility-implied NIFTY ranges directly on their charts.
Pine Script®指标
由pranavkojha提供
Volatility Stop SelectorThe Volatility Stop Selector is a comprehensive trend-following tool designed to automatically identify the optimal volatility stop strategy. It features adjustable parameters and an integrated backtester that delivers institutional-grade insights into the recommended strategy. The model continuously adapts to new data in real time by evaluating multiple volatility length and factor combinations, determining the best-performing configuration, and presenting the backtest results in a clear, color-coded table that benchmarks performance against the buy-and-hold strategy. At its core, the model systematically backtests a wide range of volatility stop combinations to identify the configuration that maximizes the selected optimization metric. Users can choose to optimize for absolute returns or risk-adjusted returns using metrics such as the Sharpe, Sortino, Martin, or Calmar ratios. The Martin ratio is particularly well suited for volatility-based risk management strategies, as it evaluates returns relative to the Ulcer Index, capturing both the depth and duration of drawdowns and therefore favoring smoother equity curves. Alternatively, users can enable manual optimization to test custom volatility length and factor settings and view the corresponding backtest results. The label displays the Compounded Annual Growth Rate (CAGR) of the strategy, with the buy-and-hold CAGR in parentheses for comparison. The table presents the backtest results based on the volatility length and factor displayed at the top: Sharpe = CAGR per unit of standard deviation. Sortino = CAGR per unit of downside deviation. Calmar = CAGR relative to maximum drawdown. Max DD = Largest peak-to-trough decline in value. Beta (β) = Return sensitivity relative to buy-and-hold. Alpha (α) = Excess annualized risk-adjusted returns. Win Rate = Ratio of profitable trades to total trades. Profit Factor = Total gross profit per unit of losses. Expectancy = Average expected return per trade. Trades/Year = Average number of trades per year. This indicator is designed with flexibility in mind, enabling users to specify the start date of the backtesting period, the preferred volatility type, and the price source. Supported volatility types include the Average True Range (ATR), Standard Deviation (SD), and Mean Absolute Deviation (MAD). Supported price sources include Close, Heikin Ashi, HL2, HLC3, and OHLC4. To minimize overfitting, users can define constraints such as a minimum and maximum number of trades per year, as well as an optional optimization margin that prioritizes more robust combinations by requiring more reactive combinations to exceed this threshold. The table follows an intuitive color-coded logic that enables quick performance comparison against buy-and-hold (B&H): Sharpe = Green indicates better than B&H, while red indicates worse. Sortino = Green indicates better than B&H, while red indicates worse. Calmar = Green indicates better than B&H, while red indicates worse. Max DD = Green indicates better than B&H, while red indicates worse. Beta (β) = Green indicates better than B&H, while red indicates worse. Alpha (α) = Green indicates above 0%, while red indicates below 0%. Win Rate = Green indicates above 50%, while red indicates below 50%. Profit Factor = Green indicates above 2, while red indicates below 1. Expectancy = Green indicates above 0%, while red indicates below 0%. In summary, the Volatility Stop Selector is a powerful tool designed to help investors make data-driven decisions when selecting volatility-based trend-following strategies. By optimizing for risk-adjusted returns, investors can identify the best configurations using institutional-grade metrics. While results are based on the selected historical period, users should be mindful of overfitting, as past results may not persist under future market conditions. Since the model continuously recalibrates to incorporate new data, the recommended length and factor may evolve over time.
Pine Script®指标
由QuantitativeAlpha提供
1
SPY VVIX VIX Buy Hold Sell SystemSPY VVIX VIX Buy Hold Sell System is a volatility-regime-based decision tool designed to help traders classify SPY conditions into BUY, HOLD, or SELL / DEFEND. The system uses VVIX, VIX, SPY trend, and a Range Filter to identify when volatility risk is calm, rising, or dangerous. BUY SPY: This signal appears when SPY is in a bullish trend, the Range Filter confirms upward movement, VVIX is calm and falling, and VIX is not rising. This suggests volatility risk is contained and long SPY exposure is favored. HOLD: This signal means the SPY trend remains constructive, but the volatility backdrop is not strong enough for aggressive fresh buying. Existing positions may be held, but caution is advised. SELL / DEFEND: This signal appears when VVIX rises strongly, VIX confirms risk expansion, VVIX reaches danger or panic levels, or SPY trend weakens. Traders may consider selling SPY, reducing exposure, tightening stops, or defending with hedges such as puts or collars. VVIX is treated as an early warning indicator because it measures the volatility of volatility. Rising VVIX can warn that the options market is preparing for future volatility expansion before SPY fully reacts. This script is intended as a decision-support tool, not financial advice. Use with risk management, position sizing, and additional confirmation.
Pine Script®指标
由asdpwr提供
Apex First-Touch PredictorApex First-Touch Predictor — Which Zone Gets Hit First? A multi-factor framework for the question every SMC trader actually asks: when price sits between an Order Block and a Fair Value Gap, which one gets touched first? The Problem This Indicator Was Built To Solve You spot a clean setup on the 5m chart. There's an unmitigated Order Block above. There's a Fair Value Gap below. Both are valid draws. Both are well-formed. So which one does price hit first? This is the question that costs SMC and ICT traders more money than any other. You enter long, expecting the OB to get tagged — price takes out the FVG instead, stops you out, then runs to the OB without you. Or the reverse. The setup wasn't wrong. The sequence was wrong. Most SMC indicators draw the zones beautifully but tell you nothing about which side resolves first. This one tries to. What It Does The First-Touch Predictor analyzes six independent factors to estimate which zone — upper or lower — price is more likely to touch first from its current position: Higher-Timeframe Bias (BOS / CHoCH detection on HTF-1 and HTF-2) Premium / Discount Position in the HTF dealing range Liquidity Draw (composite of equal highs/lows, prior day H/L, zone proximity) Session / Killzone Strength (NY AM, London, NY PM, Asia, Lunch) Recent Structural Displacement (signed body/range over last 3 bars) Volatility Regime (ATR expansion vs contraction) Each factor produces a signed vote in . The weighted aggregate produces a directional call with a confidence estimate (capped at 75% — no market prediction deserves higher). A Strong-Liquidity Override kicks in when one side has heavily stacked liquidity within reasonable reach. This encodes the well-known ICT principle that price tends to sweep obvious liquidity before HTF bias plays out. What You'll See On Your Chart Auto-detected Order Blocks with text labels (Bull OB / Bear OB) pinned to the origin candle Auto-detected Fair Value Gaps with text labels (Bull FVG / Bear FVG) pinned to the imbalance bars Premium / Discount zones with explicit text markings: Heavy Premium, Premium, EQ (50%), Discount, Heavy Discount — drawn as faint background bands Directional arrow on the current bar pointing toward the predicted first-touch zone Dashed target line marking the midpoint of the predicted zone Info panel at middle-right showing First Target, Confidence, HTF Bias, P/D Zone, Session, Liquidity, Target Mid, and Raw Score Tooltips on every panel row explaining what the metric means and how to interpret it A ⚡ icon appears next to the Liquidity row when the override rule is active. How To Use It This is a bias-confirmation tool, not a signal generator. The intended workflow: Identify a setup yourself using your own framework Check the panel — does the directional call agree with your read? When your analysis and the indicator agree: higher conviction setup, consider standard sizing When they disagree: stand aside or wait for additional confluence Always pre-define both scenarios — entry, stop, and target for if first-touch goes upper and if it goes lower. The indicator's job is to bias which scenario is more likely; your job is execution. The Confidence number is meant to inform position sizing, not certainty. 65–75% means the factors are aligned. 52–58% means thin edge. Below 52% means stand aside. Configurable Inputs HTF-1 (Bias) timeframe — default 60m HTF-2 (Regime) timeframe — default 240m Swing pivot length, max zones per type, liquidity tolerance Toggle every visual element independently (zones, labels, P/D zones, panel, arrow, target line) All six factor weights are exposed and tunable Strong-Liquidity Override on/off Color customization (Material palette by default, color-blind friendly) Built-In Alerts High-Confidence UP Target (score crosses above +0.60) High-Confidence DOWN Target (score crosses below -0.60) Directional Flip (score crosses zero) Honest Limitations You Should Know I built this to be useful, not to oversell. Here's what it cannot do: It is not predictive in any guaranteed sense. No indicator is. Markets are probabilistic; this tool tries to score probability, not prophesy outcomes. It does not see news. Major economic events (CPI, FOMC, NFP) will override any structural read. Use an economic calendar alongside. The factor weights are reasoned, not statistically optimized. Future versions may include backtested weight tuning. Order Block detection is simplified. It catches the most common BOS-origin OB pattern; institutional-grade detection requires more nuance. Confidence is capped at 75% by design. If you ever see a tool claim 85%+ confidence on directional calls, be skeptical. Best on liquid instruments during major sessions. Performance degrades in low-volume hours and illiquid pairs. Why I'm Releasing This Free This is part of a broader project on multi-factor SMC analysis. I'd rather have thousands of traders stress-testing it on real charts and giving feedback than charge a few people for an unrefined tool. If you find it useful, drop a comment with what's working, what's not, or what factor you'd like to see added. That feedback shapes future versions. Best Timeframes & Markets Designed for intraday trading on the 5m, 15m, and 1H charts. Works on: Index futures (ES, NQ, YM) Major FX pairs (EURUSD, GBPUSD, USDJPY, etc.) Liquid crypto (BTC, ETH) High-volume stocks during regular trading hours Less reliable on low-volume pairs, illiquid stocks, or weekend crypto. Credits & Methodology Built on Smart Money Concepts (SMC) and Inner Circle Trader (ICT) methodology — specifically: Order Blocks, Fair Value Gaps, Premium/Discount theory, liquidity engineering, killzone timing, and displacement analysis. The weighted multi-factor approach is original; the underlying concepts are not. Disclaimer This indicator is provided for educational and analytical purposes only. It is not financial advice. Past performance of any market structure does not guarantee future results. Always use proper risk management. Trade your own analysis, not any single indicator's output. Engagement Boost the script if it earns its place on your chart. Drop a comment with your timeframe, instrument, and what's working or breaking. I read every comment and the feedback shapes the next version.
Pine Script®指标
由arunsinghhelios提供
Shannon Entropy Terrain 3D [LliterH]SHANNON ENTROPY TERRAIN 3D ───────────────────────────────────────────────────────────────────── Most market regime filters give you a single number. This indicator gives you a landscape. Shannon Entropy of price log-returns is computed simultaneously across 20 different lookback windows and rendered as a 3D thermal terrain surface. Each layer of that surface answers the same question with a different time horizon: is the market currently structured or random? The result is a visual map that reveals what a flat oscillator cannot — whether the order or chaos you see in one timeframe is isolated or systemic. THE MATHEMATICS ───────────────────────────────────────────────────────────────────── Shannon Entropy quantifies the unpredictability of a system: H = -sum( p_i * log2(p_i) ) Applied to the distribution of price log-returns ln(close/close ) over a rolling window, H measures how randomly recent price behavior has been distributed — independent of direction or magnitude. This indicator normalizes H to a 0-100 scale using: H_norm = H / log2(N) * 100 where N is the number of histogram bins. Result interpretation: 0 Perfect order One directional move dominates the window 30 Order boundary Market is structured, patterns are reliable 50 Neutral plane Mixed regime, no clear consensus 70 Chaos boundary Market begins to lose predictability 100 Maximum chaos Returns distributed uniformly at random READING THE TERRAIN ───────────────────────────────────────────────────────────────────── The surface is colored by entropy VALUE at each point, not by depth. This means color always communicates information — not decoration. Deep navy to cyan Entropy 0-30 Order zone Cyan to warm yellow Entropy 30-50 Transitioning toward order Yellow to orange Entropy 50-70 Transitioning toward chaos Orange to crimson Entropy 70-100 Chaos zone Two floating wireframe rectangles mark the critical thresholds: Cyan frame at 30 The order boundary Red frame at 70 The chaos boundary When the terrain surface breaches a frame, the market has crossed a regime threshold across that lookback range. The left wall shows the entropy profile of the oldest bar in view. The right wall shows the live entropy profile of the current bar. The label at the right edge displays two readings: LOCAL Short lookback layer — fast, reactive to recent bars MACRO Long lookback layer — slow, structural regime PRACTICAL READING GUIDE ───────────────────────────────────────────────────────────────────── The most valuable insight this indicator provides is CONSENSUS. SCENARIO 1 — All layers cold (terrain below neutral plane, blue-cyan) The market is ordered across every time horizon simultaneously. Short lookback and long lookback agree: structure is present. This is the highest-conviction environment for any systematic strategy. Trend-following entries have high reliability. Range plays are clean. Set-and-forget methodology works in this condition. SCENARIO 2 — All layers hot (terrain above 70% frame, orange-red) The market is noisy across every time horizon simultaneously. No lookback window finds structure. Returns are near-random. Breakouts are likely false. Stop hunts increase in frequency. The correct response is to reduce position size or stand aside. This condition often precedes or follows major news events. SCENARIO 3 — Front cold, back cold (local and macro both ordered) The strongest structural signal. The market has been trending long enough for even the longer lookback windows to confirm order. This is the ideal session condition for a London momentum setup. SCENARIO 4 — Front hot, back cold (local chaos, macro structure) A brief disruption within a structurally ordered trend. The long-term framework is intact. The short-term is noisy. Wait for the front layers to cool before entering. The entry timing is off, but the directional bias may still be valid. SCENARIO 5 — Front cold, back hot (local order, macro chaos) A short-term window of clarity within a chaotic structural backdrop. Trade with reduced size. The macro environment does not support clean follow-through. Tighter management required. SCENARIO 6 — Terrain crossing the 70% frame in real time Watch the right wall and the label. When the LOCAL reading crosses above 70 during a trade, consider whether the entry signal was generated during the chaos or before it. If after — reassess. HOW THIS BENEFITS YOUR TRADING ───────────────────────────────────────────────────────────────────── The fundamental problem in discretionary trading is not finding signals — it is knowing which signals to trust on a given day. A strategy with a 60% win rate in ordered markets may drop to 40% in chaotic markets. The signal logic does not change. The market regime does. This indicator addresses that problem directly by: 1. Showing regime quality BEFORE you look for an entry. If the terrain is red before your session opens, the day is compromised before it starts. You know this at 3:00 AM, not after two losing trades. 2. Showing whether the regime is LOCAL or SYSTEMIC. A brief spike in local entropy during an otherwise ordered session is different from full-spectrum chaos. The terrain makes this distinction visual and immediate. 3. Providing a historical map of the last 45 bars. You can see when the chaos started, whether it is intensifying or resolving, and how the current bar relates to the recent structural pattern. 4. Operating independently of direction. It does not tell you to buy or sell. It tells you whether the environment supports the signals your system generates. This separation is intentional and correct. PARAMETERS ───────────────────────────────────────────────────────────────────── Terrain Time History (Width) Bars displayed on X axis. Default 45. Entropy Layers (Depth) Lookback windows on Y axis. Default 18. Min Lookback (Front) Shortest window. Most reactive. Default 8. Max Lookback (Back) Longest window. Most structural. Default 55. Histogram Bins Entropy resolution. Default 10. Terrain Smoothing EMA smoothing per layer. Default 4. Camera Yaw Rotates the landscape left or right. Pitch Tilts the viewing angle up or down. Scale X, Y, Z Stretch each axis independently. Offset X, Y Reposition the terrain within the pane. Style Wireframe Grid overlay on or off. Surface Fill Filled terrain on or off. Side Walls Left and right entropy profile panels. Threshold Contour Frames 30% and 70% floating reference frames. Thermal palette All five color anchors are adjustable. METHODOLOGY ───────────────────────────────────────────────────────────────────── Input signal: natural log returns ln(close/close ). Log returns are scale-independent, symmetric, and stationary — the mathematically correct input for entropy analysis of financial time series. Histogram method: returns are binned into N equal-width intervals over the lookback window. Probabilities are relative frequencies. This is a direct implementation of Shannon's discrete entropy formula. Normalization: H divided by log2(N), the theoretical maximum entropy for N equiprobable bins. Output is always bounded in . Thermal color: each strip is colored by the mean entropy of its two constituent rows, not by its position in the Y axis. Color always encodes information value, not spatial location. Rendering: all drawing is performed exclusively on the last bar. The terrain matrix updates incrementally every bar for efficiency. WHAT MAKES THIS DIFFERENT ───────────────────────────────────────────────────────────────────── Existing 3D terrain indicators color by depth (Y axis position). That choice is arbitrary — the color tells you where you are in the structure, not what the data says at that point. This indicator colors by entropy value (Z axis). The thermal gradient is a direct encoding of the measurement. You read regime from color without consulting a separate legend. The floating threshold frames at 30% and 70% are 3D reference planes that the terrain can physically breach. This is not available in any other published terrain indicator. The side wall panels render the full entropy distribution across all lookback layers at a single point in time, functioning as entropy histograms projected in 3D space. Shannon Entropy applied to multi-lookback terrain is, to the author's knowledge, not represented in the published TradingView library. LIMITATIONS ───────────────────────────────────────────────────────────────────── This indicator measures regime quality, not direction. It does not generate buy or sell signals and should be used alongside a complete entry methodology. Histogram entropy with lookback windows below 8 bars approaches the Nyquist sampling limit and produces unreliable readings. The minimum lookback is constrained to 5 bars as a safeguard. Performance: the indicator is computationally intensive for TradingView's rendering engine. If responsiveness is slow, reduce Entropy Layers and Time History. Recommended maximums: 20 layers, 55 bars width. █ DISCLAIMER This indicator is provided for educational and informational purposes only. It does not constitute financial, investment, or trading advice. Past performance is not indicative of future results. Trading involves substantial risk of loss and is not suitable for every investor. Always conduct your own research and consult a qualified financial professional before making any trading decisions. The author is not responsible for any losses incurred from the use of this tool ───────────────────────────────────────────────────────────────────── Published under Mozilla Public License 2.0 © LliterH ─────────────────────────────────────────────────────────────────────
Pine Script®指标
由LliterH提供
Quantum Master EngineQuantum Master Engine Description: The Quantum Master Engine is an institutional-grade quantitative trading suite designed for scalpers and day traders. It unifies a multi-timeframe dashboard (HUD) with a highly advanced visual Strike Box execution system. Core Features: MTF Confluence Matrix: Natively processes up to four timeframes simultaneously, analyzing Hidden Markov Model (HMM) shifts, Volatility Regimes, and Macro Trend EMAs in a clean, non-intrusive HUD. Kinetic Strike Boxes: Visualizes entry triggers directly on the chart based on Neon Velocity algorithms, Whale Volume Bubbles, and structural Confluence engines. Quantum Auto Targets: Say goodbye to static stop losses. The algorithm actively scans the chart for unmitigated High-Volume Nodes (HVNs) to use as magnetic Take Profits and Stop Losses. If no nodes are present, it intelligently falls back to real-time Dynamic ATR targets. Risk Management Integration: Calculates safe lot sizes automatically based on strict account percentage rules and dynamic stop-loss distances. Webhooks: Fully wired for external JSON automation, broadcasting precision entry points, exact pip distances, and momentum exhaustion exit triggers.
Pine Script®指标
由kimlombard提供
1
Sigmoid Transition Trailing Stop [LuxAlgo]The Sigmoid Transition Trailing Stop indicator provides a dynamic risk management tool that utilizes a mathematical sigmoid function to smoothly adjust stop levels when price trends become overextended. Unlike traditional trailing stops that move linearly or remain static until a new high/low is formed, this script proactively "catches up" to price during periods of high momentum using a non-linear transition. 🔶 USAGE The indicator functions as a trend-following stop-loss or take-profit level. When the price crosses the trailing stop, the trend direction flips, and a new stop is calculated based on the ATR-derived distance. The unique behavior of this tool occurs during "Adjustment" phases. When the distance between the price and the trailing stop becomes too wide (exceeding the initial ATR multiplier), the script triggers a sigmoid transition. During this phase: The trailing stop line becomes solid (0% transparency) to indicate an active adjustment. The stop level moves towards the price following an S-curve, providing a smooth acceleration and deceleration of the stop level. The adjustment concludes once the sigmoid duration is reached or if the stop gets too close to the price (defined by the Min Distance setting). 🔹 Visual Cues Green Line/Fill: Indicates a bullish trend where the stop is below the price. Red Line/Fill: Indicates a bearish trend where the stop is above the price. Solid Line: The sigmoid adjustment is currently active, moving the stop closer to price. Transparent Line: The stop is in its standard state, maintaining its level or trailing highs/lows. 🔶 DETAILS The core of the script is the sigmoid function, which creates a natural-looking transition between two price levels. Mathematically, it maps a linear time input (bars passed) to an S-shaped curve between 0 and 1. The logic ensures that the "trailing" property is never violated; even during a sigmoid transition, the stop level can only move in the direction of the trend (up for bullish, down for bearish). If the price moves against the trend during an adjustment, the stop will simply hold its level until the price moves favorably again or the adjustment period ends. 🔶 SETTINGS 🔹 General Settings ATR Length: The lookback period used to calculate the Average True Range, which determines the volatility-based distance of the stop. ATR Multiplier: The base distance (in ATR units) used when the trend first flips. Sigmoid Length (Bars): The number of bars over which the sigmoid transition occurs once triggered. Sigmoid Amplitude (ATR Units): The maximum distance the stop is allowed to travel toward the price during an adjustment phase. Min Distance (ATR Units): A safety buffer that prevents the sigmoid adjustment from moving the stop too close to the current price, preventing premature exits.
Pine Script®指标
由LuxAlgo提供
4
ATR Trailing Stops for Hyperliquid Spot + Perps [HYPR-run]DESCRIPTION: A drop-in ATR trailing exits module. Four architectures that maximize profit on winning trades using volume weighted volatility instead of fixed levels or plain ATR. Built modular; the trailing logic is self-contained so you can drop it into any existing indicator or strategy as a plug-and-play exits block. Two independent stops (long/short), spot and perps. DISCOVERING EDGE ATR trailing exits are popular, everyone uses them, but this indicator doesn't just trail on volatility, it trails on meaningful volatility that very few people measure. In order to gain a persistent, mechanical edge in how winners run and protect capital on the trades that don't work, we explored a more meaningful expression of ATR trailing exits. VOLUME-WEIGHTED ATR vs PLAIN ATR Plain ATR treats every candle equally. Volume-weighted ATR will only expand stops when volume validates the volatility, preventing premature exits on noise and letting winners run further on real moves. Over hundreds of trades this single difference can compound in the spirit of letting winners run further, losers stay controlled versus fixed levels or vanilla ATR. - Four modes (A3.1, A4.0, A4.1, A4.2) cover different trailing behaviors: ratcheting, chandelier anchor, free-floating, and raw baseline. All size stop distance from volatility, not fixed levels. - Modular engine. The trailing logic is self-contained; drop it into any existing indicator or strategy as a plug-and-play exits block. - Alerts fire built-in JSON webhook payloads. Paste your webhook URL, create the alert, execute on the exchange of your choice. ATR MODES A3.1: LinReg + plain ATR, no ratchet. The baseline. Linear regression projects where price is heading, plain ATR sets the distance. Stop moves freely in both directions. Use as a reference or when you want a raw trailing stop. A4.0: LinReg + VWATR + Efficiency Ratio + ratchet (default). The all-rounder. Volume-weighted ATR discounts low-liquidity candles. The Efficiency Ratio (Kaufman) measures trend quality: in a clean trend it widens the stop to let price run; in chop it tightens. Ratchet floor means the stop only moves in your favor. A4.1: Chandelier + VWATR + ratchet + first-bar multiplier. Anchored to the highest high (longs) or lowest low (shorts). First-bar multiplier sets a tighter initial stop, then the standard multiplier takes over as the ratchet locks in gains. Use when entering off key levels. A4.2: LinReg + VWATR, no ratchet. Same as A4.0 but without ratchet floor or Efficiency Ratio. Stop moves freely with the projection, giving the trade room through consolidation at the cost of less locked profit. FEATURES - Four ATR architectures selectable via dropdown - Volume-weighted ATR: low-liquidity candles contribute less - Efficiency Ratio: tightens in chop, widens in trend (A4.0) - Ratchet floor: stop only moves in your favor (A4.0, A4.1) - First-bar multiplier for tighter initial protection (A4.1) - Separate ATR lookbacks for longs and shorts - Separate multipliers for longs and shorts - Two-bar confirmation prevents single-wick fakeouts - Gradient fill between price and stop (intensifies near danger) - Stop line color shifts with ATR regime (green stable, amber expanding) - Ratchet circles mark each new locked-in level on the stop line - Dashboard: mode, stop price, gap %, ER, VWATR %, regime state - Dark/light theme toggle for any chart background - Independent long/short alert toggles - No JSON snippet needed; close payload is built into the script HOW IT WORKS Volume-weighted ATR scales each bar's true range by its volume relative to the lookback average. High-volume bars contribute more; thin candles contribute less. This prevents low-liquidity spikes from inflating stop distance. Separate lookbacks for longs (default 14) and shorts (default 26) reflect that crypto drops faster than it climbs. The Efficiency Ratio measures directional movement versus noise on a 0-1 scale. It scales the ATR multiplier between 0.8x (choppy) and 1.2x (trending), adapting stop width to market regime. Only active in A4.0. Two-bar confirmation requires a confirmed close beyond the stop level. A single wick does not trigger the exit. The cross must hold for at least one additional bar close. ALERTS Close Long fires as SPOT (sell spot position). Close Short fires as PERPS (close short; spot is long-only). Toggle each independently. Alert payload is built into the script as JSON; works with any webhook receiver that accepts market/ticker/position fields. CREDITS ATR: J. Welles Wilder (1978) Efficiency Ratio: Perry Kaufman
Pine Script®指标
由HYPR-run提供
Trend filter [Jamallo](2025) This is a trend qualification and filtering tool. Breakdown: Signal Line — Vervoort ATR Trailing Stop The signal line is an implementation of the ATR Trailing Stop developed by Sylvain Vervoort, first published in Technical Analysis of Stocks & Commodities, Volume 27, Issue 6 (June 2009), in his article "Average True Range Trailing Stops" . The stop uses a modified ATR calculated on the average price (O+H+L+C)/4 rather than the plain close. Baseline — Kalman Filter The baseline is derived from the Kalman Filter, a recursive algorithm developed in 1960 by Rudolf E. Kálmán, a Hungarian-American engineer and mathematician. Originally developed for aerospace applications in the early 1960s — including guidance of the Apollo spacecraft. In this indicator, the Kalman Filter acts as a smooth, low-lag trend estimate. Rather than applying fixed weights to historical data like conventional moving averages, it dynamically adjusts its trust between the predicted trend and observed prices. Bands — Volatility Channels Two pairs of bands (inner and outer) are built around the Kalman baseline using a 200-bar WMA.
Pine Script®指标
由Jamallo22提供
OSOK SafePadOSOK SafePad plots a dynamic price corridor to help intraday traders visualise immediate order entries for 'One Shot One Kill' style trades. How It Works The indicator draws two horizontal lines around the price at a fixed point distance. While the market is active, these lines trail the current price in real time. You can enable the freeze feature to lock these lines at a specific session time. By default, this is set to 8:30 AM New York (the usual time for CPI, PPI and NFP data releases). When the session begins, the lines anchor to that candle's opening price and remain static for the rest of the day. This provides a clear reference point around the macro data release volatility. Configuration - Pad Size — Number of points for the upper and lower offset (default: 25) - Upper / Lower Line — Independently configure color, width, and style (solid, dashed, dotted) for each line - Enable Freeze — Toggle to lock lines at a specific session time (default: true) - Freeze Window — Define the session start time for the freeze (default: 0830–1659) - Timezone — Select the timezone for the session anchor (default: America/New_York) Usage Tips This indicator is designed for OSOK-style high-risk tradings during high impact news release, but may also be helpful for plotting a 'safety' corridor if that's what needed in your own system.
Pine Script®指标
由NixYoung提供
Stop Loss Cascades (Breakouts) [Kioseff Trading]Hello friends and traders! 🔹Introduction This indicator " Stop-Loss Clustering (Breakouts) " attempts to model trader stop-loss placement logic and identify price areas where a large amount of stop losses might cluster. The idea is, if stop losses are indeed highly concentrated in a specific area, price extending through that area may produce high-velocity breakout conditions via forced order flow . I'll cover this topic more thoroughly throughout the description. For now, just know that stop loss location & size data is not publicly available . Any model of their concentration locations is highly assumptive. However, there's some reasonable academic research we can reference to make worthwhile estimates. Academic references supporting the concepts discussed are listed at the end of this description. To maintain readability, I won't cite individual statements inline. 🔹The Premise 🔸Liquidity, Behavior, and Stop Cascades Markets operate through a continuous limit order book , where two fundamental order types interact: Limit orders , which provide liquidity by resting in the book Market orders , which consume liquidity by exhausting those resting orders This mechanical interaction drives price movement - incoming order flow consuming available liquidity . This begs the question.. Does liquidity distribute evenly across the LOB? If it did : If liquidity were evenly distributed, price impact could be modeled as a relatively smooth function of incoming order flow. But it doesn’t : Liquidity is unevenly distributed. Academic research supports this claim and, regardless, this is an intuitive conclusion most traders arrive at. Liquidity forms localized concentrations and gaps. Liquidity concentrations are commonly referenced as: liquidity shelves , liquidity clusters , liquidity zones . Liquidity gaps are commonly referenced as: liquidity vacuums , thin book zones . As a result, identical order flow can produce very different price movements depending on the state of the order book. Let’s consider an example.. Assume price is trading at $99. The price levels $100, $101, $102 have resting sell limit order concentrations of 100. This is where you come in. You execute a market order buy for 300 size. Your order first exhausts all sell-side resting order concentrations at the $100 level. You still have 200 size that needs to be filled, and the ask price has moved from $100 to $101. Your order will now sequentially exhaust available liquidity at the $101 level, the ask price will increase to $102, and your final 100 size will exhaust the $102 level. To keep the example simple, we’ll say that your order moved price from $99 to $102, and now the ask price is $103. But, you still want to accumulate. The nearest sell-side levels in the LOB are $103, $104, $105. The $103 level has a sell limit order concentration of 500. $104 and $105 both have concentrations of 50. You execute your same market order buy for 300 size. This time, price doesn’t move.. At all.. Instead, you consumed 300 of the 500 size at $103 with your order, and the level remains a barrier. Your order was absorbed by available liquidity. This example demonstrates how price movement depends on available liquidity , not simply the size of incoming orders. In the first scenario, liquidity was thin and the order walked through multiple price levels, causing price to move quickly. In the second scenario, a large concentration of resting liquidity absorbed the same order, preventing price from advancing. 🔸Liquidity Does Not Distribute Evenly Alright, we understand that liquidity doesn’t distribute evenly. And we understand that high concentrations of liquidity can act as price barriers (liquidity shelves) while sparse liquidity can permit rapid price movement - we saw this in our example above. There’s an important question we should ask next before we move on.. If liquidity distributes unevenly, then where does it tend to cluster? And where does it tend to thin? Of course, knowing these tendencies provides multi-purpose advantages. If price approaches a liquidity vacuum - a local block of the order book with thin resting liquidity - rapid price movement can occur without requiring unusually strong aggressive order flow. If price approaches a liquidity shelf - a local block of the order book with thick resting liquidity - price can stall or contract even if the same level of aggressive order flow that previously moved price continues. With this in mind, order flow intensity alone does not determine price movement . The distribution of liquidity across surrounding price levels plays a similarly important role. So, is there any evidence of where liquidity tends to concentrate ? 🔸Empirical Observations Empirical research on limit order books shows that liquidity does not distribute smoothly across the LOB . Instead, depth tends to concentrate at specific price levels, producing irregular profiles with localized peaks in resting liquidity. These concentrations arise because order placement is not random . Traders frequently anchor decisions to widely observed reference prices such as: • prior highs • prior lows • round numbers • widely referenced price extremes Because many traders monitor the same price history, order placement decisions often reference similar price levels. This concept is simpler than it sounds. Let’s use market structure traders for example. Market structure traders frequently reference prior swing highs and swing lows when making decisions about entries, exits, and risk. A trader entering a long position may place their stop-loss below a recent swing low , reasoning that if price breaks that level, the trade idea is invalidated. A trader entering a short position may place their stop-loss above a recent swing high for the same reason. Timeframe price aggregation may differ; however, we’re all looking at roughly the same recent highs and lows when evaluating a chart (structure). When many traders collectively reference the same prices, orders may accumulate near those levels. This produces localized depth concentrations, which traders refer to as liquidity shelves . Liquidity shelves act as temporary barriers where the book contains disproportionately large resting liquidity compared to surrounding prices. 🔸Research documenting liquidity clustering includes : Bourghelle & Cellier (2007) , who find that limit orders cluster at prominent price levels (especially round numbers), creating localized depth concentrations that can act as price barriers. Kavajecz & Odders-White (2004) , who demonstrate that prices identified as support or resistance coincide with higher resting limit order depth These findings suggest that many commonly observed price levels may correspond to real concentrations of liquidity rather than being purely visual artifacts on a chart. Kavajecz & Odders-White (2004) is an important observation for support/resistance traders! Kavajecz & Odders-White (2004) show that levels traders commonly call support and resistance often align with areas where more limit orders are resting in the order book. This suggests a plausible mechanical pathway through which support and resistance levels can emerge! 🔸Liquidity Shelves and Price Interaction When liquidity clusters around a price level, the resulting liquidity shelf can influence how price behaves when it approaches that area. Price interaction with these shelves is state-dependent : If incoming order flow is absorbed, price may stall or reverse If resting liquidity is consumed, price may transition rapidly to the next liquidity zone Once a shelf is depleted, follow-through can accelerate due to thinner liquidity beyond the level Research on order book dynamics supports this mechanical view of price movement. For example: Jean-Philippe Bouchaud, J. Doyne Farmer, and Fabrizio Lillo (2009) demonstrate that price impact emerges from the interaction between order flow and finite liquidity From this perspective, price does not move simply because a level is crossed. Price moves because available liquidity at that level has been consumed. 🔸Latent Liquidity and Stop Clustering In addition to visible liquidity from limit orders, markets also contain latent liquidity . This is where ”Stop-Loss Clustering (Breakouts)” becomes important - we’re almost done! Latent liquidity consists of conditional orders such as stop-losses that are not visible in the order book until triggered . Although these orders aren’t public information, empirical studies show that stop orders tend to cluster near widely referenced price levels . Research by Carol Osler (2001, 2002) using institutional FX order data finds that stop-loss orders frequently accumulate just beyond salient price levels such as prior highs and lows. When these stops trigger, they convert into aggressive market orders and can generate bursts of directional order flow that may accelerate price movement. 🔸Stop-Loss Cascades Stop losses add another layer of latent order flow that isn’t visible in the order book until it triggers. If enough of them sit around the same price area.. Think “hidden pressure” waiting to activate. Nothing happens while price trades nearby, but once that level is traded at, those stops convert into market orders and immediately begin consuming available liquidity. This matters because stop placement is unlikely to be random in most instances. Traders frequently anchor stops to widely observed prices such as prior highs, prior lows, or other prominent structure points, or use volatility methods such as ATR, etc. So when price approaches one of these areas, two things can happen. If the resting liquidity there is large enough, the incoming orders can be absorbed and price may stall or reject. But if that liquidity gets consumed, the stops sitting just beyond the level begin triggering. Those triggered stops add additional market orders, which consume more liquidity and can push price further into the next layer of stops. This creates a cascading effect: price reaches a stop cluster stops trigger and convert into market orders liquidity gets consumed faster price moves further, triggering more stops When this chain reaction starts, price can transition very quickly from a slow battle near the level to rapid expansion through it. This is one of the mechanical reasons why some reference-point breaks barely move, while others accelerate rapidly. 🔹How It Works Now that we understand the why - let’s discuss how the indicator works. 🔸Absorbtion Extremes The image above shows the absorption extremes model. In this model, the indicator treats recent & relevant swing points as plausible stop clustering candidates. You can find similar swing point identification mechanics in other indicators. However, this model assigns subsequent volume to the swing level after its formation. There are limitations and assumptions - let’s go over them. The images above explain how the indicator determines the intensity of a possible stop-cluster around a swing level. There are limitations and assumptions 1: The indicator assigns all “directional volume” to a swing level after it’s formed and while it remains the closest active swing point to the current price. “Buy volume” is assigned to the closest active swing low. “Sell volume” is assigned to the closest active swing high. I say “buy volume” and “sell volume” because there’s assumptions on what constitutes the relevant classification. The indicators follow the traditional two-region tick model for classifying buy volume and sell volume. Higher close = “buy volume” proxy Lower close = “sell volume” proxy Depending on the granularity you select (the indicator is capable of using tick data), this model can be more/less accurate. However, even with tick-level data and bid/ask quotes, trade direction must still be inferred using classification rules. Because some trades occur inside the spread or involve hidden liquidity, perfect classification is not possible without exchange aggressor flags. For assumptions.. The model assigns ALL classified volume to the swing level. In reality, traders use a wide range of risk management methods, and not every position will place a stop loss directly at the most recent swing point. ATR-based stops, percentage-based stops, and other volatility-based methods are also common. Because the true distribution of stop placement is unobservable, the model assumes that positions entered are structurally invalidated at the closest swing level based on their classified direction. As a result, the values displayed by the indicator should be interpreted as relative proxies for potential stop concentration, rather than precise estimates of actual stop-loss size. The displayed magnitudes are intentionally exaggerated and comparative, designed to highlight where stop pressure may accumulate relative to other levels. The images above show how to interpret the indicator when using this model. The image above shows the triggered stop-cluster graph. Each point corresponds to a triggered stop-cluster - assuming it exists. The greater the size attached to that cluster, the further distant the data point is placed. Far away from zero line = large size. Close to zero line = low size. Radiating/glowing points indicate a potentially large cluster trigger. 🔸 Volatility-At-Entry Model (Time Scaled) The Volatility-At-Entry model uses ATR scaled by various timeframes to predict plausible stop loss placements. For this model, the indicator uses the same tick classification model to assign volume directionally. Volume is then dispersed across six common timeframes (1m, 5m, 15m, 30m, 1h, 4h) and 3 common ATR multiples for risk management (1ATR, 1.5ATR, 2ATR). This model assumes traders are entering positions across various timeframes and are scaling risk congruent with those timeframes. For instance, A trader using the 1-minute chart for opportunity is more likely to use a stop loss closer to entry than a trader using the 4-hour chart for opportunity. If this assumption is reasonable to you - great, we can move forward! The image above visualizes the model. Purple-shaded regions indicate a price area with less opportunity for stop loss clustering. Either transaction intensity around eligible price areas was low, or position accumulation wasn’t given sufficient time. Pink-shaded regions indicate a price area with greater opportunity for stop loss clustering. Volume was significant around these regions or price has traded within proximity for extended periods. This model naturally shows more future opportunity than historical outcomes. You can select to show historical outcomes in the settings, this image shows examples of such outcomes. The image above shows the triggered stop loss graph in effect for this model. Stop clustered are distributed across more price areas with this model - from low intensity to high intensity. Therefore, a cluster is almost always “triggering” to some degree. A classification model for what’s typical and what’s unusual is used for the graph in this case. Radiating points always indicate large stop clusters triggered. Anything within the green/pink line indicates usual size. Typical Move The image above explains the nearest cluster information table. The size and location of the nearest buy-stop cluster and sell-stop cluster are recorded. Additionally, the indicator identifies whether clusters of similar size were triggered in the past, and how price behaved following those events. Since all models here are highly assumptive, and similar sized clusters might only have one or two relative neighbors, treat these measurements as a description of history rather than a prediction. The model takes the logarithm of the current stop-volume (buy or sell) to normalize its scale and compare it with a historical dataset of previously observed stop-volume sizes that have also been log-scaled. It then identifies historical observations whose sizes are most similar to the current value, either by selecting all observations within a tolerance range around that value (where the range is based on the typical spacing between historical observations), or by selecting the single closest match. Finally, the model retrieves the historical price moves associated with those matched observations, producing a sample of “typical moves” that occurred when stop-volume magnitude was similar to the current situation. Ratio Meter The stop-cluster ratio meter shows the current sum of active and triggered all buy-side clusters and sell-side clusters. This meter is useful for quick scanning across assets to see if active or recently triggered stop clusters are lopsided. Additional Features The single most important setting outside model selection is the lower timeframe used to retrieve volume from. This setting is set to 1-minute data by default because it works with paid and free plans. If you want better granularity, I strongly suggest changing this setting to either 1-second or 1-tick. This will sacrifice the number of identifiable cluster locations, because better granularity data has less programmatically retrievable values. 🔹Closing Remarks Stop-loss clustering is an appealing concept because it offers a plausible explanation for why some breakouts accelerate so quickly while others stall. When a large number of conditional orders sit near the same price, a breakout through that area can trigger a cascade of market orders that rapidly consume liquidity and push price toward the next available zone. However, it’s important to remember that the models used in this indicator are approximations, not direct measurements. True stop-loss locations and sizes are not publicly observable, and many traders use different risk management techniques that cannot be perfectly inferred from chart data alone. The goal of this indicator is therefore not to identify exact stop locations, but to highlight price areas where stop pressure may plausibly accumulate relative to surrounding levels. Like any model based on behavioral assumptions and historical observations, results should be interpreted probabilistically. Large clusters do not guarantee breakouts, and small clusters do not guarantee quiet price behavior. Instead, the indicator is best used as a tool for context and situational awareness. References General Microstructure and Price Formation Madhavan, A. (2000). Market microstructure: A survey. Journal of Financial Markets, 3(3), 205–258. O'Hara, M. (1995). Market Microstructure Theory. Blackwell. Biais, B., Glosten, L., & Spatt, C. (2005). Market microstructure: A survey of microfoundations, empirical results, and policy implications. Journal of Financial Markets, 8(2), 217–264. Limit Order Books and Liquidity as Resting Orders Gould, M. D., Porter, M. A., Williams, S., McDonald, M., Fenn, D. J., & Howison, S. D. (2013). Limit order books. Quantitative Finance, 13(11), 1709–1742. Rosu, I. (2009). A dynamic model of the limit order book. Review of Financial Studies, 22(11), 4601–4641. Biais, B., Hillion, P., & Spatt, C. (1995). An empirical analysis of the limit order book and the order flow in the Paris Bourse. Journal of Finance, 50(5), 1655–1689. Liquidity Clustering and Depth Concentration Kavajecz, K. A., & Odders-White, E. R. (2004). Technical analysis and liquidity provision. Review of Financial Studies, 17(4), 1043–1071. Bourghelle, D., & Cellier, A. (2007). Limit order clustering and price barriers on financial markets. Working paper / SSRN. Order Flow and Price Impact Bouchaud, J.-P., Farmer, J. D., & Lillo, F. (2009). How markets slowly digest changes in supply and demand. In Handbook of Financial Markets: Dynamics and Evolution. Stop Orders and Price Cascades Osler, C. L. (2003). Currency orders and exchange-rate dynamics: Explaining the success of technical analysis. Journal of Finance, 58(5), 1791–1819. Osler, C. L. (2005). Stop-loss orders and price cascades in currency markets. Journal of International Money and Finance, 24(2), 219–241. Liquidity Provision and Execution Ho, T., & Stoll, H. (1981). Optimal dealer pricing under transactions and return uncertainty. Journal of Financial Economics, 9(1), 47–73. Almgren, R., & Chriss, N. (2000). Optimal execution of portfolio transactions. Journal of Risk, 3(2), 5–39. Menkveld, A. J. (2013). High frequency trading and the new market makers. Journal of Financial Markets, 16(4), 712–740. Behavioral Anchoring and Attention Kahneman, D., & Tversky, A. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies, 21(2), 785–818. George, T. J., & Hwang, C. Y. (2004). The 52-week high and momentum investing. Journal of Finance, 59(5), 2145–2176. Mizrach, B., & Weerts, S. (2007). Highs and lows: A behavioral and technical analysis. SSRN working paper.
Pine Script®指标
由KioseffTrading提供
19
Neighboring Trailing Stop [LuxAlgo]The Neighboring Trailing Stop indicator provides a dynamic, data-driven trailing stop-loss mechanism by analyzing the local distribution of prices relative to the current market position. It uses a "k-nearest neighbors" approach to identify support and resistance levels based on historical price clusters, offering a unique alternative to standard volatility-based stops like ATR. 🔶 USAGE The indicator helps traders identify trend reversals and manage open positions by providing a protective stop that adjusts based on where price has historically "congested" near the current level. 🔹 Trend Interpretation Bullish Trend: When the stop-loss line is below the price (colored green), the market is in a bullish state. The trailing stop will only move upward, securing profits as the price creates new local distribution highs. Bearish Trend: When the stop-loss line is above the price (colored red), the market is in a bearish state. The trailing stop will only move downward, following the price as it explores lower distribution zones. 🔹 Signals The script plots "B" (Buy) and "S" (Sell) labels at the point of trend reversals. These occurs when the price breaks through the current "neighboring" distribution boundary, suggesting a shift in the local market structure. 🔶 DETAILS The methodology behind this indicator is rooted in non-parametric statistics and price distribution analysis rather than simple moving averages or volatility. 🔹 Price Distribution & K-Neighbors The script maintains a "Historical Buffer" of recent prices in a sorted array. For every new bar, it identifies the position of the current price within this sorted distribution. It then looks at k neighbors above the price and k neighbors below the price. 🔹 Percentile Bands Within these local "neighborhoods" of price data, the script calculates a specific percentile. This allows the indicator to ignore outliers and focus on where the bulk of historical price action occurred near the current level. If the price moves into a "discovery" phase where it exceeds the range of its historical neighbors, a trend reversal is triggered. 🔹 Trailing Logic The resulting levels are smoothed via an SMA to prevent erratic jumps. The trailing stop is "ratcheted"—meaning it can only move in the direction of the trend (up for long, down for short)—ensuring that realized gains are protected even if the distribution neighborhood expands. 🔶 SETTINGS Historical Buffer (Bars): Determines how many historical price points are kept in the distribution memory. A larger buffer considers more history, leading to more stable but slower-reacting levels. Neighboring Range (K): The number of price points to analyze immediately above and below the current price. Smaller values make the stop more sensitive to local price changes. Percentile: Controls the depth within the neighborhood used for the stop level. A higher percentile (e.g., 90) places the stop further away from the current price. Smoothing: Applies a Simple Moving Average (SMA) to the raw distribution levels to create a smoother trailing line.
Pine Script®指标
由LuxAlgo提供
3
Evasive SuperTrend [LuxAlgo]The Evasive SuperTrend indicator provides a modernized approach to trend following by implementing a unique "evasive" logic designed to reduce premature trend flips during periods of high market noise and consolidation. Unlike the traditional SuperTrend which can only move closer to the price (or stay flat), this tool identifies when price action gets too close to the volatility band and "evades" the price by pushing the band further away, effectively giving the current trend more breathing room. 🔶 USAGE The indicator can be used similarly to a standard SuperTrend to identify trend direction and potential trailing stop levels, but with added intelligence regarding market noise. 🔹 Identifying Trend and Noise Solid Line: Indicates a standard trend phase where the price is maintaining a healthy distance from the volatility band. Dotted Line: Indicates the "Evasive" mode is active. This occurs when price action enters a "Noise Zone," causing the indicator to proactively push the band away to prevent a false breakout. Gradient Fill: The background fill provides a visual heat map of the trend strength relative to the source price, with higher transparency indicating proximity to a potential trend flip. 🔹 Trading Signals Users can look for "BULL" and "BEAR" labels which mark the points where price successfully penetrates the evasive band. Because the band actively retreats during noise, a trend flip often carries more significance as it requires price to overcome an adaptive volatility threshold. 🔶 DETAILS The core innovation of this script is the "Noise Avoidance Logic." Standard SuperTrends are strictly monotonic (they only move up in a bull trend and down in a bear trend). This often leads to "whipsaws" where a minor price spike touches the band and flips the trend unnecessarily. The Evasive SuperTrend calculates a Noise Threshold . If the distance between the price and the band is less than this threshold (calculated in ATR units), the band is pushed back by a specified Expansion Alpha . This allows the trend to persist through choppy sideways movement that would otherwise trigger a reversal. 🔶 SETTINGS 🔹 Supertrend Settings ATR Length: Determines the lookback period for the Average True Range calculation used to set the band distance. Base Multiplier: Sets the initial distance of the band from the price. Higher values result in fewer, longer-term trend signals. 🔹 Noise Avoidance Logic Noise Threshold (xATR): Defines the "danger zone" around the band. If price enters this ATR-based range, the band begins to evade. Expansion Alpha (xATR): The distance the band moves away from the price when noise is detected. Higher values make the indicator more "stubborn" against trend changes. 🔹 Visualization Show Signal Labels: Toggles the visibility of the BULL and BEAR trend change labels. Show Gradient Fills: Toggles the dynamic background coloring between the price and the band.
Pine Script®指标
由LuxAlgo提供
11
SuperTrend Recovery [LuxAlgo]The SuperTrend Recovery indicator provides a modified version of the classic SuperTrend algorithm that incorporates a dynamic "recovery" mechanism designed to adjust the trailing stop when price action moves significantly against the current trend. This script aims to help traders manage volatile environments by tightening the trend band when the market experiences deep pullbacks without triggering a full trend reversal. 🔶 USAGE The indicator can be used similarly to a standard SuperTrend to identify market direction and potential trailing stop-loss levels. However, the unique recovery logic allows for a more adaptive response to price deviations. 🔹 Trend Detection When the price is above the band, the indicator signals a BULL trend. When the price is below the band, the indicator signals a BEAR trend. Trend switches are marked with "BULL" or "BEAR" labels and a colored circle at the switch point. 🔹 Recovery Mechanism In a standard SuperTrend, the band stays flat if price moves against the trend (but doesn't break it). In the SuperTrend Recovery version, if the price drops significantly below the "Switch Price" (for a bull trend) or rises above it (for a bear trend), the band begins to move toward the price based on the Recovery Alpha . This allows the trailing stop to "catch up" during high-volatility pullbacks, potentially securing a faster exit if the recovery fails. 🔶 DETAILS The core of this indicator lies in its two-stage calculation: the Base SuperTrend and the Recovery Logic. 🔹 Base Logic The indicator calculates a base upper and lower band using the Average True Range (ATR) multiplied by a user-defined factor. Under normal conditions, the band follows the classic rules: it can only move up during a bull trend and only down during a bear trend. 🔹 Recovery Logic When price enters a "loss" state relative to the price where the trend initially started (the Switch Price), the script checks if the deviation exceeds the Recovery Threshold . If the threshold is met, the band is calculated as an exponential moving average (EMA) of the current price and the previous band value, weighted by the Recovery Alpha : targetBand = alpha * close + (1.0 - alpha) * prevBand This creates a "tapering" effect where the band aggressively tightens toward the price during deep retracements, helping to mitigate drawdown by providing an earlier exit signal compared to the standard static band. 🔶 SETTINGS 🔹 Supertrend Settings ATR Length : The lookback period used to calculate market volatility. Base Multiplier : The factor applied to the ATR to determine the distance of the band from the price. 🔹 Recovery Logic Recovery Alpha (%) : Determines how quickly the band adjusts toward the price when the recovery logic is active. Higher values make the band more reactive. Recovery Threshold (xATR) : The distance (in ATR units) the price must deviate from the switch price before the recovery mechanism activates. 🔹 Visualization Show Gradient Fills : Toggles the background gradient between the price (source) and the SuperTrend band. Show Signal Labels : Toggles the "BULL" and "BEAR" labels at trend reversal points.
Pine Script®指标
由LuxAlgo提供
6