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Forex Cross-Asset Correlation Mapper Forex Cross-Asset Correlation Mapper — an indicator for tracking correlations between major currency pairs and the US Dollar Index (DXY). The indicator automatically detects the currency pair on the current chart and calculates a rolling Pearson correlation with DXY in real time. What it shows: Heatmap (matrix) — visualizes correlations between all enabled pairs. Green indicates positive correlation, red indicates negative. Cells with anomalies are highlighted with a yellow marker. Correlation plot — the dynamic correlation of the current pair with DXY over time. The yellow line represents the mean value, red dashed lines mark the normal range boundaries (±N standard deviations). When an anomaly is detected, the chart background is highlighted in yellow. Info panel — displays the current correlation value, mean, deviation in sigma units, and status (Normal / ANOMALY). An anomaly is triggered when the correlation moves beyond N standard deviations from its rolling mean. This signals a breakdown in the typical relationship between the pair and the dollar. Settings: correlation period, anomaly threshold, mean calculation period, enable/disable each pair individually. Supported pairs: EURUSD, GBPUSD, USDJPY, AUDUSD, USDCAD, USDCHF, NZDUSD + DXY (DX1!).
Wskaźnik Pine Script®
od Smart-Day-Trader
Parkinson Range Oscillator [BackQuant]Parkinson Range Oscillator Overview Parkinson Range Oscillator is a volatility regime indicator built around the Parkinson volatility estimator , a high-low based variance model originally proposed as a more statistically efficient alternative to close-to-close volatility. Instead of measuring volatility from closing returns, this script measures volatility from the intrabar price range using ln(H/L), then converts it into a normalized oscillator (z-score) so you can identify volatility expansion vs compression relative to the asset’s own history. The indicator is designed to answer questions like: Is volatility currently elevated or suppressed relative to its baseline? Is volatility expanding (risk rising) or compressing (coiling)? How extreme is the current vol state in percentile terms? How does range-based vol compare to a more common ATR-based vol read? It plots: A Parkinson-based volatility z-score oscillator with gradient fills. A signal line (EMA) for expansion/compression transitions. An ATR-based z-score for context comparison. A dashboard with current vol %, z-score, percentile rank, regime label, and ATR z-score. Where Parkinson volatility comes from (origin and intuition) The Parkinson estimator comes from academic finance and the study of volatility estimation. The key insight is simple: The daily high and low contain more information about variability than the close alone. Close-to-close volatility only uses one price per bar (the close), throwing away intrabar information. The high-low range captures the realized dispersion inside the bar, so under ideal assumptions it can estimate variance more efficiently. The Parkinson model is derived assuming: Price follows a continuous-time diffusion process (often framed like geometric Brownian motion). No drift matters for the variance estimate over the interval. No jumps and no microstructure distortions (idealized). Even though real markets violate these assumptions (gaps, jumps, wicks from order flow), the estimator remains useful because: Range is still a strong proxy for realized volatility. It reacts to intrabar expansion earlier than close-based methods. It is less dependent on where the bar closes. Core Parkinson formula (what the script implements) Parkinson variance for a window of n bars is: Var = (1 / (4 * n * ln(2))) * Σ This script computes it in the common rolling form: logHL2 = (ln(high/low))² parkVar = SMA(logHL2, n) / (4 * ln(2)) parkVol = sqrt(parkVar) * 100 Key details: ln(H/L) makes the range scale-invariant (percent-like), so it behaves more consistently across price levels. Squaring gives variance contribution. The 1/(4 ln 2) constant comes from the expected distribution of high-low range under a Brownian diffusion. sqrt converts variance to standard deviation (volatility). *100 expresses it as a percentage for readability. So parkVol is a “range-based realized volatility proxy” in percent terms. Why range-based volatility behaves differently than ATR ATR measures average true range, which is a linear range magnitude measure (high-low plus gaps). Parkinson uses ln(H/L) which is: Log-scaled (closer to a return-based measure). More directly tied to variance estimation theory. In practice: ATR can be driven by gaps and absolute range. Parkinson is driven by proportional range and tends to emphasize how wide the bar is relative to its price level. Parkinson often reacts sharply when wicks expand even if closes are stable. Normalization into an oscillator (making it comparable through time) Raw volatility values are hard to interpret across regimes because every market has different “normal.” This script normalizes Parkinson volatility against its own rolling baseline using a z-score: parkMA = SMA(parkVol, baselineLen) parkSD = stdev(parkVol, baselineLen) osc = (parkVol - parkMA) / parkSD Interpretation: osc = 0 means current vol is at its baseline average. osc = +1 means 1 standard deviation above normal (high vol). osc = -1 means 1 standard deviation below normal (compressed). osc > +2 flags extreme expansion states. This is the core output. It turns “volatility” into “volatility regime” in standardized units. Signal line and expansion/compression transitions The oscillator is smoothed with an EMA to create a signal line: signal = EMA(osc, signalLen) Then transitions are defined as: Expansion cross: crossover(osc, signal) and osc > 0 Compression cross: crossunder(osc, signal) and osc < 0 Why the extra osc > 0 and osc < 0 conditions: It prevents treating small oscillations around zero as meaningful. It forces expansion signals to occur in above-average volatility territory. It forces compression signals to occur in below-average volatility territory. So signals are regime-confirming, not constant cross spam. Percentile rank (how extreme is vol relative to the past) In addition to the z-score, the script computes the percentile rank of the raw Parkinson volatility: pctRank = percentrank(parkVol, pctRankLookback) Interpretation: pctRank near 90–100 means current vol is among the highest levels seen in that lookback. pctRank near 0–10 means it is among the lowest (compression). Z-score tells you “how many SDs from mean.” Percentile tells you “how rare is this state historically.” Those are different but complementary. ATR comparison line (context, not the main engine) The indicator also computes an ATR-based volatility proxy and normalizes it in the same way: atrVol = ATR(n) / close * 100 atrOsc = zscore(atrVol, baselineLen) This gives you a direct visual comparison: If Parkinson oscillator is high but ATR oscillator isn’t, range expansion may be happening in a way ATR is not emphasizing (or vice versa). If both agree, you have stronger confirmation of a true volatility regime shift. ATR is included as a “common benchmark,” not as the primary signal. Regime classification (human-readable state mapping) The script labels regimes from osc: osc > 2.0 → EXTREME osc > 1.0 → HIGH osc > 0.0 → ABOVE AVG osc > -1.0 → BELOW AVG else → COMPRESSED This is a practical mapping for dashboards and quick reads. It is not pretending that 2.0 is a universal constant, it is just a standardized “rare expansion” threshold. Coloring follows the same logic: More positive = more “expansion” coloring (bearCol). More negative = more “compression” coloring (bullCol). Note: the color naming is semantic here: “Low Vol / Compression” is bullCol because compression often precedes trend expansion opportunities. “High Vol / Expansion” is bearCol because high vol often implies risk, disorder, liquidation, or unstable conditions. You can interpret those however you prefer, the tool is measuring volatility regime, not directional bias. Plot design (why the oscillator is split into positive/negative) The oscillator is split into two series: oscPos = osc if osc > 0 else na oscNeg = osc if osc < 0 else na This is purely for visuals: Positive region is drawn with expansion color and expansion gradient fill to zero. Negative region is drawn with compression color and compression gradient fill to zero. This makes it obvious at a glance which side of “normal volatility” you’re on. How to interpret the indicator correctly 1) The oscillator is volatility regime, not price direction High osc does not mean price will go down. It means the market is moving violently relative to its baseline. That can occur in: Selloffs, liquidations, panic. Breakouts and momentum expansions. News-driven repricing. Low osc does not mean price will go up. It means the market is quiet relative to baseline: Ranges, coils, low realized movement. Slow grind trends with suppressed pullbacks. Pre-breakout compressions. 2) Compression regimes are often “setup states” When osc is deeply negative (compressed), it often indicates that realized movement has collapsed. In many markets this precedes: Breakouts (vol expansion from compression). Trend acceleration. Mean reversion bursts. But compression can also persist. This is why the script includes signal crosses and percentile rank to judge when compression is shifting. 3) Expansion regimes are often “risk states” When osc is positive and rising, the environment is more chaotic: Stops are more likely to be hit. Mean reversion can get violent. Trend continuation can be strong but timing becomes harder. In those regimes, the tool can be used to: Reduce leverage. Widen stops (if your system supports it). Switch to volatility-aware sizing. Wait for stabilization if you trade mean reversion. 4) Use percentile rank to identify “rare” volatility Two markets can both show osc = +1, but one might be at the 95th percentile and the other at the 70th depending on distribution shape. Percentile tells you whether the current vol is truly rare in that lookback. Cross dots (how to treat them) ExpansionCross and CompressionCross are not buy/sell signals. They are “volatility phase change” markers: ExpansionCross: vol regime moving up, above baseline, acceleration risk increases. CompressionCross: vol regime moving down, below baseline, quieting environment. These are useful for: Strategy toggles (trend mode vs chop mode). Sizing changes. Timing filters (avoid entries during extreme expansion if your edge hates noise). Dashboard (what it gives you at a glance) The table summarizes everything that matters without you needing to interpret plots manually: Parkinson Vol %: current raw range-based volatility level. Z-Score: current standardized regime reading. Percentile: rarity of current vol in the lookback. Regime: discrete label based on z-score thresholds. ATR Z-Score: comparison metric in standardized units. The dashboard is positioned and sized via inputs so it can fit different chart layouts. Parameter tuning guidance Parkinson Length Controls how quickly the raw Parkinson vol responds: Shorter = more reactive to immediate range changes. Longer = smoother volatility estimate, less noisy. Baseline Length Controls what “normal” means: Long baseline (like 100) creates stable regime definitions. Short baseline makes z-scores jump around and can overreact. Signal Length Controls how quickly you detect regime turning points: Short signal = more crosses, earlier detection, more noise. Long signal = fewer crosses, later detection, cleaner regime shifts. Percentile Lookback Controls rarity context: 252 approximates one trading year on daily charts. On intraday, it becomes “252 bars,” so adjust to match your horizon. Limitations and what to watch for Parkinson assumes continuous diffusion. Jumps and gaps can distort it. Wicks caused by illiquidity can inflate ln(H/L) and produce false “expansion.” Z-score assumes the baseline distribution is reasonably stable. If volatility distribution shifts structurally, your z-scores can be biased until baseline catches up. Percentile rank is lookback-dependent. Different lookbacks can change “rarity” classification materially. Summary Parkinson Range Oscillator converts a statistically grounded high-low volatility estimator into a regime oscillator by z-scoring Parkinson volatility against its own rolling baseline. It highlights expansion vs compression states with clear gradients, flags volatility phase changes via oscillator-signal crosses, ranks current volatility by percentile for rarity context, and overlays an ATR-based z-score for comparison. This makes it a practical tool for volatility-aware trading, regime filtering, sizing adjustments, and identifying compression-to-expansion transitions.
Wskaźnik Pine Script®
od BackQuant
Sigma Distance From Moving AverageThis is an indicator I've wanted to build for a while. The advent of AI and vibe coding circa 2026 made it feasible. This is an iteration of the previous indicator I shared - % distance from moving average - which, as explained in the self-described title, displays the % distance an asset's current price sits above or below whatever moving average you enter on whatever timeframe you choose. This new version expresses this distance as a z score rather than a %. The high level is we're going one level deeper - knowing how far an asset's current price is from its moving average is interesting, but what's more interesting is knowing how often this asset reaches this level above/below its moving average. Some assets are volatile, others less so. For something that moves a lot, being 15-20% above its 50 day moving average isn't all that rare. For something that doesn't move a lot, that might be a once in a ten thousand occurrence - we want to be able to quantify this. That's what this one does. Enjoy!
Wskaźnik Pine Script®
od dkncrypto
Volatility Visualizer Percentiles (VIXFix, ATR, VIX)Summary A volatility regime dashboard for liquid instruments that converts three volatility lenses into 0 to 100 percentile ranks versus the last 252 closed daily bars. It is built to answer one question: is volatility unusually low or unusually high relative to the last year . Use it to adjust position sizing, stop width, and trade selectivity. It is not a directional signal. Scope and intent Markets : US indices and index ETFs, index futures, large cap equities, liquid crypto proxies, and other symbols where daily volatility regimes matter Timeframes : best on Daily. It can be applied on other chart timeframes, but the reference window remains 252 closed daily bars Default demo : SPX on Daily Purpose : provide a simple, testable volatility context layer that you can plug into any daily system as a risk filter or risk scaler What makes it original and useful Most “volatility tools” show raw ATR or a single volatility index. This script standardizes three distinct sources into the same unit (percentile), so you can compare them and combine them without guessing thresholds. Unique fusion : internal realized volatility (ATR%), internal stress proxy (VIXFix), and external implied volatility (input VIX symbol) expressed in the same 0 to 100 scale Practical outcome : the table gives a regime read and an action posture, so the output is directly usable for risk decisions Testable : all components are visible and thresholdable; you can backtest rules like “only trade when composite is between 30 and 75” Portable : percentiles remove the need to hardcode market specific “ATR is high” numbers across different symbols Method overview in plain language Base measures VIXFix : a price based fear proxy derived from the instrument’s own daily behavior (using the relationship between recent high closes and current lows) ATR% : daily ATR normalized by daily close, expressed as a percentage for cross symbol comparability External VIX : a user selected volatility index or proxy pulled via input symbol (default CBOE:VIX) Normalization to percentiles For each metric, the script stores the last 252 closed daily values It then computes where the most recent closed daily value sits inside that history as a percentile from 0 to 100 Tie handling is configurable (Midrank, StrictLess, LessOrEqual) to define how repeated values are ranked Fusion rule Composite percentile is the simple average of the available percentiles (VIXFix, ATR%, VIX) If one component is missing (for example the external symbol is unavailable), the composite averages the remaining components How to use it on Daily This tool is most effective as a risk regime layer on top of an existing strategy. Use the Composite row as the primary dial, and the individual components as confirmation. Recommended operating zones 0–20 Very Low : quiet regime. Tight stops often survive, but breakouts can underperform. Favor mean reversion or require stronger breakout confirmation. 20–40 Low : constructive for many systems. Use baseline sizing and baseline stops. 40–60 Mid : neutral. Run your base playbook. 60–80 High : volatility expansion. Reduce size and widen stops, or trade only higher quality setups. 80–100 Very High : stress regime. Smallest size, widest stops, and skip marginal setups. Gap risk and slippage risk are higher. How to interpret disagreements If ATR% is high but VIX is mid , realized vol is elevated but the market is not pricing extreme fear. Treat as a caution zone, not panic. If VIX is high but ATR% is mid , implied vol is elevated ahead of potential events. Expect expansion risk even if realized vol has not moved yet. If all three are high , treat it as a full stress regime and enforce strict risk limits. What you will see on the chart A compact table with one row per metric and optional composite For each row: last closed daily value, 252D percentile, a progress bar, and an action posture Optional stats: min, median, max for the 252D window (useful for sanity checks, adds CPU) Table fields quick guide Last closed daily : the value used for ranking, taken from the last fully closed daily bar 252D percentile : where the current reading ranks versus the last 252 closed daily readings Bar : quick visual map of percentile from 0 to 100 Action : risk posture suggestion tied to the percentile bucket Inputs with guidance Core Window (closed daily bars) : default 252. Higher values make the regime slower and more structural. Lower values make it more reactive. VIX VIX symbol : default CBOE:VIX. You can replace it with another implied volatility proxy appropriate for your market. VIXFix VIXFix lookback : typical range 21/22. Smaller reacts faster, larger smooths regimes. ATR ATR length : typical range 10–21 on Daily ATR as % of close : recommended on for comparability across symbols and long history UI Show composite volatility score : recommended on. Best single dial. Show action guide : recommended on if you want direct posture cues. Show min, median, max : optional. Useful for diagnostics, higher CPU. Table position : place it where it does not cover price. Usage recipes Daily trend following overlay Trade your trend system normally when Composite is between 25 and 75 If Composite is above 75, reduce size and widen stops, and require stronger trend confirmation Daily mean reversion overlay Focus on Composite below 40 Avoid Composite above 80 where gaps and cascading moves reduce mean reversion reliability Daily risk parity style scaling Use Composite percentile as a coarse risk throttle: higher percentile equals lower exposure Example posture: 0–40 normal exposure, 40–80 reduced exposure, above 80 minimal exposure Alerts This script is intentionally a dashboard and does not emit buy or sell signals. If you want alerts, create them from percentile thresholds in your own fork. For conservative workflows, trigger alerts on bar close. // Example alert conditions (add to your fork if desired) high_vol = comp_pct > 80 low_vol = comp_pct < 20 Honest limitations and failure modes This is not a directional predictor. Volatility can rise in both bull and bear markets. Percentiles are relative to the last 252 closed daily bars. A “high percentile” is high versus recent history, not an absolute guarantee of future movement. Implied volatility (VIX) can move ahead of realized volatility (ATR%). Treat divergence as information, not a signal. Very high volatility regimes can include gap risk and slippage risk that are not visible in indicator values alone. Legal Education and research only. Not investment advice. You are responsible for your decisions. Test on historical data and in simulation before any live use.
Wskaźnik Pine Script®
od exlux
Market Internals SPY[TP]# Market Internals SPY Dashboard - TradingView Publication ## 📊 Overview **Market Internals SPY ** is a comprehensive multi-factor market sentiment dashboard designed specifically for SPY (S&P 500 ETF) traders. This indicator combines four powerful market breadth signals into one easy-to-read interface, helping traders identify high-probability setups and avoid false breakouts. --- ## 🎯 What Makes This Indicator Unique? Unlike single-indicator tools, this dashboard synthesizes **multiple market internals** to provide confluence-based trading signals: - **CPR (Central Pivot Range)** - Institutional pivot levels - **VIX (Volatility Index)** - Fear gauge - **Put/Call Ratio** - Options sentiment with dynamic crossover alerts - ** USI:ADD (Advance/Decline Line)** - Market breadth strength All presented in a clean, real-time dashboard with visual alerts directly on your chart. --- ## 📈 Key Features ### 1. **Static Daily CPR Levels** - Automatically plots Top CPR, Pivot, and Bottom CPR - Levels remain fixed throughout the trading day (no repainting) - **Trend Bias Indicator**: Green = Current Pivot > Previous Pivot (Bullish structure) ### 2. **Put/Call Ratio Crossover System** - 10-period SMA smoothing for cleaner signals - **Bullish Signal** (Green background): Put/Call crosses below SMA - Indicates decreasing hedging activity (bullish) - **Bearish Signal** (Red background): Put/Call crosses above SMA - Indicates increasing hedging activity (bearish) ### 3. **Price/Breadth Divergence Detection** - **Yellow Candles**: Highlight when price and USI:ADD diverge - Price rising but USI:ADD falling = Potential reversal - Price falling but USI:ADD rising = Possible bottom ### 4. **Comprehensive Real-Time Dashboard** A top-right table displaying: - **CPR Trend Bias**: Bullish/Bearish structure - **VIX Level**: Current value + directional bias - **Put/Call Ratio**: Live value + trend arrows - **AD Line**: Breadth strength with directional indicators ### 5. **Intelligent Bar Coloring** - **Green bars**: USI:ADD rising (breadth improving) - **Red bars**: USI:ADD falling (breadth deteriorating) - **Yellow bars**: Divergence warning (potential reversal) --- ## 🔧 How to Use ### Setup Instructions 1. **Add to Chart**: Apply to SPY on your preferred intraday timeframe (5m, 15m, 30m, 1H) 2. **Configure Symbols** (if needed): - Default settings work for most platforms - If "PCC" doesn't load, try: `PCCR`, `INDEX:PCC`, `USI:PCC`, or `CBOE:PCC` - Ensure you have market internals data access ( USI:ADD , VIX) ### Trading Signals #### 🟢 **Bullish Confluence** (High-Probability Long Setup) - CPR Trend = BULLISH - VIX falling or low (<20) - Put/Call below SMA (or green background crossover) - USI:ADD rising (green bars) - **Entry**: Look for bullish price action at support levels #### 🔴 **Bearish Confluence** (High-Probability Short Setup) - CPR Trend = BEARISH - VIX rising or elevated (>25) - Put/Call above SMA (or red background crossover) - USI:ADD falling (red bars) - **Entry**: Look for bearish rejection at resistance #### ⚠️ **Divergence Warning** - Yellow candles indicate mismatch between price and breadth - Consider profit-taking or reversals when divergence appears at extremes ### Best Practices - **Multi-Timeframe Confirmation**: Check higher timeframes (4H, Daily) for trend alignment - **Volume Confirmation**: Combine with volume analysis for stronger signals - **Risk Management**: Always use stop losses; no indicator is 100% accurate - **News Awareness**: Be cautious around major economic releases --- ## 📚 Understanding the Components ### CPR (Central Pivot Range) Traditional floor trader pivot levels calculated from previous day's High, Low, Close: - **Pivot (PP)** = (High + Low + Close) / 3 - **Top CPR (TC)** = (PP - BC) + PP - **Bottom CPR (BC)** = (High + Low) / 2 ### VIX (Volatility Index) - **< 15**: Complacency, potential for sudden moves - **15-20**: Normal conditions - **20-30**: Elevated uncertainty - **> 30**: High fear, potential bottoming process ### Put/Call Ratio - **< 0.7**: Excessive optimism (contrarian bearish) - **0.7-1.0**: Balanced sentiment - **> 1.0**: Defensive positioning (contrarian bullish potential) ### USI:ADD (NYSE Advance/Decline) - **> 0**: More stocks advancing than declining (bullish breadth) - **< 0**: More stocks declining than advancing (bearish breadth) - **Extreme readings** (±2000+): Potential exhaustion --- ## ⚙️ Customization Options ### Input Parameters - **AD Line Symbol**: Default "ADD" (try "ADVN" or "NYSE:ADD" if needed) - **VIX Symbol**: Default "VIX" (try "CBOE:VIX" if needed) - **Put/Call Symbol**: Default "PCC" (alternatives listed above) ### Color Scheme - Blue: CPR levels - Purple: Pivot point - Green: Bullish signals/backgrounds - Red: Bearish signals/backgrounds - Yellow: Divergence warnings --- ## 💡 Pro Tips 1. **Wait for Confluence**: Don't trade on a single indicator - wait for 3+ signals to align 2. **Use CPR as Dynamic S/R**: Price tends to react at TC and BC levels 3. **Watch the Crossovers**: Put/Call crossovers often precede significant moves 4. **Monitor Divergences**: Yellow candles at key levels are high-value signals 5. **Combine with Price Action**: This tool confirms direction - you still need entry triggers --- ## ⚠️ Limitations & Disclaimers - Requires **premium data** for USI:ADD and VIX on most platforms - Best suited for **intraday SPY trading** (may adapt to other indices) - **Not a standalone system** - use with proper risk management - Past performance does not guarantee future results - Always backtest before live trading --- ## 🎓 Example Scenario **Bullish Setup**: - 9:45 AM EST: Price pulls back to Bottom CPR - Dashboard shows: ✅ Bullish CPR Bias, ✅ VIX 16.5 (falling), ✅ Put/Call 0.68 ⬇️ Bull, ✅ USI:ADD +850 ⬆️ - Green background flashes (Put/Call crossunder) - **Action**: Enter long at BC with stop below TC of previous day --- ## 📊 Ideal Timeframes - **Primary**: 5-minute, 15-minute (day trading) - **Secondary**: 30-minute, 1-hour (swing entries) - **Confirmation**: Daily chart for trend context --- ## 🔄 Updates & Support This indicator is actively maintained. If you encounter symbol loading issues: 1. Check your data provider includes market internals 2. Try alternative symbols in inputs 3. Ensure you're using a premium TradingView plan (if required) --- ## 📝 Version Information - **Version**: 5 (Pine Script v5) - **Type**: Overlay Indicator - **Author**: tapaspattanaik - **Category**: Market Internals / Breadth Analysis --- ## 🏆 Final Thoughts This indicator is designed for **serious traders** who understand that edge comes from confluence, not single signals. By combining institutional pivot levels with real-time market internals, you gain a significant advantage in reading market sentiment and timing entries with precision. **Remember**: The best trades happen when multiple independent factors align. Use this dashboard to find those moments. --- ## 📌 How to Add This Indicator 1. Open TradingView and navigate to Pine Editor 2. Copy the complete script code 3. Click "Add to Chart" 4. Configure symbols if needed (see Setup Instructions above) 5. Adjust position/colors to your preference --- **Happy Trading! 📈** *This indicator is for educational purposes. Always manage risk appropriately and never risk more than you can afford to lose.* --- ### Tags `#SPY` `#MarketInternals` `#CPR` `#VIX` `#PutCallRatio` `#BreadthAnalysis` `#DayTrading` `#SwingTrading` `#TechnicalAnalysis` `#PivotPoints`
Wskaźnik Pine Script®
od Tapaspattanaik
EEQI [Environment Quality Index] PyraTime The Problem: Why Good Strategies Fail The number one reason traders lose capital is not a lack of strategy—it is forced execution in poor environments. Most indicators (RSI, MACD, Stochastic) are continuously active, generating signals even when the market is dead, choppy, or chaotic. A breakout strategy that prints money in a trend will destroy your account in a consolidation range. A mean-reversion system that works in chop will fail during a parabolic expansion. The Solution: PyraTime EEQI The Execution Environment Quality Index (EEQI) is a "Gatekeeper" layer for your trading. It does not tell you what to buy or sell; it tells you if you should be trading at all. By aggregating Volatility, Price Structure, and Efficiency into a single composite score, the EEQI answers the most critical question in discretionary trading: "Is the market efficient enough to deploy capital right now?" How It Works: The 3 Core Engines The EEQI calculates a raw "Environment Score" (from -2 to +4) by analyzing three distinct dimensions of price action. 1. Volatility Engine (Usability) The Logic: Measures the "Alive-ness" of the market using ATR Percentiles. The Filter: It detects "Dead Zones" (where price is too flat to hit targets) and "Chaos Zones" (where volatility is too dangerous). Smart Feature (Parabolic Override): If price moves significantly (>2x ATR) in a single candle, the engine recognizes this as "High Momentum" rather than chaos, unlocking Green signals during breakouts. 2. Structure Engine (Bar Quality) The Logic: Analyzes the relationship between candle bodies, wicks, and overlap. The Filter: It penalizes "Barbed Wire" price action—candles with long wicks and high overlap—which indicate indecision and algo-chop. The Goal: We want to trade during "Clean Flow," where candle bodies are large and overlap is low. 3. Efficiency Engine (Directional Flow) The Logic: Compares Net Displacement (start-to-finish distance) vs. Total Distance Traveled. The Filter: Identifies "Whipsaw" conditions where price moves a lot but goes nowhere. Smart Feature (Velocity Lock): If price travels a massive distance quickly, the efficiency requirement is relaxed to catch explosive moves that might otherwise look "messy." The "Smart Gatekeepers" Even if the Core Engines look good, the EEQI applies three final safety checks before granting a PRIME status. Regime Persistence (Stability Check): The market must hold a high score for a set number of bars (default: 1) before the signal turns Green. This prevents "fake-outs" where a single anomaly candle tricks you into entering a bad trend. Volume Validation (Liquidity Check): Price movement without participation is a trap. The EEQI checks Relative Volume (RVOL). If volume is below average (e.g., lunch hour, holidays, or late-night sessions), the score is capped at "Fair" or "Low Vol," preventing execution in thin liquidity. Macro Context (HTF Filter): You cannot trade against the higher timeframe. The EEQI checks the trend and volatility of the Higher Timeframe (default: Weekly). If the macro view is compressed or dead, the local signal is vetoed. How to Read the HUD The Dashboard (Bottom Right) gives you an instant read on the market state. 🟢 PRIME (+4): Execution Optimal. The market is trending, efficient, and backed by volume. This is the "Green Light" for your strategy. 🔵 FAIR (+1 to +3): Tradeable. Conditions are decent, but one factor (e.g., volume or structure) is imperfect. Exercise caution. ⚪ NEUTRAL (0): Indecision. The market is transitioning. Stand aside. 🟡 BUILDING: Wait. The market is good, but hasn't proven itself yet (Persistence Check). 🟠 POOR / LOW VOL: Chop. Price is messy or lacking participation. 🔴 AVOID (-2): Danger Zone. The market is either dead flat or violently chaotic. Do not trade. Settings & Customization The indicator comes with calibrated presets for different asset classes: Crypto: Tolerates higher volatility and requires stronger efficiency confirmation. Forex: Stricter dead-zone filters to handle ranging sessions. Indices: Balanced settings for standard equity hours. Disclaimer This tool is designed for environment analysis only. It does not provide buy or sell signals, entry prices, or stop-losses. It is intended to be used as a filter to improve the performance of your own discretionary strategies.
Wskaźnik Pine Script®
od PyraTime
Yang-Zhang Stop Lines Yang-Zhang Stop Lines - Advanced Volatility Indicator 📊 Description The Yang-Zhang Stop Lines is an advanced technical indicator that uses the Yang-Zhang volatility estimator to calculate dynamic stop loss and take profit levels. Unlike traditional methods such as ATR or Bollinger Bands, Yang-Zhang considers multiple components of market volatility, offering a more accurate and robust measurement. 🎯 Key Features Superior Volatility Calculation: Implements the complete Yang-Zhang estimator, considering overnight volatility, open-close, and Rogers-Satchell components More accurate than traditional ATR for markets with gaps and distinct sessions Automatically adapts to market conditions Intelligent Levels: Buy Stop (Green): Lower level calculated for long position protection Sell Stop (Red): Upper level calculated for short position protection Mirrored Levels: Additional projections based on daily amplitude Continuous Bands: Real-time visualization of intraday volatility Daily Anchoring: Fixed levels calculated at the beginning of each day Facilitates trade planning with stable references Horizontal lines extending throughout the trading session ⚙️ Configurable Parameters Calculation Timeframe: Defines the period for volatility analysis (default: 60min) Period: Lookback window for statistical calculations (default: 20) Multiplier: Adjusts level sensitivity (default: 1.0) Base Price: Reference for stop calculations (default: close) Visual Options: Bands, fixed lines, labels, fill, and customizable colors 💡 How to Use For Day Traders: Use daily fixed levels as reference for stop loss and targets Watch for price crossovers at levels for reversal signals Mirrored levels serve as extended targets For Swing Traders: Configure higher timeframes (4h, daily) for medium-term analysis Use the multiplier to adjust to your risk/reward objectives Combine with trend analysis and support/resistance Risk Management: Position stops just below/above calculated levels Adjust position size based on amplitude Monitor the info table to check current volatility 📈 Information Table The indicator displays in the top-right corner: Current Yang-Zhang Volatility (in %) Buy Stop Level Sell Stop Level Calculated Amplitude 🔔 Included Alerts Alert when price crosses Buy Stop Alert when price crosses Sell Stop 🎨 Visual Customization Independent colors for each element Adjustable line width Optional fill between bands Optional informative labels 📝 Technical Notes This indicator correctly implements the complete Yang-Zhang estimator formula, including: Overnight variance Open-close variance Rogers-Satchell component Optimized k weighting Ideal for traders seeking a scientific and statistically robust approach to stop definition and volatility analysis. Compatible with all assets and timeframes. Recommended for liquid markets.
Wskaźnik Pine Script®
od SonicTheHedgehog
Toby Crabel's HisVolAs in Linda Raschke's Street smarts..... . This indicator shows the signals of Toby Crabel's Historical Volatility 6/100 strategy. The strategy assumes, that volatility contraction measured by two measures would give better results. There is one other script that is a strategy , but it assumes that the signal requires both inside bar and narrowest range, what is not as in Linda Raschke's. The strategy and what does the script do: 1) measures short-term unannualized volatility (by default six), long term uannualized volatility (by default 100), and measures the ratio of short volatility / long volatility. 2) checks if the current bar is an inside bar or has narrowest range out of last X bar (by default 4), or both, 3) puts an etiquette if short volatility / long volatility is equal to or smaller than 0,5 AND the day is inside bar, has narrowest range, or both. Next day both buy-stop and sell-stop should be set. Buy-stop at the high and sell-stop at the low of the bar with etiquette. This is by no means any financial advice, nor the historical results guarantee future gain.
Wskaźnik Pine Script®
od nikt_inny
1
Asset Volatility Heatmap [SeerQuant]Asset Volatility Heatmap (AVH) AVH is a cross-sectional volatility dashboard that ranks up to 30 assets and visualizes regime shifts as a time-series heatmap. It computes annualized historical volatility (%) on a fixed 1D basis, then maps each asset’s volatility into a configurable color spectrum for fast, intuitive scanning of risk conditions across cryptocurrencies. ⚙️ How It Works 1. Daily, Annualized Historical Volatility Each asset is measured on a fixed 1D timeframe (independent of your chart timeframe). Volatility is annualized and expressed in percentage terms. The user can choose between 1 of 4 volatility estimators: Close-Close (log returns stdev), Parkinson (H/L), Garman-Klass or Rogers-Satchell. 2. Heatmap A heatmap is plotted on the lower window (sorting is turned on by default). Each row represents a rank position. (Rank #1 highest vol ... Rank #30 lowest vol). This means that tokens will move between rows over time as their volatility changes. The asset labels show the current token sitting in each rank bucket. This setting can be turned off for more of a "random" look. 3. Color Scaling The user can select how the color range is normalized for visualization. n = (v - scaleMin) / (scaleMax - scaleMin) Cross-Section: Scales colors using the current bar’s cross-sectional min/max across the asset list. Rolling: Scales colors using a lookback window of cross-sectional ranges, so today’s values are judged relative to recent volatility history. Fixed: Uses your chosen Fixed Scale Min / Max for consistent benchmarking across time. 4. Contrast Control The Color Contrast control option changes how aggressively the palette emphasizes extremes (useful for making “risk spikes” pop vs keeping gradients smooth). 5. Summary Table + Composite Read The table highlights the highest vol / lowest vol token, along with average / median volatility, and a simple regime read (low / medium / high cross-sectional volatility). ✨ How to Use (Practical Reads) Spot risk-on / risk-off transitions: When the heatmap “heats up” broadly (more hot colors across ranks), cross-sectional volatility is expanding (higher dispersion / risk). Identify which names are driving the narrative: With sorting ON, the top ranks show which assets are currently the volatility leaders — often where attention, liquidity, and positioning stress is concentrated. Use it as a regime overlay: Low/steady colors across most ranks tends to align with calmer conditions; sharp bright bursts signal volatility events. ✨ Customizable Settings 1. Assets 30 symbol inputs (defaults to crypto, but works across markets) 2. Calculation Settings Length (lookback) Volatility Estimator (Close-Close / Parkinson / GK / RS) 3. Style Settings Color Scheme (SeerQuant / Viridis / Plasma / Magma / Turbo / Red-Blue) Color Scaling (Cross-Section / Rolling / Fixed) Scaling Lookback (for Rolling) Fixed Scale Min / Max (for Fixed) Color Contrast (emphasize extremes vs smooth gradients) Sort Heatmap (High → Low) Gradient Legend toggle Focus Mode (highlights the chart symbol if included) Ticker Label Right Padding 🚀 Features & Benefits Cross-sectional volatility at a glance (dispersion/risk conditions) Sortable rank heatmap for tracking “who’s hot” in volatility Multiple estimators for different volatility philosophies Flexible normalization (current cross-section, rolling context, or fixed benchmarks) Clean legend + summary stats for quick context 📌 Notes Sorting changes which token appears in each row over time (rows are rank buckets). Volatility is computed on 1D even if your chart is lower/higher timeframe. 📜 Disclaimer This indicator is for educational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Always consult a licensed financial advisor before making trading decisions. Use at your own risk.
Wskaźnik Pine Script®
od SeerQuant
[GYTS] Volatility Toolkit Volatility Toolkit 🌸 Part of GoemonYae Trading System (GYTS) 🌸 🌸 --------- INTRODUCTION --------- 🌸 💮 What is Volatility Toolkit? Volatility Toolkit is a comprehensive volatility analysis indicator featuring academically-grounded range-based estimators. Unlike simplistic measures like ATR, these estimators extract maximum information from OHLC data — resulting in estimates that are 5-14× more statistically efficient than traditional close-to-close methods. The indicator provides two configurable estimator slots, weighted aggregation, adaptive threshold detection, and regime identification — all with flexible smoothing options via GYTS FiltersToolkit integration. 💮 Why Use This Indicator? Standard volatility measures (like simple standard deviation) are highly inefficient, requiring large amounts of data to produce stable estimates. Academic research has shown that range-based estimators extract far more information from the same price data: • Statistical Efficiency — Yang-Zhang achieves up to 14× the efficiency of close-to-close variance, meaning you can achieve the same estimation accuracy with far fewer bars • Drift Independence — Rogers-Satchell and Yang-Zhang correctly isolate variance even in strongly trending markets where simpler estimators become biased • Gap Handling — Yang-Zhang properly accounts for overnight gaps, critical for equity markets • Regime Detection — Built-in threshold modes identify when volatility enters elevated or suppressed states ↑ Overview showing Yang-Zhang volatility with dynamic threshold bands and regime background colouring 🌸 --------- HOW IT WORKS --------- 🌸 💮 Core Concept The toolkit groups volatility estimators by their output scale to ensure valid comparisons and aggregations: • Log-Return Scale (σ) — Close-to-Close, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang. These are comparable and can be aggregated. Annualisable via √(periods_per_year) scaling. • Price Unit Scale ($) — ATR. Measures volatility in absolute price terms, directly usable for stop-loss placement. • Percentage Scale (%) — Chaikin Volatility. Measures the rate of change of the trading range — whether volatility is expanding or contracting. Only estimators with the same scale can be meaningfully compared or aggregated. The indicator enforces this and warns when mixing incompatible scales. 💮 Range-Based Estimator Overview Range-based estimators utilise High, Low, Open, and Close prices to extract significantly more information about the underlying diffusion process than close-only methods: • Parkinson (1980) — Uses High-Low range. ~5× more efficient than close-to-close. Assumes zero drift. • Garman-Klass (1980) — Incorporates Open and Close. ~7.4× more efficient. Assumes zero drift, no gaps. • Rogers-Satchell (1991) — Drift-independent. Superior in trending markets where Parkinson/GK become biased. • Yang-Zhang (2000) — Composite estimator handling both drift and overnight gaps. Up to 14× more efficient. 💮 Theoretical Background • Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53 (1), 61–65. DOI • Garman, M.B. & Klass, M.J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53 (1), 67–78. DOI • Rogers, L.C.G. & Satchell, S.E. (1991). Estimating Variance from High, Low and Closing Prices. Annals of Applied Probability, 1 (4), 504–512. DOI • Yang, D. & Zhang, Q. (2000). Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices. Journal of Business, 73 (3), 477–491. DOI 🌸 --------- KEY FEATURES --------- 🌸 💮 Feature Reference Estimators (8 options across 3 scale groups): • Close-to-Close — Classical benchmark using closing prices only. Least efficient but useful as baseline. Log-return scale. • Parkinson — Range-based (High-Low), ~5× more efficient than close-to-close. Assumes zero drift. Log-return scale. • Garman-Klass — OHLC-optimised, ~7.4× more efficient. Assumes zero drift, no gaps. Log-return scale. • Rogers-Satchell — Drift-independent, handles trending markets where Parkinson/GK become biased. Log-return scale. • Yang-Zhang — Gap-aware composite, most comprehensive (up to 14× efficient). Uses internal rolling variance (unsmoothed). Log-return scale. • Std Dev — Standard deviation of log returns. Log-return scale. • ATR — Average True Range in absolute price units. Useful for stop-loss placement. Price unit scale. • Chaikin — Rate of change of range. Measures volatility expansion/contraction, not level. Percentage scale. Smoothing Filters (10 options via FiltersToolkit): • SMA / EMA — Classical moving averages • Super Smoother (2-Pole / 3-Pole) — Ehlers IIR filter with excellent noise reduction • Ultimate Smoother (2-Pole / 3-Pole) — Near-zero lag in passband • BiQuad — Second-order IIR with configurable Q factor • ADXvma — Adaptive smoothing, flat during ranging periods • MAMA — MESA Adaptive Moving Average (cycle-adaptive) • A2RMA — Adaptive Autonomous Recursive MA Threshold Modes: • Static — Fixed threshold values you define (e.g., 0.025 annualised) • Dynamic — Adaptive bands: baseline ± (standard deviation × multiplier) • Percentile — Threshold at Nth percentile of recent history (e.g., 80th percentile for high) Visual Features: • Level-based colour gradient — Line colour shifts with percentile rank (warm = high vol, cool = low vol) • Fill to zero — Gradient fill intensity proportional to volatility level • Threshold fills — Intensity-scaled fills when thresholds are breached • Regime background — Chart background indicates HIGH/NORMAL/LOW volatility state • Legend table — Displays estimator names, parameters, current values with percentile ranks (P##) 💮 Dual Estimator Slots Compare two volatility estimators side-by-side. Each slot independently configures: • Estimator type (8 options across three scale groups) • Lookback period and smoothing filter • Colour palette and visual style This enables direct comparison between estimators (e.g., Yang-Zhang vs Rogers-Satchell) or between different parameterisations of the same estimator. ↑ Yang-Zhang (reddish) and Rogers-Satchell (greenish) 💮 Flexible Smoothing via FiltersToolkit All estimators (except Yang-Zhang, which uses internal rolling variance) support configurable smoothing through 10 filter types. Using Infinite Impulse Response (IIR) filters instead of SMA avoids the "drop-off artefact" where volatility readings crash when old spikes exit the window. Example: Same estimator (Parkinson) with different smoothing filters Add two instances of Volatility Toolkit to your chart: • Instance 1: Parkinson with SMA smoothing (lookback 14) • Instance 2: Parkinson with Super Smoother 2-Pole (lookback 14) Notice how SMA creates sharp drops when volatile bars exit the window, while Super Smoother maintains a gradual transition. ↑ Two Parkinson estimators — SMA (red mono-colour, showing drop-off artefacts) vs Super Smoother (turquoise mono colour, with smooth transitions) ↑ Garman-Klass with BiQuad (orangy) and 2-pole SuperSmoother filters (greenish) 💮 Weighted Aggregation Combine multiple estimators into a single weighted average. The indicator automatically: • Validates scale compatibility (only same-scale estimators can be aggregated) • Normalises weights (so 2:1 means 67%:33%) • Displays clear warnings when scales differ Example: Robust volatility estimate Combine Yang-Zhang (handles gaps) with Rogers-Satchell (handles drift) using equal weights: • E1: Yang-Zhang (14) • E2: Rogers-Satchell (14) • Aggregation: Enabled, weights 1:1 The aggregated line (with "fill to zero" enabled) provides a more robust estimate by averaging two complementary methodologies. ↑ Yang-Zhang + Rogers-Satchell with aggregation line (thicker) showing combined estimate (notice how opening gaps are handled differently) Example: Trend-weighted aggregation In strongly trending markets, weight Rogers-Satchell more heavily since it's drift-independent: • Estimator 1: Garman-Klass (faster, higher weight in ranging) • Estimator 2: Rogers-Satchell (drift-independent, higher weight in trends) • Aggregation: weights 1:2 (favours RS during trends) 💮 Adaptive Threshold Detection Three threshold modes for identifying volatility regime shifts. Threshold breaches are visualised with intensity-scaled fills that grow stronger the further volatility exceeds the threshold. Example: Dynamic thresholds for regime detection Configure dynamic thresholds to automatically adapt to market conditions: • High Threshold Mode: Dynamic (baseline + 2× std dev) • Low Threshold Mode: Dynamic (baseline - 2× std dev) • Show threshold fills: Enabled This creates adaptive bands that widen during volatile periods and narrow during calm periods. Example: Percentile-based thresholds Use percentile mode for context-aware regime detection: • High Threshold Mode: Percentile (96th) • Low Threshold Mode: Percentile (4th) • Percentile Lookback: 500 This identifies when volatility enters the top/bottom 4% of its recent distribution. ↑ Different threshold settings, where the dynamic and percentile methods show adaptive bands that widen during volatile periods, with fill intensity varying by breach magnitude. Regime detection (see next) is enabled too. 💮 Regime Background Colouring Optional background colouring indicates the current volatility regime: • High Volatility — Warm/alert background colour • Normal — No background (neutral) • Low Volatility — Cool/calm background colour Select which source (Estimator 1, Estimator 2, or Aggregation) drives the regime display. Example: Regime filtering for trade decisions Use regime background to filter trading signals from other indicators: • Regime Source: Aggregation • Background Transparency: 90 (subtle) When the background shows HIGH volatility (warm), consider tighter stops. When LOW (cool), watch for breakout setups. ↑ Regime background emphasis for breakout strategies. Note the interesting A2RMA smoothing for this case. 🌸 --------- USAGE GUIDE --------- 🌸 💮 Getting Started 1. Add the indicator to your chart 2. Estimator 1 defaults to Yang-Zhang (14) — the most comprehensive estimator for gapped markets 3. Keep "Annualise Volatility" enabled to express values in standard annualised form 4. Observe the legend table for current values and percentile ranks (P##). Hover over the table cells to see a little more info in the tooltip. 💮 Choosing an Estimator • Trending equities with gaps — Yang-Zhang. Handles both drift and overnight gaps optimally. • Crypto (24/7 trading) — Rogers-Satchell. Drift-independent without Yang-Zhang's multi-period lag. • Ranging markets — Garman-Klass or Parkinson. Simpler, no drift adjustment needed. • Price-based stops — ATR. Output in price units, directly usable for stop distances. • Regime detection — Combine any estimator with threshold modes enabled. 💮 Interpreting Output • Value (P##) — The volatility reading with percentile rank. "0.1523 (P75)" means 0.1523 annualised volatility at the 75th percentile of recent history. • Colour gradient — Warmer colours = higher percentile (elevated volatility), cooler colours = lower percentile. • Threshold fills — Intensity indicates how far beyond the threshold the current reading is. • ⚠️ HIGH / 🔻 LOW — Table indicators when thresholds are breached. 🌸 --------- ALERTS --------- 🌸 💮 Direction Change Alerts • Estimator 1/2 direction change — Triggers when volatility inflects (rising to falling or vice versa) 💮 Cross Alerts • E1 crossed E2 — Triggers when the two estimator lines cross 💮 Threshold Alerts • E1/E2/Aggr High Volatility — Triggers when volatility breaches the high threshold • E1/E2/Aggr Low Volatility — Triggers when volatility falls below the low threshold 💮 Regime Change Alerts • E1/E2/Aggr Regime Change — Triggers when the volatility regime transitions (High ↔ Normal ↔ Low) 🌸 --------- LIMITATIONS --------- 🌸 • Drift bias in Parkinson/GK — These estimators overestimate variance in trending conditions. Switch to Rogers-Satchell or Yang-Zhang for trending markets. • Yang-Zhang minimum lookback — Requires at least 2 bars (enforced internally). Cannot produce instantaneous readings like other estimators. • Flat candles — Single-tick bars produce near-zero variance readings. Use higher timeframes for illiquid assets. • Discretisation bias — Estimates degrade when ticks-per-bar is very small. Consider higher timeframes for thinly traded instruments. • Scale mixing — Different scale groups (log-return, price unit, percentage) cannot be meaningfully compared or aggregated. The indicator warns but does not prevent display. 🌸 --------- CREDITS --------- 🌸 💮 Academic Sources • Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53 (1), 61–65. DOI • Garman, M.B. & Klass, M.J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53 (1), 67–78. DOI • Rogers, L.C.G. & Satchell, S.E. (1991). Estimating Variance from High, Low and Closing Prices. Annals of Applied Probability, 1 (4), 504–512. DOI • Yang, D. & Zhang, Q. (2000). Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices. Journal of Business, 73 (3), 477–491. DOI • Wilder, J.W. (1978). New Concepts in Technical Trading Systems . Trend Research. 💮 Libraries Used • VolatilityToolkit Library — Range-based estimators, smoothing, and aggregation functions • FiltersToolkit Library — Advanced smoothing filters (Super Smoother, Ultimate Smoother, BiQuad, etc.) • ColourUtilities Library — Colour palette management and gradient calculations
Wskaźnik Pine Script®
od GoemonYae
End Of MooveINDICATOR: END OF MOOVE (EOM) 1. Overview The EndOfMoove (EOM) is a specialized volatility analysis tool designed to detect market exhaustion and potential price reversals. By utilizing a modified Williams Vix Fix (WVF) logic, it identifies when fear or selling pressure has reached a statistical extreme relative to recent history. --- 2. Core Logic & Calculation The script functions by measuring the "synthetic" volatility created during sharp price drops and momentum shifts. * Williams Vix Fix (WVF) Logic: It calculates the distance between the current low and the highest close over a specific lookback period ( 20 bars by default ). This creates a volatility spike during market bottoms or rapid corrections. * Dynamic Normalization: The indicator continuously tracks the Historical Maximum of this volatility over a long window ( 250 bars ). * Statistical Thresholding: It sets a "Danger Zone" at a specific percentage ( 75% ) of that historical maximum to filter out noise and isolate significant exhaustion events. --- 3. Adaptive Intelligence (Detection & Smoothing) The EOM adapts to different market conditions through its detection engine: 1. Spike Confirmation: To avoid premature entries, the script uses a confirmation window ( 3 bars ). A signal is only "confirmed" if the current volatility spike is the highest within this local window. 2. Variable Smoothing: Traders can apply an internal SMA smoothing to the raw volatility data to filter out erratic price action on lower timeframes. --- 4. Visual Anatomy The interface uses a high-contrast design to highlight institutional exhaustion: * The Histogram: * Faded Gray: Represents standard market volatility. The transparency is dynamic ; it darkens as volatility rises, signaling a buildup in pressure. * Bright White: Activates when the volatility crosses the Dynamic Threshold , marking a high-probability exhaustion zone. * The Threshold Line: A continuous horizontal boundary that represents the 75% of historical max , acting as the "Trigger Line." * Signal Triangles: A small white triangle appears at the top of the indicator when a Volatility Spike is statistically confirmed. --- 5. How to Trade with EndOfMoove * Spotting Bottoms: Large white columns often coincide with "capitulation" phases. When the histogram reaches these levels, the current downward move is likely overextended. * Divergence Watch: If price makes a new low but the EOM histogram shows a lower spike than the previous one, it indicates that selling pressure is drying up. * Volatility Breakouts: A sudden transition from faded gray to bright white suggests an impulse move that is reaching its peak velocity. --- 6. Technical Parameters * WVF Period: Controls the sensitivity of the raw volatility calculation. * Historical Max Period: Determines the depth of the statistical database (50 to 500 bars). * Threshold %: Allows the trader to tighten or loosen the "Extreme" zone (set to 75% for balanced results).
Wskaźnik Pine Script®
od AlchimistOfCrypto
Stock School IRL & ERLThis indicator is designed to help traders clearly identify liquidity levels on the chart using IRL (Internal Range Liquidity) and ERL (External Range Liquidity). Liquidity is where the market is attracted. Price does not move randomly — it moves from one liquidity pool to another. With this indicator, you can: • Visually mark IRL (internal liquidity resting inside the range) • Identify ERL (external liquidity above highs & below lows) • Understand where Smart Money targets stops • Anticipate liquidity sweeps, fake breakouts, and reversals • Improve entries, exits, and trade patience This tool helps you stop guessing and start reading market intent. Best used with: Price Action Market Structure Smart Money Concepts (SMC) Works across: Stocks • Indices • Forex • Crypto ⚠️ This indicator does not give buy/sell signals. It provides context, so you trade with logic, not emotions. If you understand liquidity, you understand where the market is going next.
Wskaźnik Pine Script®
od stockschoolindia
2
IV Rank as a Label (Top Right)IV Rank (HV Proxy) – Label Displays an IV Rank–style metric using Historical Volatility (HV) as a proxy, since TradingView Pine Script does not provide access to true per-strike implied volatility or IV Rank. The script: Calculates annualized Historical Volatility (HV) from price returns Ranks current HV relative to its lookback range (default 252 bars) Displays the result as a clean, color-coded label in the top-right corner Color logic: 🟢 Green: Low volatility regime (IV Rank < 20) 🟡 Yellow: Neutral volatility regime (20–50) 🔴 Red: High volatility regime (> 50) This tool is intended for options context awareness, risk framing, and volatility regime identification, not as a substitute for broker-provided IV Rank. Best used alongside: Options chain implied volatility Delta / extrinsic value Time-to-expiration analysis Note: This indicator does not use true implied volatility data.
Wskaźnik Pine Script®
od NikoRojas
Volatility-Dynamic Risk Manager MNQ [HERMAN]Title: Volatility-Dynamic Risk Manager MNQ Description: The Volatility-Dynamic Risk Manager is a dedicated risk management utility designed specifically for traders of Micro Nasdaq 100 Futures (MNQ). Many traders struggle with position sizing because they use a fixed Stop Loss size regardless of market conditions. A 10-point stop might be safe in a slow market but easily stopped out in a high-volatility environment. This indicator solves that problem by monitoring real-time volatility (using ATR) and automatically suggesting the appropriate Stop Loss size and Position Size (Contracts) to keep your dollar risk constant. Note: This tool is hardcoded for MNQ (Micro Nasdaq) with a tick value calculation of $2 per point. 📈 How It Works -This script operates on a logical flow that adapts to market behavior: -Volatility Measurement: It calculates the Average True Range (ATR) over a user-defined length (Default: 14) to gauge the current "speed" of the market. -State Detection: Based on the current ATR, the script classifies the market into one of three states: Low Volatility: The market is chopping or moving slowly. Normal Volatility: Standard trading conditions. High Volatility: The market is moving aggressively. Dynamic Stop Loss Selection: Depending on the detected state, the script selects a pre-defined Stop Loss (in points) that you have configured for that specific environment. Position Sizing Calculation: Finally, it calculates how many MNQ contracts you can trade so that if your Stop Loss is hit, you do not lose more than your defined "Max Risk per Trade." 🧮 Methodology & Calculations Since this script handles risk management, transparency in calculation is vital. Here is the exact math used: ATR Calculation: Contracts = Max Risk / Risk Per Contract ⚙️ Settings You can fully customize the behavior of the risk manager via the settings panel: Risk Management -Max Risk per Trade ($): The maximum amount of USD you are willing to lose on a single trade. Volatility Thresholds (ATR) -ATR Length: The lookback period for volatility calculation. -Upper Limit for LOW Volatility: If ATR is below this number, the market is "Low Volatility." -Lower Limit for HIGH Volatility: If ATR is above this number, the market is "High Volatility." (Anything between Low and High is considered "Normal"). Stop Loss Settings (Points) -SL for Low/Normal/High: Define how wide your stop loss should be in points for each of the three market states. Visual Settings -Color Theme: Switch between Light and Dark modes. -Panel Position: Move the dashboard to any corner or center of your chart. -Panel Size: Adjust the scale (Tiny to Large) to fit your screen resolution. 📊 Dashboard Overview -The on-screen panel provides a quick-glance summary for live execution: -Market State: Color-coded status (Green = Low Vol, Orange = Normal, Red = High Vol). -Current ATR: The live volatility reading. -Suggested SL: The Stop Loss size you should enter in your execution platform. -CONTRACTS: The calculated position size. -Est. Loss: The actual dollar amount you will lose if the stop is hit (usually slightly less than your Max Risk due to rounding down). Who is this for? -Discretionary and systematic futures traders on MNQ (/MNQ or MES also works with small adjustments) -Anyone who wants perfect risk consistency regardless of whether the market is asleep or exploding -Traders who hate manual position-size calculations on every trade No repainting Works on any timeframe Real-time updates on every bar Overlay indicator (no signals, pure risk-management tool) ⚠️ Disclaimer This tool is for informational and educational purposes only. It calculates mathematical position sizes based on user inputs. It does not execute trades, nor does it guarantee profits. Past performance (volatility) is not indicative of future results. Always manually verify your order size before executing trades on your broker platform.
Wskaźnik Pine Script®
od helmans13
3
Volatility Risk PremiumTHE INSURANCE PREMIUM OF THE STOCK MARKET Every day, millions of investors face a fundamental question that has puzzled economists for decades: how much should protection against market crashes cost? The answer lies in a phenomenon called the Volatility Risk Premium, and understanding it may fundamentally change how you interpret market conditions. Think of the stock market like a neighborhood where homeowners buy insurance against fire. The insurance company charges premiums based on their estimates of fire risk. But here is the interesting part: insurance companies systematically charge more than the actual expected losses. This difference between what people pay and what actually happens is the insurance premium. The same principle operates in financial markets, but instead of fire insurance, investors buy protection against market volatility through options contracts. The Volatility Risk Premium, or VRP, measures exactly this difference. It represents the gap between what the market expects volatility to be (implied volatility, as reflected in options prices) and what volatility actually turns out to be (realized volatility, calculated from actual price movements). This indicator quantifies that gap and transforms it into actionable intelligence. THE FOUNDATION The academic study of volatility risk premiums began gaining serious traction in the early 2000s, though the phenomenon itself had been observed by practitioners for much longer. Three research papers form the backbone of this indicator's methodology. Peter Carr and Liuren Wu published their seminal work "Variance Risk Premiums" in the Review of Financial Studies in 2009. Their research established that variance risk premiums exist across virtually all asset classes and persist over time. They documented that on average, implied volatility exceeds realized volatility by approximately three to four percentage points annualized. This is not a small number. It means that sellers of volatility insurance have historically collected a substantial premium for bearing this risk. Tim Bollerslev, George Tauchen, and Hao Zhou extended this research in their 2009 paper "Expected Stock Returns and Variance Risk Premia," also published in the Review of Financial Studies. Their critical contribution was demonstrating that the VRP is a statistically significant predictor of future equity returns. When the VRP is high, meaning investors are paying substantial premiums for protection, future stock returns tend to be positive. When the VRP collapses or turns negative, it often signals that realized volatility has spiked above expectations, typically during market stress periods. Gurdip Bakshi and Nikunj Kapadia provided additional theoretical grounding in their 2003 paper "Delta-Hedged Gains and the Negative Market Volatility Risk Premium." They demonstrated through careful empirical analysis why volatility sellers are compensated: the risk is not diversifiable and tends to materialize precisely when investors can least afford losses. HOW THE INDICATOR CALCULATES VOLATILITY The calculation begins with two separate measurements that must be compared: implied volatility and realized volatility. For implied volatility, the indicator uses the CBOE Volatility Index, commonly known as the VIX. The VIX represents the market's expectation of 30-day forward volatility on the S&P 500, calculated from a weighted average of out-of-the-money put and call options. It is often called the "fear gauge" because it rises when investors rush to buy protective options. Realized volatility requires more careful consideration. The indicator offers three distinct calculation methods, each with specific advantages rooted in academic literature. The Close-to-Close method is the most straightforward approach. It calculates the standard deviation of logarithmic daily returns over a specified lookback period, then annualizes this figure by multiplying by the square root of 252, the approximate number of trading days in a year. This method is intuitive and widely used, but it only captures information from closing prices and ignores intraday price movements. The Parkinson estimator, developed by Michael Parkinson in 1980, improves efficiency by incorporating high and low prices. The mathematical formula calculates variance as the sum of squared log ratios of daily highs to lows, divided by four times the natural logarithm of two, times the number of observations. This estimator is theoretically about five times more efficient than the close-to-close method because high and low prices contain additional information about the volatility process. The Garman-Klass estimator, published by Mark Garman and Michael Klass in 1980, goes further by incorporating opening, high, low, and closing prices. The formula combines half the squared log ratio of high to low prices minus a factor involving the log ratio of close to open. This method achieves the minimum variance among estimators using only these four price points, making it particularly valuable for markets where intraday information is meaningful. THE CORE VRP CALCULATION Once both volatility measures are obtained, the VRP calculation is straightforward: subtract realized volatility from implied volatility. A positive result means the market is paying a premium for volatility insurance. A negative result means realized volatility has exceeded expectations, typically indicating market stress. The raw VRP signal receives slight smoothing through an exponential moving average to reduce noise while preserving responsiveness. The default smoothing period of five days balances signal clarity against lag. INTERPRETING THE REGIMES The indicator classifies market conditions into five distinct regimes based on VRP levels. The EXTREME regime occurs when VRP exceeds ten percentage points. This represents an unusual situation where the gap between implied and realized volatility is historically wide. Markets are pricing in significantly more fear than is materializing. Research suggests this often precedes positive equity returns as the premium normalizes. The HIGH regime, between five and ten percentage points, indicates elevated risk aversion. Investors are paying above-average premiums for protection. This often occurs after market corrections when fear remains elevated but realized volatility has begun subsiding. The NORMAL regime covers VRP between zero and five percentage points. This represents the long-term average state of markets where implied volatility modestly exceeds realized volatility. The insurance premium is being collected at typical rates. The LOW regime, between negative two and zero percentage points, suggests either unusual complacency or that realized volatility is catching up to implied volatility. The premium is shrinking, which can precede either calm continuation or increased stress. The NEGATIVE regime occurs when realized volatility exceeds implied volatility. This is relatively rare and typically indicates active market stress. Options were priced for less volatility than actually occurred, meaning volatility sellers are experiencing losses. Historically, deeply negative VRP readings have often coincided with market bottoms, though timing the reversal remains challenging. TERM STRUCTURE ANALYSIS Beyond the basic VRP calculation, sophisticated market participants analyze how volatility behaves across different time horizons. The indicator calculates VRP using both short-term (default ten days) and long-term (default sixty days) realized volatility windows. Under normal market conditions, short-term realized volatility tends to be lower than long-term realized volatility. This produces what traders call contango in the term structure, analogous to futures markets where later delivery dates trade at premiums. The RV Slope metric quantifies this relationship. When markets enter stress periods, the term structure often inverts. Short-term realized volatility spikes above long-term realized volatility as markets experience immediate turmoil. This backwardation condition serves as an early warning signal that current volatility is elevated relative to historical norms. The academic foundation for term structure analysis comes from Scott Mixon's 2007 paper "The Implied Volatility Term Structure" in the Journal of Derivatives, which documented the predictive power of term structure dynamics. MEAN REVERSION CHARACTERISTICS One of the most practically useful properties of the VRP is its tendency to mean-revert. Extreme readings, whether high or low, tend to normalize over time. This creates opportunities for systematic trading strategies. The indicator tracks VRP in statistical terms by calculating its Z-score relative to the trailing one-year distribution. A Z-score above two indicates that current VRP is more than two standard deviations above its mean, a statistically unusual condition. Similarly, a Z-score below negative two indicates VRP is unusually low. Mean reversion signals trigger when VRP reaches extreme Z-score levels and then shows initial signs of reversal. A buy signal occurs when VRP recovers from oversold conditions (Z-score below negative two and rising), suggesting that the period of elevated realized volatility may be ending. A sell signal occurs when VRP contracts from overbought conditions (Z-score above two and falling), suggesting the fear premium may be excessive and due for normalization. These signals should not be interpreted as standalone trading recommendations. They indicate probabilistic conditions based on historical patterns. Market context and other factors always matter. MOMENTUM ANALYSIS The rate of change in VRP carries its own information content. Rapidly rising VRP suggests fear is building faster than volatility is materializing, often seen in the early stages of corrections before realized volatility catches up. Rapidly falling VRP indicates either calming conditions or rising realized volatility eating into the premium. The indicator tracks VRP momentum as the difference between current VRP and VRP from a specified number of bars ago. Positive momentum with positive acceleration suggests strengthening risk aversion. Negative momentum with negative acceleration suggests intensifying stress or rapid normalization from elevated levels. PRACTICAL APPLICATION For equity investors, the VRP provides context for risk management decisions. High VRP environments historically favor equity exposure because the market is pricing in more pessimism than typically materializes. Low or negative VRP environments suggest either reducing exposure or hedging, as markets may be underpricing risk. For options traders, understanding VRP is fundamental to strategy selection. Strategies that sell volatility, such as covered calls, cash-secured puts, or iron condors, tend to profit when VRP is elevated and compress toward its mean. Strategies that buy volatility tend to profit when VRP is low and risk materializes. For systematic traders, VRP provides a regime filter for other strategies. Momentum strategies may benefit from different parameters in high versus low VRP environments. Mean reversion strategies in VRP itself can form the basis of a complete trading system. LIMITATIONS AND CONSIDERATIONS No indicator provides perfect foresight, and the VRP is no exception. Several limitations deserve attention. The VRP measures a relationship between two estimates, each subject to measurement error. The VIX represents expectations that may prove incorrect. Realized volatility calculations depend on the chosen method and lookback period. Mean reversion tendencies hold over longer time horizons but provide limited guidance for short-term timing. VRP can remain extreme for extended periods, and mean reversion signals can generate losses if the extremity persists or intensifies. The indicator is calibrated for equity markets, specifically the S&P 500. Application to other asset classes requires recalibration of thresholds and potentially different data sources. Historical relationships between VRP and subsequent returns, while statistically robust, do not guarantee future performance. Structural changes in markets, options pricing, or investor behavior could alter these dynamics. STATISTICAL OUTPUTS The indicator presents comprehensive statistics including current VRP level, implied volatility from VIX, realized volatility from the selected method, current regime classification, number of bars in the current regime, percentile ranking over the lookback period, Z-score relative to recent history, mean VRP over the lookback period, realized volatility term structure slope, VRP momentum, mean reversion signal status, and overall market bias interpretation. Color coding throughout the indicator provides immediate visual interpretation. Green tones indicate elevated VRP associated with fear and potential opportunity. Red tones indicate compressed or negative VRP associated with complacency or active stress. Neutral tones indicate normal market conditions. ALERT CONDITIONS The indicator provides alerts for regime transitions, extreme statistical readings, term structure inversions, mean reversion signals, and momentum shifts. These can be configured through the TradingView alert system for real-time monitoring across multiple timeframes. REFERENCES Bakshi, G., and Kapadia, N. (2003). Delta-Hedged Gains and the Negative Market Volatility Risk Premium. Review of Financial Studies, 16(2), 527-566. Bollerslev, T., Tauchen, G., and Zhou, H. (2009). Expected Stock Returns and Variance Risk Premia. Review of Financial Studies, 22(11), 4463-4492. Carr, P., and Wu, L. (2009). Variance Risk Premiums. Review of Financial Studies, 22(3), 1311-1341. Garman, M. B., and Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53(1), 67-78. Mixon, S. (2007). The Implied Volatility Term Structure of Stock Index Options. Journal of Empirical Finance, 14(3), 333-354. Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53(1), 61-65.
Wskaźnik Pine Script®
od EdgeTools
6
Linear Moments█ OVERVIEW The Linear Moments indicator, also known as L-moments, is a statistical tool used to estimate the properties of a probability distribution. It is an alternative to conventional moments and is more robust to outliers and extreme values. █ CONCEPTS █ Four moments of a distribution We have mentioned the concept of the Moments of a distribution in one of our previous posts. The method of Linear Moments allows us to calculate more robust measures that describe the shape features of a distribution and are anallougous to those of conventional moments. L-moments therefore provide estimates of the location, scale, skewness, and kurtosis of a probability distribution. The first L-moment, λ₁, is equivalent to the sample mean and represents the location of the distribution. The second L-moment, λ₂, is a measure of the dispersion of the distribution, similar to the sample standard deviation. The third and fourth L-moments, λ₃ and λ₄, respectively, are the measures of skewness and kurtosis of the distribution. Higher order L-moments can also be calculated to provide more detailed information about the shape of the distribution. One advantage of using L-moments over conventional moments is that they are less affected by outliers and extreme values. This is because L-moments are based on order statistics, which are more resistant to the influence of outliers. By contrast, conventional moments are based on the deviations of each data point from the sample mean, and outliers can have a disproportionate effect on these deviations, leading to skewed or biased estimates of the distribution parameters. █ Order Statistics L-moments are statistical measures that are based on linear combinations of order statistics, which are the sorted values in a dataset. This approach makes L-moments more resistant to the influence of outliers and extreme values. However, the computation of L-moments requires sorting the order statistics, which can lead to a higher computational complexity. To address this issue, we have implemented an Online Sorting Algorithm that efficiently obtains the sorted dataset of order statistics, reducing the time complexity of the indicator. The Online Sorting Algorithm is an efficient method for sorting large datasets that can be updated incrementally, making it well-suited for use in trading applications where data is often streamed in real-time. By using this algorithm to compute L-moments, we can obtain robust estimates of distribution parameters while minimizing the computational resources required. █ Bias and efficiency of an estimator One of the key advantages of L-moments over conventional moments is that they approach their asymptotic normal closer than conventional moments. This means that as the sample size increases, the L-moments provide more accurate estimates of the distribution parameters. Asymptotic normality is a statistical property that describes the behavior of an estimator as the sample size increases. As the sample size gets larger, the distribution of the estimator approaches a normal distribution, which is a bell-shaped curve. The mean and variance of the estimator are also related to the true mean and variance of the population, and these relationships become more accurate as the sample size increases. The concept of asymptotic normality is important because it allows us to make inferences about the population based on the properties of the sample. If an estimator is asymptotically normal, we can use the properties of the normal distribution to calculate the probability of observing a particular value of the estimator, given the sample size and other relevant parameters. In the case of L-moments, the fact that they approach their asymptotic normal more closely than conventional moments means that they provide more accurate estimates of the distribution parameters as the sample size increases. This is especially useful in situations where the sample size is small, such as when working with financial data. By using L-moments to estimate the properties of a distribution, traders can make more informed decisions about their investments and manage their risk more effectively. Below we can see the empirical dsitributions of the Variance and L-scale estimators. We ran 10000 simulations with a sample size of 100. Here we can clearly see how the L-moment estimator approaches the normal distribution more closely and how such an estimator can be more representative of the underlying population. █ WAYS TO USE THIS INDICATOR The Linear Moments indicator can be used to estimate the L-moments of a dataset and provide insights into the underlying probability distribution. By analyzing the L-moments, traders can make inferences about the shape of the distribution, such as whether it is symmetric or skewed, and the degree of its spread and peakedness. This information can be useful in predicting future market movements and developing trading strategies. One can also compare the L-moments of the dataset at hand with the L-moments of certain commonly used probability distributions. Finance is especially known for the use of certain fat tailed distributions such as Laplace or Student-t. We have built in the theoretical values of L-kurtosis for certain common distributions. In this way a person can compare our observed L-kurtosis with the one of the selected theoretical distribution. █ FEATURES Source Settings Source - Select the source you wish the indicator to calculate on Source Selection - Selec whether you wish to calculate on the source value or its log return Moments Settings Moments Selection - Select the L-moment you wish to be displayed Lookback - Determine the sample size you wish the L-moments to be calculated with Theoretical Distribution - This setting is only for investingating the kurtosis of our dataset. One can compare our observed kurtosis with the kurtosis of a selected theoretical distribution.
Wskaźnik Pine Script®
od Motgench
1
Orbital Barycenter Matrix @darshaksscThe Orbital Barycenter Matrix is a visual, informational-only tool that models how price behaves around a dynamically calculated barycenter —a type of moving equilibrium derived entirely from historical price data. Instead of focusing on signals, this indicator focuses on market structure symmetry, distance, compression, expansion, and volatility-adjusted movement. This script does not predict future price and does not provide buy/sell signals . All values and visuals come solely from confirmed historical data , in full compliance with TradingView policy. 📘 How the Indicator Works 1. Dynamic Barycenter (Core Mean Line) The barycenter is calculated from a smoothed blend of historical price components. It represents the center of mass around which price tends to oscillate. This is not a forecast line—only a representation of historical average behavior. 2. Orbital Rings (Distance Zones) Around the barycenter, the indicator draws several “orbital rings.” Each ring shows a volatility-scaled distance from the barycenter using ATR-based calculations. These rings help visualize: How far price has drifted from its historical center Whether price is moving in an inner, mid, or outer region How volatility influences the spacing of the rings Rings do not imply future targets and are informational only. 3. Orbital Extension Range Beyond the outermost ring, a wider band (extension range) shows a high-volatility reference distance. It represents extended displacement relative to past price behavior—not a projected target. 4. Orbit Trail (Motion Trace) The Orbit Trail plots small circles behind price, helping visualize how price has moved through the orbital regions over time. Colors adjust with “pressure” (distance from center), making compression and expansion easy to observe. 5. Satellite Nodes (Swing Markers) Confirmed swing highs and lows (using fixed pivots) are marked as small dots. Their color reflects the orbital zone they formed in, giving context to how significant or extended each pivot was. These swing markers do not repaint because they use confirmed pivots. 6. Pressure & Distance Calculations The indicator converts price displacement away from the barycenter into a pressure metric, scaled between 0%–100%. Higher pressure means price is further from its historical center relative to volatility. The dashboard displays: Zone classification ATR-based distance Pressure level A small intensity gauge All are informational readings—no direction or forecast. 📊 Key Features ✔ Dynamic barycenter core ✔ Up to four orbital rings ✔ Informational orbital extension band ✔ Visual orbit trail showing recent movement ✔ Non-repainting satellite swing nodes ✔ Distance & pressure analytics ✔ Fully adjustable HUD ✔ Always-visible floating dashboard (screen-anchored) ✔ Zero repainting on confirmed elements ✔ 100% sourced from historical data only ✔ Policy-safe: no predictions, no signals, no targets 🎯 What to Look For 1. How close price is to the barycenter This can reveal whether price is in: The inner region The mid zone The outer region The extended field 2. Pressure level Shows how “stretched” price is relative to its past behavior. 3. Satellite nodes Indicate where confirmed pivots formed and in which orbital band. 4. Ring interactions Observe how price moves between rings—inside, outside, or oscillating around them. 5. Color changes in the orbit trail These show changes in market compression/expansion. 🧭 How to Read the Indicator Inner Orbit Price close to its historical equilibrium. Mid Orbit Moderate displacement from typical range. Outer Orbit Historically extended movement. Beyond Extension Field Price has moved further than usual relative to historical volatility. These are descriptive conditions only , not trade recommendations. 🛠 How to Apply It on the Chart Use the barycenter to understand where price has historically balanced. Observe how volatility changes the spacing between rings. Use pressure readings to identify when price is compressed, neutral, or extended. Use swing nodes to contextualize historical pivot formation. Watch how price interacts with rings to better understand rhythm, velocity, and structural behavior. This tool is meant to enhance visual understanding—not to generate trade entries or exits. ⚠️ Important Disclosure This indicator is strictly informational. It does not predict or project future price movement. It does not provide buy/sell/long/short signals. All lines, zones, and values are derived solely from past market data. Any interpretation is at the user’s discretion.
Wskaźnik Pine Script®
od darshakssc
VIX Calm vs Choppy (Bar Version, VIX High Threshold)This indicator tracks market stability by measuring how long the VIX stays below or above a chosen intraday threshold. Instead of looking at VIX closes, it uses VIX high, so even a brief intraday spike will flip the regime into “choppy.” The tool builds a running clock of consecutive bars spent in each regime: Calm regime: VIX high stays below the threshold Choppy regime: VIX high hits or exceeds the threshold Calm streaks plot as positive bars (light blue background). Choppy streaks plot as negative bars (dark pink background). This gives a clean picture of how long the market has been stable vs volatile — useful for trend traders, breakout traders, and anyone who watches risk-on/risk-off conditions. A table shows the current regime and streak length for quick reference.
Wskaźnik Pine Script®
od Nik-Vora
Liquidity & Momentum Master (LMM)💎 Liquidity & Momentum Master (LMM) A professional dual-system indicator that combines: 📦 High-Volume Support/Resistance Zones and 📊 RSI + Bollinger Band Combo Signals — to visualize both smart money footprints and momentum reversals in one clean tool. 🧱 1. High-Volume Liquidity Zones (Support/Resistance Boxes) Conditions Visible only on 1H and higher timeframes (1H, 4H, 1D, etc.) Detects candles with abnormally high volume and strong ATR-based range Separates bullish (support) and bearish (resistance) zones Visualization All boxes are white, with adjustable transparency (alphaW, alphaBorder) Each box extends to the right automatically Only the most important (Top-N) zones are kept — weaker ones are removed automatically Interpretation White boxes = price areas with heavy liquidity and volume concentration Price approaching these zones often leads to bounces or rejections Narrow spacing = consolidation, wide spacing = potential large move 💎 2. RSI Exit + BB-RSI Combo Signals RSI Exit (Overbought/Oversold Recovery) RSI drops from overbought (>70) → plots red “RSI” above the candle RSI rises from oversold (<30) → plots green “RSI” below the candle Works on 15m, 30m, 1H, 4H, 1D → Indicates short-term exhaustion recovery BB-RSI Combo (Momentum Reversal Confirmation) Active on 1H and higher only Requires both: ✅ RSI divergence (bullish or bearish) ✅ Bollinger Band re-entry (after temporary breakout) Combo Buy (Green Diamond) Bullish RSI divergence Candle closes back above lower Bollinger Band Combo Sell (Red Diamond) Bearish RSI divergence Candle closes back below upper Bollinger Band → Confirms stronger reversal momentum compared to standard RSI signals
Wskaźnik Pine Script®
od HSeungpyo
NY VIX Channel Trend US Futures Day Trade StrategyNY VIX Channel Trend Strategy Summary in one paragraph Session anchored intraday strategy for index futures such as ES and NQ on one to fifteen minute charts. It acts only after the first configurable window of New York Regular Trading Hours and uses a VIX derived daily implied move to form a realistic channel from the session open. Originality comes from using a pure implied volatility yardstick as portable support and resistance, then committing in the direction of the first window close relative to the open. Add it to a clean chart and trade the simple visuals. For conservative alerts use on bar close. Scope and intent • Markets. Index futures ES and NQ • Timeframes. One to thirty minutes • Default demo. ES1 on five minutes • Purpose. Provide a portable intraday yardstick for entries and exits without curve fitting • Limits. This is a strategy. Orders are simulated on standard candles Originality and usefulness • Unique concept. A VIX only channel anchored at 09:30 New York plus a single window trend test • Addresses. False urgency at session open and unrealistic bands from arbitrary multipliers • Testability. Every input is visible and the channel is plotted so users can audit behavior • Portable yardstick. Daily implied move equals VIX percent divided by square root of two hundred fifty two • Protected status. None. Method and use are fully disclosed Method overview in plain language Take the daily VIX or VIX9D value, convert it to a daily fraction by dividing by square root of two hundred fifty two, then anchor a symmetric channel at the New York session open. Observe the first N minutes. If that window closes above the open the bias is long. If it closes below the open the bias is short. One trade per session. Exits occur at the channel boundary or at a bracket based on a user selected VIX factor. Positions are closed a set number of minutes before the session ends. Base measures Return basis. The daily implied move unit equals VIX percent divided by square root of two hundred fifty two and serves as the distance unit for targets and stops. Components • VIX Channel. Top, mid, bottom lines anchored at 09:30 New York. No extra multipliers • Window Trend. Close of the first N minutes relative to the session open sets direction • Risk Bracket. Take profit and stop loss equal to VIX unit times user factor • Session Window. Uses the exchange time of the chart Fusion rule Minimum gates count equals one. The trade only arms after the window has elapsed and a direction exists. One entry per session. Signal rule • Long when the window close is above the session open and the window has completed • Short when the window close is below the session open and the window has completed • Exit on channel touch. Long exits at the top. Short exits at the bottom • Flat thirty minutes before the session close or at the user setting Inputs with guidance Setup • Use VIX9D. Width source. Typical true for fast tone or false for baseline • Use daily OPEN. Toggle for sensitivity to overnight changes Logic • Window minutes. Five to one hundred twenty. Larger values delay entries and reduce whipsaw • VIX factor for TP. Zero point five to two. Raising it widens the profit target • VIX factor for SL. Zero point five to two. Raising it widens the stop • Exit minutes before close. Fifteen to ninety. Raising it exits earlier Properties visible in this publication • Initial capital one hundred thousand USD • Base currency USD • request.security uses lookahead off • Commission cash per contract two point five $ per each contract. Slippage one tick • Default order size method FIXED with value one contract. Pyramiding zero. Process orders on close ON. Bar magnifier OFF. Recalculate after order is filled OFF. Calc on every tick ON Realism and responsible publication No performance claims. Past results never guarantee future outcomes. Fills and slippage vary by venue. Shapes can move while a bar forms and settle on close. Strategy uses standard candles. Honest limitations and failure modes Economic releases and thin liquidity can break the channel. Very quiet regimes can reduce signal contrast. Session windows follow the exchange time of the chart. If both stop and target can be hit within one bar, assume stop first for conservative reading without bar magnifier. Works best in liquid hours of New York RTH. Very large gaps and surprise news may exceed the implied channel. Always validate on the symbols you trade. Entries and exits • Entry logic. After the first window, go long if the window close is above the session open, go short if below • Exit logic. Long exits at the channel top or at the take profit or stop. Short exits at the channel bottom or at the take profit or stop. Flat before session close by the configured minutes • Risk model. Initial stop and target based on the VIX unit times user factors. No trail and no break even. No cooldown • Tie handling. Treat as stop first for conservative interpretation Position sizing Fixed size one contract per trade. Target risk per trade should generally remain near one percent of account equity. Risk is based on the daily volatility value, the max loss from the tests for one year duration with 5min chart was 4%, while the avg loss was below <1% of the total capital. If you have any questions please let me know. Thank you for coming by !
Strategia Pine Script®
od exlux
10
HV Spike Strategy (HVP + OR Breakout + Reversal + TP/SL Modes)Here is a script that I tried to make it simple, although it has several parameters, I will try to explain, here we go: Logic: Open Range Breakout: otherwise knows as First Candle Rule, usually used for the first candle in the opening of a market session, in my strategy there is an option to use it even for Crypto that operate 24/7, how to do that? Simply by detecting Volatility from the HVP (Historical Volatility Percentile). Then the ORB logic kicks in and the first candle with high volatility gives the ranges for the trades. The proper HVP Activation Threshold has to be selected for each currency pair/index/crypto in order to have maximum profit. Enter a trade: when the price goes 100% above/below the First Candle Rule Range. That way it is filtering fake breakouts. Also if the price reverses back into the range the strategy takes the opposite trade. Exit a trade: SL/TP By percentage or ATR, selection in the input menu. My intention is to avoid using lagging indicators or guessing of Price Action, purely Bull/Bear indication by the first candle. I hope you find this helpful! Wishing all successful Trades!
Strategia Pine Script®
od kostastrovas
1