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ATR multiple from High & LowA simple numerical indicator measuring ATR multiple from recent 252 days high and low. ATR multiples from high (and low) are used as a base in many systematic trading and trend following systems. As an example many systems buy after a 2.5–4 ATR multiple pullback in a strong stock if the regime allows it. This would then be paired with an entry tactic, for example buy as it recaptures the a pivot within the upper range, a MA or breaks out again after this mid term pullback/shakeout. This indicator uses a function which captures the recent high and low no matter if we have 252 bars or not, which is not how standard high/low works in Tradingview. This means it also works with recent IPO:s. I prefer to overlay the indicator in one of the lower panes, for example the volume pane and then right click on the indicator and select Pin to scale > No scale (fullscreen).
Pine Script® 인디케이터
kulturdesken의
Static K-means Clustering | InvestorUnknownStatic K-Means Clustering is a machine-learning-driven market regime classifier designed for traders who want a data-driven structure instead of subjective indicators or manually drawn zones. This script performs offline (static) K-means training on your chosen historical window. Using four engineered features: RSI (Momentum) CCI (Price deviation / Mean reversion) CMF (Money flow / Strength) MACD Histogram (Trend acceleration) It groups past market conditions into K distinct clusters (regimes). After training, every new bar is assigned to the nearest cluster via Euclidean distance in 4-dimensional standardized feature space. This allows you to create models like: Regime-based long/short filters Volatility phase detectors Trend vs. chop separation Mean-reversion vs. breakout classification Volume-enhanced money-flow regime shifts Full machine-learning trading systems based solely on regimes Note: This script is not a universal ML strategy out of the box. The user must engineer the feature set to match their trading style and target market. K-means is a tool, not a ready made system, this script provides the framework. Core Idea K-means clustering takes raw, unlabeled market observations and attempts to discover structure by grouping similar bars together. // STEP 1 — DATA POINTS ON A COORDINATE PLANE // We start with raw, unlabeled data scattered in 2D space (x/y). // At this point, nothing is grouped—these are just observations. // K-means will try to discover structure by grouping nearby points. // // y ↑ // | // 12 | • // | • // 10 | • // | • // 8 | • • // | // 6 | • // | // 4 | • // | // 2 |______________________________________________→ x // 2 4 6 8 10 12 14 // // // // STEP 2 — RANDOMLY PLACE INITIAL CENTROIDS // The algorithm begins by placing K centroids at random positions. // These centroids act as the temporary “representatives” of clusters. // Their starting positions heavily influence the first assignment step. // // y ↑ // | // 12 | • // | • // 10 | • C2 × // | • // 8 | • • // | // 6 | C1 × • // | // 4 | • // | // 2 |______________________________________________→ x // 2 4 6 8 10 12 14 // // // // STEP 3 — ASSIGN POINTS TO NEAREST CENTROID // Each point is compared to all centroids. // Using simple Euclidean distance, each point joins the cluster // of the centroid it is closest to. // This creates a temporary grouping of the data. // // (Coloring concept shown using labels) // // - Points closer to C1 → Cluster 1 // - Points closer to C2 → Cluster 2 // // y ↑ // | // 12 | 2 // | 1 // 10 | 1 C2 × // | 2 // 8 | 1 2 // | // 6 | C1 × 2 // | // 4 | 1 // | // 2 |______________________________________________→ x // 2 4 6 8 10 12 14 // // (1 = assigned to Cluster 1, 2 = assigned to Cluster 2) // At this stage, clusters are formed purely by distance. Your chosen historical window becomes the static training dataset , and after fitting, the centroids never change again. This makes the model: Predictable Repeatable Consistent across backtests Fast for live use (no recalculation of centroids every bar) Static Training Window You select a period with: Training Start Training End Only bars inside this range are used to fit the K-means model. This window defines: the market regime examples the statistical distributions (means/std) for each feature how the centroids will be positioned post-trainin Bars before training = fully transparent Training bars = gray Post-training bars = full colored regimes Feature Engineering (4D Input Vector) Every bar during training becomes a 4-dimensional point: This combination balances: momentum, volatility, mean-reversion, trend acceleration giving the algorithm a richer "market fingerprint" per bar. Standardization To prevent any feature from dominating due to scale differences (e.g., CMF near zero vs CCI ±200), all features are standardized: standardize(value, mean, std) => (value - mean) / std Centroid Initialization Centroids start at diverse coordinates using various curves: linear sinusoidal sign-preserving quadratic tanh compression init_centroids() => // Spread centroids across using different shapes per feature for c = 0 to k_clusters - 1 frac = k_clusters == 1 ? 0.0 : c / (k_clusters - 1.0) // 0 → 1 v = frac * 2 - 1 // -1 → +1 array.set(cent_rsi, c, v) // linear array.set(cent_cci, c, math.sin(v)) // sinusoidal array.set(cent_cmf, c, v * v * (v < 0 ? -1 : 1)) // quadratic sign-preserving array.set(cent_mac, c, tanh(v)) // compressed This makes initial cluster spread “random” even though true randomness is hardly achieved in pinescript. K-Means Iterative Refinement The algorithm repeats these steps: (A) Assignment Step, Each bar is assigned to the nearest centroid via Euclidean distance in 4D: distance = sqrt(dx² + dy² + dz² + dw²) (B) Update Step, Centroids update to the mean of points assigned to them. This repeats iterations times (configurable). LIVE REGIME CLASSIFICATION After training, each new bar is: Standardized using the training mean/std Compared to all centroids Assigned to the nearest cluster Bar color updates based on cluster No re-training occurs. This ensures: No lookahead bias Clean historical testing Stable regimes over time CLUSTER BEHAVIOR & TRADING LOGIC Clusters (0, 1, 2, 3…) hold no inherent meaning. The user defines what each cluster does. Example of custom actions: Cluster 0 → Cash Cluster 1 → Long Cluster 2 → Short Cluster 3+ → Cash (noise regime) This flexibility means: One trader might have cluster 0 as consolidation. Another might repurpose it as a breakout-loading zone. A third might ignore 3 clusters entirely. Example on ETHUSD Important Note: Any change of parameters or chart timeframe or ticker can cause the “order” of clusters to change The script does NOT assume any cluster equals any actionable bias, user decides. PERFORMANCE METRICS & ROC TABLE The indicator computes average 1-bar ROC for each cluster in: Training set Test (live) set This helps measure: Cluster profitability consistency Regime forward predictability Whether a regime is noise, trend, or reversion-biased EQUITY SIMULATION & FEES Designed for close-to-close realistic backtesting. Position = cluster of previous bar Fees applied only on regime switches. Meaning: Staying long → no fee Switching long→short → fee applied Switching any→cash → fee applied Fee input is percentage, but script already converts internally. Disclaimers ⚠️ This indicator uses machine-learning but does not predict the future. It classifies similarity to past regimes, nothing more. ⚠️ Backtest results are not indicative of future performance. ⚠️ Clusters have no inherent “bullish” or “bearish” meaning. You must interpret them based on your testing and your own feature engineering.
Pine Script® 인디케이터
InvestorUnknown의
2
Huli-Huli (Reversal) WindowHuli-Huli (Reversal) Time-Zone Highlighter Huli (Hawaiian for "turn/flip") highlights specific time regions on your chart where price reversals and pivots are statistically more common during major trading sessions (Asian, London, NY). This indicator identifies potential turning points based on historical session transitions and market behavior patterns. It does NOT predict or guarantee reversals - it simply marks time zones where pivots frequently occur. When combined with key support/resistance levels, supply/demand zones, or other confluence factors, these highlighted periods may provide additional context for timing entries and exits. Use this indicator as one piece of your trading puzzle, not as a standalone signal. Always combine with proper risk management and other technical analysis tools. Note: Past performance and statistical tendencies do not guarantee future results. Trade responsibly. ***UTC Time should match EST - So depending on Daylight Savings or not you will want to select UTC 4 or UTC 5***
Pine Script® 인디케이터
PonoTrading의
GVI-1 - Guendogan Valuation Index 1The Guendogan Valuation Index 1 (GVI-1) incorporates the total market capitalization of all U.S. companies, U.S. GDP, and the share of revenues generated outside the United States to provide an undistorted long-term valuation of the U.S. equity market across the past decades. Disclaimer: The Guendogan Valuation Index 1 (GVI-1) is a research-based macro indicator provided solely for educational and informational purposes. It does not constitute financial advice, investment advice, trading advice, or a recommendation to buy or sell any asset. Financial markets involve risk, and past performance does not guarantee future results. All users are solely responsible for their own investment decisions.
Pine Script® 인디케이터
Cueneyt_Guendogan의
Open Interest Anomaly DetectorOpen Interest Anomaly Indicator This indicator is designed to detect anomalies in Open Interest (OI) and highlight moments when capital is aggressively entering or exiting the market. The indicator plots raw Open Interest values as a column histogram. A moving average is applied to establish the baseline behavior of OI, while standard deviation bands define thresholds for abnormal deviations. These deviation levels can be customized in the settings. When Open Interest rises above the upper deviation band, the indicator marks these events in green, signaling positive anomalies, often associated with sudden inflows of capital. When Open Interest falls below the lower deviation band, it highlights these points in red, indicating negative anomalies, which may reflect capital leaving the market due to stop-loss triggers, take-profit executions, or liquidations. It is important to note that Open Interest alone does not generate entry signals. Instead, it serves as a contextual layer, helping traders understand market dynamics and confirm other tools. For cleaner signals with reduced noise, we recommend using the indicator on the 15-minute timeframe. Using Open Interest Together With Delta The indicator becomes even more powerful when combined with Delta, providing clear insight into who is entering or exiting the market: Delta > 0 and Open Interest rising → Long positions are entering the market. Delta < 0 and Open Interest rising → Short positions are entering the market. Open Interest falling (regardless of Delta) → Money is leaving the market; long or short positions are being closed, either by profit-taking or by forced exits. This synergy between Open Interest and Delta offers a deeper understanding of market flow and can produce highly informative signals when used together.
Pine Script® 인디케이터
bboydaler의
Uptrick: Dynamic Z-Score DivergenceIntroduction Uptrick: Dynamic Z-Score Divergence is an oscillator that combines multiple momentum sources within a Z-Score framework, allowing for the detection of statistically significant mean-reversion setups, directional shifts, and divergence signals. It integrates a multi-source normalized oscillator, a slope-based signal engine, structured divergence logic, a slope-adaptive EMA with dynamic bands, and a modular bar coloring system. This script is designed to help traders identify statistically stretched conditions, evolving trend dynamics, and classical divergence behavior using a unified statistical approach. Overview At its core, this script calculates the Z-Score of three momentum sources—RSI, Stochastic RSI, and MACD—using a user-defined lookback period. These are averaged and smoothed to form the main oscillator line. This normalized oscillator reflects how far short-term momentum deviates from its mean, highlighting statistically extreme areas. Signals are triggered when the oscillator reverses slope within defined inner zones, indicating a shift in direction while the signal remains in a statistically stretched state. These mean-reversion flips (referred to as TP signals) help identify turning points when price momentum begins to revert from extended zones. In addition, the script includes a divergence detection engine that compares oscillator pivot points with price pivot points. It confirms regular bullish and bearish divergence by validating spacing between pivots and visualizes both the oscillator-side and chart-side divergences clearly. A dynamic trend overlay system is included using a Slope Adaptive EMA (SA-EMA). This trend line becomes more responsive when Z-Score deviation increases, allowing the trend line to adapt to market conditions. It is paired with ATR-based bands that are slope-sensitive and selectively visible—offering context for dynamic support and resistance. The script includes configurable bar coloring logic, allowing users to color candles based on oscillator slope, last confirmed divergence, or the most recent signal of any type. A full alert system is also built-in for key signals. Originality The script is based on the well-known concept of Z-Score valuation, which is a standard statistical method for identifying how far a signal deviates from its mean. This foundation—normalizing momentum values such as RSI or MACD to measure relative strength or weakness—is not unique to this script and is widely used in quantitative analysis. What makes this implementation original is how it expands the Z-Score foundation into a fully featured, signal-producing system. First, it introduces a multi-source composite oscillator by combining three momentum inputs—RSI, Stochastic RSI, and MACD—into a unified Z-Score stream. Second, it builds on that stream with a directional slope logic that identifies turning points inside statistical zones. The most distinctive additions are the layered features placed on top of this normalized oscillator: A structured divergence detection engine that compares oscillator pivots with price pivots to validate regular bullish and bearish divergence using precise spacing and timing filters. A fully integrated slope-adaptive EMA overlay, where the smoothing dynamically adjusts based on real-time Z-Score movement of RSI, allowing the trend line to become more reactive during high-momentum environments and slower during consolidation. ATR-based dynamic bands that adapt to slope direction and offer real-time visual zones for support and resistance within trend structures. These features are not typically found in standard Z-Score indicators and collectively provide a unique approach that bridges statistical normalization, structure detection, and adaptive trend modeling within one script. Features Z-Score-based oscillator combining RSI, StochRSI, and MACD Configurable smoothing for stable composite signal output Buy/Sell TP signals based on slope flips in defined zones Background highlighting for extreme outer bands Inner and outer zones with fill logic for statistical context Pivot-based divergence detection (regular bullish/bearish) Divergence markers on oscillator and price chart Slope-Adaptive EMA (SA-EMA) with real-time adaptivity based on RSI Z-Score ATR-based upper and lower bands around the SA-EMA, visibility tied to slope direction Configurable bar coloring (oscillator slope, divergence, or most recent signal) Alerts for TP signals and confirmed divergences Optional fixed Y-axis scaling for consistent oscillator view The full setup mode can be seen below: Input Parameters General Settings Full Setup: Enables rendering of the full visual system (lines, bands, signals) Z-Score Lookback: Lookback period for normalization (mean and standard deviation) Main Line Smoothing: EMA length applied to the averaged Z-Score Slope Detection Index: Used to calculate directional flips for signal logic Enable Background Highlighting: Enables visual region coloring in overbought/oversold areas Force Visible Y-Axis Scale: Forces max/min bounds for a consistent oscillator range Divergence Settings Enable Divergence Detection: Toggles divergence logic Pivot Lookback Left / Right: Defines the structure of oscillator pivot points Minimum / Maximum Bars Between Pivots: Controls the allowed spacing range for divergence validation Bar Coloring Settings Bar Coloring Mode: ➜ Line Color: Colors bars based on oscillator slope ➜ Latest Confirmed Signal: Colors bars based on the most recent confirmed divergence ➜ Any Latest Signal: Colors based on the most recent signal (TP or divergence) SA-EMA Settings RSI Length: RSI period used to determine adaptivity Z-Score Length: Lookback for normalizing RSI in adaptive logic Base EMA Length: Base length for smoothing before adaptivity Adaptivity Intensity: Scales the smoothing responsiveness based on RSI deviation Slope Index: Determines slope direction for coloring and band logic Band ATR Length / Band Multiplier: Controls the width and responsiveness of the trend-following bands Alerts The script includes the following alert conditions: Buy Signal (TP reversal detected in oversold zone) Sell Signal (TP reversal detected in overbought zone) Confirmed Bullish Divergence (oscillator HL, price LL) Confirmed Bearish Divergence (oscillator LH, price HH) These alerts allow integration into automation systems or signal monitoring setups. Summary Uptrick: Dynamic Z-Score Divergence is a statistically grounded trading indicator that merges normalized multi-momentum analysis with real-time slope logic, divergence detection, and adaptive trend overlays. It helps traders identify mean-reversion conditions, divergence structures, and evolving trend zones using a modular system of statistical and structural tools. Its alert system, layered visuals, and flexible input design make it suitable for discretionary traders seeking to combine quantitative momentum logic with structural pattern recognition. Disclaimer This script is for educational and informational purposes only. No indicator can guarantee future performance, and trading involves risk. Always use risk management and test strategies in a simulated environment before deploying with live capital.
Pine Script® 인디케이터
Uptrick의
7
Pair Cointegration & Static Beta Analyzer (v6)Pair Cointegration & Static Beta Analyzer (v6) This indicator evaluates whether two instruments exhibit statistical properties consistent with cointegration and tradable mean reversion. It uses long-term beta estimation, spread standardization, AR(1) dynamics, drift stability, tail distribution analysis, and a multi-factor scoring model. 1. Static Beta and Spread Construction A long-horizon static beta is estimated using covariance and variance of log-returns. This beta does not update on every bar and is used throughout the entire model. Beta = Cov(r1, r2) / Var(r2) Spread = PriceA - Beta * PriceB This “frozen” beta provides structural stability and avoids rolling noise in spread construction. 2. Correlation Check Log-price correlation ensures the instruments move together over time. Correlation ≥ 0.85 is required before deeper cointegration diagnostics are considered meaningful. 3. Z-Score Normalization and Distribution Behavior The spread is standardized: Z = (Spread - MA(Spread)) / Std(Spread) The following statistical properties are examined: Z-Mean: Should be close to zero in a stationary process Z-Variance: Measures amplitude of deviations Tail Probability: Frequency of |Z| being larger than a threshold (e.g. 2) These metrics reveal whether the spread behaves like a mean-reverting equilibrium. 4. Mean Drift Stability A rolling mean of the spread is examined. If the rolling mean drifts excessively, the spread may not represent a stable long-term equilibrium. A normalized drift ratio is used: Mean Drift Ratio = Range( RollingMean(Spread) ) / Std(Spread) Low drift indicates stable long-run equilibrium behavior. 5. AR(1) Dynamics and Half-Life An AR(1) model approximates mean reversion: Spread(t) = Phi * Spread(t-1) + error Mean reversion requires: 0 < Phi < 1 Half-life of reversion: Half-life = -ln(2) / ln(Phi) Valid half-life for 10-minute bars typically falls between 3 and 80 bars. 6. Composite Scoring Model (0–100) A multi-factor weighted scoring system is applied: Component Score Correlation 0–20 Z-Mean 0–15 Z-Variance 0–10 Tail Probability 0–10 Mean Drift 0–15 AR(1) Phi 0–15 Half-Life 0–15 Score interpretation: 70–100: Strong Cointegration Quality 40–70: Moderate 0–40: Weak A pair is classified as cointegrated when: Total Score ≥ Threshold (default = 70) 7. Main Cointegration Panel Displays: Static beta Log-price correlation Z-Mean, Z-Variance, Tail Probability Drift Ratio AR(1) Phi and Half-life Composite score Overall cointegration assessment 8. Beta Hedge Position Sizing (Average-Price Based) To provide a more stable hedge ratio, hedge sizing is computed using average prices, not instantaneous prices: AvgPriceA = SMA(PriceA, N) AvgPriceB = SMA(PriceB, N) Required B per 1 A = Beta * (AvgPriceA / AvgPriceB) Using averaged prices results in a smoother, more reliable hedge ratio, reducing noise from bar-to-bar volatility. The panel displays: Required B security for 1 A security (average) This represents the beta-neutral quantity of B required to hedge one unit of A. Overview of Classical Stationarity & Cointegration Methods The principal econometric tools commonly used in assessing stationarity and cointegration include: Augmented Dickey–Fuller (ADF) Test Phillips–Perron (PP) Test KPSS Test Engle–Granger Cointegration Test Phillips–Ouliaris Cointegration Test Johansen Cointegration Test Since these procedures rely on regression residuals, matrix operations, and distribution-based critical values that are not supported in TradingView Pine Script, a practical multi-criteria scoring approach is employed instead. This framework leverages metrics that are fully computable in Pine and offers an operational proxy for evaluating cointegration-like behavior under platform constraints. References Engle & Granger (1987), Co-integration and Error Correction Poterba & Summers (1988), Mean Reversion in Stock Prices Vidyamurthy (2004), Pairs Trading Explanation structured with assistance from OpenAI’s ChatGPT Regards.
Pine Script® 인디케이터
Noldo의
Prob Stats PPIBW Prob Stats PPIBW - Data-Driven Trading Decisions Transform historical price patterns into actionable probabilities. This indicator analyzes thousands of periods to show you the real odds behind pivot hits, range expansions, inside bars, and weekend breakouts. What It Tracks Pivot Hit Rates (D/W/M/Q/6M/Y) What percentage of pivot points get touched during their period? Includes recent period comparison to spot regime changes. Example: "Daily: 82.3% (450/547) | L30: 76.7% (23/30)" Previous Period Levels (D/W/M) How often does current period break previous period's high or low? Only counts actual range expansion, not equilibrium crossings. Helps gauge breakout probability. Inside Bar Analysis (D/W/M) When price consolidates inside previous period's range, what are the odds of a breakout? Only appears when currently in an inside bar. Weekend Breakdown When Sat/Sun breaks Mon-Fri range, does the following week continue? Critical for crypto traders and weekend gap analysis. Key Features - Recent Period Comparison: See if recent behavior differs from historical averages - Self-Documenting: Hover over any header for instant explanations - Color-Coded Sections: Yellow (Pivots), Orange (Prev Period), Pink (Inside Bar), Green (Weekend) - Blue Background: Recent stats highlighted for easy identification - Dynamic Layout: Adapts based on market conditions - Real-Time Updates: Includes current period for live probability tracking How To Use 1. Add to any chart (best on Daily+ for maximum historical data) 2. Hover over column headers to understand each statistic 3. Compare historical vs recent probabilities 4. Use probabilities to inform position sizing and expectations Example: Weekly pivot shows 78% historical hit rate but only 60% in last 30 weeks. Recent regime change suggests lower probability of test. Technical Details - Pine Script v6 - Rolling window arrays track last 30/30/12 periods for D/W/M - Previous Period excludes EQ crossings for accurate stats - Works on all timeframes, optimized for Daily+ - Configurable table position Perfect For Traders seeking data-driven confirmation, those wanting to quantify probability vs guessing, regime change detection, and crypto traders analyzing weekend patterns. Note: Past performance doesn't guarantee future results. Use these statistics as one input in your overall trading strategy.
Pine Script® 인디케이터
flashmatrix의
FVG – (auto close + age) GR V1.0FVG – Fair Value Gaps (auto close + age counter) Short Description Automatically detects Fair Value Gaps (FVGs) on the current timeframe, keeps them open until price fully fills the gap or a maximum bar age is reached, and shows how many candles have passed since each FVG was created. Full Description This indicator automatically finds and visualizes Fair Value Gaps (FVGs) using the classic 3-candle ICT logic on any timeframe. It works on whatever timeframe you apply it to (M1, M5, H1, H4, etc.) and adapts to the current chart. FVG detection logic The script uses a 3-candle pattern: Bullish FVG Condition: low > high Gap zone: Lower boundary: high Upper boundary: low Bearish FVG Condition: high < low Gap zone: Lower boundary: high Upper boundary: low Each detected FVG is drawn as a colored box (green for bullish, red for bearish in this version, but you can adjust colors in the inputs). Auto-close rules An FVG remains on the chart until one of the following happens: Full fill / mitigation A bullish FVG closes when any candle’s low goes down to or below the lower boundary of the gap. A bearish FVG closes when any candle’s high goes up to or above the upper boundary of the gap. Maximum bar age reached Each FVG has a maximum lifetime measured in candles. When the number of candles since its creation reaches the configured maximum (default: 200 bars), the FVG is automatically removed even if it has not been fully filled. This keeps the chart cleaner and prevents very old gaps from cluttering the view. Age counter (labels inside the boxes) Inside every FVG box there is a small label that: Shows how many bars have passed since the FVG was created. Moves together with the right edge of the box and stays vertically centered in the gap. This makes it easy to distinguish fresh gaps from older ones and prioritize which zones you want to pay attention to. Inputs FVG color – Main fill color for all FVG boxes. Show bullish FVGs – Turn bullish gaps on/off. Show bearish FVGs – Turn bearish gaps on/off. Max bar age – Maximum number of candles an FVG is allowed to stay on the chart before it is removed. Usage Works on any symbol and any timeframe. Can be combined with your own ICT / SMC concepts, order blocks, session ranges, market structure, etc. You can also choose to only display bullish or only bearish FVGs depending on your directional bias. Disclaimer This script is for educational and informational purposes only and is not financial advice. Always do your own research and use proper risk management when trading.
Pine Script® 인디케이터
GRIDFX의
PIP BOOSTER (Desktop) DemoversionThe PIP BOOSTER from underground-traders.com is a very intelligent indicator with integrated win-rate tracking (%), which can be used on all markets and timeframes. Thanks to its two fundamentally different algorithms, the PIP BOOSTER is able to find a profitable setup in over 80% of all charts. The win-rate tracking (%) is highly detailed and can be applied to up to 5,000 candles. It updates after every single signal, ensuring that performance monitoring is always up to date. Additionally, PIP BOOSTER users can apply different time filters, which can further optimize performance. There is both a desktop version and a mobile version, which can be used with the TradingView mobile app. All signals are displayed clearly in the mobile app, making it possible to trade directly from your smartphone. Please note that the demo version does not include any live signals. The demo version is only for you to evaluate the performance (win-rate %) of the two algorithms. We guarantee that there are no repaint signals, and the signals in the demo version are 100% identical to those in the full version. For any questions, please visit: underground-traders.com Or contact us at: help@underground-traders.com
Pine Script® 인디케이터
UNDERGROUND-TRADERS의
PIP BOOSTER (Mobile) Demoversion​The PIP BOOSTER from underground-traders.com is a very intelligent indicator with integrated win-rate tracking (%), which can be used on all markets and timeframes. Thanks to its two fundamentally different algorithms, the PIP BOOSTER is able to find a profitable setup in over 80% of all charts. The win-rate tracking (%) is highly detailed and can be applied to up to 5,000 candles. It updates after every single signal, ensuring that performance monitoring is always up to date. Additionally, PIP BOOSTER users can apply different time filters, which can further optimize performance. There is both a desktop version and a mobile version, which can be used with the TradingView mobile app. All signals are displayed clearly in the mobile app, making it possible to trade directly from your smartphone. Please note that the demo version does not include any live signals. The demo version is only for you to evaluate the performance (win-rate %) of the two algorithms. We guarantee that there are no repaint signals, and the signals in the demo version are 100% identical to those in the full version. For any questions, please visit: underground-traders.com Or contact us at: help@underground-traders.com
Pine Script® 인디케이터
UNDERGROUND-TRADERS의
PIP BOOSTER (Desktop) Fullversion​The PIP BOOSTER from underground-traders.com is a very intelligent indicator with integrated win-rate tracking (%), which can be used on all markets and timeframes. Thanks to its two fundamentally different algorithms, the PIP BOOSTER is able to find a profitable setup in over 80% of all charts. The win-rate tracking (%) is highly detailed and can be applied to up to 5,000 candles. It updates after every single signal, ensuring that performance monitoring is always up to date. Additionally, PIP BOOSTER users can apply different time filters, which can further optimize performance. There is both a desktop version and a mobile version, which can be used with the TradingView mobile app. All signals are displayed clearly in the mobile app, making it possible to trade directly from your smartphone. Please note that the demo version does not include any live signals. The demo version is only for you to evaluate the performance (win-rate %) of the two algorithms. We guarantee that there are no repaint signals, and the signals in the demo version are 100% identical to those in the full version. For any questions, please visit: underground-traders.com Or contact us at: help@underground-traders.com
Pine Script® 인디케이터
UNDERGROUND-TRADERS의
PIP BOOSTER (Mobile) FullversionThe PIP BOOSTER from underground-traders.com is a very intelligent indicator with integrated win-rate tracking (%), which can be used on all markets and timeframes. Thanks to its two fundamentally different algorithms, the PIP BOOSTER is able to find a profitable setup in over 80% of all charts. The win-rate tracking (%) is highly detailed and can be applied to up to 5,000 candles. It updates after every single signal, ensuring that performance monitoring is always up to date. Additionally, PIP BOOSTER users can apply different time filters, which can further optimize performance. There is both a desktop version and a mobile version, which can be used with the TradingView mobile app. All signals are displayed clearly in the mobile app, making it possible to trade directly from your smartphone. Please note that the demo version does not include any live signals. The demo version is only for you to evaluate the performance (win-rate %) of the two algorithms. We guarantee that there are no repaint signals, and the signals in the demo version are 100% identical to those in the full version. For any questions, please visit: underground-traders.com Or contact us at: help@underground-traders.com
Pine Script® 인디케이터
UNDERGROUND-TRADERS의
VWAP ±2σ Entry Signals (volume Weighted)This indicator builds a session based VWAP and plots the upper and lower 2nd standard deviation bands around it. These bands act as dynamic volatility edges for the session. When price reaches these outer bands, it often represents an extreme stretch away from fair value a place where mean reversion or exhaustion can occur. The indicator generates trade signals only when price approaches the band from the correct direction, which filters out a lot of noise and reduces false touches. How It Works VWAP is calculated from the start of each session. Standard deviation is computed using volume weighted prices, so the bands expand and contract with real market activity. The upper and lower 2σ bands form natural "overextended" zones around VWAP. Most VWAP band strategies fire signals every time price touches a band which produces a lot of junk signals. This version avoids that by requiring direction based touches, meaning: If price is already above the band, no sell signal appears. If price is already below the band, no buy signal appears.
Pine Script® 인디케이터
sandunmathers의
BTC Macro Heatmap (Fed Cuts & Hikes)🔴 1. Red line – Fed Funds Rate (policy trend) This line tells you what stage of the monetary cycle we’re in. Rising red line = the Fed is hiking → liquidity is tightening → money leaves risk assets like BTC. Flat = pause → markets start pricing in the next move (often sideways BTC). Falling = easing / cutting → liquidity returns → bullish environment builds. The rate of change matters more than the level. When the slope turns down, capital starts seeking yield again — BTC benefits first because it’s the most volatile asset. 💚 2. Dim green zones – detected cuts These are data-based easing events pulled directly from FRED. They show when the actual effective rate began moving down, not necessarily the exact meeting day. Think of them as the Fed’s “foot off the brake” — that’s when risk markets begin responding. 🟩 3. Bright green lines – official FOMC cuts These are the real policy shifts — the Fed formally changed direction. After these appear, BTC historically transitions from accumulation → markup phase. Look at 2020: the bright green lines came right before BTC’s full reversal. You’re seeing the same thing now with the 2025 lines — early-stage liquidity return. 🟠 4. Orange line – DXY (US Dollar Index) DXY is your “risk-off” gauge. When DXY rises, global investors flock to dollars → BTC usually weakens. When DXY peaks and starts dropping, it means risk appetite is coming back → BTC rallies. BTC and DXY are inversely correlated about 70–80% of the time. Watch for DXY lower highs after rate cuts — that’s your macro confirmation of a BTC-friendly environment. 🟦 5. Aqua line – BTC (normalized) You’re not looking for the price itself here, but its shape relative to DXY and the Fed line. When BTC curls up as the red line flattens and DXY rolls over → that’s historically the start of a major bull phase. BTC tends to bottom before the first cut and explode once DXY decisively breaks down. 🧠 Putting it together Here’s the rhythm this chart shows over and over: Fed hikes (red line rising) → BTC weakens, DXY climbs. Fed pauses (red line flat) → BTC stops falling, DXY tops. Fed cuts (dim + bright green) → DXY turns down → BTC begins long recovery → bull cycle starts.
Pine Script® 인디케이터
NFA-의
Weekday Close vs Open — Last N (per weekday)# Weekday Close vs Open - Last N Occurrences This indicator distills every weekday's historical open-to-close behavior into a compact table so you can see how "typical" the current session is before the day even closes. It runs independently of your chart timeframe by pulling daily OHLCV data under the hood, tracking the last **N** completed occurrences for each weekday, and refreshing only when a daily bar closes. On daily charts you can also shade every past bar that matches today's weekday (excluding the in-progress session) to reinforce the pattern visually while the table remains non-repainting. ## What It Shows - **Win/Loss/Tie counts** - how many of the last `N` occurrences closed above the open (wins), below (losses), or inside the tie threshold you define as "flat". - **Win % heatmap** - the win column is color-coded (deep green > deep red) so you immediately recognize strong or weak weekdays. - **Advanced metrics (optional)** - average daily volume plus the average percentage excursion above/below the open (`AvgUp%`, `AvgDn%`) for that weekday. - **Totals row** - aggregates every weekday into one row to estimate overall hit rate and average stats across the entire data set. - **Weekday shading (optional)** - on daily charts you can tint every bar that matches today's weekday (all Mondays, all Fridays, etc.) for instant pattern recognition. ## How It Works 1. The script requests daily OHLCV data (non-repainting) regardless of the chart timeframe. 2. When a new daily bar confirms, it packs that day's data into one of seven arrays (one per weekday). Each day contributes five floats (O/H/L/C/V) so trimming and statistics stay in lockstep. 3. A helper function (`f_dayMetrics`) scans daily history to compute average volume, average excursion above/below the open, and win/loss/tie counts for the requested weekday. 4. The table populates on the last bar of the chart session, respecting your advanced/totals toggles and keeping text at `size.normal`. ## Reading the Table - **Win/Loss/Tie columns**: raw counts taken from your chosen `N`. - **Win %***: excludes ties from the denominator so it reflects only decisive closes. - **AvgUp% / AvgDn%**: typical intraday extension (high vs open, open vs low) in percent. - **Avg Vol**: arithmetic mean of daily volume for that weekday. - **TOTAL row**: provides a global win rate plus volume/up/down averages weighted by how many samples each weekday contributed. ## Practical Uses - Spot weekdays that historically trend higher or lower before entering a trade. - Compare current price action against the typical intraday range (`AvgUp%` vs today's move). - Filter mean-reversion vs breakout setups based on the most reliable weekday patterns. - Quickly gauge whether today is behaving "in character" by referencing the highlighted row or the optional whole-chart weekday shading. > **Tip:** Use smaller `N` values (e.g., 10-20) for adaptive, recent behavior and larger values (50+) to capture longer-term seasonality. Tighten the tie threshold if you want almost every candle to register as win/loss, or widen it to focus only on meaningful moves.
Pine Script® 인디케이터
Zippy111의