立即開始

指標和策略

Bollinger Band Width Oscillator %🧠 Bollinger Band Width Oscillator % The Bollinger Band Width Oscillator % is a volatility-focused tool that measures the relative width of Bollinger Bands and transforms it into an oscillator format. It helps traders visualize volatility expansions and contractions directly in an indicator pane — a powerful way to anticipate breakout or consolidation phases. 🔍 How It Works Band Width %: Calculates the percentage distance between the upper and lower Bollinger Bands relative to the basis (SMA). Smoothed Output: The raw bandwidth is smoothed using a moving average for cleaner, more stable signals. Dynamic Volatility Zones: The script automatically computes average, high, and low volatility thresholds — each dynamically adapting to market conditions. User-Adjustable Multipliers: Control how sensitive your high/low zones are with the High Zone Multiplier and Low Zone Multiplier inputs. ⚙️ Key Features 📊 Oscillator Format – Easy-to-read visualization of volatility compression and expansion. 🔥 High/Low Volatility Detection – Automatic labeling and color-coded alerts for shifts in volatility. 🧩 Dynamic Thresholds – Zones adjust automatically with market activity for adaptive sensitivity. 🧠 Hysteresis Logic – Prevents rapid signal flipping, improving clarity and reliability. 🎨 Custom Visuals – Adjustable smoothing and background highlights for quick interpretation. 📈 Trading Applications Identify Breakouts: Rising bandwidth often precedes price breakouts. Spot Consolidations: Low bandwidth indicates tightening volatility and potential range trades. Volatility Regime Analysis: Understand market rhythm and adapt strategies accordingly. ⚡ Inputs Parameter Description Band Length Period for Bollinger Band calculation Band Multiplier Standard deviation multiplier for the bands Source Price source (default: close) Smoothing Period for smoothing the oscillator line High Zone Multiplier Adjusts the high-volatility threshold Low Zone Multiplier Adjusts the low-volatility threshold Highlight Volatility Zones Optional background color overlay 🧊 Usage Tip Combine this indicator with momentum tools or price action analysis to confirm trade setups. Watch for transitions from low to high volatility zones — these often signal the beginning of major market moves.
Pine Script®指標
由Bmaloney提供
SC_Reversal Confirmation 30 minutes by Claude (Version 1)📉 When to Use Use this setup when the stock is in a downtrend and a bullish reversal is anticipated. 🔍 Recommended Usage This model is designed for pullback phases, where the asset is declining and a reversal is expected. It helps filter out weak signals and waits for technical confirmation before triggering an entry. ✅ Entry Signal Green triangles appear only when all reversal conditions are fully met. Entry may occur slightly after the bottom, but with a reduced likelihood of false signals. 📊 Suggested Settings Apply on a 30-minute chart using a 100-period Exponential Moving Average (EMA) based on close. Recommended for Cobalt Chart 0. --------------------------------------------------------------------------------------
Pine Script®指標
由torotrap提供
Smart Money Concept: FVG Block Filter Smart Money Concept: FVG Block Filter (FVG Block Range vs N Range) with Candle Highlighter Summary: Smart Money Concept (SMC): An advanced indicator designed to visualize and filter Fair Value Gaps (FVG) blocks based on their size (Range) compared to the preceding N Range candle movement. It also includes a customizable Candle Highlighter function that marks the specific candle responsible for creating the FVG. The indicator allows full color customization for both blocks and the highlighter, and features clean, label-free charts by default. Key Features: FVG Block Detection: Automatically identifies and groups sequential FVG imbalances to form consolidated FVG blocks. FVG Block Filtering (N Range): Filters blocks based on a user-defined rule, comparing the block's size (Range) to the range of the preceding N candles (e.g., requiring the FVG block to be larger than the range of the previous 6 candles). Customizable Candle Highlighter: Marks the central candle (B) within the FVG structure (A-B-C) to highlight the source of the price imbalance. Highlighter colors are fully adjustable via inputs. Visualization Control: Labels are turned OFF by default to keep the chart clean but can be easily enabled via the indicator settings. Full Color Customization: Allows independent customization of Bullish and Bearish FVG Block colors, Block Transparency, and Bullish/Bearish Highlighter colors. Keywords: Smart Money Concept, SMC, Fair Value Gap, FVG, Imbalance, Block Filter, Candle Highlighter, Range.
Pine Script®指標
由alexas77442211提供
1
VWAP & Band Cross Strategy v6VWAP & Band Cross Strategy v6: Script Summary This Pine Script implements a highly flexible, multi-layered trading strategy centered around the Volume Weighted Average Price (VWAP) and its associated Standard Deviation Bands. The strategy is designed to test various entry/exit models based on how the price interacts with the central VWAP line and the upper/lower volatility bands, with extensive risk management and confirmation filters. 1. Core Mechanics (VWAP & Bands) VWAP Calculation: Calculates the VWAP based on a user-defined source (default is the close price). Standard Deviation Bands: Creates upper and lower bands by calculating the standard deviation of the price (over 20 periods by default) and multiplying it by a user-defined Multiplier (default is 2.0). These bands dynamically expand and contract with volatility. Plotting: The script clearly plots the VWAP (purple), the Upper Band (green), and the Lower Band (red), with a colored fill between the bands. 2. Entry Triggers The core entry logic is based on a single, user-selected cross event between the price and the VWAP/Bands. The user can choose from six predefined entry types: Entry Type Category Entry Trigger (Long) Entry Trigger (Short) Mean Reversion Price crosses over the Lower Band. Price crosses under the Upper Band. Trend Following Price crosses over the Upper Band (Breakout). Price crosses under the Lower Band (Breakout). VWAP Cross Price crosses over the VWAP. Price crosses under the VWAP. 3. Filters and Confirmation Trades are only executed if they pass a series of optional filters, making the strategy highly customizable: Technical Confirmation (Optional): Users can enable and configure up to three additional indicators that must align with the trade direction: RSI: Price must be Oversold (for Long) or Overbought (for Short). SMMA: Price must be above the SMMA (for Long) or below (for Short). MACD: MACD line must cross the Signal line and the Histogram must be positive/negative. Time and Day Filters: Trades are restricted to a defined Entry Start/End Hour/Minute window, and only execute on user-selected Trading Days of the week. Trade Direction: Can be toggled to execute Long Only, Short Only, or Both. 4. Advanced Risk Management (Daily Limits) The strategy incorporates robust daily limits that reset at a configured Daily Reset Hour/Minute: Daily Profit/Loss Limits: If the running total of Realized PnL (closed trades) + Unrealized PnL (open position) exceeds a user-defined Daily Take Profit (in Ticks) or falls below the Daily Stop Loss (in Ticks), the strategy locks out new trades and immediately closes any open position. Max Daily Trades: Prevents the strategy from entering more than a specified number of trades per day. 5. Exit Logic The strategy exit is also highly configurable via the Exit Type setting: Fixed Ticks / ATR / Capped ATR: If one of these is selected, the script calculates a static Stop Loss and Take Profit level upon entry, using either fixed tick values or dynamic values based on the Average True Range (ATR), which are then executed using Pine Script's strategy.exit function. Cross Exits (VWAP/Bands): If selected, the position is closed when the price crosses the VWAP or a specific band in the opposite direction. End-of-Day Close: An unconditional exit that closes all open positions at a user-defined Close All Hour/Minute, regardless of profit/loss or limit status, preventing positions from being held overnight.
Pine Script®策略
由mcward302提供
Reactive Curvature Smoother Moving Average IndicatorSummary in one paragraph RCS MA is a reactive curvature smoother for any liquid instrument on intraday through swing timeframes. It helps you act only when context strengthens by adapting its window length with a normalized path energy score and by smoothing with robust residual weights over a quadratic fit, then optionally blending a capped one step forecast. Add it to a clean chart and watch the single colored line. Shapes can shift while a bar forms and settle on close. For conservative use, judge on bar close. Scope and intent • Markets: major FX pairs, index futures, large cap equities, liquid crypto • Timeframes: one minute to daily • Purpose: reduce lag in trends while resisting chop and outliers • Limits: indicator only, no orders Originality and usefulness • Novelty: adaptive window selection by minimizing normalized path energy with directionality bias, plus Huber weighted residuals and curvature aware penalty, finished with a mintick capped forecast blend • Failure modes addressed: whipsaws from fixed length MAs and outlier spikes that pull means • Testable: Inputs expose all components and optional diagnostics show chosen length, directionality, and energy • Portable yardstick: forecast cap uses mintick to stay symbol aware Method overview in plain language Base measures • Range span of the tested window and a path energy defined as the sum of squared price increments, normalized by span Components Adaptive window chooser: scans L between Min and Max using an energy over trend score and picks the lowest score Robust smoother: fits a quadratic to the last L bars, computes residuals, applies Huber weights and an exponential residual penalty scaled down when curvature is high Forecast blend: projects one step ahead from the quadratic, caps displacement by a multiple of mintick, blends by user weight Fusion rule • Final line equals robust mean plus optional capped forecast blend Signal rule • Visual bias only: color turns lime when close is above the line, red otherwise What you will see on the chart • One colored line that tightens in trends and relaxes in chop • Optional debug overlays for core value, chosen L, directionality, and energy • Optional last bar label with L, directionality, and energy • Reminder: drawings can move intrabar and settle on close Inputs with guidance Setup • Source: price series to smooth Logic • Min window l_min. Typical 5 to 21. Higher increases stability, adds lag • Max window l_max. Typical 40 to 128. Higher reduces noise, adds lag ceiling • Length step grid_step. Typical 1 to 8. Smaller is finer and heavier • Trend bias trend_bias. Typical 0.50 to 0.80. Higher favors trend persistence • Residual penalty lambda_base. Typical 0.8 to 2.0. Higher downweights large residuals more • Huber threshold huber_k. Typical 1.5 to 3.0. Higher admits more outliers • Curvature guard curv_guard. Typical 0.3 to 1.0. Higher reduces influence when curve is tight • Forecast blend lead_blend. 0 disables. Typical 0.10 to 0.40 • Forecast cap lead_limit. Typical 1 to 5 minticks • Show chosen L and metrics show_debug. Diagnostics toggle Optional: enable diagnostics to see length, direction, and energy Realism and responsible publication • No performance claims. Past results never guarantee future outcomes • Shapes can move while bars are open and settle on close • Use on standard candles for analysis and combine with your own risk process Honest limitations and failure modes • Very quiet regimes can reduce energy contrast, length selection may hover near the bounds • Gap heavy symbols can disrupt quadratic fit on the window edges • Excessive forecast blend may look anticipatory; use low values and the cap
Pine Script®指標
由exlux提供
Average Daily Session Range PRO [Capitalize Labs]Average Daily Session Range PRO The Average Daily Session Range PRO (ADSR PRO) is a professional-grade analytical tool designed to quantify and visualize the probabilistic range behavior of intraday sessions. It calculates directional range statistics using historical session data to show how far price typically moves up or down from the session open. This helps traders understand session volatility profiles, range asymmetry, and probabilistic extensions relative to prior performance. Key Features Asymmetric Range Modeling: Separately tracks average upside and downside excursions from each session open, revealing directional bias and volatility imbalance. Probability Engine Modes: Choose between Rolling Window (fixed-length lookback) and Exponential Decay (weighted historical memory) to control how recent or historic data influences probabilities. Session-Aware Statistics: Calculates values independently for each defined session, allowing region-specific insights (e.g., Tokyo, London, New York). Dynamic Range Table: Displays key metrics such as average up/down ticks, expected range extensions, and percentage probabilities. Adaptive Display: Works across timeframes and instruments, automatically aligning with user-defined session start and end times. Visual Clarity: Includes clean range markers and labels optimized for both backtesting and live-chart analysis. Intended Use ADSR PRO is a statistical reference indicator. It does not generate buy/sell signals or predictive forecasts. Its purpose is to help users observe historical session behavior and volatility tendencies to support their own discretionary analysis. Credits Developed by Capitalize Labs, specialists in quantitative and discretionary market research tools. Risk Warning This material is educational research only and does not constitute financial advice, investment recommendation, or a solicitation to buy or sell any instrument. Foreign exchange and CFDs are complex, leveraged products that carry a high risk of rapid losses; leverage amplifies both gains and losses, and you should not trade with funds you cannot afford to lose. Market conditions can change without notice, and news or illiquidity may cause gaps and slippage; stop-loss orders are not guaranteed. The analysis presented does not take into account your objectives, financial situation, or risk tolerance. Before acting, assess suitability in light of your circumstances and consider seeking advice from a licensed professional. Past performance and back-tested or hypothetical scenarios are not reliable indicators of future results, and no outcome or level mentioned here is assured. You are solely responsible for all trading decisions, including position sizing and risk management. No external links, promotions, or contact details are provided, in line with TradingView House Rules.
Pine Script®指標
由CapitalizeLabs提供
LogNormalLibrary "LogNormal" A collection of functions used to model skewed distributions as log-normal. Prices are commonly modeled using log-normal distributions (ie. Black-Scholes) because they exhibit multiplicative changes with long tails; skewed exponential growth and high variance. This approach is particularly useful for understanding price behavior and estimating risk, assuming continuously compounding returns are normally distributed. Because log space analysis is not as direct as using math.log(price) , this library extends the Error Functions library to make working with log-normally distributed data as simple as possible. - - - QUICK START Import library into your project Initialize model with a mean and standard deviation Pass model params between methods to compute various properties var LogNorm model = LN.init(arr.avg(), arr.stdev()) // Assumes the library is imported as LN var mode = model.mode() Outputs from the model can be adjusted to better fit the data. var Quantile data = arr.quantiles() var more_accurate_mode = mode.fit(model, data) // Fits value from model to data Inputs to the model can also be adjusted to better fit the data. datum = 123.45 model_equivalent_datum = datum.fit(data, model) // Fits value from data to the model area_from_zero_to_datum = model.cdf(model_equivalent_datum) - - - TYPES There are two requisite UDTs: LogNorm and Quantile . They are used to pass parameters between functions and are set automatically (see Type Management ). LogNorm   Object for log space parameters and linear space quantiles .   Fields:      mu (float) : Log space mu ( µ ).      sigma (float) : Log space sigma ( σ ).      variance (float) : Log space variance ( σ² ).      quantiles (Quantile) : Linear space quantiles. Quantile   Object for linear quantiles, most similar to a seven-number summary .   Fields:      Q0 (float) : Smallest Value      LW (float) : Lower Whisker Endpoint      LC (float) : Lower Whisker Crosshatch      Q1 (float) : First Quartile      Q2 (float) : Second Quartile      Q3 (float) : Third Quartile      UC (float) : Upper Whisker Crosshatch      UW (float) : Upper Whisker Endpoint      Q4 (float) : Largest Value      IQR (float) : Interquartile Range      MH (float) : Midhinge      TM (float) : Trimean      MR (float) : Mid-Range - - - TYPE MANAGEMENT These functions reliably initialize and update the UDTs. Because parameterization is interdependent, avoid setting the LogNorm and Quantile fields directly . init(mean, stdev, variance)   Initializes a LogNorm object.   Parameters:      mean (float) : Linearly measured mean.      stdev (float) : Linearly measured standard deviation.      variance (float) : Linearly measured variance.   Returns: LogNorm Object set(ln, mean, stdev, variance)   Transforms linear measurements into log space parameters for a LogNorm object.   Parameters:      ln (LogNorm) : Object containing log space parameters.      mean (float) : Linearly measured mean.      stdev (float) : Linearly measured standard deviation.      variance (float) : Linearly measured variance.   Returns: LogNorm Object quantiles(arr)   Gets empirical quantiles from an array of floats.   Parameters:      arr (array) : Float array object.   Returns: Quantile Object - - - DESCRIPTIVE STATISTICS Using only the initialized LogNorm parameters, these functions compute a model's central tendency and standardized moments. mean(ln)   Computes the linear mean from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ median(ln)   Computes the linear median from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ mode(ln)   Computes the linear mode from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ variance(ln)   Computes the linear variance from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ skewness(ln)   Computes the linear skewness from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ kurtosis(ln, excess)   Computes the linear kurtosis from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.      excess (bool) : Excess Kurtosis (true) or regular Kurtosis (false).   Returns: Between 0 and ∞ hyper_skewness(ln)   Computes the linear hyper skewness from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ hyper_kurtosis(ln, excess)   Computes the linear hyper kurtosis from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.      excess (bool) : Excess Hyper Kurtosis (true) or regular Hyper Kurtosis (false).   Returns: Between 0 and ∞ - - - DISTRIBUTION FUNCTIONS These wrap Gaussian functions to make working with model space more direct. Because they are contained within a log-normal library, they describe estimations relative to a log-normal curve, even though they fundamentally measure a Gaussian curve. pdf(ln, x, empirical_quantiles)   A Probability Density Function estimates the probability density . For clarity, density is not a probability .   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate for which a density will be estimated.      empirical_quantiles (Quantile) : Quantiles as observed in the data (optional).   Returns: Between 0 and ∞ cdf(ln, x, precise)   A Cumulative Distribution Function estimates the area under a Log-Normal curve between Zero and a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 ccdf(ln, x, precise)   A Complementary Cumulative Distribution Function estimates the area under a Log-Normal curve between a linear X coordinate and Infinity.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 cdfinv(ln, a, precise)   An Inverse Cumulative Distribution Function reverses the Log-Normal cdf() by estimating the linear X coordinate from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ ccdfinv(ln, a, precise)   An Inverse Complementary Cumulative Distribution Function reverses the Log-Normal ccdf() by estimating the linear X coordinate from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ cdfab(ln, x1, x2, precise)   A Cumulative Distribution Function from A to B estimates the area under a Log-Normal curve between two linear X coordinates (A and B).   Parameters:      ln (LogNorm) : Object of log space parameters.      x1 (float) : First linear X coordinate .      x2 (float) : Second linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 ott(ln, x, precise)   A One-Tailed Test transforms a linear X coordinate into an absolute Z Score before estimating the area under a Log-Normal curve between Z and Infinity.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 0.5 ttt(ln, x, precise)    A Two-Tailed Test transforms a linear X coordinate into symmetrical ± Z Scores before estimating the area under a Log-Normal curve from Zero to -Z, and +Z to Infinity.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 ottinv(ln, a, precise)   An Inverse One-Tailed Test reverses the Log-Normal ott() by estimating a linear X coordinate for the right tail from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Half a normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ tttinv(ln, a, precise)   An Inverse Two-Tailed Test reverses the Log-Normal ttt() by estimating two linear X coordinates from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Linear space tuple : - - - UNCERTAINTY Model-based measures of uncertainty, information, and risk. sterr(sample_size, fisher_info)   The standard error of a sample statistic.   Parameters:      sample_size (float) : Number of observations.      fisher_info (float) : Fisher information.   Returns: Between 0 and ∞ surprisal(p, base)   Quantifies the information content of a single event.   Parameters:      p (float) : Probability of the event .      base (float) : Logarithmic base (optional).   Returns: Between 0 and ∞ entropy(ln, base)   Computes the differential entropy (average surprisal).   Parameters:      ln (LogNorm) : Object of log space parameters.      base (float) : Logarithmic base (optional).   Returns: Between 0 and ∞ perplexity(ln, base)   Computes the average number of distinguishable outcomes from the entropy.     Parameters:      ln (LogNorm)      base (float) : Logarithmic base used for Entropy (optional).   Returns: Between 0 and ∞ value_at_risk(ln, p, precise)   Estimates a risk threshold under normal market conditions for a given confidence level.   Parameters:      ln (LogNorm) : Object of log space parameters.      p (float) : Probability threshold, aka. the confidence level .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ value_at_risk_inv(ln, value_at_risk, precise)   Reverses the value_at_risk() by estimating the confidence level from the risk threshold.   Parameters:      ln (LogNorm) : Object of log space parameters.      value_at_risk (float) : Value at Risk.      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 conditional_value_at_risk(ln, p, precise)   Estimates the average loss beyond a confidence level, aka. expected shortfall.   Parameters:      ln (LogNorm) : Object of log space parameters.      p (float) : Probability threshold, aka. the confidence level .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ conditional_value_at_risk_inv(ln, conditional_value_at_risk, precise)   Reverses the conditional_value_at_risk() by estimating the confidence level of an average loss.   Parameters:      ln (LogNorm) : Object of log space parameters.      conditional_value_at_risk (float) : Conditional Value at Risk.      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 partial_expectation(ln, x, precise)   Estimates the partial expectation of a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and µ partial_expectation_inv(ln, partial_expectation, precise)   Reverses the partial_expectation() by estimating a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      partial_expectation (float) : Partial Expectation .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ conditional_expectation(ln, x, precise)   Estimates the conditional expectation of a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between X and ∞ conditional_expectation_inv(ln, conditional_expectation, precise)   Reverses the conditional_expectation by estimating a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      conditional_expectation (float) : Conditional Expectation .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ fisher(ln, log)   Computes the Fisher Information Matrix for the distribution, not a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      log (bool) : Sets if the matrix should be in log (true) or linear (false) space.   Returns: FIM for the distribution fisher(ln, x, log)   Computes the Fisher Information Matrix for a linear X coordinate, not the distribution itself.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      log (bool) : Sets if the matrix should be in log (true) or linear (false) space.   Returns: FIM for the linear X coordinate confidence_interval(ln, x, sample_size, confidence, precise)   Estimates a confidence interval for a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      sample_size (float) : Number of observations.      confidence (float) : Confidence level .      precise (bool) : Double precision (true) or single precision (false).   Returns: CI for the linear X coordinate - - - CURVE FITTING An overloaded function that helps transform values between spaces. The primary function uses quantiles, and the overloads wrap the primary function to make working with LogNorm more direct. fit(x, a, b)   Transforms X coordinate between spaces A and B.   Parameters:      x (float) : Linear X coordinate from space A .      a (LogNorm | Quantile | array) : LogNorm, Quantile, or float array.      b (LogNorm | Quantile | array) : LogNorm, Quantile, or float array.   Returns: Adjusted X coordinate - - - EXPORTED HELPERS Small utilities to simplify extensibility. z_score(ln, x)   Converts a linear X coordinate into a Z Score.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate.   Returns: Between -∞ and +∞ x_coord(ln, z)   Converts a Z Score into a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      z (float) : Standard normal Z Score.   Returns: Between 0 and ∞ iget(arr, index)   Gets an interpolated value of a pseudo -element (fictional element between real array elements). Useful for quantile mapping.   Parameters:      arr (array) : Float array object.      index (float) : Index of the pseudo element.   Returns: Interpolated value of the arrays pseudo element.
Pine Script®腳本庫
由liquid-trader提供
Liquidity Grab + RSI Divergence═══════════════════════════════════════════════════════════════ LIQUIDITY GRAB + RSI DIVERGENCE INDICATOR ═══════════════════════════════════════════════════════════════ 📌 OVERVIEW This indicator identifies high-probability reversals by combining: • Liquidity sweeps (stop hunts) • RSI divergence confirmation • Filters false breakouts automatically ═══════════════════════════════════════════════════════════════ 🟢 BUY SIGNAL (Green Triangle Up) REQUIRES BOTH CONDITIONS: 1. Liquidity Grab Below Previous Low • Price breaks BELOW recent low • Candle CLOSES ABOVE that low • Traps sellers who shorted the breakdown 2. Bullish RSI Divergence • Price: Lower Low (LL) • RSI: Higher Low (HL) • Shows weakening downward momentum ➜ Result: Potential bullish reversal ═══════════════════════════════════════════════════════════════ 🔴 SELL SIGNAL (Red Triangle Down) REQUIRES BOTH CONDITIONS: 1. Liquidity Grab Above Previous High • Price breaks ABOVE recent high • Candle CLOSES BELOW that high • Traps buyers who bought the breakout 2. Bearish RSI Divergence • Price: Higher High (HH) • RSI: Lower High (LH) • Shows weakening upward momentum ➜ Result: Potential bearish reversal ═══════════════════════════════════════════════════════════════ 📊 VISUAL INDICATORS Main Signals: 🔺 Large Green Triangle = BUY (Liq Grab + Bullish Div) 🔻 Large Red Triangle = SELL (Liq Grab + Bearish Div) Reference Levels: ━ Red Line = Previous High Level ━ Green Line = Previous Low Level Additional Markers (Optional): ○ Small Green Circle = Liquidity grab low only ○ Small Red Circle = Liquidity grab high only ✕ Small Blue Cross = Bullish divergence only ✕ Small Orange Cross = Bearish divergence only ═══════════════════════════════════════════════════════════════ ⚙️ SETTINGS 1. Lookback Period (Default: 20) • Range: 5-100 • Sets how far back to identify previous highs/lows • Higher = fewer but stronger levels • Lower = more frequent but weaker levels 2. RSI Length (Default: 14) • Range: 5-50 • Standard RSI calculation period • 14 is industry standard 3. RSI Divergence Lookback (Default: 5) • Range: 3-20 • Controls pivot point sensitivity • Higher = fewer divergence signals • Lower = more divergence signals 4. Show Labels (Default: ON) • Toggle BUY/SELL text labels • Disable for cleaner chart view ═══════════════════════════════════════════════════════════════ 💡 HOW TO USE Step 1: WAIT FOR CONFIRMATION • Only trade LARGE TRIANGLE signals • Ignore small circles/crosses alone Step 2: CHECK TIMEFRAME • Best on: 15min, 1H, 4H, Daily • Avoid: 1min, 5min (too noisy) Step 3: CONFIRM CONTEXT • Check overall market trend • Identify key support/resistance • Look for confluence with price action Step 4: ENTRY & RISK MANAGEMENT • Enter on signal candle close or pullback • Stop loss below/above the liquidity grab wick • Target: Previous swing high/low or key levels • Risk/Reward: Minimum 1:2 ratio Step 5: SET ALERTS • Create alert for "BUY Signal" • Create alert for "SELL Signal" • Never miss opportunities ═══════════════════════════════════════════════════════════════ ✅ BEST PRACTICES DO: ✓ Use on multiple timeframes for confluence ✓ Combine with support/resistance zones ✓ Wait for both conditions (liq grab + divergence) ✓ Practice on demo account first ✓ Use proper position sizing DON'T: ✗ Trade every small circle/cross ✗ Use on very low timeframes (<15min) ✗ Ignore overall market context ✗ Trade without stop loss ✗ Risk more than 1-2% per trade ═══════════════════════════════════════════════════════════════ ⚠️ IMPORTANT NOTES • This is a CONFIRMATION tool, not a holy grail • No indicator is 100% accurate • Combine with your trading strategy • Backtest on your preferred instruments • Adjust parameters for your trading style • Higher timeframes = more reliable signals • Always use risk management ═══════════════════════════════════════════════════════════════ 🔔 ALERTS INCLUDED Two alert conditions are built-in: 1. "BUY Signal" - Liquidity Grab + Bullish RSI Divergence 2. "SELL Signal" - Liquidity Grab + Bearish RSI Divergence ═══════════════════════════════════════════════════════════════ 📈 RECOMMENDED SETTINGS BY TIMEFRAME 5-15 Min Charts: • Lookback: 10-15 • RSI Length: 14 • RSI Div Lookback: 3-5 1H-4H Charts: • Lookback: 20-30 • RSI Length: 14 • RSI Div Lookback: 5-7 Daily Charts: • Lookback: 30-50 • RSI Length: 14 • RSI Div Lookback: 7-10 ═══════════════════════════════════════════════════════════════ Good luck and trade safe! 🚀
Pine Script®指標
由hassanejaz1提供
Volume + MA5 & MA10This Volume + MA5 & MA10 (Technical Volume Trend Analysis) The Volume + MA5 & MA10 indicator provides a precise view of market participation and volume momentum by combining raw volume data with two moving averages (MA5 and MA10). It’s designed for traders who rely on volume-based confirmation to validate price movements, breakouts, and trend reversals. 🔍 Overview This indicator displays volume bars alongside two smooth volume averages — MA5 (short-term) and MA10 (medium-term) — making it easier to detect shifts in market activity. When the short-term average crosses above or below the long-term average, it signals a potential change in trading intensity or market sentiment. ⚙️ Key Features Dual Volume Moving Averages (MA5 & MA10) for short- and medium-term analysis. Dynamic Bar Coloring based on whether current volume exceeds MA5 or MA10. Crossover Detection with visual markers for MA5/MA10 intersections. Alert Conditions to notify you of significant volume trend shifts. Fully customizable appearance and smoothing options. 📊 How to Interpret MA5 > MA10 → Increasing short-term volume activity (strengthening momentum). MA5 < MA10 → Decreasing short-term volume (weakening participation). Rising volume with price → Confirms trend strength. Falling volume with rising/falling price → Suggests potential reversal or reduced conviction. 💡 Applications Confirm breakouts and trend continuations. Identify momentum divergences between price and volume. Filter out low-volume or weak-trend setups. Combine with RSI, MACD, or moving averages for enhanced signal validation. ✅ Advantages Simple yet powerful structure for clean visual analysis. Works across all timeframes and markets (crypto, stocks, forex, indices). No repainting — reliable for both live and historical backtesting. Use Volume + MA5 & MA10 to strengthen your technical analysis and gain a deeper understanding of how market participation drives price trends.
Pine Script®指標
由Davidovff提供
FVG SizeFVG Size Indicator – Description Overview This Pine Script v5 indicator detects and visualizes Fair Value Gaps (FVGs) on the chart. It draws colored boxes for FVGs, center lines (CE), and displays the size of each FVG as a label. The indicator is designed for manual analysis, helping traders identify potential price imbalances. Key Features FVG Detection: Identifies bullish and bearish FVGs based on price structure. Draws colored boxes for FVGs and dotted center lines (CE). FVG Size Display: Shows the size of each FVG as a label inside the box. Customizable minimum size threshold to filter out smaller FVGs. Dynamic Adjustments: Extends FVG boxes to the right as new bars form. Removes FVGs that are filled (mitigated) by price action. Customizable Settings: Adjustable colors, text size, and display options. Settings and Translations Here are the German settings with their English translations: FVG Settings Long FVG Farbe → Long FVG Color (Color for bullish FVG boxes) Short FVG Farbe → Short FVG Color (Color for bearish FVG boxes) CE Farbe → CE Color (Color for the center line) Tage Rückblick → Lookback Days (Number of days to look back for FVGs) Lösche gefüllte Boxen & Linien → Delete Filled Boxes & Lines (Removes FVGs that have been filled by price) FVG Display FVG Größe anzeigen → Show FVG Size (Displays the size of each FVG as a label) Text → Text Size (Size of the FVG size label text) Mindestgröße → Minimum Size (Minimum FVG size to display, filtering out smaller FVGs) How It Works FVG Detection Logic: A bullish FVG is detected if the high of the 3rd bar is lower than the low of the 1st bar. A bearish FVG is detected if the low of the 3rd bar is higher than the high of the 1st bar. Drawing FVGs: The indicator draws a box between the high/low of the 1st and 3rd bars. A center line (CE) is drawn at the midpoint of the FVG. The size of the FVG is displayed as a label inside the box. Dynamic Adjustments: FVG boxes are extended to the right as new bars form. If the price fills the FVG, the box and line are removed (depending on settings). Mitigation Logic: If the price closes beyond the FVG boundaries, the FVG is considered "filled" and removed.
Pine Script®指標
由kp_eu_de提供
BullishBuzz ORB – CALL/PUT with Chart Alerts (Final)⚙️ The Bullish BuzzBot System 1️⃣ Data Feeds (Input Layer) BuzzBot connects to live market data through TradingView’s chart engine (or via API for more advanced builds). It continuously pulls: Price data (open, high, low, close per bar) Volume RSI, MACD, VWAP, EMA 9/21 values Timestamps & bar intervals (1m, 5m, 15m) That’s the raw fuel — the same data you’d use for charting. 2️⃣ Indicator Engine (Signal Layer) This is where the logic lives — it calculates conditions in real time. BuzzBot checks for patterns like: EMA 9/21 Cross: detects momentum shift VWAP Reclaim or Reject: confirms intraday bias RSI < 50 or > 70: momentum confirmation MACD Cross: trend continuation signal Volume > 2x average: validates conviction
Pine Script®指標
由jakobzahabi提供
EMA HeatmapEMA Heatmap — Indicator Description The EMA Order Heatmap is a visual trend-structure tool designed to show whether the market is currently trending bullish, trending bearish, or moving through a neutral consolidation phase. It evaluates the alignment of multiple exponential moving averages (EMAs) at three different structural layers: short-term daily, medium-term daily, and weekly macro trend. This creates a quick and intuitive picture of how well price movement is organized across timeframes. Each layer of the heatmap is scored from bearish to bullish based on how the EMAs are stacked relative to each other. When EMAs are in a fully bullish configuration, the row displays a bright green or lime color. Fully bearish alignment is shown in red. Yellow tones appear when the EMAs are mixed or compressing, indicating uncertainty, trend exhaustion, or a change in market character. The three rows combined offer a concise view of whether strength or weakness is isolated to one timeframe or broad across the market. This indicator is best used as a trend filter before making trading decisions. Traders may find more consistent setups when the majority of the heatmap supports the direction of their trade. Green-dominant conditions suggest a trending bullish environment where long trades can be favored. Red-dominant conditions indicate bearish momentum and stronger potential for short opportunities. When yellow becomes more prominent, the market may be transitioning, ranging, or gearing up for a breakout, making timing more challenging and risk higher. • Helps quickly identify directional bias • Highlights when trends strengthen, weaken, or turn • Provides insight into whether momentum is supported by higher timeframes • Encourages traders to avoid fighting market structure It is important to recognize the limitations. EMAs are lagging indicators, so the heatmap may confirm a trend after the initial move is underway, especially during fast reversals. In sideways or low-volume environments, the structure can shift frequently, reducing clarity. This tool does not generate entry or exit signals on its own and should be paired with price action, momentum studies, or support and resistance analysis for precise trade execution. The EMA Order Heatmap offers a clean and reliable way to stay aligned with the broader market environment and avoid lower-quality trades in indecisive conditions. It supports more disciplined decision-making by helping traders focus on setups that match the prevailing structural trend.
Pine Script®指標
由Scatman_Jeff提供
THAIT Moving Averages Tight within # ATR EMA SMA convergence THAIT(tight) indicator is a powerful tool for identifying moving average convergence in price action. This indicator plots four user-defined moving averages (EMA or SMA). It highlights moments when the MAs converge within a user specified number of ATRs, adjusted by the 14-period ATR, signaling potential trend shifts or consolidation. A convergence is flagged when MA1 is the maximum, the spread between MAs is tight, and the price is above MA1, excluding cases where the longest MA (MA4) is the highest. The indicator alerts and visually marks convergence zones with a shaded green background, making it ideal for traders seeking precise entry or exit points.
Pine Script®指標
由Imready4提供
Reverse RSI LevelsSimple reverse RSI calculation As default RSI values 30-50-70 are calculated into price. This can be used similar to a bollinger band, but has also multiple other uses. 70 RSI works as overbought/resistance level. 50 RSI works as both support and resistance depending on the trend. 30 RSI works as oversold/support level. Keep in mind that RSI levels can go extreme, specially in Crypto. I haven't made it possible to adjust the default levels, but I've added 4 more calculations where you can plot reverse RSI calculations of your desired RSI values. If you're a RSI geek, you probably use RSI quite often to see how high/low the RSI might go before finding a new support or resistance level. Now you can just put the RSI level into on of the 4 slots in the settings and see where that support/resistance level might be on the chart.
Pine Script®指標
由GizmoTheInvestor提供