立即開始

指標和策略

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
Yield Curve RegimesCurrently we are seeing equities and all other risk assets rallying to new all time high. But when will this stop? There are multiple risks/signals i am monitoring to stay at the right side of the macro trade. Macro is everything: “When you get the Big-Picture wrong you wont live long.” So lets go through a major risk that could be the catalyst for the next deeper correction Capital needs to begin to move BACK across the risk curve as the yield curve steepens. We don't know if the source of the the crash will be from bear steepening or bull steepening because its unclear if long end rates blowing out will be the source of the crash. If the Fed continues to make the policy error of being overly accommodative at this high level of nominal GDP + Inflation risk, the long end of the curve will price this. Simple: If the Fed is to lose the long end can move up to price the inflation risk, which could ultimately pull risk assets down. We have not seen this yet because the last inflation prints came in flat, but I expect these to come in higher over the next 6 months. This means watching long end rates and their potential drag on equities will be critical. We are not seeing this yet as the Russell is sitting at all time highs and capital continues to move into low quality factors. Look where the long end is moving + the attribution analysis for the move. → Down growth risk → Up Inflation risk + look what the 2s10s & the 10s30 are pricing and how these changes in the curve connect to the current yield curve regimes. You can get the Trading view Skript 100% free here
Pine Script®指標
由The-white-goat提供
SMC by ASHY-JAYASHY-JAY "Smart Money" refers to funds under the control of institutional investors, central banks, funds, market makers, and other financial entities. Ordinary people recognize investments made by those who have a deep understanding of market performance and possess information typically inaccessible to regular investors as "Smart Money". Consequently, when market movements often diverge from expectations, traders identify the footprints of smart money. For example, when a classic pattern forms in the market, traders take short positions. However, the market might move upward instead. They attribute this contradiction to smart money and seek to capitalize on such inconsistencies in their trades. The "Smart Money Concept" (SMC) is one of the primary styles of technical analysis that falls under the subset of "Price Action". Price action encompasses various subcategories, with one of the most significant being "Supply and Demand", in which SMC is categorized. The SMC method aims to identify trading opportunities by emphasizing the impact of large traders (Smart Money) on the market, offering specific patterns, techniques, and trading strategies. 🟣Key Terms of Smart Money Concept (SMC) • Market Structure (Trend) • Change of Character (ChoCh) • Break of Structure (BoS) • Order Blocks (Supply and Demand) • Imbalance (IMB) • Inefficiency (IFC) • Fair Value Gap (FVG) • Liquidity • Premium and Discount
Pine Script®指標
由Jay-Jover提供
Svopex Session Highlighter# Session Highlighter ## Description **Session Highlighter** is a powerful Pine Script indicator designed to visually identify and mark specific trading hours on your chart. This tool helps traders focus on their preferred trading sessions by highlighting the background during active hours and marking the session start with customizable visual markers. ## Key Features - **📊 Session Background Highlighting**: Automatically shades the chart background during your defined trading hours (default: 7:00 - 23:00) - **🎯 Smart Session Start Marker**: Places a marker on the last candle before session start, intelligently adapting to your timeframe: - 1 Hour chart: Marker at 6:00 - 15 Minute chart: Marker at 6:45 - 5 Minute chart: Marker at 6:55 - 1 Minute chart: Marker at 6:59 - **🌍 Timezone Support**: Choose from multiple timezones (Europe/Prague, Europe/London, America/New_York, UTC) - **🎨 5 Marker Styles**: Customize your session start indicator: - Triangle - Circle - Diamond - Label with time text - Vertical line - **⚙️ Fully Customizable**: Adjust start/end hours, timezone, and marker style through simple settings ## Settings - **Start Hour**: Set your session start time (0-23) - **End Hour**: Set your session end time (0-23) - **Timezone**: Select your trading timezone - **Marker Style**: Choose your preferred visual marker ## Use Cases - Identify London/New York trading sessions - Mark Asian session hours - Highlight your personal trading windows - Avoid trading during off-hours - Perfect for day traders and scalpers ## Installation 1. Copy the Pine Script code 2. Open TradingView Pine Editor 3. Paste the code and click "Add to Chart" 4. Configure settings to match your trading schedule
Pine Script®指標
由svopex提供
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!
Pine Script®策略
由kostastrovas提供
TrendFlowTrendFlow is a visual trend analysis tool that helps identify changes in market direction and momentum. It uses three exponential moving averages (EMA 21, EMA 50, and EMA 200) to define short-, medium-, and long-term trend structure. A dynamic color fill highlights the relationship between the 21 and 50 EMAs: When EMA 21 is above EMA 50, a green fill indicates upward momentum. When EMA 21 is below EMA 50, a red fill shows downward momentum. The EMA 200 line adapts its color based on its position relative to the shorter EMAs: Red when above both EMAs (strong upward structure) Green when below both EMAs (strong downward structure) Gray when between them (neutral or consolidation phase) This setup provides a clear visual framework for observing trend flow and directional bias over multiple timeframes. Developed by The Volume Hub Fintech and Strategy Development
Pine Script®指標
由FXGOD22提供
3
Buyside & Sellside Liquidity The Buyside & Sellside Liquidity Indicator is an advanced Smart Money Concepts (SMC) tool that automatically detects and visualizes liquidity zones and liquidity voids (imbalances) directly on the chart. 🟢 Function and meaning: 1. Buyside Liquidity (green): Highlights price zones above current price where short traders’ stop-loss orders are likely resting. When price sweeps these areas, it often indicates a liquidity grab or stop hunt. 👉 These zones are labeled with 💵💰 emojis for a clear visual cue where smart money collects liquidity. 2. Sellside Liquidity (red): Highlights zones below the current price where long traders’ stop-losses are likely placed. Once breached, these often signal a potential reversal upward. 👉 The 💵💰🪙 emojis make these liquidity targets visually intuitive on the chart. 3. Liquidity Voids (bright areas): Indicate inefficient price areas, where the market moved too quickly without filling orders. These zones are often revisited later as the market seeks balance (fair value). 👉 Shown as light shaded boxes with 💰 emojis to emphasize imbalance regions. 💡 Usage: • Helps spot smart money manipulation and stop hunts. • Marks potential reversal or breakout zones. • Great for traders applying SMC, ICT, or Fair Value Gap strategies. ✨ Highlight: Dollar and money bag emojis (💵💰🪙💸) are integrated directly into chart labels to create a clear and visually engaging representation of liquidity areas.
Pine Script®指標
由morris220768提供
1
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
NOVA Breakout Signals v2.2 (TF M30)A clean, rules-based breakout signal tool for 30-minute charts. It detects Dow swing breakouts and filters them with RSI, MACD and Volume so you only see the higher-quality entries. The script does not place trades and does not calculate SL/TP – it only prints clear LONG/SHORT labels at the entry price. ⸻ How it works 1. Timeframe enforcement – Signals are generated only on M30. On other timeframes the script shows a notice and stays silent. 2. Breakout engine (Dow swings) – The last confirmed swing high/low (pivots) is tracked. • Breakout Up: bar closes above the last swing high by a small buffer. • Breakout Down: bar closes below the last swing low by a small buffer. 3. Quality filters (all must be true): • RSI (default length 30): • Long: RSI > threshold and rising. • Short: RSI < threshold and falling. • MACD (12/26/9): • Long: histogram > 0 and line > signal. • Short: histogram < 0 and line < signal. • Volume: current volume > SMA(volume, 20) × multiplier. 4. Debounce / anti-spam • Cooldown of 4 hours (8 M30 bars) after any signal. • Minimum price distance from the previous signal to avoid clustered labels. Signals appear once the bar closes (barstate.isconfirmed). No swing lines are drawn to keep the chart clean; only entry labels are shown. ⸻ Inputs (key) • RSI length & thresholds for Long/Short confirmation. • MACD uses 12/26/9 (fixed). • Volume multiplier (relative to SMA 20). • Breakout buffer %, Cooldown hours, Min distance %. • Show labels (on/off). ⸻ Usage tips • Start with gold/major FX/indices on M30; use “Once per bar close” if you attach alerts. • Increase the breakout buffer and volume multiplier in choppy markets. • Tighten RSI thresholds (e.g., 55/45) if you want fewer but stronger signals. ⸻ Notes & limitations • Pivots confirm after a few bars by definition; signals themselves are printed only on confirmed bar close and do not repaint once shown. • This is a signal indicator, not investment advice. Always manage risk.
Pine Script®指標
由kenny_nguyen提供
Kalman VWAP Filter [BackQuant]Kalman VWAP Filter A precision-engineered price estimator that fuses Kalman filtering with the Volume-Weighted Average Price (VWAP) to create a smooth, adaptive representation of fair value. This hybrid model intelligently balances responsiveness and stability, tracking trend shifts with minimal noise while maintaining a statistically grounded link to volume distribution. If you would like to see my original Kalman Filter, please find it here: Concept overview The Kalman VWAP Filter is built on two core ideas from quantitative finance and control theory: Kalman filtering — a recursive Bayesian estimator used to infer the true underlying state of a noisy system (in this case, fair price). VWAP anchoring — a dynamic reference that weights price by traded volume, representing where the majority of transactions have occurred. By merging these concepts, the filter produces a line that behaves like a "smart moving average": smooth when noise is high, fast when markets trend, and self-adjusting based on both market structure and user-defined noise parameters. How it works Measurement blend : Combines the chosen Price Source (e.g., close or hlc3) with either a Session VWAP or a Rolling VWAP baseline. The VWAP Weight input controls how much the filter trusts traded volume versus price movement. Kalman recursion : Each bar updates an internal "state estimate" using the Kalman gain, which determines how much to trust new observations vs. the prior state. Noise parameters : Process Noise controls agility — higher values make the filter more responsive but also more volatile. Measurement Noise controls smoothness — higher values make it steadier but slower to adapt. Filter order (N) : Defines how many parallel state estimates are used. Larger orders yield smoother output by layering multiple one-dimensional Kalman passes. Final output : A refined price trajectory that captures VWAP-adjusted fair value while dynamically adjusting to real-time volatility and order flow. Why this matters Most smoothing techniques (EMA, SMA, Hull) trade off lag for smoothness. Kalman filtering, however, adaptively rebalances that tradeoff each bar using probabilistic weighting, allowing it to follow market state changes more efficiently. Anchoring it to VWAP integrates microstructure context — capturing where liquidity truly lies rather than only where price moves. Use cases Trend tracking : Color-coded candle painting highlights shifts in slope direction, revealing early trend transitions. Fair value mapping : The line represents a continuously updated equilibrium price between raw price action and VWAP flow. Adaptive moving average replacement : Outperforms static MAs in variable volatility regimes by self-adjusting smoothness. Execution & reversion logic : When price diverges from the Kalman VWAP, it may indicate short-term imbalance or overextension relative to volume-adjusted fair value. Cross-signal framework : Use with standard VWAP or other filters to identify convergence or divergence between liquidity-weighted and state-estimated prices. Parameter guidance Process Noise : 0.01–0.05 for swing traders, 0.1–0.2 for intraday scalping. Measurement Noise : 2–5 for normal use, 8+ for very smooth tracking. VWAP Weight : 0.2–0.4 balances both price and VWAP influence; 1.0 locks output directly to VWAP dynamics. Filter Order (N) : 3–5 for reactive short-term filters; 8–10 for smoother institutional-style baselines. Interpretation When price > Kalman VWAP and slope is positive → bullish pressure; buyers dominate above fair value. When price < Kalman VWAP and slope is negative → bearish pressure; sellers dominate below fair value. Convergence of price and Kalman VWAP often signals equilibrium; strong divergence suggests imbalance. Crosses between Kalman VWAP and the base VWAP can hint at shifts in short-term vs. long-term liquidity control. Summary The Kalman VWAP Filter blends statistical estimation with market microstructure awareness, offering a refined alternative to static smoothing indicators. It adapts in real time to volatility and order flow, helping traders visualize balance, transition, and momentum through a lens of probabilistic fair value rather than simple price averaging.
Pine Script®指標
由BackQuant提供
4
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
High Zone MapperHigh Zone and Low Zone Mapper — Quick Manual (Short-Term Trading) Author: hkpress | Script date: 2025-10-26 This indicator draws: PDH/PDL (Prior Day High/Low), PWH/PWL (Prior Week High/Low), ORH/ORL (Opening Range High/Low), IDH/IDL (Intraday High/Low), plus a shaded Opening Range box. I built this script after watching an interview on TradingLion with a Hong Kong trader who uses prior-day, opening-range, intraday, and prior-week levels to plan entries and exits. The approach is especially useful for traders who run tight stops (about 1.5%–3%) while aiming to size up into bigger positions. (Youtube: www.youtube.com) 1) Quick Start (15-minute default) Timeframe: use 1–15m for scalps, 5–30m for intraday. Opening Range (OR — Opening Range): default 15 minutes. Turn on “Show OR lines while opening range builds” if you want to see ORH/ORL during the first 15 minutes. Session mode: Stocks → Use Trading Session = ON (RTH — Regular Trading Hours, e.g., 09:30–16:00). Crypto/24h → Use Trading Session = OFF (day-start mode). Visuals: enable PDH/PDL, PWH/PWL, ORH/ORL, IDH/IDL, and the Opening Range box (fill). 2) What each line means PDH/PDL (Prior Day High/Low): Yesterday’s extremes; frequent reaction zones. PWH/PWL (Prior Week High/Low): Last week’s extremes; stronger “fences.” ORH/ORL (Opening Range High/Low): High/low of the first 15 minutes by default; key breakout compass. IDH/IDL (Intraday High/Low): Today’s high/low so far; confirms momentum after a break. 3) Short-Term Playbook (step-by-step) A. Before the open Note where price is vs PDH/PDL and PWH/PWL to set a bias. Above PDH and pushing up → bullish lean. Below PDL and slipping → bearish lean. B. First 15 minutes (Opening Range forms) Let the Opening Range box print (ORH top, ORL bottom). Think of this box as the day’s first “battlefield.” C. Breakout entries Long: Clean break above ORH (preferably with momentum/volume). Stop: just below ORH (aggressive) or below ORL (conservative). Targets: step up through PDH → PWH. Short: Clean break below ORL. Stop: just above ORL (aggressive) or above ORH (conservative). Targets: step down through PDL → PWL. D. Retest entries (missed the first move?) After a break, wait for a retest of ORH/ORL from the other side. Enter on rejection/continuation; place stop on the opposite side of the retested level. E. Momentum confirmation New IDH (Intraday High) after an ORH break = trend strengthening (consider add/hold). New IDL after an ORL break = downtrend strengthening. Trail stops below higher lows (long) or above lower highs (short). F. Range mode (no break yet) If price stays inside the box, fade edges: buy near ORL, sell near ORH, until a decisive break. 4) Risk rules (keep it simple) Aim for R:R (Risk-to-Reward) ≥ 1:2. Set a daily max loss (e.g., 1–2R) and respect it. Invalidation: if price breaks and then re-enters the box and holds, exit—don’t argue. 5) Quick example The 15-minute OR prints: ORL = 100, ORH = 105. Price breaks 106 with momentum → Long. Stop 104.8 (below ORH) or 99.8 (below ORL). Targets: PDH, then PWH. Trail as IDH keeps making new highs. 6) Handy tweaks Noisy/news days: widen to 30-minute OR to reduce whipsaws. Strong trend open: tighten to 5–10-minute OR to engage earlier. Choppy session: stick to box-edge fades or stand aside after two failed breaks. 7) Built-in alerts to consider “Break Above ORH / Below ORL” → entry triggers. “New IDH / New IDL” → momentum confirms; tighten stops or scale. “Break Above PDH / Below PDL / Above PWH / Below PWL” → target hits or bigger trend shifts. 8) Troubleshooting No lines? Switch to an intraday timeframe (1–60m). ORH/ORL missing? Turn ON Show OR lines while opening range builds. Session mismatch? Use correct RTH hours, or turn session OFF for 24h symbols. Abbreviation cheat-sheet OR (Opening Range), ORH/ORL (Opening Range High/Low) PDH/PDL (Prior Day High/Low) PWH/PWL (Prior Week High/Low) IDH/IDL (Intraday High/Low) RTH (Regular Trading Hours), R:R (Risk-to-Reward)
Pine Script®指標
由hkpress提供