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Baseline Deviation Oscillator [Alpha Extract]A sophisticated normalized oscillator system that measures price deviation from a customizable moving average baseline using ATR-based scaling and dynamic threshold adaptation. Utilizing advanced HL median filtering and multi-timeframe threshold calculations, this indicator delivers institutional-grade overbought/oversold detection with automatic zone adjustment based on recent oscillator extremes. The system's flexible baseline architecture supports six different moving average types while maintaining consistent ATR normalization for reliable signal generation across varying market volatility conditions. 🔶 Advanced Baseline Construction Framework Implements flexible moving average architecture supporting EMA, RMA, SMA, WMA, HMA, and TEMA calculations with configurable source selection for optimal baseline customization. The system applies HL median filtering to the raw baseline for exceptional smoothing and outlier resistance, creating ultra-stable trend reference levels suitable for precise deviation measurement. // Flexible Baseline MA System ma(src, length, type) => if type == "EMA" ta.ema(src, length) else if type == "TEMA" ema1 = ta.ema(src, length) ema2 = ta.ema(ema1, length) ema3 = ta.ema(ema2, length) 3 * ema1 - 3 * ema2 + ema3 // Baseline with HL Median Smoothing Baseline_Raw = ma(src, MA_Length, MA_Type) Baseline = hlMedian(Baseline_Raw, HL_Filter_Length) 🔶 ATR Normalization Engine Features sophisticated ATR-based scaling methodology that normalizes price deviations relative to current volatility conditions, ensuring consistent oscillator readings across different market regimes. The system calculates ATR bands around the baseline and uses half the band width as the normalization factor for volatility-adjusted deviation measurement. 🔶 Dynamic Threshold Adaptation System Implements intelligent threshold calculation using rolling window analysis of oscillator extremes with configurable smoothing and expansion parameters. The system identifies peak and trough levels over dynamic windows, applies EMA smoothing, and adds expansion factors to create adaptive overbought/oversold zones that adjust to changing market conditions. 1D 3D 1W 🔶 Multi-Source Configuration Architecture Provides comprehensive source selection including Close, Open, HL2, HLC3, and OHLC4 options for baseline calculation, enabling traders to optimize oscillator behavior for specific trading styles. The flexible source system allows adaptation to different market characteristics while maintaining consistent ATR normalization methodology. 🔶 Signal Generation Framework Generates bounce signals when oscillator crosses back through dynamic thresholds and zero-line crossover signals for trend confirmation. The system identifies both standard threshold bounces and extreme zone bounces with distinct alert conditions for comprehensive reversal and continuation pattern detection. Bull_Bounce = ta.crossover(OSC, -Active_Lower) or ta.crossover(OSC, -Active_Lower_Extreme) Bear_Bounce = ta.crossunder(OSC, Active_Upper) or ta.crossunder(OSC, Active_Upper_Extreme) // Zero Line Signals Zero_Cross_Up = ta.crossover(OSC, 0) Zero_Cross_Down = ta.crossunder(OSC, 0) 🔶 Enhanced Visual Architecture Provides color-coded oscillator line with bullish/bearish dynamic coloring, signal line overlay for trend confirmation, and optional cloud fills between oscillator and signal. The system includes gradient zone fills for overbought/oversold regions with configurable transparency and threshold level visualization with automatic label generation. snapshot 🔶 HL Median Filter Integration Features advanced high-low median filtering identical to DEMA Flow for exceptional baseline smoothing without lag introduction. The system constructs rolling windows of baseline values, performs median extraction for both odd and even window lengths, and eliminates outliers for ultra-clean deviation measurement baseline. 🔶 Comprehensive Alert System Implements multi-tier alert framework covering bullish bounces from oversold zones, bearish bounces from overbought zones, and zero-line crossovers in both directions. The system provides real-time notifications for critical oscillator events with customizable message templates for automated trading integration. 🔶 Performance Optimization Framework Utilizes efficient calculation methods with optimized array management for median filtering and minimal computational overhead for real-time oscillator updates. The system includes intelligent null value handling and automatic scale factor protection to prevent division errors during extreme market conditions. 🔶 Why Choose Baseline Deviation Oscillator ? This indicator delivers sophisticated normalized oscillator analysis through flexible baseline architecture and dynamic threshold adaptation. Unlike traditional oscillators with fixed levels, the BDO automatically adjusts overbought/oversold zones based on recent oscillator behavior while maintaining consistent ATR normalization for reliable cross-market and cross-timeframe comparison. The system's combination of multiple MA type support, HL median filtering, and intelligent zone expansion makes it essential for traders seeking adaptive momentum analysis with reduced false signals and comprehensive reversal detection across cryptocurrency, forex, and equity markets.
Pine Script®指標
由AlphaExtract提供
3
Session Range Boxes (Budapest time) GR V2.0Session Range Boxes (Budapest time) This indicator draws intraday range boxes for the main Forex sessions based on Europe/Budapest time (CET/CEST). Tracked sessions (Budapest time): Asia: 01:00 – 08:00 Frankfurt (pre-London): 08:00 – 09:00 London: 09:00 – 18:00 New York: 14:30 – 23:00 For each session, the script: Detects the session start and session end using the current chart timeframe and the Europe/Budapest time zone. Tracks the high and low of price during the entire session. Draws a box (rectangle) from session open to session close, covering the full price range between session high and low. Optionally prints a small label above the first bar of each session (Asia, Fra, London, NY). Color scheme: Asia: soft orange box Frankfurt: light aqua box London: darker blue box New York: light lime box Use this tool to: Quickly see which session created the high/low of the day, Identify liquidity zones and session ranges that price may revisit, Visually separate Asia, Frankfurt, London and New York volatility on intraday charts. Optimized for intraday trading (Forex / indices), but it works on any symbol where session behavior matters.
Pine Script®指標
由GRIDFX提供
Michael's Custom Watermark🔷 MICHAEL'S CUSTOM WATERMARK INDICATOR ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 OVERVIEW A comprehensive chart watermark overlay that displays essential fundamental and technical information for stocks in a clean, customizable table format. Perfect for traders who want quick access to key metrics without cluttering their charts. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ✨ KEY FEATURES 📊 Fundamental Data Display — Shows Industry, Sector, Market Cap, and P/E Ratio 📅 Earnings Information — Displays next earnings date with countdown timer 📈 ATR Volatility Indicator — 14-day ATR with color-coded visual alerts (🔴🟡🟢) 🎨 Auto Theme Detection — Automatically adjusts text color based on chart background ⚙️ Fully Customizable — Position, colors, size, and displayed metrics all adjustable 🏢 GICS Sector Mapping — Heuristic-based sector classification aligned with industry standards ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 WHAT MAKES THIS INDICATOR UNIQUE? Unlike basic watermarks, this indicator provides: Real-time fundamental data integration Smart theme-aware color adaptation for both light and dark charts Configurable volatility alerts using ATR thresholds Earnings countdown feature to never miss important dates Optimized display that only shows relevant data for the current symbol type ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📖 HOW TO USE 1. BASIC SETUP Add the indicator to your chart. By default, it displays in the top-left corner with all features enabled. 2. POSITIONING Vertical Location: Top, Middle, or Bottom Horizontal Location: Left, Center, or Right Vertical Offset: Fine-tune position with 0-50 pixel offset from top 3. CUSTOMIZATION OPTIONS TEXT APPEARANCE: Auto Text Color — Enable to automatically adapt text color to your chart theme Manual Color — Set a fixed text color if auto-color is disabled Text Size — Choose from Huge, Large, Normal, or Small Theme Colors — Customize text color for light and dark backgrounds separately DATA DISPLAY TOGGLES: Show Industry & Sector — Display heuristic-based GICS-aligned sector and industry classification Show Market Cap — View market capitalization in T/B/M format Show P/E Ratio — Display Price-to-Earnings ratio (stocks only) Show ATR (14-Day) — Display Average True Range with percentage and visual indicator Show Next Earnings — Display upcoming earnings information Show Earnings Countdown — Show days remaining until next earnings (requires earnings display) 4. ATR VOLATILITY ALERTS Configure custom thresholds to monitor volatility: Red Threshold — ATR percentage that triggers red alert 🔴 (default: 6%) Yellow Threshold — ATR percentage that triggers yellow alert 🟡 (default: 3%) Green — Shows automatically when ATR is below yellow threshold 🟢 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📐 UNDERSTANDING THE DISPLAY 🏢 SECTOR & INDUSTRY Shows the GICS sector classification followed by the specific industry. The indicator uses heuristic-based mapping to align TradingView sectors with standard GICS classifications. Note that this mapping is based on keyword detection and industry analysis, so while generally accurate, it may not perfectly match official GICS classifications in all cases. 💰 MARKET CAP Displays market capitalization using standard abbreviations: T = Trillion B = Billion M = Million 📊 P/E RATIO Shows the trailing twelve-month Price-to-Earnings ratio. Only displayed for stocks when enabled. Shows "N/A" if data is unavailable. 📈 ATR (14-DAY) Displays the 14-period Average True Range in both absolute value and percentage terms, with a color-coded indicator: 🔴 Red: High volatility (above red threshold) 🟡 Yellow: Moderate volatility (between yellow and red thresholds) 🟢 Green: Low volatility (below yellow threshold) 📅 EARNINGS Shows earnings information in three formats: "X days remaining" — When countdown is enabled and earnings date is known "Upcoming" — When date is in the future but countdown is disabled "Recently Reported" — When earnings just occurred "N/A" — When no earnings data is available ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ TECHNICAL DETAILS SUPPORTED INSTRUMENTS: Optimized for stocks with full fundamental data Works with other instruments (crypto, forex, futures) but only displays applicable metrics Automatically suppresses irrelevant data (e.g., P/E for non-stocks) PERFORMANCE: Lightweight overlay with minimal resource usage Updates only on last bar for efficiency No historical recalculation needed COMPATIBILITY: Pine Script v6 Works on all timeframes Compatible with all chart types Auto-adapts to theme changes ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 TIPS & BEST PRACTICES Enable Auto Text Color for seamless theme switching between light and dark modes Adjust vertical offset to avoid overlap with price action in high-volatility periods Use ATR thresholds appropriate to your trading style and asset class Disable features you don't use to keep the watermark clean and focused Position in corners to maximize chart viewing space Use smaller text size for multi-panel layouts ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔧 TROUBLESHOOTING "N/A" SHOWING FOR P/E RATIO: This is normal for non-stock instruments May occur for stocks with negative earnings Check if fundamental data is available for the symbol EARNINGS SHOWING "N/A": Earnings data may not be available for all stocks Check TradingView's data coverage for your symbol TEXT COLOR NOT VISIBLE: Enable Auto Text Color feature Manually set text color to contrast with your chart background Adjust custom light/dark text colors in settings ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ DISCLAIMER This indicator is for informational purposes only. The fundamental data displayed is sourced from TradingView's data providers. Always verify critical information before making trading decisions. Past performance is not indicative of future results. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ If you find this indicator helpful, please give it a boost 🚀 and share your feedback in the comments! Version: 1.0 Pine Script Version: v6 Created by: Michael
Pine Script®指標
由ehudm1提供
MTF Candle Countdown — HUD V1 (By Price-Action-Art) MTF Candle Countdown — HUD V1 (By Price-Action-Art) A clean, lightweight HUD that shows you exactly how much time is left in multiple higher-timeframe candles — all in one place. This tool is designed for traders who rely on multi-timeframe precision. Instead of constantly switching charts or checking timers, the HUD gives you a real-time countdown for up to six timeframes (Daily, 4H, 1H, 30m, 15m, 5m by default). You can fully customize the timeframes, text size, and HUD position on your chart. Perfect for: Intraday and scalping timing Swing traders waiting for HTF candle closes ICT / SMC structure-based traders Anyone who needs exact candle close timing without distractions Features: Real-time multi-timeframe candle countdown Fully adjustable HUD placement (all corners) Customizable timeframes and text size Clean, minimal, and non-intrusive design Updates only on the last bar for performance efficiency Optional border for a sharper HUD look Whether you’re waiting for a Daily close to confirm structure or timing your entries around 5m/15m candles, this HUD keeps everything visible and precise at a glance. If you find this tool helpful, feel free to like, comment, and follow — it motivates me to keep releasing more tools for the community.
Pine Script®指標
由Price-Action-Art提供
Rons Custom WatermarkRon's Custom Watermark (RCW) This is a lightweight, all-in-one watermark indicator that displays essential fundamental and technical data directly on your chart. It's designed to give you a quick, at-a-glance overview of any asset without cluttering your screen. Features The watermark displays the following information in a clean table: * Company Info: Full Name & Market Cap (e.g., "AST SpaceMobile, Inc. (18.85B)") * Symbol & Timeframe: Ticker and current chart period (e.g., "ASTS, 1D") * Sector & Industry: The asset's classification. * Technical Status (MA): Shows if the price is Above or Below the SMA (with a 🟢/🔴 emoji). * Technical Status (EMA): Shows if the price is Above or Below the EMA (with a 🟢/🔴 emoji). * Earnings: A countdown showing "X days remaining" until the next earnings report. * (Optional) Volatility: The 14-day ATR value and its percentage of the current price.
Pine Script®指標
由irthetraders提供
1
Market Extreme Zones IndexThe Market Extreme Zones Index is a new mean reversion (valuation) tool focused on catching long term oversold/overbought zones. Combining an enhanced RSI with a smoothed Z-score this indicator allows traders to find oppurtunities during highly oversold/overbought zones. I will separate the explanation into the following parts: 1. How does it work? 2. Methodologies & Concepts 3. Use cases How does it work? The indicator attempts to catch highly unprobable events in either direction to capture reversal points over the long term. This is done by calculating the Z-Score of an enhanced RSI. First we need to calculate the Enhanced RSI: For this we need to calculate 2 additional lengths: Length1 = user defined length Length2 = Length1/2 Length3 = √Length Now we need to calculate 3 different RSIs: 1st RSI => uses classic user defined source and classic user defined length. 2nd RSI => uses classic user defined source and Length 2. 3rd RSI => uses RSI 2 as source and Length 2 Now calculate the divergence: RSI_base => 2nd RSI * 3 - 1st RSI - 3rd RSI After this we need to calculate the median of the RSI_base over √Length and make a divergence of these 2: RSI => RSI_base*2 - median All that remains now is the Z-score calculations: We need: Average RSI value Standard Deviation = a measure of how dispersed or spread out a set of data values are from their average Z-score = (Current Value - Average Value) / Standard Deviation After this we just smooth the Z-score with a Weighted Moving average with √Length Methodology & Concepts Mean Reversion Methodology: The methodology behind mean reversion is the theory that asset prices will eventually return to their long-term average after deviating significantly, driven by the belief that extreme moves are temporary. Z-Score Methodology: A Z-score, or standard score, is a statistical measure that indicates how many standard deviations a data point is from the mean of a dataset. A positive z-score means the value is above the mean, a negative score means it's below, and a score of zero means the value is equal to the mean. You might already be able to see where I am going with this: Z-Score could be used for the extreme moves to capture reversal points. By applying it to the RSI rather than the Price, we get a more accurate measurement that allow us to get a banger indicator. Use Cases Capturing reversal points Trend Direction - while the main use it for mean reversion, the values can indicate whether we are in an uptrend or a downtrend. Advantages: Visualization: The indicator has many plots to ensure users can easily see what the indicator signals, such as highlighting extreme conditions with background colors. Versatility: This indicator works across multiple assets, including the S&P500 and more, so it is not only for crypto. Final note: No indicator alone is perfect. Backtests are not indicative of future performance. Hope you enjoy Gs! Good luck!
Pine Script®指標
由MisinkoMaster提供
LapseBacktestingTableLibrary "LapseBacktestingMetrics" This library provides a robust set of quantitative backtesting and performance evaluation functions for Pine Script strategies. It’s designed to help traders, quants, and developers assess risk, return, and robustness through detailed statistical metrics — including Sharpe, Sortino, Omega, drawdowns, and trade efficiency. Built to enhance any trading strategy’s evaluation framework, this library allows you to visualize performance with the quantlapseTable() function, producing an interactive on-chart performance table. Credit to EliCobra and BikeLife76 for original concept inspiration. curve(disp_ind)   Retrieves a selected performance curve of your strategy.   Parameters:     disp_ind (simple string): Type of curve to plot. Options include "Equity", "Open Profit", "Net Profit", "Gross Profit".   Returns: (float) Corresponding performance curve value. cleaner(disp_ind, plot)   Filters and displays selected strategy plots for clean visualization.   Parameters:     disp_ind (simple string): Type of display.     plot (simple float): Strategy plot variable.   Returns: (float) Filtered plot value. maxEquityDrawDown()   Calculates the maximum equity drawdown during the strategy’s lifecycle.   Returns: (float) Maximum equity drawdown percentage. maxTradeDrawDown()   Computes the worst intra-trade drawdown among all closed trades.   Returns: (float) Maximum intra-trade drawdown percentage. consecutive_wins()   Finds the highest number of consecutive winning trades.   Returns: (int) Maximum consecutive wins. consecutive_losses()   Finds the highest number of consecutive losing trades.   Returns: (int) Maximum consecutive losses. no_position()   Counts the maximum consecutive bars where no position was held.   Returns: (int) Maximum flat days count. long_profit()   Calculates total profit generated by long positions as a percentage of initial capital.   Returns: (float) Total long profit %. short_profit()   Calculates total profit generated by short positions as a percentage of initial capital.   Returns: (float) Total short profit %. prev_month()   Measures the previous month’s profit or loss based on equity change.   Returns: (float) Monthly equity delta. w_months()   Counts the number of profitable months in the backtest.   Returns: (int) Total winning months. l_months()   Counts the number of losing months in the backtest.   Returns: (int) Total losing months. checktf()   Returns the time-adjusted scaling factor used in Sharpe and Sortino ratio calculations based on chart timeframe.   Returns: (float) Annualization multiplier. stat_calc()   Performs complete statistical computation including drawdowns, Sharpe, Sortino, Omega, trade stats, and profit ratios.   Returns: (array)      . f_colors(x, nv)   Generates a color gradient for performance values, supporting dynamic table visualization.   Parameters:     x (simple string): Metric label name.     nv (simple float): Metric numerical value.   Returns: (color) Gradient color value for table background. quantlapseTable(option, position)   Displays an interactive Performance Table summarizing all major backtesting metrics.   Includes Sharpe, Sortino, Omega, Profit Factor, drawdowns, profitability %, and trade statistics.   Parameters:     option (simple string): Table type — "Full", "Simple", or "None".     position (simple string): Table position — "Top Left", "Middle Right", "Bottom Left", etc.   Returns: (table) On-chart performance visualization table. This library empowers advanced quantitative evaluation directly within Pine Script®, ideal for strategy developers seeking deeper performance diagnostics and intuitive on-chart metrics.
Pine Script®腳本庫
由QuantLapse提供
1
LibVeloLibrary "LibVelo" This library provides a sophisticated framework for **Velocity Profile (Flow Rate)** analysis. It measures the physical speed of trading at specific price levels by relating volume to the time spent at those levels. ## Core Concept: Market Velocity Unlike Volume Profiles, which only answer "how much" traded, Velocity Profiles answer "how fast" it traded. It is calculated as: `Velocity = Volume / Duration` This metric (contracts per second) reveals hidden market dynamics invisible to pure Volume or TPO profiles: 1. **High Velocity (Fast Flow):** * **Aggression:** Initiative buyers/sellers hitting market orders rapidly. * **Liquidity Vacuum:** Price slips through a level because order book depth is thin (low resistance). 2. **Low Velocity (Slow Flow):** * **Absorption:** High volume but very slow price movement. Indicates massive passive limit orders ("Icebergs"). * **Apathy:** Little volume over a long time. Lack of interest from major participants. ## Architecture: Triple-Engine Composition To ensure maximum performance while offering full statistical depth for all metrics, this library utilises **object composition** with a lazy evaluation strategy: #### Engine A: The Master (`vpVol`) * **Role:** Standard Volume Profile. * **Purpose:** Maintains the "ground truth" of volume distribution, price buckets, and ranges. #### Engine B: The Time Container (`vpTime`) * **Role:** specialized container for time duration (in ms). * **Hack:** It repurposes standard volume arrays (specifically `aBuy`) to accumulate time duration for each bucket. #### Engine C: The Calculator (`vpVelo`) * **Role:** Temporary scratchpad for derived metrics. * **Purpose:** When complex statistics (like Value Area or Skewness) are requested for **Velocity**, this engine is assembled on-demand to leverage the full statistical power of `LibVPrf` without rewriting complex algorithms. --- **DISCLAIMER** This library is provided "AS IS" and for informational and educational purposes only. It does not constitute financial, investment, or trading advice. The author assumes no liability for any errors, inaccuracies, or omissions in the code. Using this library to build trading indicators or strategies is entirely at your own risk. As a developer using this library, you are solely responsible for the rigorous testing, validation, and performance of any scripts you create based on these functions. The author shall not be held liable for any financial losses incurred directly or indirectly from the use of this library or any scripts derived from it. create(buckets, rangeUp, rangeLo, dynamic, valueArea, allot, estimator, cdfSteps, split, trendLen)   Construct a new `Velo` controller, initializing its engines.   Parameters:      buckets (int) : series int Number of price buckets ≥ 1.      rangeUp (float) : series float Upper price bound (absolute).      rangeLo (float) : series float Lower price bound (absolute).      dynamic (bool) : series bool Flag for dynamic adaption of profile ranges.      valueArea (int) : series int Percentage for Value Area (1..100).      allot (series AllotMode) : series AllotMode Allocation mode `Classic` or `PDF` (default `PDF`).      estimator (series PriceEst enum from AustrianTradingMachine/LibBrSt/1) : series PriceEst PDF model for distribution attribution (default `Uniform`).      cdfSteps (int) : series int Resolution for PDF integration (default 20).      split (series SplitMode) : series SplitMode Buy/Sell split for the master volume engine (default `Classic`).      trendLen (int) : series int Look‑back for trend factor in dynamic split (default 3).   Returns: Velo Freshly initialised velocity profile. method clone(self)   Create a deep copy of the composite profile.   Namespace types: Velo   Parameters:      self (Velo) : Velo Profile object to copy.   Returns: Velo A completely independent clone. method clear(self)   Reset all engines and accumulators.   Namespace types: Velo   Parameters:      self (Velo) : Velo Profile object to clear.   Returns: Velo Cleared profile (chaining). method merge(self, srcVolBuy, srcVolSell, srcTime, srcRangeUp, srcRangeLo, srcVolCvd, srcVolCvdHi, srcVolCvdLo)   Merges external data (Volume and Time) into the current profile. Automatically handles resizing and re-bucketing if ranges differ.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      srcVolBuy (array) : array Source Buy Volume bucket array.      srcVolSell (array) : array Source Sell Volume bucket array.      srcTime (array) : array Source Time bucket array (ms).      srcRangeUp (float) : series float Upper price bound of the source data.      srcRangeLo (float) : series float Lower price bound of the source data.      srcVolCvd (float) : series float Source Volume CVD final value.      srcVolCvdHi (float) : series float Source Volume CVD High watermark.      srcVolCvdLo (float) : series float Source Volume CVD Low watermark.   Returns: Velo `self` (chaining). method addBar(self, offset)   Main data ingestion. Distributes Volume and Time to buckets.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      offset (int) : series int Offset of the bar to add (default 0).   Returns: Velo `self` (chaining). method setBuckets(self, buckets)   Sets the number of buckets for the profile.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      buckets (int) : series int New number of buckets.   Returns: Velo `self` (chaining). method setRanges(self, rangeUp, rangeLo)   Sets the price range for the profile.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      rangeUp (float) : series float New upper price bound.      rangeLo (float) : series float New lower price bound.   Returns: Velo `self` (chaining). method setValueArea(self, va)   Set the percentage of volume/time for the Value Area.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      va (int) : series int New Value Area percentage (0..100).   Returns: Velo `self` (chaining). method getBuckets(self)   Returns the current number of buckets in the profile.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.   Returns: series int The number of buckets. method getRanges(self)   Returns the current price range of the profile.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.   Returns: rangeUp series float The upper price bound of the profile. rangeLo series float The lower price bound of the profile. method getArrayBuyVol(self)   Returns the internal raw data array for **Buy Volume** directly.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.   Returns: array The internal array for buy volume. method getArraySellVol(self)   Returns the internal raw data array for **Sell Volume** directly.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.   Returns: array The internal array for sell volume. method getArrayTime(self)   Returns the internal raw data array for **Time** (in ms) directly.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.   Returns: array The internal array for time duration. method getArrayBuyVelo(self)   Returns the internal raw data array for **Buy Velocity** directly. Automatically executes _assemble() if data is dirty.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.   Returns: array The internal array for buy velocity. method getArraySellVelo(self)   Returns the internal raw data array for **Sell Velocity** directly. Automatically executes _assemble() if data is dirty.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.   Returns: array The internal array for sell velocity. method getBucketBuyVol(self, idx)   Returns the **Buy Volume** of a specific bucket.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      idx (int) : series int The index of the bucket.   Returns: series float The buy volume. method getBucketSellVol(self, idx)   Returns the **Sell Volume** of a specific bucket.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      idx (int) : series int The index of the bucket.   Returns: series float The sell volume. method getBucketTime(self, idx)   Returns the raw accumulated time (in ms) spent in a specific bucket.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      idx (int) : series int The index of the bucket.   Returns: series float The time in milliseconds. method getBucketBuyVelo(self, idx)   Returns the **Buy Velocity** (Aggressive Buy Flow) of a bucket.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      idx (int) : series int The index of the bucket.   Returns: series float The buy velocity in . method getBucketSellVelo(self, idx)   Returns the **Sell Velocity** (Aggressive Sell Flow) of a bucket.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      idx (int) : series int The index of the bucket.   Returns: series float The sell velocity in . method getBktBnds(self, idx)   Returns the price boundaries of a specific bucket.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      idx (int) : series int The index of the bucket.   Returns: up series float The upper price bound of the bucket. lo series float The lower price bound of the bucket. method getPoc(self, target)   Returns Point of Control (POC) information for the specified target metric. Calculates on-demand if the target is 'Velocity' and data changed.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).   Returns: pocIdx series int The index of the POC bucket. pocPrice series float The mid-price of the POC bucket. method getVA(self, target)   Returns Value Area (VA) information for the specified target metric. Calculates on-demand if the target is 'Velocity' and data changed.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).   Returns: vaUpIdx series int The index of the upper VA bucket. vaUpPrice series float The upper price bound of the VA. vaLoIdx series int The index of the lower VA bucket. vaLoPrice series float The lower price bound of the VA. method getMedian(self, target)   Returns the Median price for the specified target metric distribution. Calculates on-demand if the target is 'Velocity' and data changed.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).   Returns: medianIdx series int The index of the bucket containing the median. medianPrice series float The median price. method getAverage(self, target)   Returns the weighted average price (VWAP/TWAP) for the specified target. Calculates on-demand if the target is 'Velocity' and data changed.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).   Returns: avgIdx series int The index of the bucket containing the average. avgPrice series float The weighted average price. method getStdDev(self, target)   Returns the standard deviation for the specified target distribution. Calculates on-demand if the target is 'Velocity' and data changed.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).   Returns: series float The standard deviation. method getSkewness(self, target)   Returns the skewness for the specified target distribution. Calculates on-demand if the target is 'Velocity' and data changed.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).   Returns: series float The skewness. method getKurtosis(self, target)   Returns the excess kurtosis for the specified target distribution. Calculates on-demand if the target is 'Velocity' and data changed.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).   Returns: series float The excess kurtosis. method getSegments(self, target)   Returns the fundamental unimodal segments for the specified target metric. Calculates on-demand if the target is 'Velocity' and data changed.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).   Returns: matrix A 2-column matrix where each row is an pair. method getCvd(self, target)   Returns Cumulative Volume/Velo Delta (CVD) information for the target metric.   Namespace types: Velo   Parameters:      self (Velo) : Velo The profile object.      target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).   Returns: cvd series float The final delta value. cvdHi series float The historical high-water mark of the delta. cvdLo series float The historical low-water mark of the delta. Velo   Velo Composite Velocity Profile Controller.   Fields:      _vpVol (VPrf type from AustrianTradingMachine/LibVPrf/2) : LibVPrf.VPrf Engine A: Master Volume source.      _vpTime (VPrf type from AustrianTradingMachine/LibVPrf/2) : LibVPrf.VPrf Engine B: Time duration container (ms).      _vpVelo (VPrf type from AustrianTradingMachine/LibVPrf/2) : LibVPrf.VPrf Engine C: Scratchpad for velocity stats.      _aTime (array) : array Pointer alias to `vpTime.aBuy` (Time storage).      _valueArea (series float) : int Percentage of total volume to include in the Value Area (1..100)      _estimator (series PriceEst enum from AustrianTradingMachine/LibBrSt/1) : LibBrSt.PriceEst PDF model for distribution attribution.      _allot (series AllotMode) : AllotMode Attribution model (Classic or PDF).      _cdfSteps (series int) : int Integration resolution for PDF.      _isDirty (series bool) : bool Lazy evaluation flag for vpVelo.
Pine Script®腳本庫
由AustrianTradingMachine提供
1
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent. Table of contents What the script does at a glance Momentum, what it is, how to know if it is present Volatility targeting, why and how it is done here Portfolio construction modes: Long Only, Short Only, Delta Neutral Regime filter and when the strategy goes to cash Transaction cost modelling in this script Backtest metrics and definitions Performance attribution chart Monte Carlo simulation Scatter plot analysis modes Asset allocation pie chart Inputs, presets, and deployment checklist Suggested workflow 1) What the script does at a glance Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility. Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter. Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite. Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views. 2) Momentum: definition, detection, and validation Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints. Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization. How to know momentum is present . Leaders and laggards persist across adjacent windows rather than flipping every bar. Spread between average momentum of leaders and laggards is materially positive in sample. Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak. Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries. Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime. 3) Volatility targeting: purpose and implementation here Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk. Computation in this script (per asset, rolling): Return series ret = log(price/price ). Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays). Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0). This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero. Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations. Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure. 4) Portfolio construction modes Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties. Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult. Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs. Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3). Notes . The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage. If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta. 5) Regime filter A fast EMA(12) vs EMA(21) filter gates exposure. Long Only active when EMA12 > EMA21. Otherwise the book is set to cash. Short Only active when EMA12 < EMA21. Otherwise cash. Delta Neutral is always active. This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar. 6) Transaction cost modelling There are two cost touchpoints in the script. Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar. Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots. Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders. Practical advice . If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue. For crypto perp microcaps, stress higher cost assumptions and add slippage buffers. If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag. 7) Backtest metrics and definitions The script computes a standard set of portfolio statistics once the start date is reached. Net Profit percent over the full test. Max Drawdown percent, tracked from running peaks. Annualized Mean and Stdev using the chosen trading day count. Variance is the square of annualized stdev. Sharpe uses daily mean adjusted by risk-free rate and annualized. Sortino uses downside stdev only. Omega ratio of sum of gains to sum of losses. Gain-to-Pain total gains divided by total losses absolute. CAGR compounded annual growth from start date to now. Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM. Skewness of daily returns. VaR 95 linear-interpolated 5th percentile of daily returns. CVaR average of the worst 5 percent of daily returns. Benchmark Buy-and-Hold equity path for comparison. 8) Performance attribution Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels. 9) Monte Carlo simulation The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed. Normal uses Box-Muller with your seed. Good for quick, smooth envelopes. Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption. Bands show 10th, 25th, 75th, 90th percentiles and the path mean. 10) Scatter plot analysis Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels. Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite. Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations. Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta. Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk. 11) Asset allocation pie chart Builds a wheel of current allocations. Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent. Short Only, weights show the short book as positive slices that sum to 100 percent. Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional. Labels can show asset, percent, and current leverage. 12) Inputs and quick presets Core Portfolio Strategy . Long Only, Short Only, Delta Neutral. Initial Capital . For equity scaling in the panel. Trading Days/Year . 252 for stocks, 365 for crypto. Target Volatility . Annualized, drives volMult. Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above. Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier. Start Date . Ensure every symbol has data back to this date to avoid bias. Benchmark . Used for alpha, beta, and B&H line. Diagnostics Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill. Attribution panel. Toggle winners only to focus on what matters. Monte Carlo mode with Normal or Bootstrap and confidence bands. Scatter plot type and styling, labels, and portfolio star. Pie chart and labels for current allocation. Presets Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands. Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward. Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta. 13) Suggested workflow Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees. Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe. Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity. Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear. Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it. Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence. Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier. Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs. Important implementation details mapped to code Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting. Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult. Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively. Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate. Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison. Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures. Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds. Final notes Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call. Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it. Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
Pine Script®指標
由BackQuant提供
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion . Core Architecture Why These Components Are Combined: The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention. The Four Trading Agents 1. Spoofing Detector Agent 🎭 Detects iceberg orders through persistent volume at similar price levels over 5 bars Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average) Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model) Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation Best Markets: Consolidations, institutional trading windows, low-liquidity hours 2. Exhaustion Detector Agent ⚡ Calculates RSI divergence between price movement and momentum indicator over 5-bar window Detects VWAP exhaustion (price at 2σ bands with declining volume) Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure Signal Logic: Counter-trend—enters when momentum extreme shows weakness Best Markets: Trending markets reaching climax points, over-extended moves 3. Liquidity Void Detector Agent 💧 Measures Bollinger Band squeeze (width <60% of 50-period average) Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR) Signal Logic: Breakout anticipation—enters after liquidity grab but before main move Best Markets: Range-bound pre-breakout, volatility compression zones 4. Mean Reversion Agent 📊 Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ) Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model) Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability) Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments Adaptive Selection: Multi-Armed Bandit System The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance: Thompson Sampling (Default - Recommended): Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent) Balances exploration (trying underused agents) vs. exploitation (using proven winners) Each agent's win/loss history informs its selection probability Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation UCB1 (Upper Confidence Bound): Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls) Deterministic algorithm favoring agents with high uncertainty (potential upside) More conservative than Thompson Sampling Epsilon-Greedy: Exploits best-performing agent (1-ε)% of the time Explores randomly ε% of the time (default 10%, configurable 1-50%) Simple, transparent, easily tuned via epsilon parameter Gradient Bandit: Uses softmax probability distribution over agent preference weights Updates weights via gradient ascent based on rewards Best for Blend mode where all agents contribute Selection Modes: Switch Mode: Uses only the selected agent's signal (clean, decisive) Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified) Lock Agent Feature: Optional manual override to force one specific agent Useful after identifying which agent dominates your specific instrument Only applies in Switch mode Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion Memory System Dual-Layer Architecture: Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50) Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars) Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20% Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds) How Memory Works: Agent generates signal → outcome tracked after 8 bars (performance horizon) Result stored in short-term memory (win = 1.0, loss = 0.0) Short-term average influences agent's future scores (positive feedback loop) After threshold met (default 10 results), episode averaged into long-term storage Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost Market Microstructure Analysis These advanced metrics quantify institutional order flow dynamics: Order Flow Toxicity (Simplified VPIN): Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol) Detects informed trading activity (institutional players with non-public information) Values >0.4 indicate "toxic flow" (informed traders active) Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification Price Impact Analysis (Simplified Kyle's Lambda): Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10) Low values = large orders with minimal price impact ( stealth accumulation ) High values = retail-dominated moves with high slippage Lite Approximation: Uses simplified denominator instead of regression-based signed order flow Market Randomness (Entropy Analysis): Counts unique price changes over 20 bars / 20 Measures market predictability High entropy (>0.6) = human-driven, chaotic price action Low entropy (<0.25) = algorithmic trading dominance (predictable patterns) Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x)) Order Clustering (Simplified Hawkes Process): Tracks self-exciting event intensity (coordinated order activity) Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average High intensity (>0.7) indicates clustering (potential spoofing/accumulation) Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE Signal Generation Process Multi-Stage Validation: Stage 1: Agent Scoring Each agent calculates internal score based on its detection criteria Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier) Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0) Stage 2: Memory Boost Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance) Successful agents get amplified, failing agents get dampened Stage 3: Bandit Selection/Blending If Adaptive Mode ON: Switch: Bandit selects single best agent, uses only its signal Blend: All agents combined using softmax-weighted confidence scores If Adaptive Mode OFF: Traditional consensus voting with confidence-squared weighting Signal fires when consensus exceeds threshold (default 70%) Stage 4: Confirmation Filter Raw signal must repeat for consecutive bars (default 3, configurable 2-4) Minimum confidence threshold: 0.25 (25%) enforced regardless of mode Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2 Stage 5: Cooldown Enforcement Minimum bars between signals (default 10, configurable 5-15) Prevents over-trading during choppy conditions Stage 6: Performance Tracking After 8 bars (performance horizon), signal outcome evaluated Win = price moved in signal direction, Loss = price moved against Results fed back into memory and bandit statistics Trading Modes (Presets) Pre-configured parameter sets: Conservative: 85% consensus, 4 confirmations, 15-bar cooldown Expected: 60-70% win rate, 3-8 signals/week Best for: Swing trading, capital preservation, beginners Balanced: 70% consensus, 3 confirmations, 10-bar cooldown Expected: 55-65% win rate, 8-15 signals/week Best for: Day trading, most traders, general use Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown Expected: 50-58% win rate, 15-30 signals/week Best for: Scalping, high-frequency trading, active management Elite: 75% consensus, 3 confirmations, 12-bar cooldown Expected: 58-68% win rate, 5-12 signals/week Best for: Selective trading, high-conviction setups Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown Expected: Varies based on learning Best for: Experienced users leveraging bandit system How to Use 1. Initial Setup (5 Minutes): Select Trading Mode matching your style (start with Balanced) Enable Adaptive Learning (recommended for automatic agent selection) Choose Thompson Sampling algorithm (best all-around performance) Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume) 2. Agent Tuning (Optional): Adjust Agent Sensitivity multipliers (0.5-2.0): <0.8 = Highly selective (fewer signals, higher quality) 0.9-1.2 = Balanced (recommended starting point) 1.3 = Aggressive (more signals, lower individual quality) Monitor dashboard for 20-30 signals to identify dominant agent If one agent consistently outperforms, consider using Lock Agent feature 3. Bandit Configuration (Advanced): Blend Temperature (0.1-2.0): 0.3 = Sharp decisions (best agent dominates) 0.5 = Balanced (default) 1.0+ = Smooth (equal weighting, democratic) Memory Decay (0.8-0.99): 0.90 = Fast adaptation (volatile markets) 0.95 = Balanced (most instruments) 0.97+ = Long memory (stable trends) 4. Signal Interpretation: Green triangle (▲): Long signal confirmed Red triangle (▼): Short signal confirmed Dashboard shows: Active agent (highlighted row with ► marker) Win rate per agent (green >60%, yellow 40-60%, red <40%) Confidence bars (█████ = maximum confidence) Memory size (short-term buffer count) Colored zones display: Entry level (current close) Stop-loss (1.5× ATR) Take-profit 1 (2.0× ATR) Take-profit 2 (3.5× ATR) 5. Risk Management: Never risk >1-2% per signal (use ATR-based stops) Signals are entry triggers, not complete strategies Combine with your own market context analysis Consider fundamental catalysts and news events Use "Confirming" status to prepare entries (not to enter early) 6. Memory Persistence (Optional): After 50-100 trades, check Memory Export Panel Record displayed alpha/beta/weight values for each agent Record VPIN and Kyle threshold values Enable "Restore From Memory" and input saved values to continue learning Useful when switching timeframes or restarting indicator Visual Components On-Chart Elements: Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend) Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers) Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR) Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal) Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets Signal Markers: Triangle up/down at validated entry points Dashboard (Configurable Position & Size): Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear) Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus) Agent Performance Table: Real-time win%, confidence, and memory stats Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled) Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress Memory Panel (Configurable Position & Size): Live Parameter Export: Alpha, beta, and weight values per agent Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold Save Reminder: Visual indicator if parameters should be recorded What Makes This Original This script's originality lies in three key innovations: 1. Genuine Meta-Learning Framework: Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning. 2. Episodic Memory Architecture with Transfer Learning: The dual-layer memory system mimics human learning patterns: Short-term memory captures recent performance (recency bias) Long-term memory preserves historical patterns (experience) Automatic transfer mechanism consolidates knowledge Memory boost creates positive feedback loops (successful strategies become stronger) This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates. 3. Institutional Microstructure Integration: Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis. Mashup Justification: The four agents are combined specifically for risk diversification across failure modes: Spoofing Detector: Prevents false breakout losses from manipulation Exhaustion Detector: Prevents chasing extended trends into reversals Liquidity Void: Exploits volatility compression (different regime than trending) Mean Reversion: Provides mathematical anchoring when patterns fail The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals. Why "ML-lite"? Simplifications and Approximations This is the "lite" version due to necessary simplifications for Pine Script execution: 1. Simplified VPIN Calculation: Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume) Impact: May misclassify volume during ranging/choppy markets; works best in directional moves 2. Pseudo-Random Sampling: Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100 Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection 3. Hawkes Process Approximation: Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event) Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments 4. Kyle's Lambda Simplification: Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals 5. Entropy Calculation: Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude This Implementation: Simple ratio of unique price changes to total observations (variety measure) Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape 6. Memory System Constraints: Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking 7. Limited Feature Engineering: Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations 8. Single-Instrument Data: Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes This Implementation: Only OHLCV data from displayed instrument Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets 9. Fixed Performance Horizon: Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement This Implementation: Fixed 8-bar evaluation window Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach Performance Impact Summary: These simplifications make the script: ✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations ✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers ✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models) ✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint ⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations ⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows ⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized When to Upgrade to Full Implementation: Consider professional Python/C++ versions with institutional data feeds if: Trading with >$100K capital where precision differences materially impact returns Operating in microsecond-competitive environments (HFT, market making) Requiring regulatory-grade audit trails and reproducibility Backtesting with tick-level precision for strategy validation Need true real-time adaptation with neural network-based learning For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact. Technical Notes All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap ) VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above) Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling No repainting: All calculations use confirmed bar data (no forward-looking) Maximum lookback: 500 bars (set via max_bars_back parameter) Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed) Confidence threshold: Minimum 0.25 (25%) enforced on all signals Memory arrays: Dynamic sizing with FIFO queue management Limitations and Disclaimers Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty. Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context. Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms. Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts. Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets. No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss. Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics. Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes. Recommended Settings Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning) Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices) Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable) Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens) Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0) After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent Alert Configuration The script includes built-in alert conditions: Long Signal: Fires when validated long entry confirmed Short Signal: Fires when validated short entry confirmed Alerts fire once per bar (after confirmation requirements met) Set alert to "Once Per Bar Close" for reliability Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Pine Script®指標
由DskyzInvestments提供
14
Market Profile Dominance Analyzer# Market Profile Dominance Analyzer ## 📊 OVERVIEW **Market Profile Dominance Analyzer** is an advanced multi-factor indicator that combines Market Profile methodology with composite dominance scoring to identify buyer and seller strength across higher timeframes. Unlike traditional volume profile indicators that only show volume distribution, or simple buyer/seller indicators that only compare candle colors, this script integrates six distinct analytical components into a unified dominance measurement system. This indicator helps traders understand **WHO controls the market** by analyzing price position relative to Market Profile key levels (POC, Value Area) combined with volume distribution, momentum, and trend characteristics. ## 🎯 WHAT MAKES THIS ORIGINAL ### **Hybrid Analytical Approach** This indicator uniquely combines two separate methodologies that are typically analyzed independently: 1. **Market Profile Analysis** - Calculates Point of Control (POC) and Value Area (VA) using volume distribution across price channels on higher timeframes 2. **Multi-Factor Dominance Scoring** - Weights six independent factors to produce a composite dominance index ### **Six-Factor Composite Analysis** The dominance score integrates: - Price position relative to POC (equilibrium assessment) - Price position relative to Value Area boundaries (acceptance/rejection zones) - Volume imbalance within Value Area (institutional bias detection) - Price momentum (directional strength) - Volume trend comparison (participation analysis) - Normalized Value Area position (precise location within fair value zone) ### **Adaptive Higher Timeframe Integration** The script features an intelligent auto-selection system that automatically chooses appropriate higher timeframes based on the current chart period, ensuring optimal Market Profile structure regardless of the trading timeframe being analyzed. ## 💡 HOW IT WORKS ### **Market Profile Construction** The indicator builds a Market Profile structure on a higher timeframe by: 1. **Session Identification** - Detects new higher timeframe sessions using `request.security()` to ensure accurate period boundaries 2. **Data Accumulation** - Stores high, low, and volume data for all bars within the current higher timeframe session 3. **Channel Distribution** - Divides the session's price range into configurable channels (default: 20 rows) 4. **Volume Mapping** - Distributes each bar's volume proportionally across all price channels it touched ### **Key Level Calculation** **Point of Control (POC)** - Identifies the price channel with the highest accumulated volume - Represents the price level where the most trading activity occurred - Serves as a magnetic level where price often returns **Value Area (VA)** - Starts at POC and expands both upward and downward - Includes channels until reaching the specified percentage of total volume (default: 70%) - Expansion algorithm compares adjacent volumes and prioritizes the direction with higher activity - Defines the "fair value" zone where most market participants agreed to trade ### **Dominance Score Formula** ``` Dominance Score = (price_vs_poc × 10) + (price_vs_va × 5) + (volume_imbalance × 0.5) + (price_momentum × 100) + (volume_trend × 5) + (va_position × 15) ``` **Component Breakdown:** - **price_vs_poc**: +1 if above POC, -1 if below (shows which side of equilibrium) - **price_vs_va**: +2 if above VAH, -2 if below VAL, 0 if inside VA - **volume_imbalance**: Percentage difference between upper and lower VA volumes - **price_momentum**: 5-period SMA of price change (directional acceleration) - **volume_trend**: Compares 5-period vs 20-period volume averages - **va_position**: Normalized position within Value Area (-1 to +1) The composite score is then smoothed using EMA with configurable sensitivity to reduce noise while maintaining responsiveness. ### **Market State Determination** - **BUYERS Dominant**: Smooth dominance > +10 (bullish control) - **SELLERS Dominant**: Smooth dominance < -10 (bearish control) - **NEUTRAL**: Between -10 and +10 (balanced market) ## 📈 HOW TO USE THIS INDICATOR ### **Trend Identification** - **Green background** indicates buyers are in control - look for long opportunities - **Red background** indicates sellers are in control - look for short opportunities - **Gray background** indicates neutral market - consider range-bound strategies ### **Signal Interpretation** **Buy Signals** (green triangle) appear when: - Dominance crosses above -10 from oversold conditions - Previous state was not already bullish - Suggests shift from seller to buyer control **Sell Signals** (red triangle) appear when: - Dominance crosses below +10 from overbought conditions - Previous state was not already bearish - Suggests shift from buyer to seller control ### **Value Area Context** Monitor the information table (top-right) to understand market structure: - **Price vs POC**: Shows if trading above/below equilibrium - **Volume Imbalance**: Positive values favor buyers, negative favors sellers - **Market State**: Current dominant force (BUYERS/SELLERS/NEUTRAL) ### **Multi-Timeframe Strategy** The auto-timeframe feature analyzes higher timeframe structure: - On 1-minute charts → analyzes 2-hour structure - On 5-minute charts → analyzes Daily structure - On 15-minute charts → analyzes Weekly structure - On Daily charts → analyzes Yearly structure This higher timeframe context helps avoid counter-trend trades against the dominant force. ### **Confluence Trading** Strongest signals occur when multiple factors align: 1. Price above VAH + positive volume imbalance + buyers dominant = Strong bullish setup 2. Price below VAL + negative volume imbalance + sellers dominant = Strong bearish setup 3. Price at POC + neutral state = Potential breakout/breakdown pivot ## ⚙️ INPUT PARAMETERS - **Higher Time Frame**: Select specific HTF or use 'Auto' for intelligent selection - **Value Area %**: Percentage of volume contained in VA (default: 70%) - **Show Buy/Sell Signals**: Toggle signal triangles visibility - **Show Dominance Histogram**: Toggle histogram display - **Signal Sensitivity**: EMA period for dominance smoothing (1-20, default: 5) - **Number of Channels**: Market Profile resolution (10-50, default: 20) - **Color Settings**: Customize buyer, seller, and neutral colors ## 🎨 VISUAL ELEMENTS - **Histogram**: Shows smoothed dominance score (green = buyers, red = sellers) - **Zero Line**: Neutral equilibrium reference - **Overbought/Oversold Lines**: ±50 levels marking extreme dominance - **Background Color**: Highlights current market state - **Information Table**: Displays key metrics (state, dominance, POC relationship, volume imbalance, timeframe, bars in session, total volume) - **Signal Shapes**: Triangle markers for buy/sell signals ## 🔔 ALERTS The indicator includes three alert conditions: 1. **Buyers Dominate** - Fires on buy signal crossovers 2. **Sellers Dominate** - Fires on sell signal crossovers 3. **Dominance Shift** - Fires when dominance crosses zero line ## 📊 BEST PRACTICES ### **Timeframe Selection** - **Scalping (1-5min)**: Focus on 2H-4H dominance shifts - **Day Trading (15-60min)**: Monitor Daily and Weekly structure - **Swing Trading (4H-Daily)**: Track Weekly and Monthly dominance ### **Confirmation Strategies** 1. **Trend Following**: Enter in direction of dominance above/below ±20 2. **Reversal Trading**: Fade extreme readings beyond ±50 when diverging with price 3. **Breakout Trading**: Look for dominance expansion beyond ±30 with increasing volume ### **Risk Management** - Avoid trading during NEUTRAL states (dominance between -10 and +10) - Use POC levels as logical stop-loss placement - Consider VAH/VAL as profit targets for mean reversion ## ⚠️ LIMITATIONS & WARNINGS **Data Requirements** - Requires sufficient historical data on current chart (minimum 100 bars recommended) - Lower timeframes may show fewer bars per HTF session initially - More accurate results after several complete HTF sessions have formed **Not a Standalone System** - This indicator analyzes market structure and participant control - Should be combined with price action, support/resistance, and risk management - Does not guarantee profitable trades - past dominance does not predict future results **Repainting Characteristics** - Higher timeframe levels (POC, VAH, VAL) update as new bars form within the session - Dominance score recalculates with each new bar - Historical signals remain fixed, but current session data is developing **Volume Limitations** - Uses exchange-provided volume data which varies by instrument type - Forex and some CFDs use tick volume (not actual transaction volume) - Most accurate on instruments with reliable volume data (stocks, futures, crypto) ## 🔍 TECHNICAL NOTES **Performance Optimization** - Uses `max_bars_back=5000` for extended historical analysis - Efficient array management prevents memory issues - Automatic cleanup of session data on new period **Calculation Method** - Market Profile uses actual volume distribution, not TPO (Time Price Opportunity) - Value Area expansion follows traditional Market Profile auction theory - All calculations occur on the chart's current symbol and timeframe ## 📚 EDUCATIONAL VALUE This indicator helps traders understand: - How institutional traders use Market Profile to identify fair value - The relationship between price, volume, and market acceptance - Multi-factor analysis techniques for assessing market conditions - The importance of higher timeframe structure in trade planning ## 🎓 RECOMMENDED READING To better understand the concepts behind this indicator: - "Mind Over Markets" by James Dalton (Market Profile foundations) - "Markets in Profile" by James Dalton (Value Area analysis) - Volume Profile analysis in institutional trading ## 💬 USAGE TERMS This indicator is provided as an educational and analytical tool. It does not constitute financial advice, investment recommendations, or trading signals. Users are responsible for their own trading decisions and should conduct their own research and due diligence. Trading involves substantial risk of loss. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
Pine Script®指標
由Smart-Day-Trader提供
[Statistics] killzone SFPSFP Statistics (ICT Sessions) This indicator automatically finds and draws the high and low of the Asia, London, and New York trading sessions. It then hunts for Swing Failure Patterns (SFPs) that sweep these key session levels. The main purpose of this script is to gather statistics on when these high-probability SFPs occur, allowing you to map out and identify the times of day when they are most frequent. How to Use This Indicator Set Your SFP Timeframe: In the settings, choose the timeframe you want to hunt for SFPs on (e.g., 1H, 15m). Important: You must also set your main chart to this exact same timeframe for the statistics to be collected correctly. Define Your Sessions: Go to the "Session Definitions" tab. Set the Global Timezone to your preferred trading timezone (e.g., "America/New_York"). This controls all session times and table times. Adjust the start and end times for Asia, London, and NY AM sessions. You can turn off sessions you don't want to track (like NY Lunch or NY PM). You can also change the colors and text style for the session boxes here. Set Confirmation Bars: In "SFP Engine Settings," the "Confirmation Bars" (default is 2) defines how many bars must close after the SFP bar without invalidating the level. An SFP is only "confirmed" and drawn after this period. 0 = Confirms immediately on the SFP candle's close. 2 = Confirms 2 bars after the SFP candle's close. Read the Statistics: The "Custom SFP Statistics" table will appear on your chart. This table logs every confirmed SFP and tells you: Which time of day they happen most. How many were Bearish (swept a high) vs. Bullish (swept a low). It's set by default to show the "Top 20" most frequent times, sorted chronologically. Filter Your Chart (Optional): If your chart feels cluttered, go to "Visual Time Filter" and turn it ON. Set a time window (e.g., "09:30-11:00"). The indicator will now only draw SFP signals that occurred within that specific time window. This is perfect for focusing on a single killzone. How to Set Up Alerts You can set up server-side alerts to be notified every time a new SFP is confirmed. Check the "Enable SFP Alerts" box at the top of the indicator's settings. Click the "Alert" button (alarm clock icon) on the TradingView toolbar. In the "Condition" dropdown, select "SFP Statistics (ICT Sessions)". In the second dropdown, choose "Any alert() function call". Most Important Step: In the "Message" box, delete any default text and type in this exact placeholder: {{alert_message}} Set the trigger to "Once Per Bar Close". Click "Create". How Alerts Work (Triggers & Filtering) Trigger: Alerts are tied to the confirmed signal. An alert will only fire after your "Confirmation Bars" have passed and the SFP is locked in. This prevents you from getting alerts on fake-outs. Alert Filtering: The alerts are linked to the "Visual Time Filter". If you turn on the Visual Time Filter (e.g., to 09:30-11:00), you will only receive alerts for SFPs that are confirmed within that time window. If an SFP happens at 14:00, the script will ignore it, it will not be drawn, and it will not send you an alert. This allows you to get alerts only for the session you are actively trading. Note: This is a first draft of this indicator. I will continue to work on it and improve it over time, as it may still contain small bugs. Acknowledgements: A big thank you to TFO (tradeforopp). The session detection logic and the visual style for the session boxes were adapted from his excellent "ICT Killzones & Pivots " indicator.
Pine Script®指標
由nickbonenkamp提供
1
Price Z-ScoreThe goal of this Pine Script is to visually represent how much price deviates from its rolling average via the Z-score statistic in TradingView's indicators section. This script first uses a user-definable "lookback" period (the default is 20 bars) to calculate a moving average and standard deviation. Then it displays the price as an amount of standard deviation greater than or less than the moving average. A moving Z-score is plotted along with three reference lines (+2, 0, -2) to indicate areas of unusual extremes statistically; also, it colors the chart green for when price is in the lower half of the Z-score range (oversold) and red for when price is in the upper half of the Z-score range (overbought). These can be used by traders to recognize price has moved out of normal ranges and is due to revert to its mean.
Pine Script®指標
由JBr3AK提供
Aquantprice: Institutional Structure MatrixSETUP GUIDE Open TradingView Go to Indicators Search: Aquantprice: Institutional Structure Matrix Click Add to Chart Customize: Min Buy = 10, Min Sell = 7 Show only PP, R1, S1, TC, BC Set Decimals = 5 (Forex) or 8 (Crypto) USE CASES & TRADING STRATEGIES 1. CPR Confluence Trading (Most Popular) Rule: Enter when ≥3 timeframes show Buy ≥10/15 or Sell ≥7/13 text Example: Daily: 12/15 Buy Weekly: 11/15 Buy Monthly: 10/15 Buy → **STRONG LONG BIAS** Enter on pullback to nearest **S1 or L3** 2. Hot Zone Scalping (Forex & Indices) Rule: Trade only when price is in Hot Zone (closest 2 levels) text Hot: S1-PP → Expect bounce or breakout Action: - Buy at S1 if Buy Count ↑ - Sell at PP if Sell Count ↑ 3. Institutional Reversal Setup Rule: Price at H3/L3 + Reversal Condition text Scenario: Price touches **Monthly L3** L3 in **Hot Zone** Buy Count = 13/15 → **High-Probability Reversal Long** 4. CPR Width Filter (Avoid Choppy Markets) Rule: Trade only if CPR Label = "Strong Trend" text CPR Size < 0.25 → Trending CPR Size > 0.75 → Sideways (Avoid) 5. Multi-Timeframe Bias Dashboard Use "Buy" and "Sell" columns as a sentiment meter TimeframeBuySellBiasDaily123BullishWeekly89BearishMonthly112Bullish → Wait for alignment before entering HOW TO READ THE TABLE Column Meaning Time frame D, W, M, 3M, 6M, 12MOpen Price Current session open PP, TC, BC, etc. Pivot levels (color-coded if in Hot Zone) Buy X/15 conditions met (≥10 = Strong Buy)Sell X/13 conditions met (≥7 = Strong Sell)CPR Size Histogram + Label (Trend vs Range)Zone Hot: PP-S1, Med: S2-L3, etc. + PP Distance PRO TIPS Best on 5M–1H charts for entries Use with volume or order flow for confirmation Set alerts on Buy ≥12/15 or Sell ≥10/13 Hide unused levels to reduce clutter Combine with AQuantPrice Dashboard (Small TF) for full system IDEAL MARKETS Forex (EURUSD, GBPUSD, USDJPY) Indices (NAS100, SPX500, DAX) Crypto (BTC, ETH – use 6–8 decimals) Commodities (Gold, Oil) 🚀 **NEW INDICATOR ALERT** **Aquantprice: Institutional Structure Matrix** The **ALL-IN-ONE CPR Dashboard** used by smart money traders. ✅ **6 Timeframes in 1 Table** (Daily → Yearly) ✅ **15 Buy + 13 Sell Conditions** (Institutional Logic) ✅ **Hot Zones, CPR Width, PP Distance** ✅ **Fully Customizable – Show/Hide Any Level** ✅ **Real-Time Zone Detection** (Hot, Med, Low) ✅ **Precision up to 8 Decimals** **No more switching charts. No more confusion.** See **where institutions are positioned** — instantly. 👉 **Add to Chart Now**: Search **"Aquantprice: Institutional Structure Matrix"** 🔥 **Free Access | Pro-Level Insights** *By AQuant – Trusted by 10,000+ Traders* #CPR #PivotTrading #SmartMoney #TradingView FINAL TAGLINE "See What Institutions See — Before They Move." Aquantprice: Institutional Structure Matrix Your Edge. One Dashboard.
Pine Script®指標
由Nmn0708提供
Put Option Profits inspired by Travis Wilkerson; SPX BacktesterPut Option Profits — Travis Wilkerson inspired. This tester evaluates a simple monthly SPX at-the-money credit-spread timing idea: enter on a fixed calendar rule (e.g., 1st Friday or 8th day with business-day shifting) at Open or Close, then exit exactly N calendar days later (first tradable day >= target, at Close). A trade is marked WIN if price at exit is above the entry price (1:1 risk proxy). The book suggests forward testing 60-day and 180-day expirations to prove the concept. This tool lets you backtest both (and more) to see what actually works best. In the book, profits are taken when the spread reaches ~80% of max credit; losers are left to expire and cash-settle. This backtester does not model early profit-taking—every trade is held to the configured hold period and evaluated on price vs entry at the exit close. Think of it as a pure “set it and forget it” stress test. In live trading, you can still follow Travis’s 80% take-profit rule; TradingView just doesn’t simulate that here. Happy trading! Features: Schedule: Day-of-Month (with Prev/Next business-day shift, optional “stay in month”) or Nth Weekday (e.g., 1st Friday). Entry timing: Open or Close. Exit: N calendar days later at Close (holiday/weekend aware). Filters: Optional EMA-200 “risk-on” filter. Scope: Date range limiter. Visuals: Entry/exit bubbles (paired colors) or simple win/loss dots. Table: Overall Win% and N (within range). Alerts: Entry alert (static condition + dynamic alert() message). How to use: [* ]Choose Start Mode (NthWeekday or DayOfMonth) and parameters (e.g., 1st Friday or DOM=8, PrevBizDay). Pick Entry Timing (Open or Close). Set Days In Trade (e.g., 150). (Optional) Enable EMA filter and set Date Range. Turn Bubbles on/off and/or Dots on/off. Create alert: Simple ping: Condition = this indicator -> Monthly Entry Signal -> “Once per bar” (Open) or “Once per bar close” (Close). Rich message: Condition = this indicator -> Any alert() function call. Notes: Keep DOM shift in same month: when a DOM falls on a weekend/holiday, PrevBizDay/NextBizDay shift will stay inside the month if enabled; otherwise it can spill into the prior/next month. (Ignored for NthWeekday.) Credits: Concept sparked by “Put Option Profits – How to turn ten minutes of free time into consistent cash flow each month” by Travis Wilkerson; this script is a neutral research tool (not financial advice).
Pine Script®指標
由MoMoPower提供
1
Slick Strategy Weekly PCS TesterInspired by the book “The Slick Strategy: A Unique Profitable Options Trading Method.” This indicator tests weekly SPX put-credit spreads set below Monday’s open and judged at Friday’s close. WHAT IT DOES • Sets weekly PCS level = Monday (or first trading day) OPEN − your offset; win/loss checked at Friday close. • Optional core filter at entry: Price ≥ 200-SMA AND 10-SMA ≥ 20-SMA; pause if Price < both 10 & 20 while > 200. • Reference modes: Strict = Mon OPEN vs Fri SMAs (no repaint); Mid = Mon OPEN vs Mon SMAs KEY INPUTS • Date range (Start/End) to limit backtest window. • Offset mode/value (Points or Percent). • Entry day (Monday only or first trading day). • Core filters (On/Off) and Strict/Mid reference. • SMA settings (source; 10/20/200 lengths). • Table settings (position, size, padding, border). VISUALS • Active week line: Orange = trade taken; Gray = skipped. • History: Green = win; Red = loss; Purple = skipped. • Optional week bands highlight active/win/loss/skipped weeks (adjustable opacity). TABLE • Shows Date range, Trades, Wins, Losses, Win rate, and Active level (this week’s PCS price). NOTES • PCS level freezes at week open and persists through the week.
Pine Script®指標
由MoMoPower提供
1
High/Low from Set Period with LabelsMark high and low from a set period. I use it to mark Overnight Low and High of FDAX instrument, to achieve that : - you need to use candle chart - you need to use regular trading hours ( to include overnight trades ) - you need to set that on M2 timeframe - you need to set time begin : 17:30 - you need to set time end : 08:58 - when it will be drawn in 09:02, then let extend it via a hand and then you can disable Issues : - it will be visible after finished miminum period time : -- after 2 minutes on M2 ( 9:02 ) -- after 5 minutes on M5 ( 9:05 ) etc ...
Pine Script®指標
由hushedOrange28448提供