Auf geht's

Statistics

Price Drop CounterThe Price Drop Counter is a very basic statistical indicator. See it as an analytical tool that tracks how many times an asset's price has dropped by a specified percentage from its recent peak within a defined date range. The indicator monitors the highest price reached and counts each occurrence when the price falls by your chosen threshold, then resets its peak tracking point after each drop is registered. Uses Volatility Assessment: Measure how frequently significant price corrections occur during specific periods Market Behavior Analysis: Compare drop frequency across different timeframes or market conditions Risk Evaluation: Identify assets or periods with higher downside volatility Historical Pattern Recognition: Study how often major pullbacks happened during bull or bear markets Backtesting Support: Analyze how your strategy would perform based on the frequency of drawdowns How to use it Add the indicator to your TradingView chart Configure the Percent Drop (%) to define your threshold (default: 10%). The indicator will count each time price falls by this percentage from the most recent high IMPORTANT Set your Start Date and End Date to analyze a specific period of interest The blue step-line plot shows the cumulative count of drops within your date range Adjust the percentage threshold based on your analysis needs - use smaller values (2-5%) for more frequent signals or larger values (15-20%) for major corrections only The counter resets its high-water mark after each qualifying drop, allowing it to track multiple sequential drops within the same period.
Pine Script® Indikator
von HenriqueCentieiro
indicator CalibrationIndicator Calibration - Multi-Indicator Consensus System Overview Indicator Calibration is a powerful consensus-based trading indicator that leverages the MyIndicatorLibrary (NormalizedIndicators) to combine multiple trend-following indicators into a single, actionable signal. By averaging the normalized outputs of up to 8 different trend indicators, this tool provides traders with a clear consensus view of market direction, reducing noise and false signals inherent in single-indicator approaches. The indicator outputs a value between -1 (strong bearish) and +1 (strong bullish), with 0 representing a neutral market state. This creates an intuitive, easy-to-read oscillator that synthesizes multiple analytical perspectives into one coherent signal. 🎯 Core Concept Consensus Trading Philosophy Rather than relying on a single indicator that may give conflicting or premature signals, Indicator Calibration employs a democratic voting system where multiple indicators contribute their normalized opinion: Each enabled indicator votes: +1 (bullish), -1 (bearish), or 0 (neutral) The votes are averaged to create a consensus signal Strong consensus (closer to ±1) indicates high agreement among indicators Weak consensus (closer to 0) indicates market indecision or transition Key Benefits Reduced False Signals: Multiple indicators must agree before strong signals appear Noise Filtering: Individual indicator quirks are smoothed out by averaging Customizable: Enable/disable indicators and adjust parameters to suit your trading style Universal Application: Works across all timeframes and asset classes Clear Visualization: Simple line oscillator with clear bull/bear zones 📊 Included Indicators The system can utilize up to 8 normalized trend-following indicators from the library: 1. BBPct - Bollinger Bands Percent Parameters: Length (default: 20), Factor (default: 2) Type: Stationary oscillator Strength: Mean reversion and volatility detection 2. NorosTrendRibbonEMA Parameters: Length (default: 20) Type: Non-stationary trend follower Strength: Breakout detection with momentum confirmation 3. RSI - Relative Strength Index Parameters: Length (default: 9), SMA Length (default: 4) Type: Stationary momentum oscillator Strength: Overbought/oversold with smoothing 4. Vidya - Variable Index Dynamic Average Parameters: Length (default: 30), History Length (default: 9) Type: Adaptive moving average Strength: Volatility-adjusted trend following 5. HullSuite Parameters: Length (default: 55), Multiplier (default: 1) Type: Fast-response moving average Strength: Low-lag trend identification 6. TrendContinuation Parameters: MA Length 1 (default: 50), MA Length 2 (default: 25) Type: Dual HMA system Strength: Trend quality assessment with neutral states 7. LeonidasTrendFollowingSystem Parameters: Short Length (default: 21), Key Length (default: 10) Type: Dual EMA crossover Strength: Simple, reliable trend tracking 8. TRAMA - Trend Regularity Adaptive Moving Average Parameters: Length (default: 50) Type: Adaptive trend follower Strength: Adjusts to trend stability ⚙️ Input Parameters Source Settings Source: Choose your price input (default: close) Can be modified to: open, high, low, close, hl2, hlc3, ohlc4, hlcc4 Indicator Selection Each indicator can be enabled or disabled via checkboxes: use_bbpct: Enable/disable Bollinger Bands Percent use_noros: Enable/disable Noro's Trend Ribbon use_rsi: Enable/disable RSI use_vidya: Enable/disable VIDYA use_hull: Enable/disable Hull Suite use_trendcon: Enable/disable Trend Continuation use_leonidas: Enable/disable Leonidas System use_trama: Enable/disable TRAMA Parameter Customization Each indicator has its own parameter group where you can fine-tune: val 1: Primary period/length parameter val 2: Secondary parameter (multiplier, smoothing, etc.) 📈 Signal Interpretation Output Line (Orange) The main output oscillates between -1 and +1: +1.0 to +0.5: Strong bullish consensus (all or most indicators agree on uptrend) +0.5 to +0.2: Moderate bullish bias (bullish indicators outnumber bearish) +0.2 to -0.2: Neutral zone (mixed signals or transition phase) -0.2 to -0.5: Moderate bearish bias (bearish indicators outnumber bullish) -0.5 to -1.0: Strong bearish consensus (all or most indicators agree on downtrend) Reference Lines Green line (+1): Maximum bullish consensus Red line (-1): Maximum bearish consensus Gray line (0): Neutral midpoint 💡 Trading Strategies Strategy 1: Consensus Threshold Trading Entry Rules: - Long: Output crosses above +0.5 (strong bullish consensus) - Short: Output crosses below -0.5 (strong bearish consensus) Exit Rules: - Exit Long: Output crosses below 0 (consensus lost) - Exit Short: Output crosses above 0 (consensus lost) Strategy 2: Zero-Line Crossover Entry Rules: - Long: Output crosses above 0 (bullish shift in consensus) - Short: Output crosses below 0 (bearish shift in consensus) Exit Rules: - Exit on opposite crossover Strategy 3: Divergence Trading Look for divergences between: - Price making higher highs while indicator makes lower highs (bearish divergence) - Price making lower lows while indicator makes higher lows (bullish divergence) Strategy 4: Extreme Reading Reversal Entry Rules: - Long: Output reaches -0.8 or below (extreme bearish consensus = potential reversal) - Short: Output reaches +0.8 or above (extreme bullish consensus = potential reversal) Use with caution - best combined with other reversal signals 🔧 Optimization Tips For Trending Markets Enable trend-following indicators: Noro's, VIDYA, Hull Suite, Leonidas Use higher threshold levels (±0.6) to filter out minor retracements Increase indicator periods for smoother signals For Range-Bound Markets Enable oscillators: BBPct, RSI Use zero-line crossovers for entries Decrease indicator periods for faster response For Volatile Markets Enable adaptive indicators: VIDYA, TRAMA Use wider threshold levels to avoid whipsaws Consider disabling fast indicators that may overreact Custom Calibration Process Start with all indicators enabled using default parameters Backtest on your chosen timeframe and asset Identify which indicators produce the most false signals Disable or adjust parameters for problematic indicators Test different threshold levels for entry/exit Validate on out-of-sample data 📊 Visual Guide Color Scheme Orange Line: Main consensus output Green Horizontal: Bullish extreme (+1) Red Horizontal: Bearish extreme (-1) Gray Horizontal: Neutral zone (0) Reading the Chart Line above 0: Net bullish sentiment Line below 0: Net bearish sentiment Line near extremes: Strong consensus Line fluctuating near 0: Indecision or transition Smooth line movement: Stable consensus Erratic line movement: Conflicting signals ⚠️ Important Considerations Lag Characteristics This is a lagging indicator by design (consensus takes time to form) Best used for trend confirmation rather than early entry May miss the first portion of strong moves Reduces false entries at the cost of delayed entries Number of Active Indicators More indicators = smoother but slower signals Fewer indicators = faster but potentially noisier signals Minimum recommended: 4 indicators for reliable consensus Optimal: 6-8 indicators for balanced performance Market Conditions Best: Strong trending markets (up or down) Good: Volatile markets with clear directional moves Poor: Choppy, sideways markets with no clear trend Worst: Low-volume, range-bound conditions Complementary Tools Consider combining with: Volume analysis for confirmation Support/resistance levels for entry/exit points Market structure analysis (higher timeframe trends) Risk management tools (ATR-based stops) 🎓 Example Use Cases Swing Trading Timeframe: Daily or 4H Enable: All 8 indicators with default parameters Entry: Consensus > +0.5 or < -0.5 Hold: Until consensus reverses to opposite extreme Day Trading Timeframe: 15m or 1H Enable: Faster indicators (RSI, BBPct, Noro's, Hull Suite) Entry: Zero-line crossover with volume confirmation Exit: Opposite crossover or profit target Position Trading Timeframe: Weekly or Daily Enable: Slower indicators (TRAMA, VIDYA, Trend Continuation) Entry: Strong consensus (±0.7) with higher timeframe confirmation Hold: Months until consensus weakens significantly 🔬 Technical Details Calculation Method 1. Each enabled indicator calculates its normalized signal (-1, 0, or +1) 2. All active signals are stored in an array 3. Array.avg() computes the arithmetic mean 4. Result is plotted as a continuous line Output Range Theoretical: -1.0 to +1.0 Practical: Typically ranges between -0.8 to +0.8 Rare: All indicators perfectly aligned at ±1.0 Performance Lightweight calculation (simple averaging) No repainting (all indicators are non-repainting) Compatible with all Pine Script features Works on all TradingView plans 📋 License This code is subject to the Mozilla Public License 2.0 at mozilla.org 🚀 Quick Start Guide Add to Chart: Apply indicator to your chart Choose Timeframe: Select appropriate timeframe for your trading style Enable Indicators: Start with all 8 enabled Observe Behavior: Watch how consensus forms during different market conditions Calibrate: Adjust parameters and indicator selection based on observations Backtest: Validate your settings on historical data Trade: Apply with proper risk management 🎯 Key Takeaways ✅ Consensus beats individual indicators - Multiple perspectives reduce errors ✅ Customizable to your style - Enable/disable and tune to preference ✅ Simple interpretation - One line tells the story ✅ Works across markets - Stocks, crypto, forex, commodities ✅ Reduces emotional trading - Clear, objective signal generation ✅ Professional-grade - Built on proven technical analysis principles Indicator Calibration transforms complex multi-indicator analysis into a single, actionable signal. By harnessing the collective wisdom of multiple proven trend-following systems, traders gain a powerful edge in identifying high-probability trade setups while filtering out market noise.
Pine Script® Indikator
von Unicorpus
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® Indikator
von AlphaExtract
3
Earnings Move Radar (E+1 & E+2)Stop guessing earnings reactions. See them. Count them. Use them. Earnings Move Radar turns every earnings release into a clear visual story on your chart. What it does for you Automatically marks the first and second trading day after each earnings (E+1 & E+2). Shows at a glance whether the move came from a gap, an intraday trend, or a follow-through / reversal on the next day. Packs all past earnings into a compact stats panel so you instantly know: how big earnings moves usually are, how often they close up vs. down, how common your “big move” threshold really is. Highlights simple, practical patterns like “two-day runs” and “three-day runs” around earnings that many traders care about but rarely measure. Why traders like it Saves time: no more clicking through old earnings dates one by one. Puts realistic numbers behind your option ideas and post-earnings plays. Works on any stock or ETF with earnings data, with a customizable look-back window. All labels and the stats table are shown in Chinese, making it very friendly for Chinese-speaking traders. How to use Apply it to a daily chart of the stock or ETF you trade. Scan the labels to understand how the market usually reacts to earnings for this symbol. Use the stats panel to size your risk and define what “normal”, “large” and “extreme” earnings moves mean for you. This indicator is an analytical tool, not a signal generator. It does not provide financial advice.
Pine Script® Indikator
von k2jysy
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® Indikator
von GRIDFX
CCT Fear & Greed Sincere📄 CCT Fear & Greed Sincere — Technical Overview The CCT Fear & Greed Sincere indicator provides a consolidated view of macro-market conditions using exclusively price-derived and market-structure data. The goal is to translate multiple independent risk-appetite components into a unified 0–100 index. This tool does not rely on survey sentiment, alternative datasets, or social indicators; it uses only verifiable, market-observable inputs. All components are normalized into a comparable scale and combined into a composite metric representing broad risk-seeking or risk-averse behaviour in global markets. While applicable to any instrument on TradingView, the indicator is particularly effective for cryptocurrency markets due to their historical sensitivity to equity volatility, liquidity cycles, and macroeconomic shifts. 📊 Components Included in the Index Below is an overview of the internal factors used to compute the final score. Each item is independently transformed into a 0–100 range before aggregation. 1. S&P 500 Price Deviation (SPX 125-Day Distance) Measures how far the S&P 500 is trading above or below its 125-day moving average. Large positive deviations generally reflect elevated risk-taking, while negative deviations suggest conservative market posture. 2. 52-Week Range Position (NYSE Composite) Evaluates the NYSE Composite’s distance from its annual high/low range. A higher relative position indicates greater market confidence, whereas lower values capture prolonged weakness or systemic stress. 3. Advance/Decline Momentum (ADVN vs. DECN) Applies smoothing to the net difference between advancing and declining issues. This highlights internal market participation, breadth conditions, and the balance between accumulation and distribution phases. 4. Put/Call Ratio Pressure (PCC) Uses a smoothed version of the equity put/call ratio. A higher put/call ratio (inverted here) reflects risk aversion, while lower ratios align with speculative environments. 5. VIX Relative Position (VIX vs. 50-Day Average) Compares the current VIX value to its 50-day moving average. VIX above its mean implies elevated volatility and fear; values below suggest calmer conditions and stronger risk appetite. 6. Equity vs. Treasury Performance (SPX vs. US10Y) Contrasts 20-day returns of equities and U.S. 10-year bonds. Strong equity performance relative to treasuries is normally associated with risk-on flows, while the opposite reflects defensive positioning. 7. High-Yield Spread (JNK vs. US10Y) Tracks the yield differential between high-yield bonds and U.S. Treasury rates. A wider spread captures stress in credit markets; a narrower spread indicates improved confidence. 🧮 Composite Calculation The indicator computes each component independently, normalizes the values into a 0–100 scale using a consistent methodology, and then calculates the simple average. This ensures transparency and avoids hidden weighting schemes or model bias. The final index is plotted as a continuous line with adaptive coloring based on its current level, visually highlighting shifts between fear-dominant and greed-dominant market states. 📈 Suggested Usage The indicator can be applied to any tradable asset, but it tends to be especially informative for: Cryptocurrencies, due to their pronounced reaction to global liquidity and risk sentiment. High-beta stocks, which often mirror broader volatility cycles. Macro-focused analysis, where risk-on/risk-off transitions impact multiple asset classes simultaneously. This tool is intended as a contextual framework rather than a standalone signal generator. Market participants may use it to contextualize regime changes, identify extremes, or complement existing technical strategies. 📏 Fear & Greed Levels Included in the Indicator These levels are plotted with dedicated labels and tooltips to offer additional visual clarity: Level Interpretation 0 – “Holy 💩” Extreme market stress; structural capitulation environment. 20 – “Who Sells the DIP?” Strong fear signal; frequently aligns with oversold conditions. 40 – “C’mon Wake UP!” Cautious or bearish environment; transitional zone. 60 – “So What?” Neutral risk environment; consolidation or equilibrium. 80 – “To the MOON!” Elevated risk appetite; momentum-driven phases. 100 – “Sell the MOM!” Peak optimism; historically associated with overheated conditions. These thresholds do not provide direct buy/sell instructions; they are reference bands designed to help illustrate the structural context of market behavior. 📌 Why This Indicator Is Uniquely Updated Uses 7 fully technical components Zero survey sentiment Zero social media data Zero alternative datasets or search trend metrics Fully transparent, fully reproducible, and based only on market-derived inputs Built on Pine Script® v6, aligned with modern TradingView standards Does not rely on proprietary or black-box scoring systems This positions the CCT Fear & Greed Sincere as one of the most technically grounded and transparent fear/greed-style indicators available using only TradingView-native data.
Pine Script® Indikator
von Central_CryptoTraders
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® Indikator
von ehudm1
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® Indikator
von Price-Action-Art
Gold𝑺𝒀𝑵𝑪🟡 Gold𝑺𝒀𝑵𝑪 - BTC follows GOLD Gold𝑺𝒀𝑵𝑪 is a quantitative projection tool that visualizes how Bitcoin (BTC/USD) would perform if it mirrored the recent price behavior of Gold (XAU/USD). It extends Gold’s last n days of normalized performance forward on the BTC chart and builds a volatility-adjusted projection corridor. ⚙️ Core Mechanics Projection Engine: Calculates Gold’s relative performance over the selected lookback window and applies it to BTC’s last closing price. Volatility Scaling: Computes the rolling standard deviation of Gold’s logarithmic returns to estimate the potential deviation range. Dynamic Gradient Bands: Three upper and lower standard deviation layers (1σ, 2σ, 3σ) are drawn using fading gradient fills to visualize increasing uncertainty. Scenario Labels: Displays key levels for: 𝑩𝑼𝑳𝑳𝑪𝑨𝑺𝑬 — +2σ projection 𝑬𝑿𝑷𝑬𝑪𝑻𝑬𝑫 — mean projection 𝑩𝑬𝑨𝑹𝑪𝑨𝑺𝑬 — −2σ projection 📈 Usage Designed for 1D charts (daily timeframe). Provides a comparative “sync” between Gold and Bitcoin to study cross-asset momentum, volatility symmetry, and directional bias. Useful in macro correlation analysis or when modeling BTC’s potential movement under Gold-like conditions. 🧠 Interpretation Gold𝑺𝒀𝑵𝑪 doesn’t predict - it synchronizes. It offers a contextual view of BTC’s potential path if it followed Gold’s rhythm, enhanced by statistically derived volatility zones. Created by: @SP_Quant Credits: BitAura
Pine Script® Indikator
von BitAura
𝑷ortfolio𝑴𝑨𝑺𝑻𝑬𝑹 [BitAura]𝑷ortfolio𝑴𝑨𝑺𝑻𝑬𝑹 This Pine Script® indicator is meant to be used to manage a Barbell Portfolio composed of BitAura's various strategies in order to create a risk-reward balance for the investor's needs. The Portfolio is divided in two main parts, one being a lower-risk Bitcoin-only strategy while the other being the higher-risk, higher-reward 𝐑otation𝑺𝑼𝑰𝑻𝑬 V3 system. The user can choose the percentage splits between these two systems and then also configure them based on their risk profile. Logic and Core Concepts The 𝑷ortfolio𝑴𝑨𝑺𝑻𝑬𝑹 System uses the Barbell Portfolio theory to create a portfolio taylored for the final user and automatically calculates dollar allocation based on inputted settings. Features 𝐑otation𝑺𝑼𝑰𝑻𝑬 : our advanced Strategy which allocates to the strongest asset amongst a pool of 4 Major Crypto Tokens, or de-risk to USD when these lack momentum. BItcoin-only Strategy : This is theoretically a lower-risk system compared to 𝐑otation𝑺𝑼𝑰𝑻𝑬 and is made out of Universal Trend Following strategies. There are two variants, one being a Long-Term strategy (𝐂ycle𝑽𝑰𝑺𝑰𝑶𝑵) while the other one being of Medium-Term speed (𝐒wing𝑽𝑰𝑺𝑰𝑶𝑵). Customizable Inputs : Allows users to adjust table settings, backtest date ranges, portfolio splits and portfolio dollar allocations. Visual Outputs : Allocation Table : Displays calculated allocation to each system based on user settings. Equity Plots : Plots the Barbell Portfolio performance along with the two individual systems equities and allow comparisons between them and to Bitcoin Buy & Hold. Color Presets : Offers five color themes (e.g., Arctic Blast, Fire vs. Ice) or custom color options for long/cash signals. Pine Script v6 : Leverages matrices, tables, and gradient coloring for enhanced usability. How to Use Add to Chart : Apply the indicator to any chart on the 1D timeframe. The ticker doesn't matter as it doesn't affect the calculations, just make sure the ticker start date is earlier than the Backtest Start date applied in the script settings. Input Portfolio size : Adjust the Dollar Portfolio size in the script settings in order to obtain accurate Portfolio Allocations in the respective table. Note that BitAura can't and won't be able to access your portfolio size. Choose Barbell Split : Adjust based on your risk-profile how much to allocate to your preferred Bitcoin Strategy (default 70%) and how much to allocate to 𝐑otation𝑺𝑼𝑰𝑻𝑬 (default 30%). Configure Systems : Select your preferred allocation type for 𝐑otation𝑺𝑼𝑰𝑻𝑬. Aggressive allocates 100% to the dominant asset, Moderate allocates 80% to the dominant asset and 20% to the second strongest one while Conservative does a 60/40 split between the first two assets. Configure Settings : Adjust backtest start date (default: 31 Oct 2024) to properly track the Portfolio's performance. Select Color Theme : Choose a preset color mode (e.g., Arctic Blast) or enable custom colors in the Colors group. Monitor Outputs : Check the Table for Allocations and system signals, and view the equity curves to view the portfolio's performance. Notes The script is closed-source. The script avoids lookahead bias by using barmerge.lookahead_off in request.security() calls. The BitAura watermark can be toggled in the Script Settings . Disclaimer : This script is for educational and analytical purposes only and does not constitute financial advice. Investing involves significant risk, and past performance is not indicative of future results. Always conduct your own research and apply proper risk management.
Pine Script® Indikator
von BitAura
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® Indikator
von irthetraders
1
Algorithm Predator - ProAlgorithm Predator - Pro: Advanced Multi-Agent Reinforcement Learning Trading System Algorithm Predator - Pro combines four specialized market microstructure agents with a state-of-the-art reinforcement learning framework . Unlike traditional indicator mashups, this system implements genuine machine learning to automatically discover which detection strategies work best in current market conditions and adapts continuously without manual intervention. Core Innovation: Rather than forcing traders to interpret conflicting signals, this system uses 15 different multi-armed bandit algorithms and a full reinforcement learning stack (Q-Learning, TD(λ) with eligibility traces, and Policy Gradient with REINFORCE) to learn optimal agent selection policies. The result is a self-improving system that gets smarter with every trade. Target Users: Swing traders, day traders, and algorithmic traders seeking systematic signal generation with mathematical rigor. Suitable for stocks, forex, crypto, and futures on liquid instruments (>100k daily volume). Why These Components Are Combined The Fundamental Problem No single indicator works consistently across all market regimes. What works in trending markets fails in ranging conditions. Traditional solutions force traders to manually switch indicators (slow, error-prone) or interpret all signals simultaneously (cognitive overload). This system solves the problem through automated meta-learning: Deploy multiple specialized agents designed for specific market microstructure conditions, then use reinforcement learning to discover which agent (or combination) performs best in real-time. Why These Specific Four Agents? The four agents provide orthogonal failure mode coverage —each agent's weakness is another's strength: Spoofing Detector - Optimal in consolidation/manipulation; fails in trending markets (hedged by Exhaustion Detector) Exhaustion Detector - Optimal at trend climax; fails in range-bound markets (hedged by Liquidity Void) Liquidity Void - Optimal pre-breakout compression; fails in established trends (hedged by Mean Reversion) Mean Reversion - Optimal in low volatility; fails in strong trends (hedged by Spoofing Detector) This creates complete market state coverage where at least one agent should perform well in any condition. The bandit system identifies which one without human intervention. Why Reinforcement Learning vs. Simple Voting? Traditional consensus systems have fatal flaws: equal weighting assumes all agents are equally reliable (false), static thresholds don't adapt, and no learning means past mistakes repeat indefinitely. Reinforcement learning solves this through the exploration-exploitation tradeoff: Continuously test underused agents (exploration) while primarily relying on proven winners (exploitation). Over time, the system builds a probability distribution over agent quality reflecting actual market performance. Mathematical Foundation: Multi-armed bandit problem from probability theory, where each agent is an "arm" with unknown reward distribution. The goal is to maximize cumulative reward while efficiently learning each arm's true quality. The Four Trading Agents: Technical Explanation Agent 1: 🎭 Spoofing Detector (Institutional Manipulation Detection) Theoretical Basis: Market microstructure theory on order flow toxicity and information asymmetry. Based on research by Easley, López de Prado, and O'Hara on high-frequency trading manipulation. What It Detects: 1. Iceberg Orders (Hidden Liquidity Absorption) Method: Monitors volume spikes (>2.5× 20-period average) with minimal price movement (<0.3× ATR) Formula: score += (close > open ? -2.5 : 2.5) when volume > vol_avg × 2.5 AND abs(close - open) / ATR < 0.3 Interpretation: Large volume without price movement indicates institutional absorption (buying) or distribution (selling) using hidden orders Signal Logic: Contrarian—fade false breakouts caused by institutional manipulation 2. Spoofing Patterns (Fake Liquidity via Layering) Method: Analyzes candlestick wick-to-body ratios during volume spikes Formula: if upper_wick > body × 2 AND volume_spike: score += 2.0 Mechanism: Spoofing creates large wicks (orders pulled before execution) with volume evidence Signal Logic: Wick direction indicates trapped participants; trade against the failed move 3. Post-Manipulation Reversals Method: Tracks volume decay after manipulation events Formula: if volume > vol_avg × 3 AND volume / volume < 0.3: score += (close > open ? -1.5 : 1.5) Interpretation: Sharp volume drop after manipulation indicates exhaustion of manipulative orders Why It Works: Institutional manipulation creates detectable microstructure anomalies. While retail traders see "mysterious reversals," this agent quantifies the order flow patterns causing them. Parameter: i_spoof (sensitivity 0.5-2.0) - Controls detection threshold Best Markets: Consolidations before breakouts, London/NY overlap windows, stocks with institutional ownership >70% Agent 2: ⚡ Exhaustion Detector (Momentum Failure Analysis) Theoretical Basis: Technical analysis divergence theory combined with VPIN reversals from market microstructure literature. What It Detects: 1. Price-RSI Divergence (Momentum Deceleration) Method: Compares 5-bar price ROC against RSI change Formula: if price_roc > 5% AND rsi_current < rsi : score += 1.8 Mathematics: Second derivative detecting inflection points Signal Logic: When price makes higher highs but momentum makes lower highs, expect mean reversion 2. Volume Exhaustion (Buying/Selling Climax) Method: Identifies strong price moves (>5% ROC) with declining volume (<-20% volume ROC) Formula: if price_roc > 5 AND vol_roc < -20: score += 2.5 Interpretation: Price extension without volume support indicates retail chasing while institutions exit 3. Momentum Deceleration (Acceleration Analysis) Method: Compares recent 3-bar momentum to prior 3-bar momentum Formula: deceleration = abs(mom1) < abs(mom2) × 0.5 where momentum significant (> ATR) Signal Logic: When rate of price change decelerates significantly, anticipate directional shift Why It Works: Momentum is lagging, but momentum divergence is leading. By comparing momentum's rate of change to price, this agent detects "weakening conviction" before reversals become obvious. Parameter: i_momentum (sensitivity 0.5-2.0) Best Markets: Strong trends reaching climax, parabolic moves, instruments with high retail participation Agent 3: 💧 Liquidity Void Detector (Breakout Anticipation) Theoretical Basis: Market liquidity theory and order book dynamics. Based on research into "liquidity holes" and volatility compression preceding expansion. What It Detects: 1. Bollinger Band Squeeze (Volatility Compression) Method: Monitors Bollinger Band width relative to 50-period average Formula: bb_width = (upper_band - lower_band) / middle_band; triggers when < 0.6× average Mathematical Foundation: Regression to the mean—low volatility precedes high volatility Signal Logic: When volatility compresses AND cumulative delta shows directional bias, anticipate breakout 2. Volume Profile Gaps (Thin Liquidity Zones) Method: Identifies sharp volume transitions indicating few limit orders Formula: if volume < vol_avg × 0.5 AND volume < vol_avg × 0.5 AND volume > vol_avg × 1.5 Interpretation: Sudden volume drop after spike indicates price moved through order book to low-opposition area Signal Logic: Price accelerates through low-liquidity zones 3. Stop Hunts (Liquidity Grabs Before Reversals) Method: Detects new 20-bar highs/lows with immediate reversal and rejection wick Formula: if new_high AND close < high - (high - low) × 0.6: score += 3.0 Mechanism: Market makers push price to trigger stop-loss clusters, then reverse Signal Logic: Enter reversal after stop-hunt completes Why It Works: Order book theory shows price moves fastest through zones with minimal liquidity. By identifying these zones before major moves, this agent provides early entry for high-reward breakouts. Parameter: i_liquidity (sensitivity 0.5-2.0) Best Markets: Range-bound pre-breakout setups, volatility compression zones, instruments prone to gap moves Agent 4: 📊 Mean Reversion (Statistical Arbitrage Engine) Theoretical Basis: Statistical arbitrage theory, Ornstein-Uhlenbeck mean-reverting processes, and pairs trading methodology applied to single instruments. What It Detects: 1. Z-Score Extremes (Standard Deviation Analysis) Method: Calculates price distance from 20-period and 50-period SMAs in standard deviation units Formula: zscore_20 = (close - SMA20) / StdDev(50) Statistical Interpretation: Z-score >2.0 means price is 2 standard deviations above mean (97.5th percentile) Trigger Logic: if abs(zscore_20) > 2.0: score += zscore_20 > 0 ? -1.5 : 1.5 (fade extremes) 2. Ornstein-Uhlenbeck Process (Mean-Reverting Stochastic Model) Method: Models price as mean-reverting stochastic process: dx = θ(μ - x)dt + σdW Implementation: Calculates spread = close - SMA20, then z-score of spread vs. spread distribution Formula: ou_signal = (spread - spread_mean) / spread_std Interpretation: Measures "tension" pulling price back to equilibrium 3. Correlation Breakdown (Regime Change Detection) Method: Compares 50-period price-volume correlation to 10-period correlation Formula: corr_breakdown = abs(typical_corr - recent_corr) > 0.5 Enhancement: if corr_breakdown AND abs(zscore_20) > 1.0: score += zscore_20 > 0 ? -1.2 : 1.2 Why It Works: Mean reversion is the oldest quantitative strategy (1970s pairs trading at Morgan Stanley). While simple, it remains effective because markets exhibit periodic equilibrium-seeking behavior. This agent applies rigorous statistical testing to identify when mean reversion probability is highest. Parameter: i_statarb (sensitivity 0.5-2.0) Best Markets: Range-bound instruments, low-volatility periods (VIX <15), algo-dominated markets (forex majors, index futures) Multi-Armed Bandit System: 15 Algorithms Explained What Is a Multi-Armed Bandit Problem? Origin: Named after slot machines ("one-armed bandits"). Imagine facing multiple slot machines, each with unknown payout rates. How do you maximize winnings? Formal Definition: K arms (agents), each with unknown reward distribution with mean μᵢ. Goal: Maximize cumulative reward over T trials. Challenge: Balance exploration (trying uncertain arms to learn quality) vs. exploitation (using known-best arm for immediate reward). Trading Application: Each agent is an "arm." After each trade, receive reward (P&L). Must decide which agent to trust for next signal. Algorithm Categories Bayesian Approaches (probabilistic, optimal for stationary environments): Thompson Sampling Bootstrapped Thompson Sampling Discounted Thompson Sampling Frequentist Approaches (confidence intervals, deterministic): UCB1 UCB1-Tuned KL-UCB SW-UCB (Sliding Window) D-UCB (Discounted) Adversarial Approaches (robust to non-stationary environments): EXP3-IX Hedge FPL-Gumbel Reinforcement Learning Approaches (leverage learned state-action values): Q-Values (from Q-Learning) Policy Network (from Policy Gradient) Simple Baseline: Epsilon-Greedy Softmax Key Algorithm Details Thompson Sampling (DEFAULT - RECOMMENDED) Theoretical Foundation: Bayesian decision theory with conjugate priors. Published by Thompson (1933), rediscovered for bandits by Chapelle & Li (2011). How It Works: Model each agent's reward distribution as Beta(α, β) where α = wins, β = losses Each step, sample from each agent's beta distribution: θᵢ ~ Beta(αᵢ, βᵢ) Select agent with highest sample: argmaxᵢ θᵢ Update winner's distribution after observing outcome Mathematical Properties: Optimality: Achieves logarithmic regret O(K log T) (proven optimal) Bayesian: Maintains probability distribution over true arm means Automatic Balance: High uncertainty → more exploration; high certainty → exploitation ⚠️ CRITICAL APPROXIMATION: This is a pseudo-random approximation of true Thompson Sampling. True implementation requires random number generation from beta distributions, which Pine Script doesn't provide. This version uses Box-Muller transform with market data (price/volume decimal digits) as entropy source. While not mathematically pure, it maintains core exploration-exploitation balance and learns agent preferences effectively. When To Use: Best all-around choice. Handles non-stationary markets reasonably well, balances exploration naturally, highly sample-efficient. UCB1 (Upper Confidence Bound) Formula: UCB_i = reward_mean_i + sqrt(2 × ln(total_pulls) / pulls_i) Interpretation: First term (exploitation) + second term (exploration bonus for less-tested arms) Mathematical Properties: Deterministic : Always selects same arm given same state Regret Bound: O(K log T) — same optimality as Thompson Sampling Interpretable: Can visualize confidence intervals When To Use: Prefer deterministic behavior, want to visualize uncertainty, stable markets EXP3-IX (Exponential Weights - Adversarial) Theoretical Foundation: Adversarial bandit algorithm. Assumes environment may be actively hostile (worst-case analysis). How It Works: Maintain exponential weights: w_i = exp(η × cumulative_reward_i) Select agent with probability proportional to weights: p_i = (1-γ)w_i/Σw_j + γ/K After outcome, update with importance weighting: estimated_reward = observed_reward / p_i Mathematical Properties: Adversarial Regret: O(sqrt(TK log K)) even if environment is adversarial No Assumptions: Doesn't assume stationary or stochastic reward distributions Robust: Works even when optimal arm changes continuously When To Use: Extreme non-stationarity, don't trust reward distribution assumptions, want robustness over efficiency KL-UCB (Kullback-Leibler Upper Confidence Bound) Theoretical Foundation: Uses KL-divergence instead of Hoeffding bounds. Tighter confidence intervals. Formula (conceptual): Find largest q such that: n × KL(p||q) ≤ ln(t) + 3×ln(ln(t)) Mathematical Properties: Tighter Bounds: KL-divergence adapts to reward distribution shape Asymptotically Optimal: Better constant factors than UCB1 Computationally Intensive: Requires iterative binary search (15 iterations) When To Use: Maximum sample efficiency needed, willing to pay computational cost, long-term trading (>500 bars) Q-Values & Policy Network (RL-Based Selection) Unique Feature: Instead of treating agents as black boxes with scalar rewards, these algorithms leverage the full RL state representation . Q-Values Selection: Uses learned Q-values: Q(state, agent_i) from Q-Learning Selects agent via softmax over Q-values for current market state Advantage: Selects based on state-conditional quality (which agent works best in THIS market state) Policy Network Selection: Uses neural network policy: π(agent | state, θ) from Policy Gradient Direct policy over agents given market features Advantage: Can learn non-linear relationships between market features and agent quality When To Use: After 200+ RL updates (Q-Values) or 500+ updates (Policy Network) when models converged Machine Learning & Reinforcement Learning Stack Why Both Bandits AND Reinforcement Learning? Critical Distinction: Bandits treat agents as contextless black boxes: "Agent 2 has 60% win rate" Reinforcement Learning adds state context: "Agent 2 has 60% win rate WHEN trend_score > 2 and RSI < 40" Power of Combination: Bandits provide fast initial learning with minimal assumptions. RL provides state-dependent policies for superior long-term performance. Component 1: Q-Learning (Value-Based RL) Algorithm: Temporal Difference Learning with Bellman equation. State Space: 54 discrete states formed from: trend_state = {0: bearish, 1: neutral, 2: bullish} (3 values) volatility_state = {0: low, 1: normal, 2: high} (3 values) RSI_state = {0: oversold, 1: neutral, 2: overbought} (3 values) volume_state = {0: low, 1: high} (2 values) Total states: 3 × 3 × 3 × 2 = 54 states Action Space: 5 actions (No trade, Agent 1, Agent 2, Agent 3, Agent 4) Total state-action pairs: 54 × 5 = 270 Q-values Bellman Equation: Q(s,a) ← Q(s,a) + α × Parameters: α (learning rate): 0.01-0.50, default 0.10 - Controls step size for updates γ (discount factor): 0.80-0.99, default 0.95 - Values future rewards ε (exploration): 0.01-0.30, default 0.10 - Probability of random action Update Mechanism: Position opens with state s, action a (selected agent) Every bar position is open: Calculate floating P&L → scale to reward Perform online TD update When position closes: Perform terminal update with final reward Gradient Clipping: TD errors clipped to ; Q-values clipped to for stability. Why It Works: Q-Learning learns "quality" of each agent in each market state through trial and error. Over time, builds complete state-action value function enabling optimal state-dependent agent selection. Component 2: TD(λ) Learning (Temporal Difference with Eligibility Traces) Enhancement Over Basic Q-Learning: Credit assignment across multiple time steps. The Problem TD(λ) Solves: Position opens at t=0 Market moves favorably at t=3 Position closes at t=8 Question: Which earlier decisions contributed to success? Basic Q-Learning: Only updates Q(s₈, a₈) ← reward TD(λ): Updates ALL visited state-action pairs with decayed credit Eligibility Trace Formula: e(s,a) ← γ × λ × e(s,a) for all s,a (decay all traces) e(s_current, a_current) ← 1 (reset current trace) Q(s,a) ← Q(s,a) + α × TD_error × e(s,a) (update all with trace weight) Lambda Parameter (λ): 0.5-0.99, default 0.90 λ=0: Pure 1-step TD (only immediate next state) λ=1: Full Monte Carlo (entire episode) λ=0.9: Balance (recommended) Why Superior: Dramatically faster learning for multi-step tasks. Q-Learning requires many episodes to propagate rewards backwards; TD(λ) does it in one. Component 3: Policy Gradient (REINFORCE with Baseline) Paradigm Shift: Instead of learning value function Q(s,a), directly learn policy π(a|s). Policy Network Architecture: Input: 12 market features Hidden: None (linear policy) Output: 5 actions (softmax distribution) Total parameters: 12 features × 5 actions + 5 biases = 65 parameters Feature Set (12 Features): Price Z-score (close - SMA20) / ATR Volume ratio (volume / vol_avg - 1) RSI deviation (RSI - 50) / 50 Bollinger width ratio Trend score / 4 (normalized) VWAP deviation 5-bar price ROC 5-bar volume ROC Range/ATR ratio - 1 Price-volume correlation (20-period) Volatility ratio (ATR / ATR_avg - 1) EMA50 deviation REINFORCE Update Rule: θ ← θ + α × ∇log π(a|s) × advantage where advantage = reward - baseline (variance reduction) Why Baseline? Raw rewards have high variance. Subtracting baseline (running average) centers rewards around zero, reducing gradient variance by 50-70%. Learning Rate: 0.001-0.100, default 0.010 (much lower than Q-Learning because policy gradients have high variance) Why Policy Gradient? Handles 12 continuous features directly (Q-Learning requires discretization) Naturally maintains exploration through probability distribution Can converge to stochastic optimal policy Component 4: Ensemble Meta-Learner (Stacking) Architecture: Level-1 meta-learner combines Level-0 base learners (Q-Learning, TD(λ), Policy Gradient). Three Meta-Learning Algorithms: 1. Simple Average (Baseline) Final_prediction = (Q_prediction + TD_prediction + Policy_prediction) / 3 2. Weighted Vote (Reward-Based) weight_i ← 0.95 × weight_i + 0.05 × (reward_i + 1) 3. Adaptive Weighting (Gradient-Based) — RECOMMENDED Loss Function: L = (y_true - ŷ_ensemble)² Gradient: ∂L/∂weight_i = -2 × (y_true - ŷ_ensemble) × agent_contribution_i Updates weights via gradient descent with clipping and normalization Why It Works: Unlike simple averaging, meta-learner discovers which base learner is most reliable in current regime. If Policy Gradient excels in trending markets while Q-Learning excels in ranging, meta-learner learns these patterns and weights accordingly. Feature Importance Tracking Purpose: Identify which of 12 features contribute most to successful predictions. Update Rule: importance_i ← 0.95 × importance_i + 0.05 × |feature_i × reward| Use Cases: Feature selection: Drop low-importance features Market regime detection: Importance shifts reveal regime changes Agent tuning: If VWAP deviation has high importance, consider boosting agents using VWAP RL Position Tracking System Critical Innovation: Proper reinforcement learning requires tracking which decisions led to outcomes. State Tracking (When Signal Validates): active_rl_state ← current_market_state (0-53) active_rl_action ← selected_agent (1-4) active_rl_entry ← entry_price active_rl_direction ← 1 (long) or -1 (short) active_rl_bar ← current_bar_index Online Updates (Every Bar Position Open): floating_pnl = (close - entry) / entry × direction reward = floating_pnl × 10 (scale to meaningful range) reward = clip(reward, -5.0, 5.0) Update Q-Learning, TD(λ), and Policy Gradient Terminal Update (Position Close): Final Q-Learning update (no next Q-value, terminal state) Update meta-learner with final result Update agent memory Clear position tracking Exit Conditions: Time-based: ≥3 bars held (minimum hold period) Stop-loss: 1.5% adverse move Take-profit: 2.0% favorable move Market Microstructure Filters Why Microstructure Matters Traditional technical analysis assumes fair, efficient markets. Reality: Markets have friction, manipulation, and information asymmetry. Microstructure filters detect when market structure indicates adverse conditions. Filter 1: VPIN (Volume-Synchronized Probability of Informed Trading) Theoretical Foundation: Easley, López de Prado, & O'Hara (2012). "Flow Toxicity and Liquidity in a High-Frequency World." What It Measures: Probability that current order flow is "toxic" (informed traders with private information). Calculation: Classify volume as buy or sell (close > close = buy volume) Calculate imbalance over 20 bars: VPIN = |Σ buy_volume - Σ sell_volume| / Σ total_volume Compare to moving average: toxic = VPIN > VPIN_MA(20) × sensitivity Interpretation: VPIN < 0.3: Normal flow (uninformed retail) VPIN 0.3-0.4: Elevated (smart money active) VPIN > 0.4: Toxic flow (informed institutions dominant) Filter Logic: Block LONG when: VPIN toxic AND price rising (don't buy into institutional distribution) Block SHORT when: VPIN toxic AND price falling (don't sell into institutional accumulation) Adaptive Threshold: If VPIN toxic frequently, relax threshold; if rarely toxic, tighten threshold. Bounded . Filter 2: Toxicity (Kyle's Lambda Approximation) Theoretical Foundation: Kyle (1985). "Continuous Auctions and Insider Trading." What It Measures: Price impact per unit volume — market depth and informed trading. Calculation: price_impact = (close - close ) / sqrt(Σ volume over 10 bars) impact_zscore = (price_impact - impact_mean) / impact_std toxicity = abs(impact_zscore) Interpretation: Low toxicity (<1.0): Deep liquid market, large orders absorbed easily High toxicity (>2.0): Thin market or informed trading Filter Logic: Block ALL SIGNALS when toxicity > threshold. Most dangerous when price breaks from VWAP with high toxicity. Filter 3: Regime Filter (Counter-Trend Protection) Purpose: Prevent counter-trend trades during strong trends. Trend Scoring: trend_score = 0 trend_score += close > EMA8 ? +1 : -1 trend_score += EMA8 > EMA21 ? +1 : -1 trend_score += EMA21 > EMA50 ? +1 : -1 trend_score += close > EMA200 ? +1 : -1 Range: Regime Classification: Strong Bull: trend_score ≥ +3 → Block all SHORT signals Strong Bear: trend_score ≤ -3 → Block all LONG signals Neutral: -2 ≤ trend_score ≤ +2 → Allow both directions Filter 4: Liquidity Boost (Signal Enhancer) Unique: Unlike other filters (which block), this amplifies signals during low liquidity. Logic: if volume < vol_avg × 0.7: agent_scores × 1.2 Why It Works: Low liquidity often precedes explosive moves (breakouts). By increasing agent sensitivity during compression, system catches pre-breakout signals earlier. Technical Implementation & Approximations ⚠️ Critical Approximations Required by Pine Script 1. Thompson Sampling: Pseudo-Random Beta Distribution Academic Standard: True random sampling from beta distributions using cryptographic RNG This Implementation: Box-Muller transform for normal distribution using market data (price/volume decimal digits) as entropy source, then scale to beta distribution mean/variance Impact: Not cryptographically random, may have subtle biases in specific price ranges, but maintains correct mean and approximate variance. Sufficient for bandit agent selection. 2. VPIN: Simplified Volume Classification Academic Standard: Lee-Ready algorithm or exchange-provided aggressor flags with tick-by-tick data This Implementation: Bar-based classification: if close > close : buy_volume += volume Impact: 10-15% precision loss. Works well in directional markets, misclassifies in choppy conditions. Still captures order flow imbalance signal. 3. Policy Gradient: Simplified Per-Action Updates Academic Standard: Full softmax gradient updating all actions (selected action UP, others DOWN proportionally) This Implementation: Only updates selected action's weights Impact: Valid approximation for small action spaces (5 actions). Slower convergence than full softmax but still learns optimal policy. 4. Kyle's Lambda: Simplified Price Impact Academic Standard: Regression over multiple time scales with signed order flow This Implementation: price_impact = Δprice_10 / sqrt(Σvolume_10); z_score calculation Impact: 15-20% precision loss. No proper signed order flow. Still detects informed trading signals at extremes (>2σ). 5. Other Simplifications: Hawkes Process: Fixed exponential decay (0.9) not MLE-optimized Entropy: Ratio approximation not true Shannon entropy H(X) = -Σ p(x)·log₂(p(x)) Feature Engineering: 12 features vs. potential 100+ with polynomial interactions RL Hybrid Updates: Both online and terminal (non-standard but empirically effective) Overall Precision Loss Estimate: 10-15% compared to academic implementations with institutional data feeds. Practical Trade-off: For retail trading with OHLCV data, these approximations provide 90%+ of the edge while maintaining full transparency, zero latency, no external dependencies, and runs on any TradingView plan. How to Use: Practical Guide Initial Setup (5 Minutes) Select Trading Mode: Start with "Balanced" for most users Enable ML/RL System: Toggle to TRUE, select "Full Stack" ML Mode Bandit Configuration: Algorithm: "Thompson Sampling", Mode: "Switch" or "Blend" Microstructure Filters: Enable all four filters, enable "Adaptive Microstructure Thresholds" Visual Settings: Enable dashboard (Top Right), enable all chart visuals Learning Phase (First 50-100 Signals) What To Monitor: Agent Performance Table: Watch win rates develop (target >55%) Bandit Weights: Should diverge from uniform (0.25 each) after 20-30 signals RL Core Metrics: "RL Updates" should increase when position open Filter Status: "Blocked" count indicates filter activity Optimization Tips: Too few signals: Lower min_confidence to 0.25, increase agent sensitivities to 1.1-1.2 Too many signals: Raise min_confidence to 0.35-0.40, decrease agent sensitivities to 0.8-0.9 One agent dominates (>70%): Consider "Lock Agent" feature Signal Interpretation Dashboard Signal Status: ⚪ WAITING FOR SIGNAL: No agent signaling ⏳ ANALYZING...: Agent signaling but not confirmed 🟡 CONFIRMING 2/3: Building confirmation (2 of 3 bars) 🟢 LONG ACTIVE : Validated long entry 🔴 SHORT ACTIVE : Validated short entry Kill Zone Boxes: Entry price (triangle marker), Take Profit (Entry + 2.5× ATR), Stop Loss (Entry - 1.5× ATR). Risk:Reward = 1:1.67 Risk Management Position Sizing: Risk per trade = 1-2% of capital Position size = (Capital × Risk%) / (Entry - StopLoss) Stop-Loss Placement: Initial: Entry ± 1.5× ATR (shown in kill zone) Trailing: After 1:1 R:R achieved, move stop to breakeven Take-Profit Strategy: TP1 (2.5× ATR): Take 50% off TP2 (Runner): Trail stop at 1× ATR or use opposite signal as exit Memory Persistence Why Save Memory: Every chart reload resets the system. Saving learned parameters preserves weeks of learning. When To Save: After 200+ signals when agent weights stabilize What To Save: From Memory Export panel, copy all alpha/beta/weight values and adaptive thresholds How To Restore: Enable "Restore From Saved State", input all values into corresponding fields What Makes This Original Innovation 1: Genuine Multi-Armed Bandit Framework This implements 15 mathematically rigorous bandit algorithms from academic literature (Thompson Sampling from Chapelle & Li 2011, UCB family from Auer et al. 2002, EXP3 from Auer et al. 2002, KL-UCB from Garivier & Cappé 2011). Each algorithm maintains proper state, updates according to proven theory, and converges to optimal behavior. This is real learning, not superficial parameter changes. Innovation 2: Full Reinforcement Learning Stack Beyond bandits learning which agent works best globally, RL learns which agent works best in each market state. After 500+ positions, system builds 54-state × 5-action value function (270 learned parameters) capturing context-dependent agent quality. Innovation 3: Market Microstructure Integration Combines retail technical analysis with institutional-grade microstructure metrics: VPIN from Easley, López de Prado, O'Hara (2012), Kyle's Lambda from Kyle (1985), Hawkes Processes from Hawkes (1971). These detect informed trading, manipulation, and liquidity dynamics invisible to technical analysis. Innovation 4: Adaptive Threshold System Dynamic quantile-based thresholds: Maintains histogram of each agent's score distribution (24 bins, exponentially decayed), calculates 80th percentile threshold from histogram. Agent triggers only when score exceeds its own learned quantile. Proper non-parametric density estimation automatically adapts to instrument volatility, agent behavior shifts, and market regime changes. Innovation 5: Episodic Memory with Transfer Learning Dual-layer architecture: Short-term memory (last 20 trades, fast adaptation) + Long-term memory (condensed episodes, historical patterns). Transfer mechanism consolidates knowledge when STM reaches threshold. Mimics hippocampus → neocortex consolidation in human memory. Limitations & Disclaimers General Limitations No Predictive Guarantee: Pattern recognition ≠ prediction. Past performance ≠ future results. Learning Period Required: Minimum 50-100 bars for reliable statistics. Initial performance may be suboptimal. Overfitting Risk: System learns patterns in historical data. May not generalize to unprecedented conditions. Approximation Limitations: See technical implementation section (10-15% precision loss vs. academic standards) Single-Instrument Limitation: No multi-asset correlation, sector context, or VIX integration. Forward-Looking Bias Disclaimer CRITICAL TRANSPARENCY: The RL system uses an 8-bar forward-looking window for reward calculation. What This Means: System learns from rewards incorporating future price information (bars 101-108 relative to entry at bar 100). Why Acceptable: ✅ Signals do NOT look ahead: Entry decisions use only data ≤ entry bar ✅ Learning only: Forward data used for optimization, not signal generation ✅ Real-time mirrors backtest: In live trading, system learns identically ⚠️ Implication: Dashboard "Agent Win%" reflects this 8-bar evaluation. Real-time performance may differ slightly if positions held longer, slippage/fees not captured, or market microstructure changes. Risk Warnings No Guarantee of Profit: All trading involves risk of loss System Failures: Bugs possible despite extensive testing Market Conditions: Optimized for liquid markets (>100k daily volume). Performance degrades in illiquid instruments, major news events, flash crashes Broker-Specific Issues: Execution slippage, commission/fees, overnight financing costs Appropriate Use This Indicator Is: ✅ Entry trigger system ✅ Risk management framework (stop/target) ✅ Adaptive agent selection engine ✅ Learning system that improves over time This Indicator Is NOT: ❌ Complete trading strategy (requires position sizing, portfolio management) ❌ Replacement for fundamental analysis ❌ Guaranteed profit generator ❌ Suitable for complete beginners without training Recommended Complementary Analysis: Market context (support/resistance), volume profile, fundamental catalysts, correlation with related instruments, broader market regime Recommended Settings by Instrument Stocks (Large Cap, >$1B): Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Switch Agent Sensitivity: Spoofing 1.0-1.2, Exhaustion 0.9-1.1, Liquidity 0.8-1.0, StatArb 1.1-1.3 Microstructure: All enabled, VPIN 1.2, Toxicity 1.5 | Timeframe: 15min-1H Forex Majors (EURUSD, GBPUSD): Mode: Balanced to Conservative | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Blend Agent Sensitivity: Spoofing 0.8-1.0, Exhaustion 0.9-1.1, Liquidity 0.7-0.9, StatArb 1.2-1.5 Microstructure: All enabled, VPIN 1.0-1.1, Toxicity 1.3-1.5 | Timeframe: 5min-30min Crypto (BTC, ETH): Mode: Aggressive to Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling OR EXP3-IX Agent Sensitivity: Spoofing 1.2-1.5, Exhaustion 1.1-1.3, Liquidity 1.2-1.5, StatArb 0.7-0.9 Microstructure: All enabled, VPIN 1.4-1.6, Toxicity 1.8-2.2 | Timeframe: 15min-4H Futures (ES, NQ, CL): Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: UCB1 or Thompson Sampling Agent Sensitivity: All 1.0-1.2 (balanced) Microstructure: All enabled, VPIN 1.1-1.3, Toxicity 1.4-1.6 | Timeframe: 5min-30min Conclusion Algorithm Predator - Pro synthesizes academic research from market microstructure theory, reinforcement learning, and multi-armed bandit algorithms. Unlike typical indicator mashups, this system implements 15 mathematically rigorous bandit algorithms, deploys a complete RL stack (Q-Learning, TD(λ), Policy Gradient), integrates institutional microstructure metrics (VPIN, Kyle's Lambda), adapts continuously through dual-layer memory and meta-learning, and provides full transparency on approximations and limitations. The system is designed for serious algorithmic traders who understand that no indicator is perfect, but through proper machine learning, we can build systems that improve over time and adapt to changing markets without manual intervention. Use responsibly. Risk disclosure applies. Past performance ≠ future results. Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® Indikator
von DskyzInvestments
2
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® Indikator
von MisinkoMaster
Average Daily Range DashboardThis script displays a non-intrusive ADR (Average Daily Range) dashboard designed to assist traders in monitoring real-time range expansion throughout the trading session. It compares the current day's high-low range to the average daily range calculated over a user-defined number of previous completed days (default: 5). The tool provides a numerical ADR score (0–5) based on how much of the average daily range has been filled. It also includes optional visual cues and narrative descriptions to help contextualize current price behavior. 📘 Key Features: Calculates ADR using fully completed daily bars (excluding the current session) Tracks the current session’s intraday range live (high to low) Outputs a score from 0 (low range expansion) to 5 (ADR fully filled or exceeded) Optional alerts when ADR thresholds are crossed (e.g., 60%, 100%) Displays optional debug values: ADR value, today’s range, session high/low Customizable table position, size, colors, and visibility settings 🧮 Formula Transparency: ADR = Simple Moving Average of (Daily High - Low) over the last N completed days Intraday Range = Real-time (Session High - Session Low) ADR Score is derived by comparing current range to ADR: score = floor((sessionRange / adr) * 5), capped at 5 ⚠️ Disclaimer: This tool does not provide buy/sell signals, trading advice, or predictive forecasts. It is intended for educational and informational purposes only. Users should independently verify all data and apply their own analysis. Past performance of any range behavior is not indicative of future results.
Pine Script® Indikator
von originalsaucemaker
1