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Langlands-Operadic Möbius Vortex (LOMV)Langlands-Operadic Möbius Vortex (LOMV) Where Pure Mathematics Meets Market Reality A Revolutionary Synthesis of Number Theory, Category Theory, and Market Dynamics 🎓 THEORETICAL FOUNDATION The Langlands-Operadic Möbius Vortex represents a groundbreaking fusion of three profound mathematical frameworks that have never before been combined for market analysis: The Langlands Program: Harmonic Analysis in Markets Developed by Robert Langlands (Fields Medal recipient), the Langlands Program creates bridges between number theory, algebraic geometry, and harmonic analysis. In our indicator: L-Function Implementation: - Utilizes the Möbius function μ(n) for weighted price analysis - Applies Riemann zeta function convergence principles - Calculates quantum harmonic resonance between -2 and +2 - Measures deep mathematical patterns invisible to traditional analysis The L-Function core calculation employs: L_sum = Σ(return_val × μ(n) × n^(-s)) Where s is the critical strip parameter (0.5-2.5), controlling mathematical precision and signal smoothness. Operadic Composition Theory: Multi-Strategy Democracy Category theory and operads provide the mathematical framework for composing multiple trading strategies into a unified signal. This isn't simple averaging - it's mathematical composition using: Strategy Composition Arity (2-5 strategies): - Momentum analysis via RSI transformation - Mean reversion through Bollinger Band mathematics - Order Flow Polarity Index (revolutionary T3-smoothed volume analysis) - Trend detection using Directional Movement - Higher timeframe momentum confirmation Agreement Threshold System: Democratic voting where strategies must reach consensus before signal generation. This prevents false signals during market uncertainty. Möbius Function: Number Theory in Action The Möbius function μ(n) forms the mathematical backbone: - μ(n) = 1 if n is a square-free positive integer with even number of prime factors - μ(n) = -1 if n is a square-free positive integer with odd number of prime factors - μ(n) = 0 if n has a squared prime factor This creates oscillating weights that reveal hidden market periodicities and harmonic structures. 🔧 COMPREHENSIVE INPUT SYSTEM Langlands Program Parameters Modular Level N (5-50, default 30): Primary lookback for quantum harmonic analysis. Optimized by timeframe: - Scalping (1-5min): 15-25 - Day Trading (15min-1H): 25-35 - Swing Trading (4H-1D): 35-50 - Asset-specific: Crypto 15-25, Stocks 30-40, Forex 35-45 L-Function Critical Strip (0.5-2.5, default 1.5): Controls Riemann zeta convergence precision: - Higher values: More stable, smoother signals - Lower values: More reactive, catches quick moves - High frequency: 0.8-1.2, Medium: 1.3-1.7, Low: 1.8-2.3 Frobenius Trace Period (5-50, default 21): Galois representation lookback for price-volume correlation: - Measures harmonic relationships in market flows - Scalping: 8-15, Day Trading: 18-25, Swing: 25-40 HTF Multi-Scale Analysis: Higher timeframe context prevents trading against major trends: - Provides market bias and filters signals - Improves win rates by 15-25% through trend alignment Operadic Composition Parameters Strategy Composition Arity (2-5, default 4): Number of algorithms composed for final signal: - Conservative: 4-5 strategies (higher confidence) - Moderate: 3-4 strategies (balanced approach) - Aggressive: 2-3 strategies (more frequent signals) Category Agreement Threshold (2-5, default 3): Democratic voting minimum for signal generation: - Higher agreement: Fewer but higher quality signals - Lower agreement: More signals, potential false positives Swiss-Cheese Mixing (0.1-0.5, default 0.382): Golden ratio φ⁻¹ based blending of trend factors: - 0.382 is φ⁻¹, optimal for natural market fractals - Higher values: Stronger trend following - Lower values: More contrarian signals OFPI Configuration: - OFPI Length (5-30, default 14): Order Flow calculation period - T3 Smoothing (3-10, default 5): Advanced exponential smoothing - T3 Volume Factor (0.5-1.0, default 0.7): Smoothing aggressiveness control Unified Scoring System Component Weights (sum ≈ 1.0): - L-Function Weight (0.1-0.5, default 0.3): Mathematical harmony emphasis - Galois Rank Weight (0.1-0.5, default 0.2): Market structure complexity - Operadic Weight (0.1-0.5, default 0.3): Multi-strategy consensus - Correspondence Weight (0.1-0.5, default 0.2): Theory-practice alignment Signal Threshold (0.5-10.0, default 5.0): Quality filter producing: - 8.0+: EXCEPTIONAL signals only - 6.0-7.9: STRONG signals - 4.0-5.9: MODERATE signals - 2.0-3.9: WEAK signals 🎨 ADVANCED VISUAL SYSTEM Multi-Dimensional Quantum Aura Bands Five-layer resonance field showing market energy: - Colors: Theme-matched gradients (Quantum purple, Holographic cyan, etc.) - Expansion: Dynamic based on score intensity and volatility - Function: Multi-timeframe support/resistance zones Morphism Flow Portals Category theory visualization showing market topology: - Green/Cyan Portals: Bullish mathematical flow - Red/Orange Portals: Bearish mathematical flow - Size/Intensity: Proportional to signal strength - Recursion Depth (1-8): Nested patterns for flow evolution Fractal Grid System Dynamic support/resistance with projected L-Scores: - Multiple Timeframes: 10, 20, 30, 40, 50-period highs/lows - Smart Spacing: Prevents level overlap using ATR-based minimum distance - Projections: Estimated signal scores when price reaches levels - Usage: Precise entry/exit timing with mathematical confirmation Wick Pressure Analysis Rejection level prediction using candle mathematics: - Upper Wicks: Selling pressure zones (purple/red lines) - Lower Wicks: Buying pressure zones (purple/green lines) - Glow Intensity (1-8): Visual emphasis and line reach - Application: Confluence with fractal grid creates high-probability zones Regime Intensity Heatmap Background coloring showing market energy: - Black/Dark: Low activity, range-bound markets - Purple Glow: Building momentum and trend development - Bright Purple: High activity, strong directional moves - Calculation: Combines trend, momentum, volatility, and score intensity Six Professional Themes - Quantum: Purple/violet for general trading and mathematical focus - Holographic: Cyan/magenta optimized for cryptocurrency markets - Crystalline: Blue/turquoise for conservative, stability-focused trading - Plasma: Gold/magenta for high-energy volatility trading - Cosmic Neon: Bright neon colors for maximum visibility and aggressive trading 📊 INSTITUTIONAL-GRADE DASHBOARD Unified AI Score Section - Total Score (-10 to +10): Primary decision metric - >5: Strong bullish signals - <-5: Strong bearish signals - Quality ratings: EXCEPTIONAL > STRONG > MODERATE > WEAK - Component Analysis: Individual L-Function, Galois, Operadic, and Correspondence contributions Order Flow Analysis Revolutionary OFPI integration: - OFPI Value (-100% to +100%): Real buying vs selling pressure - Visual Gauge: Horizontal bar chart showing flow intensity - Momentum Status: SHIFTING, ACCELERATING, STRONG, MODERATE, or WEAK - Trading Application: Flow shifts often precede major moves Signal Performance Tracking - Win Rate Monitoring: Real-time success percentage with emoji indicators - Signal Count: Total signals generated for frequency analysis - Current Position: LONG, SHORT, or NONE with P&L tracking - Volatility Regime: HIGH, MEDIUM, or LOW classification Market Structure Analysis - Möbius Field Strength: Mathematical field oscillation intensity - CHAOTIC: High complexity, use wider stops - STRONG: Active field, normal position sizing - MODERATE: Balanced conditions - WEAK: Low activity, consider smaller positions - HTF Trend: Higher timeframe bias (BULL/BEAR/NEUTRAL) - Strategy Agreement: Multi-algorithm consensus level Position Management When in trades, displays: - Entry Price: Original signal price - Current P&L: Real-time percentage with risk level assessment - Duration: Bars in trade for timing analysis - Risk Level: HIGH/MEDIUM/LOW based on current exposure 🚀 SIGNAL GENERATION LOGIC Balanced Long/Short Architecture The indicator generates signals through multiple convergent pathways: Long Entry Conditions: - Score threshold breach with algorithmic agreement - Strong bullish order flow (OFPI > 0.15) with positive composite signal - Bullish pattern recognition with mathematical confirmation - HTF trend alignment with momentum shifting - Extreme bullish OFPI (>0.3) with any positive score Short Entry Conditions: - Score threshold breach with bearish agreement - Strong bearish order flow (OFPI < -0.15) with negative composite signal - Bearish pattern recognition with mathematical confirmation - HTF trend alignment with momentum shifting - Extreme bearish OFPI (<-0.3) with any negative score Exit Logic: - Score deterioration below continuation threshold - Signal quality degradation - Opposing order flow acceleration - 10-bar minimum between signals prevents overtrading ⚙️ OPTIMIZATION GUIDELINES Asset-Specific Settings Cryptocurrency Trading: - Modular Level: 15-25 (capture volatility) - L-Function Precision: 0.8-1.3 (reactive to price swings) - OFPI Length: 10-20 (fast correlation shifts) - Cascade Levels: 5-7, Theme: Holographic Stock Index Trading: - Modular Level: 25-35 (balanced trending) - L-Function Precision: 1.5-1.8 (stable patterns) - OFPI Length: 14-20 (standard correlation) - Cascade Levels: 4-5, Theme: Quantum Forex Trading: - Modular Level: 35-45 (smooth trends) - L-Function Precision: 1.6-2.1 (high smoothing) - OFPI Length: 18-25 (disable volume amplification) - Cascade Levels: 3-4, Theme: Crystalline Timeframe Optimization Scalping (1-5 minute charts): - Reduce all lookback parameters by 30-40% - Increase L-Function precision for noise reduction - Enable all visual elements for maximum information - Use Small dashboard to save screen space Day Trading (15 minute - 1 hour): - Use default parameters as starting point - Adjust based on market volatility - Normal dashboard provides optimal information density - Focus on OFPI momentum shifts for entries Swing Trading (4 hour - Daily): - Increase lookback parameters by 30-50% - Higher L-Function precision for stability - Large dashboard for comprehensive analysis - Emphasize HTF trend alignment 🏆 ADVANCED TRADING STRATEGIES The Mathematical Confluence Method 1. Wait for Fractal Grid level approach 2. Confirm with projected L-Score > threshold 3. Verify OFPI alignment with direction 4. Enter on portal signal with quality ≥ STRONG 5. Exit on score deterioration or opposing flow The Regime Trading System 1. Monitor Aether Flow background intensity 2. Trade aggressively during bright purple periods 3. Reduce position size during dark periods 4. Use Möbius Field strength for stop placement 5. Align with HTF trend for maximum probability The OFPI Momentum Strategy 1. Watch for momentum shifting detection 2. Confirm with accelerating flow in direction 3. Enter on immediate portal signal 4. Scale out at Fibonacci levels 5. Exit on flow deceleration or reversal ⚠️ RISK MANAGEMENT INTEGRATION Mathematical Position Sizing - Use Galois Rank for volatility-adjusted sizing - Möbius Field strength determines stop width - Fractal Dimension guides maximum exposure - OFPI momentum affects entry timing Signal Quality Filtering - Trade only STRONG or EXCEPTIONAL quality signals - Increase position size with higher agreement levels - Reduce risk during CHAOTIC Möbius field periods - Respect HTF trend alignment for directional bias 🔬 DEVELOPMENT JOURNEY Creating the LOMV was an extraordinary mathematical undertaking that pushed the boundaries of what's possible in technical analysis. This indicator almost didn't happen. The theoretical complexity nearly proved insurmountable. The Mathematical Challenge Implementing the Langlands Program required deep research into: - Number theory and the Möbius function - Riemann zeta function convergence properties - L-function analytical continuation - Galois representations in finite fields The mathematical literature spans decades of pure mathematics research, requiring translation from abstract theory to practical market application. The Computational Complexity Operadic composition theory demanded: - Category theory implementation in Pine Script - Multi-dimensional array management for strategy composition - Real-time democratic voting algorithms - Performance optimization for complex calculations The Integration Breakthrough Bringing together three disparate mathematical frameworks required: - Novel approaches to signal weighting and combination - Revolutionary Order Flow Polarity Index development - Advanced T3 smoothing implementation - Balanced signal generation preventing directional bias Months of intensive research culminated in breakthrough moments when the mathematics finally aligned with market reality. The result is an indicator that reveals market structure invisible to conventional analysis while maintaining practical trading utility. 🎯 PRACTICAL IMPLEMENTATION Getting Started 1. Apply indicator with default settings 2. Select appropriate theme for your markets 3. Observe dashboard metrics during different market conditions 4. Practice signal identification without trading 5. Gradually adjust parameters based on observations Signal Confirmation Process - Never trade on score alone - verify quality rating - Confirm OFPI alignment with intended direction - Check fractal grid level proximity for timing - Ensure Möbius field strength supports position size - Validate against HTF trend for bias confirmation Performance Monitoring - Track win rate in dashboard for strategy assessment - Monitor component contributions for optimization - Adjust threshold based on desired signal frequency - Document performance across different market regimes 🌟 UNIQUE INNOVATIONS 1. First Integration of Langlands Program mathematics with practical trading 2. Revolutionary OFPI with T3 smoothing and momentum detection 3. Operadic Composition using category theory for signal democracy 4. Dynamic Fractal Grid with projected L-Score calculations 5. Multi-Dimensional Visualization through morphism flow portals 6. Regime-Adaptive Background showing market energy intensity 7. Balanced Signal Generation preventing directional bias 8. Professional Dashboard with institutional-grade metrics 📚 EDUCATIONAL VALUE The LOMV serves as both a practical trading tool and an educational gateway to advanced mathematics. Traders gain exposure to: - Pure mathematics applications in markets - Category theory and operadic composition - Number theory through Möbius function implementation - Harmonic analysis via L-function calculations - Advanced signal processing through T3 smoothing ⚖️ RESPONSIBLE USAGE This indicator represents advanced mathematical research applied to market analysis. While the underlying mathematics are rigorously implemented, markets remain inherently unpredictable. Key Principles: - Use as part of comprehensive trading strategy - Implement proper risk management at all times - Backtest thoroughly before live implementation - Understand that past performance does not guarantee future results - Never risk more than you can afford to lose The mathematics reveal deep market structure, but successful trading requires discipline, patience, and sound risk management beyond any indicator. 🔮 CONCLUSION The Langlands-Operadic Möbius Vortex represents a quantum leap forward in technical analysis, bringing PhD-level pure mathematics to practical trading while maintaining visual elegance and usability. From the harmonic analysis of the Langlands Program to the democratic composition of operadic theory, from the number-theoretic precision of the Möbius function to the revolutionary Order Flow Polarity Index, every component works in mathematical harmony to reveal the hidden order within market chaos. This is more than an indicator - it's a mathematical lens that transforms how you see and understand market structure. Trade with mathematical precision. Trade with the LOMV. *"Mathematics is the language with which God has written the universe." - Galileo Galilei* *In markets, as in nature, profound mathematical beauty underlies apparent chaos. The LOMV reveals this hidden order.* — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® göstergesi
DskyzInvestments tarafından
27
Time-Based Fair Value Gaps (FVG) with Inversions (iFVG)Overview The Time-Based Fair Value Gaps (FVG) with Inversions (iFVG) (ICT/SMT) indicator is a specialized tool designed for traders using Inner Circle Trader (ICT) methodologies. Inspired by LuxAlgo's Fair Value Gap indicator, this script introduces significant enhancements by integrating ICT principles, focusing on precise time-based FVG detection, inversion tracking, and retest signals tailored for institutional trading strategies. Unlike LuxAlgo’s general FVG approach, this indicator filters FVGs within customizable 10-minute windows aligned with ICT’s macro timeframes and incorporates ICT-specific concepts like mitigation, liquidity grabs, and session-based gap prioritization. This tool is optimized for 1–5 minute charts, though probably best for 1 minute charts, identifying bullish and bearish FVGs, tracking their mitigation into inverted FVGs (iFVGs) as key support/resistance zones, and generating retest signals with customizable “Close” or “Wick” confirmation. Features like ATR-based filtering, optional FVG labels, mitigation removal, and session-specific FVG detection (e.g., first FVG in AM/PM sessions) make it a powerful tool for ICT traders. Originality and Improvements While inspired by LuxAlgo’s FVG indicator (credit to LuxAlgo for their foundational work), this script significantly extends the original concept by: 1. Time-Based FVG Detection: Unlike LuxAlgo’s continuous FVG identification, this script filters FVGs within user-defined 10-minute windows each hour (:00–:10, :10–:20, etc.), aligning with ICT’s emphasis on specific periods of institutional activity, such as hourly opens/closes or kill zones (e.g., New York 7:00–11:00 AM EST). This ensures FVGs are relevant to high-probability ICT setups. 2. Session-Specific First FVG Option: A unique feature allows traders to display only the first FVG in ICT-defined AM (9:30–10:00 AM EST) or PM (1:30–2:00 PM EST) sessions, reflecting ICT’s focus on initial market imbalances during key liquidity events. 3. ICT-Driven Mitigation and Inversion Logic: The script tracks FVG mitigation (when price closes through a gap) and converts mitigated FVGs into iFVGs, which serve as ICT-style support/resistance zones. This aligns with ICT’s view that mitigated gaps become critical reversal points, unlike LuxAlgo’s simpler gap display. 4. Customizable Retest Signals: Retest signals for iFVGs are configurable for “Close” (conservative, requiring candle body confirmation) or “Wick” (faster, using highs/lows), catering to ICT traders’ need for precise entry timing during liquidity grabs or Judas swings. 5. ATR Filtering and Mitigation Removal: An optional ATR filter ensures only significant FVGs are displayed, reducing noise, while mitigation removal declutters the chart by removing filled gaps, aligning with ICT’s principle that mitigated gaps lose relevance unless inverted. 6. Timezone and Timeframe Safeguards: A timezone offset setting aligns FVG detection with EST for ICT’s New York-centric strategies, and a timeframe warning alerts users to avoid ≥1-hour charts, ensuring accuracy in time-based filtering. These enhancements make the script a distinct tool that builds on LuxAlgo’s foundation while offering ICT traders a tailored, high-precision solution. How It Works FVG Detection FVGs are identified when a candle’s low is higher than the high of two candles prior (bullish FVG) or a candle’s high is lower than the low of two candles prior (bearish FVG). Detection is restricted to: • User-selected 10-minute windows (e.g., :00–:10, :50–:60) to capture ICT-relevant periods like hourly transitions. • AM/PM session first FVGs (if enabled), focusing on 9:30–10:00 AM or 1:30–2:00 PM EST for key market opens. An optional ATR filter (default: 0.25× ATR) ensures only gaps larger than the threshold are displayed, prioritizing significant imbalances. Mitigation and Inversion When price closes through an FVG (e.g., below a bullish FVG’s bottom), the FVG is mitigated and becomes an iFVG, plotted as a support/resistance zone. iFVGs are critical in ICT for identifying reversal points where institutional orders accumulate. Retest Signals The script generates signals when price retests an iFVG: • Close: Triggers when the candle body confirms the retest (conservative, lower noise). • Wick: Triggers when the candle’s high/low touches the iFVG (faster, higher sensitivity). Signals are visualized with triangular markers (▲ for bullish, ▼ for bearish) and can trigger alerts. Visualization • FVGs: Displayed as colored boxes (green for bullish, red for bearish) with optional “Bull FVG”/“Bear FVG” labels. • iFVGs: Shown as extended boxes with dashed midlines, limited to the user-defined number of recent zones (default: 5). • Mitigation Removal: Mitigated FVGs/iFVGs are removed (if enabled) to keep the chart clean. How to Use Recommended Settings • Timeframe: Use 1–5 minute charts for precision, avoiding ≥1-hour timeframes (a warning label appears if misconfigured). • Time Windows: Enable :00–:10 and :50–:60 for hourly open/close FVGs, or use the “Show only 1st presented FVG” option for AM/PM session focus. • ATR Filter: Keep enabled (multiplier 0.25–0.5) for significant gaps; disable on 1-minute charts for more FVGs during volatility. • Signal Preference: Use “Close” for conservative entries, “Wick” for aggressive setups. • Timezone Offset: Set to -5 for EST (or -4 for EDT) to align with ICT’s New York session. Trading Strategy 1. Macro Timeframes: Focus on New York (7:00–11:00 AM EST) or London (2:00–5:00 AM EST) kill zones for high institutional activity. 2. FVG Entries: Trade bullish FVGs as support in uptrends or bearish FVGs as resistance in downtrends, especially in :00–:10 or :50–:60 windows. 3. iFVG Retests: Enter on retest signals (▲/▼) during liquidity grabs or Judas swings, using “Close” for confirmation or “Wick” for speed. 4. Session FVGs: Use the “Show only 1st presented FVG” option to target the first gap in AM/PM sessions, often tied to ICT’s market maker algorithms. 5. Risk Management: Combine with ICT concepts like order blocks or breaker blocks for confluence, and set stops beyond FVG/iFVG boundaries. Alerts Set alerts for: • “Bullish FVG Detected”/“Bearish FVG Detected”: New FVGs in selected windows. • “Bullish Signal”/“Bearish Signal”: iFVG retest confirmations. Settings Description • Show Last (1–100, default: 5): Number of recent iFVGs to display. Lower values reduce clutter. • Show only 1st presented FVG : Limits FVGs to the first in 9:30–10:00 AM or 1:30–2:00 PM EST sessions (overrides time window checkboxes). • Time Window Checkboxes: Enable/disable FVG detection in 10-minute windows (:00–:10, :10–:20, etc.). All enabled by default. • Signal Preference: “Close” (default) or “Wick” for iFVG retest signals. • Use ATR Filter: Enables ATR-based size filtering (default: true). • ATR Multiplier (0–∞, default: 0.25): Sets FVG size threshold (higher values = larger gaps). • Remove Mitigated FVGs: Removes filled FVGs/iFVGs (default: true). • Show FVG Labels: Displays “Bull FVG”/“Bear FVG” labels (default: true). • Timezone Offset (-12 to 12, default: -5): Aligns time windows with EST. • Colors: Customize bullish (green), bearish (red), and midline (gray) colors. Why Use This Indicator? This indicator empowers ICT traders with a tool that goes beyond generic FVG detection, offering precise, time-filtered gaps and inversion tracking aligned with institutional trading principles. By focusing on ICT’s macro timeframes, session-specific imbalances, and customizable signal logic, it provides a clear edge for scalping, swing trading, or reversal setups in high-liquidity markets.
Pine Script® göstergesi
BigGuavaTrading tarafından
1
HL2 Moving Average with BandsThis indicator is designed to assist traders in identifying potential trade entries and exits for S&P 500 (ES) and Nasdaq-100 (NQ) futures. It calculates a Simple Moving Average (SMA) based on the HL2 value (average of high and low prices) of the current candle over a user-defined lookback period (default: 200 periods). The indicator plots this SMA as a blue line, providing a smoothed reference for price trends. Additionally, it includes upper and lower bands calculated as a percentage (default: 0.5%) above and below the SMA, plotted as green and red lines, respectively. These bands act as dynamic thresholds to identify overbought or oversold conditions. The indicator generates trade signals based on price action relative to these bands: Long Entry: A green upward triangle is plotted below the candle when the close crosses above the upper band, signaling a potential buy. Close Long: A red square is plotted above the candle when the close crosses back below the upper band, indicating an exit for the long position. Short Entry: A red downward triangle is plotted above the candle when the close crosses below the lower band, signaling a potential sell. Close Short: A green square is plotted below the candle when the close crosses back above the lower band, indicating an exit for the short position. The script is customizable, allowing users to adjust the SMA length and band percentage to suit their trading style or market conditions. It is plotted as an overlay on the price chart for easy integration with other technical analysis tools. Recommended Time Frame and Settings for Trading S&P 500 and Nasdaq-100 Futures Based on research and market dynamics for S&P 500 (ES) and Nasdaq-100 (NQ) futures, the 5-minute chart is recommended as the optimal time frame for day trading with this indicator. This time frame strikes a balance between capturing intraday trends and filtering out excessive noise, which is critical for futures trading due to their high volatility and leverage. The 5-minute chart aligns well with periods of high liquidity and volatility, such as the U.S. market open (9:30 AM–11:00 AM EST) and the afternoon session (2:00 PM–4:00 PM EST), when institutional traders are most active. Why 5-minute? It allows traders to react to short-term price movements while avoiding the rapid fluctuations of 1-minute charts, which can be prone to false signals in choppy markets. It also provides enough data points to make the SMA and bands meaningful without the lag associated with longer time frames like 15-minute or hourly charts. Recommended Settings SMA Length: Set to 200 periods. This longer lookback period smooths the HL2 data, reducing noise and providing a reliable trend reference for the 5-minute chart. A 200-period SMA helps identify significant trend shifts without being overly sensitive to minor price fluctuations. Band Percentage: 0.5% is more suitable for the volatility of ES and NQ futures on a 5-minute chart, as it generates fewer but higher-probability signals. Wider bands (e.g., 1%) may miss short-term opportunities, while narrower bands (e.g., 0.1%) may produce excessive false signals. Trading Session Recommendations Futures markets for ES and NQ are open nearly 24 hours (Sunday 6:00 PM EST to Friday 5:00 PM EST, with a daily break from 4:00 PM–5:00 PM EST), but not all hours are equally optimal due to varying liquidity and volatility. The best times to trade with this indicator are: U.S. Market Open (9:30 AM–11:00 AM EST): This period is characterized by high volume and volatility, driven by the opening of U.S. equity markets and economic data releases (e.g., 8:30 AM EST reports like CPI or GDP). The indicator’s signals are more reliable during this window due to strong order flow and price momentum. Afternoon Session (2:00 PM–4:00 PM EST): After the lunchtime lull, volume picks up as institutional traders return, and news or FOMC announcements often drive price action. The indicator can capture breakout moves as prices test the upper or lower bands. Pre-Market (7:30 AM–9:30 AM EST): For traders comfortable with lower liquidity, this period can offer opportunities, especially around 8:30 AM EST economic releases. However, use tighter risk management due to wider spreads and potential volatility spikes. Additional Tips Avoid Low-Volume Periods: Steer clear of trading during low-liquidity hours, such as the overnight session (11:00 PM–3:00 AM EST), when spreads widen and price movements can be erratic, leading to false signals from the indicator. Combine with Other Tools: Enhance the indicator’s effectiveness by pairing it with support/resistance levels, Fibonacci retracements, or volume analysis to confirm signals. For example, a long entry signal above the upper band is stronger if it coincides with a breakout above a key resistance level. Risk Management: Given the leverage in futures (e.g., Micro E-mini contracts require ~$1,200 margin for ES), use tight stop-losses (e.g., below the lower band for longs or above the upper band for shorts) to manage risk. Aim for a risk-reward ratio of at least 1:2. Test Settings: Backtest the indicator on a demo account to optimize the SMA length and band percentage for your specific trading style and risk tolerance. Micro E-mini contracts (MES for S&P 500, MNQ for Nasdaq-100) are ideal for testing due to their lower capital requirements. Why These Settings and Time Frame? The 5-minute chart with a 200-period SMA and 0.5% bands is tailored for the volatility and liquidity of ES and NQ futures during peak trading hours. The longer SMA period ensures the indicator captures meaningful trends, while the 0.5% bands are tight enough to signal actionable breakouts but wide enough to avoid excessive whipsaws. Trading during high-volume sessions maximizes the likelihood of valid signals, as institutional participation drives clearer price action. By focusing on these settings and time frames, traders can leverage the indicator to capitalize on the dynamic price movements of S&P 500 and Nasdaq-100 futures while managing the inherent risks of these markets.
Pine Script® göstergesi
BigGuavaTrading tarafından
Green*DiamondGreen*Diamond (GD1) Unleash Dynamic Trading Signals with Volatility and Momentum Overview GreenDiamond is a versatile overlay indicator designed for traders seeking actionable buy and sell signals across various markets and timeframes. Combining Volatility Bands (VB) bands, Consolidation Detection, MACD, RSI, and a unique Ribbon Wave, it highlights high-probability setups while filtering out noise. With customizable signals like Green-Yellow Buy, Pullback Sell, and Inverse Pullback Buy, plus vibrant candle and volume visuals, GreenDiamond adapts to your trading style—whether you’re scalping, day trading, or swing trading. Key Features Volatility Bands (VB): Plots dynamic upper and lower bands to identify breakouts or reversals, with toggleable buy/sell signals outside consolidation zones. Consolidation Detection: Marks low-range periods to avoid choppy markets, ensuring signals fire during trending conditions. MACD Signals: Offers flexible buy/sell conditions (e.g., cross above signal, above zero, histogram up) with RSI divergence integration for precision. RSI Filter: Enhances signals with customizable levels (midline, oversold/overbought) and bullish divergence detection. Ribbon Wave: Visualizes trend strength using three EMAs, colored by MACD and RSI for intuitive momentum cues. Custom Signals: Includes Green-Yellow Buy, Pullback Sell, and Inverse Pullback Buy, with limits on consecutive signals to prevent overtrading. Candle & Volume Styling: Blends MACD/RSI colors on candles and scales volume bars to highlight momentum spikes. Alerts: Set up alerts for VB signals, MACD crosses, Green*Diamond signals, and custom conditions to stay on top of opportunities. How It Works Green*Diamond integrates multiple indicators to generate signals: Volatility Bands: Calculates bands using a pivot SMA and standard deviation. Buy signals trigger on crossovers above the lower band, sell signals on crossunders below the upper band (if enabled). Consolidation Filter: Suppresses signals when candle ranges are below a threshold, keeping you out of flat markets. MACD & RSI: Combines MACD conditions (e.g., cross above signal) with RSI filters (e.g., above midline) and optional volume spikes for robust signals. Custom Logic: Green-Yellow Buy uses MACD bullishness, Pullback Sell targets retracements, and Inverse Pullback Buy catches reversals after downmoves—all filtered to avoid consolidation. Visuals: Ribbon Wave shows trend direction, candles blend momentum colors, and volume bars scale dynamically to confirm signals. Settings Volatility Bands Settings: VB Lookback Period (20): Adjust to 10–15 for faster markets (e.g., 1-minute scalping) or 25–30 for daily charts. Upper/Lower Band Multiplier (1.0): Increase to 1.5–2.0 for wider bands in volatile stocks like AEHL; decrease to 0.5 for calmer markets. Show Volatility Bands: Toggle off to reduce chart clutter. Use VB Signals: Enable for breakout-focused trades; disable to focus on Green*Diamond signals. Consolidation Settings: Consolidation Lookback (14): Set to 5–10 for small caps (e.g., AEHL) to catch quick consolidations; 20 for higher timeframes. Range Threshold (0.5): Lower to 0.3 for stricter filtering in choppy markets; raise to 0.7 for looser signals. MACD Settings: Fast/Slow Length (12/26): Shorten to 8/21 for scalping; extend to 15/34 for swing trading. Signal Smoothing (9): Reduce to 5 for faster signals; increase to 12 for smoother trends. Buy/Sell Signal Options: Choose “Cross Above Signal” for classic MACD; “Histogram Up” for momentum plays. Use RSI Div + MACD Cross: Enable for high-probability reversal signals. RSI Settings: RSI Period (14): Drop to 10 for 1-minute charts; raise to 20 for daily. Filter Level (50): Set to 55 for stricter buys; 45 for sells. Overbought/Oversold (70/30): Tighten to 65/35 for small caps; widen to 75/25 for indices. RSI Buy/Sell Options: Select “Bullish Divergence” for reversals; “Cross Above Oversold” for momentum. Color Settings: Adjust bullish/bearish colors for visibility (e.g., brighter green/red for dark themes). Border Thickness (1): Increase to 2–3 for clearer candle outlines. Volume Settings: Volume Average Length (20): Shorten to 10 for scalping; extend to 30 for swing trades. Volume Multiplier (2.0): Raise to 3.0 for AEHL’s volume surges; lower to 1.5 for steady stocks. Bar Height (10%): Increase to 15% for prominent bars; decrease to 5% to reduce clutter. Ribbon Settings: EMA Periods (10/20/30): Tighten to 5/10/15 for scalping; widen to 20/40/60 for trends. Color by MACD/RSI: Disable for simpler visuals; enable for dynamic momentum cues. Gradient Fill: Toggle on for trend clarity; off for minimalism. Custom Signals: Enable Green-Yellow Buy: Use for momentum confirmation; limit to 1–2 signals to avoid spam. Pullback/Inverse Pullback % (50): Set to 30–40% for small caps; 60–70% for indices. Max Buy Signals (1): Increase to 2–3 for active markets; keep at 1 for discipline. Tips and Tricks Scalping Small Caps (e.g., AEHL): Use 1-minute charts with VB Lookback = 10, Consolidation Lookback = 5, and Volume Multiplier = 3.0 to catch $0.10–$0.20 moves. Enable Green-Yellow Buy and Inverse Pullback Buy for quick entries; disable VB Signals to focus on Green*Diamond logic. Pair with SMC+ green boxes (if you use them) for reversal confirmation. Day Trading: Try 5-minute charts with MACD Fast/Slow = 8/21 and RSI Period = 10. Enable RSI Divergence + MACD Cross for high-probability setups; set Max Buy Signals = 2. Watch for volume bars turning yellow to confirm entries. Swing Trading: Use daily charts with VB Lookback = 30, Ribbon EMAs = 20/40/60. Enable Pullback Sell (60%) to exit after rallies; disable RSI Color for cleaner candles. Check Ribbon Wave gradient for trend strength—bright green signals strong bulls. Avoiding Noise: Increase Consolidation Threshold to 0.7 on volatile days to skip false breakouts. Disable Ribbon Wave or Volume Bars if the chart feels crowded. Limit Max Buy Signals to 1 for disciplined trading. Alert Setup: In TradingView’s Alerts panel, select: “GD Buy Signal” for standard entries. “RSI Div + MACD Cross Buy” for reversals. “VB Buy Signal” for breakout plays. Set to “Once Per Bar Close” for confirmed signals; “Once Per Bar” for scalping. Backtesting: Replay on small caps ( Float < 5M, Price $0.50–$5) to test signals. Focus on “GD Buy Signal” with yellow volume bars and green Ribbon Wave. Avoid signals during gray consolidation squares unless paired with RSI Divergence. Usage Notes Markets: Works on stocks, forex, crypto, and indices. Best for volatile assets (e.g., small-cap stocks, BTCUSD). Timeframes: Scalping (1–5 minutes), day trading (15–60 minutes), or swing trading (daily). Adjust settings per timeframe. Risk Management: Combine with stop-losses (e.g., 1% risk, $0.05 below AEHL entry) and take-profits (3–5%). Customization: Tweak inputs to match your strategy—experiment in replay to find your sweet spot. Disclaimer Green*Diamond is a technical tool to assist with trade identification, not a guarantee of profits. Trading involves risks, and past performance doesn’t predict future results. Always conduct your own analysis, manage risk, and test settings before live trading. Feedback Love Green*Diamond? Found a killer setup?
Pine Script® göstergesi
kwsatterfield1 tarafından
1
RSI3M3+ v.1.8RSI3M3+ v.1.8 Indicator This script is an advanced trading indicator based on Walter J. Bressert's cycle analysis methodology, combined with an RSI (Relative Strength Index) variation. Let me break it down and explain how it works. Core Concepts The RSI3M3+ indicator combines: A short-term RSI (3-period) A 3-period moving average to smooth the RSI Bressert's cycle analysis principles to identify optimal trading points RSI3M3+ Indicator VisualizationImage Walter J. Bressert's Cycle Analysis Concepts Walter Bressert was a pioneer in cycle analysis trading who believed markets move in cyclical patterns that can be measured and predicted. His key principles integrated into this indicator include: Trading Cycles: Markets move in cycles with measurable time spans from low to low Timing Bands: Projected periods when the next cyclical low or high is anticipated Oscillator Use: Using oscillators like RSI to confirm cycle position Entry/Exit Rules: Specific rules for trade entry and exit based on cycle position Key Parameters in the Script Basic RSI Parameters Required bars: Minimum number of bars needed (default: 20) Overbought region: RSI level considered overbought (default: 70) Oversold region: RSI level considered oversold (default: 30) Bressert-Specific Parameters Cycle Detection Length: Lookback period for cycle identification (default: 30) Minimum/Maximum Cycle Length: Expected cycle duration in days (default: 15-30) Buy Line: Lower threshold for buy signals (default: 40) Sell Line: Upper threshold for sell signals (default: 60) How the Indicator Works RSI3M3 Calculation: Calculates a 3-period RSI (sRSI) Smooths it with a 3-period moving average (sMA) Cycle Detection: Identifies bottoms: When the RSI is below the buy line (40) and starting to turn up Identifies tops: When the RSI is above the sell line (60) and starting to turn down Records these points to calculate cycle lengths Timing Bands: Projects when the next cycle bottom or top should occur Creates visual bands on the chart showing these expected time windows Signal Generation: Buy signals occur when the RSI turns up from below the oversold level (30) Sell signals occur when the RSI turns down from above the overbought level (70) Enhanced by Bressert's specific timing rules Bressert's Five Trading Rules (Implemented in the Script) Cycle Timing: The low must be 15-30 market days from the previous Trading Cycle bottom Prior Top Validation: A Trading Cycle high must have occurred with the oscillator above 60 Oscillator Behavior: The oscillator must drop below 40 and turn up Entry Trigger: Entry is triggered by a rise above the price high of the upturn day Protective Stop: Place stop slightly below the Trading Cycle low (implemented as 99% of bottom price) How to Use the Indicator Reading the Chart Main Plot Area: Green line: 3-period RSI Red line: 3-period moving average of the RSI Horizontal bands: Oversold (30) and Overbought (70) regions Dotted lines: Buy line (40) and Sell line (60) Yellow vertical bands: Projected timing windows for next cycle bottom Signals: Green up arrows: Buy signals Red down arrows: Sell signals Trading Strategy For Buy Signals: Wait for the RSI to drop below the buy line (40) Look for an upturn in the RSI from below this level Enter the trade when price rises above the high of the upturn day Place a protective stop at 99% of the Trading Cycle low For Sell Signals: Wait for the RSI to rise above the sell line (60) Look for a downturn in the RSI from above this level Consider exiting or taking profits when a sell signal appears Alternative exit: When price moves below the low of the downturn day Cycle Timing Enhancement: Pay attention to the yellow timing bands Signals occurring within these bands have higher probability of success Signals outside these bands may be less reliable Practical Tips for Using RSI3M3+ Timeframe Selection: The indicator works best on daily charts for intermediate-term trading Can be used on weekly charts for longer-term position trading On intraday charts, adjust cycle lengths accordingly Market Applicability: Works well in trending markets with clear cyclical behavior Less effective in choppy, non-trending markets Consider additional indicators for trend confirmation Parameter Adjustment: Different markets may have different natural cycle lengths You may need to adjust the min/max cycle length parameters Higher volatility markets may need wider overbought/oversold levels Trade Management: Enter trades when all Bressert's conditions are met Use the protective stop as defined (99% of cycle low) Consider taking partial profits at the projected cycle high timing Advanced Techniques Multiple Timeframe Analysis: Confirm signals with the same indicator on higher timeframes Enter in the direction of the larger cycle when smaller and larger cycles align Divergence Detection: Look for price making new lows while RSI makes higher lows (bullish) Look for price making new highs while RSI makes lower highs (bearish) Confluence with Price Action: Combine with support/resistance levels Use with candlestick patterns for confirmation Consider volume confirmation of cycle turns This RSI3M3+ indicator combines the responsiveness of a short-term RSI with the predictive power of Bressert's cycle analysis, offering traders a sophisticated tool for identifying high-probability trading opportunities based on market cycles and momentum shifts. THANK YOU FOR PREVIOUS CODER THAT EFFORT TO CREATE THE EARLIER VERSION THAT MAKE WALTER J BRESSERT CONCEPT IN TRADINGVIEW @ADutchTourist
Pine Script® göstergesi
kromut76 tarafından
14
RSI Signal with filters by S.Kodirov📌 English RSI Signal with Multi-Timeframe Filters This TradingView indicator generates RSI-based buy and sell signals on the 15-minute timeframe with additional filtering from other timeframes (5M, 30M, 1M). 🔹 Signal Types: ✅ 15/5B & 15/5S – RSI 15M filtered by 5M ✅ 15/30/1B & 15/30/1S – RSI 15M filtered by 30M & 1M ✅ 15B & 15S – RSI 15M without filters 🔹 How It Works: Signals are displayed as colored triangles on the chart. Labels indicate the type of signal (e.g., 15/5B, 15S). Alerts notify users when a signal appears. 🚀 Best for short-term trading with RSI confirmation from multiple timeframes! 📌 Русский Индикатор RSI с мульти-таймфрейм фильтрами Этот индикатор для TradingView генерирует сигналы покупки и продажи на 15-минутном таймфрейме, используя фильтрацию с других таймфреймов (5M, 30M, 1M). 🔹 Типы сигналов: ✅ 15/5B & 15/5S – RSI 15M с фильтром 5M ✅ 15/30/1B & 15/30/1S – RSI 15M с фильтрами 30M и 1M ✅ 15B & 15S – RSI 15M без фильтров 🔹 Как это работает: Сигналы отображаются как цветные треугольники на графике. Подписи показывают тип сигнала (например, 15/5B, 15S). Алерты уведомляют трейдера о появлении сигнала. 🚀 Идеально для краткосрочной торговли с подтверждением RSI на нескольких таймфреймах! 📌 O'zbekcha Ko'p vaqt oralig‘idagi RSI signallari Ushbu TradingView indikatori 15 daqiqalik vaqt oralig‘ida RSI asosida sotib olish va sotish signallarini yaratadi. Bundan tashqari, boshqa vaqt oralig‘idagi (5M, 30M, 1M) RSI filtrlarini ham hisobga oladi. 🔹 Signal turlari: ✅ 15/5B & 15/5S – 5M bilan filtrlangan RSI 15M ✅ 15/30/1B & 15/30/1S – 30M va 1M bilan filtrlangan RSI 15M ✅ 15B & 15S – Filtrsiz RSI 15M 🔹 Qanday ishlaydi? Signallar rangli uchburchaklar shaklida ko‘rsatiladi. Yozuvlar signal turini ko‘rsatadi (masalan, 15/5B, 15S). Xabarnomalar yangi signal paydo bo‘lganda treyderni ogohlantiradi. 🚀 Ko‘p vaqt oralig‘ida RSI tasdig‘i bilan qisqa muddatli savdo uchun ideal!
Pine Script® göstergesi
skodirov095 tarafından
Crypto Scanner v4This guide explains a version 6 Pine Script that scans a user-provided list of cryptocurrency tokens to identify high probability tradable opportunities using several technical indicators. The script combines trend, momentum, and volume-based analyses to generate potential buying or selling signals, and it displays the results in a neatly formatted table with alerts for trading setups. Below is a detailed walkthrough of the script’s design, how traders can interpret its outputs, and recommendations for optimizing indicator inputs across different timeframes. ## Overview and Key Components The script is designed to help traders assess multiple tokens by calculating several indicators for each one. The key components include: - **Input Settings:** - A comma-separated list of symbols to scan. - Adjustable parameters for technical indicators such as ADX, RSI, MFI, and a custom Wave Trend indicator. - Options to enable alerts and set update frequencies. - **Indicator Calculations:** - **ADX (Average Directional Index):** Measures trend strength. A value above the provided threshold indicates a strong trend, which is essential for validating momentum before entering a trade. - **RSI (Relative Strength Index):** Helps determine overbought or oversold conditions. When the RSI is below the oversold level, it may present a buying opportunity, while an overbought condition (not explicitly part of this setup) could suggest selling. - **MFI (Money Flow Index):** Similar in concept to RSI but incorporates volume, thus assessing buying and selling pressure. Values below the designated oversold threshold indicate potential undervaluation. - **Wave Trend:** A custom indicator that calculates two components (WT1 and WT2); a crossover where WT1 moves from below to above WT2 (particularly near oversold levels) may signal a reversal and a potential entry point. - **Scanning and Trading Zone:** - The script identifies a *bullish setup* when the following conditions are met for a token: - ADX exceeds the threshold (strong trend). - Both RSI and MFI are below their oversold levels (indicating potential buying opportunities). - A Wave Trend crossover confirms near-term reversal dynamics. - A *trading zone* condition is also defined by specific ranges for ADX, RSI, MFI, and a limited difference between WT1 and WT2. This zone suggests that the token might be in a consolidation phase where even small moves may be significant. - **Alerts and Table Reporting:** - A table is generated, with each row corresponding to a token. The table contains columns for the symbol, ADX, RSI, MFI, WT1, WT2, and the trading zone status. - Visual cues—such as different background colors—highlight tokens with a bullish setup or that are within the trading zone. - Alerts are issued based on the detection of a bullish setup or entry into a trading zone. These alerts are limited per bar to avoid flooding the trader with notifications. ## How to Interpret the Indicator Outputs Traders should use the indicator values as guidance, verifying them against their own analysis before making any trading decision. Here’s how to assess each output: - **ADX:** - **High values (above threshold):** Indicate strong trends. If other indicators confirm an oversold condition, a trader may consider a long position for a corrective reversal. - **Low values:** Suggest that the market is not trending strongly, and caution should be taken when considering entry. - **RSI and MFI:** - **Below oversold levels:** These conditions are traditionally seen as signals that an asset is undervalued, potentially triggering a bounce. - **Above typical resistance levels (not explicitly used here):** Would normally caution a trader against entering a long position. - **Wave Trend (WT1 and WT2):** - A crossover where WT1 moves upward above WT2 in an oversold environment can signal the beginning of a recovery or reversal, thereby reinforcing buy signals. - **Trading Zone:** - Being “in zone” means that the asset’s current values for ADX, RSI, MFI, and the closeness of the Wave Trend lines indicate a period of consolidation. This scenario might be suitable for both short-term scalping or as an early exit indicator, depending on further market analysis. ## Timeframe Optimization Input Table Traders can optimize indicator inputs depending on the timeframe they use. The following table provides a set of recommended input values for various timeframes. These values are suggestions and should be adjusted based on market conditions and individual trading styles. Timeframe ADX RSI MFI ADX RSI MFI WT Channel WT Average 5-min 10 10 10 20 30 20 7 15 15-min 12 12 12 22 30 20 9 18 1-hour 14 14 14 25 30 20 10 21 4-hour 16 16 16 27 30 20 12 24 1-day 18 18 18 30 30 20 14 28 Adjust these parameters directly in the script’s input settings to match the selected timeframe. For shorter timeframes (e.g., 5-min or 15-min), the shorter lengths help filter high-frequency noise. For longer timeframes (e.g., 1-day), longer input values may reduce false signals and capture more significant trends. ## Best Practices and Usage Tips - **Token Limit:** - Limit the number of tokens scanned to 10 per query line. If you need to scan more tokens, initiate a new query line. This helps manage screen real estate and ensures the table remains legible. - **Confirming Signals:** - Use this script as a starting point for identifying high potential trades. Each indicator’s output should be used to confirm your trading decision. Always cross-reference with additional technical analysis tools or market context. - **Regular Review:** - Since the script updates the table every few bars (as defined by the update frequency), review the table and alerts regularly. Market conditions change rapidly, so timely decisions are crucial. ## Conclusion This Pine Script provides a comprehensive approach for scanning multiple cryptocurrencies using a combination of trend strength (ADX), momentum (RSI and MFI), and reversal signals (Wave Trend). By using the provided recommendation table for different timeframes and limiting the tokens to 20 per query line (with a maximum of four query lines), traders can streamline their scanning process and more effectively identify high probability tradable tokens. Ultimately, the outputs should be critically evaluated and combined with additional market research before executing any trades.
Pine Script® göstergesi
Crypt0Curious tarafından
Son Model ICT [TradingFinder] HTF DOL H1 + Sweep M15 + FVG M1🔵 Introduction The ICT Son Model setup is a precise trading strategy based on market structure and liquidity, implemented across multiple timeframes. This setup first identifies a liquidity level in the 1-hour (1H) timeframe and then confirms a Market Structure Shift (MSS) in the 5-minute (5M) timeframe to validate the trend. After confirmation, the price forms a new swing in the 5-minute timeframe, absorbing liquidity. Once this level is broken, traders typically drop to the 30-second (30s) timeframe and enter trades based on a Fair Value Gap (FVG). However, since access to the 30-second timeframe is not available to most traders, we take the entry signal directly from the 5-minute timeframe, using the same liquidity zones and confirmed breakouts to execute trades. This approach simplifies execution and makes the strategy accessible to all traders. This model operates in two setups : Bullish ICT Son Model and Bearish ICT Son Model. In the bullish setup, liquidity is first accumulated at the lows of the 1-hour timeframe, and after confirming a market structure shift, a long position is initiated. Conversely, in the bearish setup, liquidity is first drawn from higher levels, and upon confirmation of a bearish trend, a short position is executed. Bullish Setup : Bearish Setup : 🔵 How to Use The ICT Son Model setup is designed around liquidity analysis and market structure shifts and can be applied in both bullish and bearish market conditions. The strategy first identifies a liquidity level in the 1-hour (1H) timeframe and then confirms a Market Structure Shift (MSS) in the 5-minute (5M) timeframe. After this shift, the price forms a new swing, absorbing liquidity. When this level is broken in the 5-minute timeframe, the trader enters based on a Fair Value Gap (FVG). While the ideal entry is in the 30-second (30s) timeframe, due to accessibility constraints, we take entry signals directly from the 5-minute timeframe. 🟣 Bullish Setup In the Bullish ICT Son Model, the 1-hour timeframe first identifies liquidity at the market lows, where price sweeps this level to absorb liquidity. Then, in the 5-minute timeframe, an MSS confirms the bullish shift. After confirmation, the price forms a new swing, absorbing liquidity at a higher level. The price then retraces into a Fair Value Gap (FVG) created in the 5-minute timeframe, where the trader enters a long position, placing the stop-loss below the FVG. 🟣 Bearish Setup In the Bearish ICT Son Model, liquidity at higher market levels is identified in the 1-hour timeframe, where price sweeps these levels to absorb liquidity. Then, in the 5-minute timeframe, an MSS confirms the bearish trend. After confirmation, the price forms a new swing, absorbing liquidity at a lower level. The price then retraces into a Fair Value Gap (FVG) created in the 5-minute timeframe, where the trader enters a short position, placing the stop-loss above the FVG. 🔵 Settings Swing period : You can set the swing detection period. Max Swing Back Method : It is in two modes "All" and "Custom". If it is in "All" mode, it will check all swings, and if it is in "Custom" mode, it will check the swings to the extent you determine. Max Swing Back : You can set the number of swings that will go back for checking. FVG Length : Default is 120 Bar. MSS Length : Default is 80 Bar. FVG Filter : This refines the number of identified FVG areas based on a specified algorithm to focus on higher quality signals and reduce noise. Types of FVG filters : Very Aggressive Filter: Adds a condition where, for an upward FVG, the last candle's highest price must exceed the middle candle's highest price, and for a downward FVG, the last candle's lowest price must be lower than the middle candle's lowest price. This minimally filters out FVGs. Aggressive Filter: Builds on the Very Aggressive mode by ensuring the middle candle is not too small, filtering out more FVGs. Defensive Filter: Adds criteria regarding the size and structure of the middle candle, requiring it to have a substantial body and specific polarity conditions, filtering out a significant number of FVGs. Very Defensive Filter: Further refines filtering by ensuring the first and third candles are not small-bodied doji candles, retaining only the highest quality signals. 🔵 Conclusion The ICT Son Model setup is a structured and precise method for trade execution based on liquidity analysis and market structure shifts. This strategy first identifies a liquidity level in the 1-hour timeframe and then confirms a trend shift using the 5-minute timeframe. Trade entries are executed based on Fair Value Gaps (FVGs), which highlight optimal entry points. By applying this model, traders can leverage existing market liquidity to enter high-probability trades. The bullish setup activates when liquidity is swept from market lows and a market structure shift confirms an upward trend, whereas the bearish setup is used when liquidity is drawn from market highs, confirming a downtrend. This approach enables traders to identify high-probability trade setups with greater precision compared to many other strategies. Additionally, since access to the 30-second timeframe is limited, the strategy remains fully functional in the 5-minute timeframe, making it more practical and accessible for a wider range of traders.
Pine Script® göstergesi
TFlab tarafından
8
Timed Ranges [mktrader]The Timed Ranges indicator helps visualize price ranges that develop during specific time periods. It's particularly useful for analyzing market behavior in instruments like NASDAQ, S&P 500, and Dow Jones, which often show reactions to sweeps of previous ranges and form reversals. ### Key Features - Visualizes time-based ranges with customizable lengths (30 minutes, 90 minutes, etc.) - Tracks high/low range development within specified time periods - Shows multiple cycles per day for pattern recognition - Supports historical analysis across multiple days ### Parameters #### Settings - **First Cycle (HHMM-HHMM)**: Define the time range of your first cycle. The duration of this range determines the length of all subsequent cycles (e.g., "0930-1000" creates 30-minute cycles) - **Number of Cycles per Day**: How many consecutive cycles to display after the first cycle (1-20) - **Maximum Days to Display**: Number of historical days to show the ranges for (1-50) - **Timezone**: Select the appropriate timezone for your analysis #### Style - **Box Transparency**: Adjust the transparency of the range boxes (0-100) ### Usage Example To track 30-minute ranges starting at market open: 1. Set First Cycle to "0930-1000" (creates 30-minute cycles) 2. Set Number of Cycles to 5 (will show ranges until 11:30) 3. The indicator will display: - Range development during each 30-minute period - Visual progression of highs and lows - Color-coded cycles for easy distinction ### Use Cases - Identify potential reversal points after range sweeps - Track regular time-based support and resistance levels - Analyze market structure within specific time windows - Monitor range expansions and contractions during key market hours ### Tips - Use in conjunction with volume analysis for better confirmation - Pay attention to breaks and sweeps of previous ranges - Consider market opens and key session times when setting cycles - Compare range sizes across different time periods for volatility analysis
Pine Script® göstergesi
mktradermmxm tarafından
Market Movement After OpenDescription: This script provides a detailed visualization of market movements during key trading hours: the German market opening (08:00–09:00 UTC+1) and the US market opening (15:30–16:30 UTC+1). It is designed to help traders analyze price behavior in these critical trading periods by capturing and presenting movement patterns and trends directly on the chart and in an interactive table. Key Features: Market Movement Analysis: Tracks the price movement during the German market's first hour (08:00–09:00 UTC+1) and the US market's opening session (15:30–16:30 UTC+1). Analyzes whether the price moved up or down during these intervals. Visual Representation: Dynamically colored price lines indicate upward (green) or downward (red) movement during the respective periods. Labels ("DE" for Germany and "US" for the United States) mark key moments in the chart. Historical Data Table: Displays the past 10 trading days' movement trends in an interactive table, including: Date: Trading date. German Market Movement: Up (▲), Down (▼), or Neutral (-) for 08:00–09:00 UTC+1. US Market Movement: Up (▲), Down (▼), or Neutral (-) for 15:30–16:30 UTC+1. The table uses color coding for easy interpretation: green for upward movements, red for downward, and gray for neutral. Real-Time Updates: Automatically updates during live trading sessions to reflect the most recent movements. Highlights incomplete periods (e.g., ongoing sessions) to indicate their status. Customizable: Suitable for intraday analysis or broader studies of market trends. Designed to overlay directly on any price chart. Use Case: This script is particularly useful for traders who focus on market openings, which are often characterized by high volatility and significant price movements. By providing a clear visual representation of historical and live data, it aids in understanding and capitalizing on market trends during these critical periods. Notes: The script works best when the chart is set to the appropriate timezone (UTC+1 for the German market or your local equivalent). For precise trading decisions, consider combining this script with other technical indicators or trading strategies. Feel free to share feedback or suggest additional features to enhance the script!
Pine Script® göstergesi
base751 tarafından
Overnight Effect High Volatility Crypto (AiBitcoinTrend)👽 Overview of the Strategy This strategy leverages the overnight effect in the cryptocurrency market, specifically targeting the two-hour window from 21:00 UTC to 23:00 UTC. The strategy is designed to be applied only during periods of high volatility, which is determined using historical volatility data. This approach, inspired by research from Padyšák and Vojtko (2022), aims to capitalize on statistically significant return patterns observed during these hours. Deep Backtesting with a High Volatility Filter Deep Backtesting without a High Volatility Filter 👽 How the Strategy Works Volatility Calculation: Each day at 00:00 UTC, the strategy calculates the 30-day historical volatility of crypto returns (typically Bitcoin). The historical volatility is the standard deviation of the log returns over the past 30 days, representing the market's recent volatility level. Median Volatility Benchmark: The median of the 30-day historical volatility is calculated over a 365-day period (one year). This median acts as a benchmark to classify each day as either: 👾 High Volatility: When the current 30-day volatility exceeds the median volatility. 👾 Low Volatility: When the current 30-day volatility is below the median. Trading Rule: If the day is classified as a High Volatility Day, the strategy executes the following trades: 👾 Buy at 21:00 UTC. 👾 Sell at 23:00 UTC. Trade Execution Details: The strategy uses a 0.02% fee per trade. Each trade is executed with 25% of the available capital. This allocation helps manage risk while allowing for compounding returns. Rationale: The returns during the 22:00 and 23:00 UTC hours have been found to be statistically significant during high volatility periods. The overnight effect is believed to drive this phenomenon due to the asynchronous closing hours of global financial markets. This creates unique trading opportunities in the cryptocurrency market, where exchanges remain open 24/7. 👽 Market Context and Global Time Zone Impact 👾 Why 21:00 to 23:00 UTC? During this window, major traditional financial markets are closed: NYSE (New York) closes at 21:00 UTC. London and European markets are closed during these hours. Asian markets (Tokyo, Hong Kong, etc.) open later, leaving this window largely unaffected by traditional trading flows. This global market inactivity creates a period where significant moves can occur in the cryptocurrency market, particularly during high volatility. 👽 Strategy Parameters Volatility Period: 30 days. The lookback period for calculating historical volatility. Median Period: 365 days. The lookback period for calculating the median volatility benchmark. Entry Time: 21:00 UTC. Adjust this to your local time if necessary (e.g., 16:00 in New York, 22:00 in Stockholm). Exit Time: 23:00 UTC. Adjust this to your local time if necessary (e.g., 18:00 in New York, 00:00 midnight in Stockholm). 👽 Benefits of the Strategy Seasonality Effect: The strategy captures consistent patterns driven by the overnight effect and high volatility periods. Risk Reduction: Since trades are executed during a specific window and only on high volatility days, the strategy helps mitigate exposure to broader market risk. Simplicity and Efficiency: The strategy is moderately complex, making it accessible for traders while offering significant returns. Global Applicability: Suitable for traders worldwide, with clear guidelines on adjusting for local time zones. 👽 Considerations Market Conditions: The strategy works best in a high-volatility environment. Execution: Requires precise timing to enter and exit trades at the specified hours. Time Zone Adjustments: Ensure you convert UTC times accurately based on your location to execute trades at the correct local times. Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
Pine Script® stratejisi
aibitcointrend tarafından
David_30-Minute Boxes This indicator draws boxes for 30-minute intervals on the chart, highlighting significant price movements. The boxes begin at 00:00 and end at 22:00. Each box is color-coded according to price action: Green Box: The closing price at the end of the 30-minute period is above the opening price. Red Box: The closing price at the end of the 30-minute period is below the opening price. Dark Green Box: The closing price at the end of the box is higher than the high of the previous box. Dark Red Box: The closing price at the end of the box is lower than the low of the previous box. The boxes dynamically adjust within each 30-minute interval to reflect the high and low of the period. The border of each box is fully transparent for a clean and uncluttered visual display. Optional Candle Numbering In the indicator settings, you can enable or disable the numbering of individual candles within each box. The numbering restarts at 1 for each new box, helping to track the progression of individual 30-minute intervals. Use Cases This indicator is particularly useful for traders who want to analyze short-term movements and the dynamics within 30-minute intervals. The color-coding of the boxes provides quick visual insights into the strength of price action within a time interval, making it easier to spot momentum shifts or important support and resistance levels.
Pine Script® göstergesi
GolfTrade tarafından
Highest Volume* 지표 설명 이 지표는 다양한 기간 동안의 최대 거래량을 시각적으로 표시하여 거래자들이 중요한 거래량 패턴을 쉽게 식별할 수 있도록 도와줍니다. 30, 60, 90, 120 캔들 기간 동안의 최대 거래량을 감지하고, 이를 차트 상에 색상 코드로 표시합니다. 다중 기간 분석: 30, 60, 90, 120 캔들 기간에 대한 최대 거래량을 동시에 추적합니다. 기간에 따른 색상 표시: 기간이 길어질수록 표시되는 색상이 짙어집니다. * 주요 기능 거래량 급증 감지: 갑작스러운 거래량 증가를 빠르게 포착할 수 있습니다. * 부가 설명 초록색 배경: 최근 120 캔들 중 최대 거래량 노란색 배경: 최근 90 캔들 중 최대 거래량 (120 캔들 최대가 아닌 경우) 주황색 배경: 최근 60 캔들 중 최대 거래량 (90, 120 캔들 최대가 아닌 경우) 빨간색 배경: 최근 30 캔들 중 최대 거래량 (60, 90, 120 캔들 최대가 아닌 경우) * Indicator Description This indicator visually displays the maximum trading volume over various periods, helping traders easily identify important volume patterns. It detects the highest volume over 30, 60, 90, and 120 candle periods and represents this on the chart using color codes. Multi-period analysis: Simultaneously tracks the maximum volume for 30, 60, 90, and 120 candle periods. Color display according to period: The color becomes darker as the period gets longer. * Key Features Rapid volume surge detection: Quickly captures sudden increases in trading volume. * Additional Explanation Green background: Highest volume among the most recent 120 candles Yellow background: Highest volume among the most recent 90 candles (when not the highest in 120 candles) Orange background: Highest volume among the most recent 60 candles (when not the highest in 90 or 120 candles) Red background: Highest volume among the most recent 30 candles (when not the highest in 60, 90, or 120 candles)
Pine Script® göstergesi
CH_WINNER tarafından
Moving Average Ratio [InvestorUnknown]Overview The "Moving Average Ratio" (MAR) indicator is a versatile tool designed for valuation, mean-reversion, and long-term trend analysis. This indicator provides multiple display modes to cater to different analytical needs, allowing traders and investors to gain deeper insights into the market dynamics. Features 1. Moving Average Ratio (MAR): Calculates the ratio of the chosen source (close, open, ohlc4, hl2 …) to a longer-term moving average of choice (SMA, EMA, HMA, WMA, DEMA) Useful for identifying overbought or oversold conditions, aiding in mean-reversion strategies and valuation of assets. For some high beta asset classes, like cryptocurrencies, you might want to use logarithmic scale for the raw MAR, below you can see the visual difference of using Linear and Logarithmic scale on BTC 2. MAR Z-Score: Computes the Z-Score of the MAR to standardize the ratio over chosen time period, making it easier to identify extreme values relative to the historical mean. Helps in detecting significant deviations from the mean, which can indicate potential reversal points and buying/selling opportunities 3. MAR Trend Analysis: Uses a combination of short-term (default 1, raw MAR) and long-term moving averages of the MAR to identify trend changes. Provides a visual representation of bullish and bearish trends based on moving average crossings. Using Logarithmic scale can improve the visuals for some asset classes. 4. MAR Momentum: Measures the momentum of the MAR by calculating the difference over a specified period. Useful for detecting changes in the market momentum and potential trend reversals. 5. MAR Rate of Change (ROC): Calculates the rate of change of the MAR to assess the speed and direction of price movements. Helps in identifying accelerating or decelerating trends. MAR Momentum and Rate of Change are very similar, the only difference is that the Momentum is expressed in units of the MAR change and ROC is expressed as % change of MAR over chosen time period. Customizable Settings General Settings: Display Mode: Select the display mode from MAR, MAR Z-Score, MAR Trend, MAR Momentum, or MAR ROC. Color Bars: Option to color the bars based on the current display mode. Wait for Bar Close: Toggle to wait for the bar to close before updating the MAR value. MAR Settings: Length: Period for the moving average calculation. Source: Data source for the moving average calculation. Moving Average Type: Select the type of moving average (SMA, EMA, WMA, HMA, DEMA). Z-Score Settings: Z-Score Length: Period for the Z-Score calculation. Trend Analysis Settings: Moving Average Type: Select the type of moving average for trend analysis (SMA, EMA). Longer Moving Average: Period for the longer moving average. Shorter Moving Average: Period for the shorter moving average. Momentum Settings: Momentum Length: Period for the momentum calculation. Rate of Change Settings: ROC Length: Period for the rate of change calculation. Calculation and Plotting Moving Average Ratio (MAR): Calculates the ratio of the price to the selected moving average type and length. Plots the MAR with a gradient color based on its Z-Score, aiding in visual identification of extreme values. // Moving Average Ratio (MAR) ma_main = switch ma_main_type "SMA" => ta.sma(src, len) "EMA" => ta.ema(src, len) "WMA" => ta.wma(src, len) "HMA" => ta.hma(src, len) "DEMA" => ta.dema(src, len) mar = (waitforclose ? src : src) / ma_main z_col = color.from_gradient(z, -2.5, 2.5, color.green, color.red) plot(disp_mode.mar ? mar : na, color = z_col, histbase = 1, style = plot.style_columns) barcolor(color_bars ? (disp_mode.mar ? (z_col) : na) : na) MAR Z-Score: Computes the Z-Score of the MAR and plots it with a color gradient indicating the magnitude of deviation from the mean. // MAR Z-Score mean = ta.sma(math.log(mar), z_len) stdev = ta.stdev(math.log(mar),z_len) z = (math.log(mar) - mean) / stdev plot(disp_mode.mar_z ? z : na, color = z_col, histbase = 0, style = plot.style_columns) plot(disp_mode.mar_z ? 1 : na, color = color.new(color.red,70)) plot(disp_mode.mar_z ? 2 : na, color = color.new(color.red,50)) plot(disp_mode.mar_z ? 3 : na, color = color.new(color.red,30)) plot(disp_mode.mar_z ? -1 : na, color = color.new(color.green,70)) plot(disp_mode.mar_z ? -2 : na, color = color.new(color.green,50)) plot(disp_mode.mar_z ? -3 : na, color = color.new(color.green,30)) barcolor(color_bars ? (disp_mode.mar_z ? (z_col) : na) : na) MAR Trend: Plots the MAR along with its short-term and long-term moving averages. Uses color changes to indicate bullish or bearish trends based on moving average crossings. // MAR Trend - Moving Average Crossing mar_ma_long = switch ma_trend_type "SMA" => ta.sma(mar, len_trend_long) "EMA" => ta.ema(mar, len_trend_long) mar_ma_short = switch ma_trend_type "SMA" => ta.sma(mar, len_trend_short) "EMA" => ta.ema(mar, len_trend_short) plot(disp_mode.mar_t ? mar : na, color = mar_ma_long < mar_ma_short ? color.new(color.green,50) : color.new(color.red,50), histbase = 1, style = plot.style_columns) plot(disp_mode.mar_t ? mar_ma_long : na, color = mar_ma_long < mar_ma_short ? color.green : color.red, linewidth = 4) plot(disp_mode.mar_t ? mar_ma_short : na, color = mar_ma_long < mar_ma_short ? color.green : color.red, linewidth = 2) barcolor(color_bars ? (disp_mode.mar_t ? (mar_ma_long < mar_ma_short ? color.green : color.red) : na) : na) MAR Momentum: Plots the momentum of the MAR, coloring the bars to indicate increasing or decreasing momentum. // MAR Momentum mar_mom = mar - mar // MAR Momentum mom_col = mar_mom > 0 ? (mar_mom > mar_mom ? color.new(color.green,0): color.new(color.green,30)) : (mar_mom < mar_mom ? color.new(color.red,0): color.new(color.red,30)) plot(disp_mode.mar_m ? mar_mom : na, color = mom_col, histbase = 0, style = plot.style_columns) MAR Rate of Change (ROC): Plots the ROC of the MAR, using color changes to show the direction and strength of the rate of change. // MAR Rate of Change mar_roc = ta.roc(mar,len_roc) // MAR ROC roc_col = mar_roc > 0 ? (mar_roc > mar_roc ? color.new(color.green,0): color.new(color.green,30)) : (mar_roc < mar_roc ? color.new(color.red,0): color.new(color.red,30)) plot(disp_mode.mar_r ? mar_roc : na, color = roc_col, histbase = 0, style = plot.style_columns) Summary: This multi-purpose indicator provides a comprehensive toolset for various trading strategies, including valuation, mean-reversion, and trend analysis. By offering multiple display modes and customizable settings, it allows users to tailor the indicator to their specific analytical needs and market conditions.
Pine Script® göstergesi
InvestorUnknown tarafından
2
PubLibTrendLibrary "PubLibTrend" trend, multi-part trend, double trend and multi-part double trend conditions for indicator and strategy development rlut()   return line uptrend condition   Returns: bool dt()   downtrend condition   Returns: bool ut()   uptrend condition   Returns: bool rldt()   return line downtrend condition   Returns: bool dtop()   double top condition   Returns: bool dbot()   double bottom condition   Returns: bool rlut_1p()   1-part return line uptrend condition   Returns: bool rlut_2p()   2-part return line uptrend condition   Returns: bool rlut_3p()   3-part return line uptrend condition   Returns: bool rlut_4p()   4-part return line uptrend condition   Returns: bool rlut_5p()   5-part return line uptrend condition   Returns: bool rlut_6p()   6-part return line uptrend condition   Returns: bool rlut_7p()   7-part return line uptrend condition   Returns: bool rlut_8p()   8-part return line uptrend condition   Returns: bool rlut_9p()   9-part return line uptrend condition   Returns: bool rlut_10p()   10-part return line uptrend condition   Returns: bool rlut_11p()   11-part return line uptrend condition   Returns: bool rlut_12p()   12-part return line uptrend condition   Returns: bool rlut_13p()   13-part return line uptrend condition   Returns: bool rlut_14p()   14-part return line uptrend condition   Returns: bool rlut_15p()   15-part return line uptrend condition   Returns: bool rlut_16p()   16-part return line uptrend condition   Returns: bool rlut_17p()   17-part return line uptrend condition   Returns: bool rlut_18p()   18-part return line uptrend condition   Returns: bool rlut_19p()   19-part return line uptrend condition   Returns: bool rlut_20p()   20-part return line uptrend condition   Returns: bool rlut_21p()   21-part return line uptrend condition   Returns: bool rlut_22p()   22-part return line uptrend condition   Returns: bool rlut_23p()   23-part return line uptrend condition   Returns: bool rlut_24p()   24-part return line uptrend condition   Returns: bool rlut_25p()   25-part return line uptrend condition   Returns: bool rlut_26p()   26-part return line uptrend condition   Returns: bool rlut_27p()   27-part return line uptrend condition   Returns: bool rlut_28p()   28-part return line uptrend condition   Returns: bool rlut_29p()   29-part return line uptrend condition   Returns: bool rlut_30p()   30-part return line uptrend condition   Returns: bool dt_1p()   1-part downtrend condition   Returns: bool dt_2p()   2-part downtrend condition   Returns: bool dt_3p()   3-part downtrend condition   Returns: bool dt_4p()   4-part downtrend condition   Returns: bool dt_5p()   5-part downtrend condition   Returns: bool dt_6p()   6-part downtrend condition   Returns: bool dt_7p()   7-part downtrend condition   Returns: bool dt_8p()   8-part downtrend condition   Returns: bool dt_9p()   9-part downtrend condition   Returns: bool dt_10p()   10-part downtrend condition   Returns: bool dt_11p()   11-part downtrend condition   Returns: bool dt_12p()   12-part downtrend condition   Returns: bool dt_13p()   13-part downtrend condition   Returns: bool dt_14p()   14-part downtrend condition   Returns: bool dt_15p()   15-part downtrend condition   Returns: bool dt_16p()   16-part downtrend condition   Returns: bool dt_17p()   17-part downtrend condition   Returns: bool dt_18p()   18-part downtrend condition   Returns: bool dt_19p()   19-part downtrend condition   Returns: bool dt_20p()   20-part downtrend condition   Returns: bool dt_21p()   21-part downtrend condition   Returns: bool dt_22p()   22-part downtrend condition   Returns: bool dt_23p()   23-part downtrend condition   Returns: bool dt_24p()   24-part downtrend condition   Returns: bool dt_25p()   25-part downtrend condition   Returns: bool dt_26p()   26-part downtrend condition   Returns: bool dt_27p()   27-part downtrend condition   Returns: bool dt_28p()   28-part downtrend condition   Returns: bool dt_29p()   29-part downtrend condition   Returns: bool dt_30p()   30-part downtrend condition   Returns: bool ut_1p()   1-part uptrend condition   Returns: bool ut_2p()   2-part uptrend condition   Returns: bool ut_3p()   3-part uptrend condition   Returns: bool ut_4p()   4-part uptrend condition   Returns: bool ut_5p()   5-part uptrend condition   Returns: bool ut_6p()   6-part uptrend condition   Returns: bool ut_7p()   7-part uptrend condition   Returns: bool ut_8p()   8-part uptrend condition   Returns: bool ut_9p()   9-part uptrend condition   Returns: bool ut_10p()   10-part uptrend condition   Returns: bool ut_11p()   11-part uptrend condition   Returns: bool ut_12p()   12-part uptrend condition   Returns: bool ut_13p()   13-part uptrend condition   Returns: bool ut_14p()   14-part uptrend condition   Returns: bool ut_15p()   15-part uptrend condition   Returns: bool ut_16p()   16-part uptrend condition   Returns: bool ut_17p()   17-part uptrend condition   Returns: bool ut_18p()   18-part uptrend condition   Returns: bool ut_19p()   19-part uptrend condition   Returns: bool ut_20p()   20-part uptrend condition   Returns: bool ut_21p()   21-part uptrend condition   Returns: bool ut_22p()   22-part uptrend condition   Returns: bool ut_23p()   23-part uptrend condition   Returns: bool ut_24p()   24-part uptrend condition   Returns: bool ut_25p()   25-part uptrend condition   Returns: bool ut_26p()   26-part uptrend condition   Returns: bool ut_27p()   27-part uptrend condition   Returns: bool ut_28p()   28-part uptrend condition   Returns: bool ut_29p()   29-part uptrend condition   Returns: bool ut_30p()   30-part uptrend condition   Returns: bool rldt_1p()   1-part return line downtrend condition   Returns: bool rldt_2p()   2-part return line downtrend condition   Returns: bool rldt_3p()   3-part return line downtrend condition   Returns: bool rldt_4p()   4-part return line downtrend condition   Returns: bool rldt_5p()   5-part return line downtrend condition   Returns: bool rldt_6p()   6-part return line downtrend condition   Returns: bool rldt_7p()   7-part return line downtrend condition   Returns: bool rldt_8p()   8-part return line downtrend condition   Returns: bool rldt_9p()   9-part return line downtrend condition   Returns: bool rldt_10p()   10-part return line downtrend condition   Returns: bool rldt_11p()   11-part return line downtrend condition   Returns: bool rldt_12p()   12-part return line downtrend condition   Returns: bool rldt_13p()   13-part return line downtrend condition   Returns: bool rldt_14p()   14-part return line downtrend condition   Returns: bool rldt_15p()   15-part return line downtrend condition   Returns: bool rldt_16p()   16-part return line downtrend condition   Returns: bool rldt_17p()   17-part return line downtrend condition   Returns: bool rldt_18p()   18-part return line downtrend condition   Returns: bool rldt_19p()   19-part return line downtrend condition   Returns: bool rldt_20p()   20-part return line downtrend condition   Returns: bool rldt_21p()   21-part return line downtrend condition   Returns: bool rldt_22p()   22-part return line downtrend condition   Returns: bool rldt_23p()   23-part return line downtrend condition   Returns: bool rldt_24p()   24-part return line downtrend condition   Returns: bool rldt_25p()   25-part return line downtrend condition   Returns: bool rldt_26p()   26-part return line downtrend condition   Returns: bool rldt_27p()   27-part return line downtrend condition   Returns: bool rldt_28p()   28-part return line downtrend condition   Returns: bool rldt_29p()   29-part return line downtrend condition   Returns: bool rldt_30p()   30-part return line downtrend condition   Returns: bool dut()   double uptrend condition   Returns: bool ddt()   double downtrend condition   Returns: bool dut_1p()   1-part double uptrend condition   Returns: bool dut_2p()   2-part double uptrend condition   Returns: bool dut_3p()   3-part double uptrend condition   Returns: bool dut_4p()   4-part double uptrend condition   Returns: bool dut_5p()   5-part double uptrend condition   Returns: bool dut_6p()   6-part double uptrend condition   Returns: bool dut_7p()   7-part double uptrend condition   Returns: bool dut_8p()   8-part double uptrend condition   Returns: bool dut_9p()   9-part double uptrend condition   Returns: bool dut_10p()   10-part double uptrend condition   Returns: bool dut_11p()   11-part double uptrend condition   Returns: bool dut_12p()   12-part double uptrend condition   Returns: bool dut_13p()   13-part double uptrend condition   Returns: bool dut_14p()   14-part double uptrend condition   Returns: bool dut_15p()   15-part double uptrend condition   Returns: bool dut_16p()   16-part double uptrend condition   Returns: bool dut_17p()   17-part double uptrend condition   Returns: bool dut_18p()   18-part double uptrend condition   Returns: bool dut_19p()   19-part double uptrend condition   Returns: bool dut_20p()   20-part double uptrend condition   Returns: bool dut_21p()   21-part double uptrend condition   Returns: bool dut_22p()   22-part double uptrend condition   Returns: bool dut_23p()   23-part double uptrend condition   Returns: bool dut_24p()   24-part double uptrend condition   Returns: bool dut_25p()   25-part double uptrend condition   Returns: bool dut_26p()   26-part double uptrend condition   Returns: bool dut_27p()   27-part double uptrend condition   Returns: bool dut_28p()   28-part double uptrend condition   Returns: bool dut_29p()   29-part double uptrend condition   Returns: bool dut_30p()   30-part double uptrend condition   Returns: bool ddt_1p()   1-part double downtrend condition   Returns: bool ddt_2p()   2-part double downtrend condition   Returns: bool ddt_3p()   3-part double downtrend condition   Returns: bool ddt_4p()   4-part double downtrend condition   Returns: bool ddt_5p()   5-part double downtrend condition   Returns: bool ddt_6p()   6-part double downtrend condition   Returns: bool ddt_7p()   7-part double downtrend condition   Returns: bool ddt_8p()   8-part double downtrend condition   Returns: bool ddt_9p()   9-part double downtrend condition   Returns: bool ddt_10p()   10-part double downtrend condition   Returns: bool ddt_11p()   11-part double downtrend condition   Returns: bool ddt_12p()   12-part double downtrend condition   Returns: bool ddt_13p()   13-part double downtrend condition   Returns: bool ddt_14p()   14-part double downtrend condition   Returns: bool ddt_15p()   15-part double downtrend condition   Returns: bool ddt_16p()   16-part double downtrend condition   Returns: bool ddt_17p()   17-part double downtrend condition   Returns: bool ddt_18p()   18-part double downtrend condition   Returns: bool ddt_19p()   19-part double downtrend condition   Returns: bool ddt_20p()   20-part double downtrend condition   Returns: bool ddt_21p()   21-part double downtrend condition   Returns: bool ddt_22p()   22-part double downtrend condition   Returns: bool ddt_23p()   23-part double downtrend condition   Returns: bool ddt_24p()   24-part double downtrend condition   Returns: bool ddt_25p()   25-part double downtrend condition   Returns: bool ddt_26p()   26-part double downtrend condition   Returns: bool ddt_27p()   27-part double downtrend condition   Returns: bool ddt_28p()   28-part double downtrend condition   Returns: bool ddt_29p()   29-part double downtrend condition   Returns: bool ddt_30p()   30-part double downtrend condition   Returns: bool
Pine Script® kütüphanesi
theEccentricTrader tarafından
2
ICT Killzones and Sessions W/ Silver Bullet + MacrosForex and Equity Session Tracker with Killzones, Silver Bullet, and Macro Times This Pine Script indicator is a comprehensive timekeeping tool designed specifically for ICT traders using any time-based strategy. It helps you visualize and keep track of forex and equity session times, kill zones, macro times, and silver bullet hours. Features: Session and Killzone Lines: Green: London Open (LO) White: New York (NY) Orange: Australian (AU) Purple: Asian (AS) Includes AM and PM session markers. Dotted/Striped Lines indicate overlapping kill zones within the session timeline. Customization Options: Display sessions and killzones in collapsed or full view. Hide specific sessions or killzones based on your preferences. Customize colors, texts, and sizes. Option to hide drawings older than the current day. Automatic Updates: The indicator draws all lines and boxes at the start of a new day. Automatically adjusts time-based boxes according to the New York timezone. Killzone Time Windows (for indices): London KZ: 02:00 - 05:00 New York AM KZ: 07:00 - 10:00 New York PM KZ: 13:30 - 16:00 Silver Bullet Times: 03:00 - 04:00 10:00 - 11:00 14:00 - 15:00 Macro Times: 02:33 - 03:00 04:03 - 04:30 08:50 - 09:10 09:50 - 10:10 10:50 - 11:10 11:50 - 12:50 Latest Update: January 15: Added option to automatically change text coloring based on the chart. Included additional optional macro times per user request: 12:50 - 13:10 13:50 - 14:15 14:50 - 15:10 15:50 - 16:15 Usage: To maximize your experience, minimize the pane where the script is drawn. This minimizes distractions while keeping the essential time markers visible. The script is designed to help traders by clearly annotating key trading periods without overwhelming their charts. Originality and Justification: This indicator uniquely integrates various time-based strategies essential for ICT traders. Unlike other indicators, it consolidates session times, kill zones, macro times, and silver bullet hours into one comprehensive tool. This allows traders to have a clear and organized view of critical trading periods, facilitating better decision-making. Credits: This script incorporates open-source elements with significant improvements to enhance functionality and user experience. Forex and Equity Session Tracker with Killzones, Silver Bullet, and Macro Times This Pine Script indicator is a comprehensive timekeeping tool designed specifically for ICT traders using any time-based strategy. It helps you visualize and keep track of forex and equity session times, kill zones, macro times, and silver bullet hours. Features: Session and Killzone Lines: Green: London Open (LO) White: New York (NY) Orange: Australian (AU) Purple: Asian (AS) Includes AM and PM session markers. Dotted/Striped Lines indicate overlapping kill zones within the session timeline. Customization Options: Display sessions and killzones in collapsed or full view. Hide specific sessions or killzones based on your preferences. Customize colors, texts, and sizes. Option to hide drawings older than the current day. Automatic Updates: The indicator draws all lines and boxes at the start of a new day. Automatically adjusts time-based boxes according to the New York timezone. Killzone Time Windows (for indices): London KZ: 02:00 - 05:00 New York AM KZ: 07:00 - 10:00 New York PM KZ: 13:30 - 16:00 Silver Bullet Times: 03:00 - 04:00 10:00 - 11:00 14:00 - 15:00 Macro Times: 02:33 - 03:00 04:03 - 04:30 08:50 - 09:10 09:50 - 10:10 10:50 - 11:10 11:50 - 12:50 Latest Update: January 15: Added option to automatically change text coloring based on the chart. Included additional optional macro times per user request: 12:50 - 13:10 13:50 - 14:15 14:50 - 15:10 15:50 - 16:15 ICT Sessions and Kill Zones What They Are: ICT Sessions: These are specific times during the trading day when market activity is expected to be higher, such as the London Open, New York Open, and the Asian session. Kill Zones: These are specific time windows within these sessions where the probability of significant price movements is higher. For example, the New York AM Kill Zone is typically from 8:30 AM to 11:00 AM EST. How to Use Them: Identify the Session: Determine which trading session you are in (London, New York, or Asian). Focus on Kill Zones: Within that session, focus on the kill zones for potential trade setups. For instance, during the New York session, look for setups between 8:30 AM and 11:00 AM EST. Silver Bullets What They Are: Silver Bullets: These are specific, high-probability trade setups that occur within the kill zones. They are designed to be "one shot, one kill" trades, meaning they aim for precise and effective entries and exits. How to Use Them: Time-Based Setup: Look for these setups within the designated kill zones. For example, between 10:00 AM and 11:00 AM for the New York AM session . Chart Analysis: Start with higher time frames like the 15-minute chart and then refine down to 5-minute and 1-minute charts to identify imbalances or specific patterns . Macros What They Are: Macros: These are broader market conditions and trends that influence your trading decisions. They include understanding the overall market direction, seasonal tendencies, and the Commitment of Traders (COT) reports. How to Use Them: Understand Market Conditions: Be aware of the macroeconomic factors and market conditions that could affect price movements. Seasonal Tendencies: Know the seasonal patterns that might influence the market direction. COT Reports: Use the Commitment of Traders reports to understand the positioning of large traders and commercial hedgers . Putting It All Together Preparation: Understand the macro conditions and review the COT reports. Session and Kill Zone: Identify the trading session and focus on the kill zones. Silver Bullet Setup: Look for high-probability setups within the kill zones using refined chart analysis. Execution: Execute the trade with precision, aiming for a "one shot, one kill" outcome. By following these steps, you can effectively use ICT sessions, kill zones, silver bullets, and macros to enhance your trading strategy. Usage: To maximize your experience, shrink the pane where the script is drawn. This minimizes distractions while keeping the essential time markers visible. The script is designed to help traders by clearly annotating key trading periods without overwhelming their charts. Originality and Justification: This indicator uniquely integrates various time-based strategies essential for ICT traders. Unlike other indicators, it consolidates session times, kill zones, macro times, and silver bullet hours into one comprehensive tool. This allows traders to have a clear and organized view of critical trading periods, facilitating better decision-making. Credits: This script incorporates open-source elements with significant improvements to enhance functionality and user experience. All credit goes to itradesize for the SB + Macro boxes
Pine Script® göstergesi
SpxGh0st tarafından
5
[imba]lance algo🟩 INTRODUCTION Hello, everyone! Please take the time to review this description and source code to utilize this script to its fullest potential. 🟩 CONCEPTS This is a trend indicator. The trend is the 0.5 fibonacci level for a certain period of time. A trend change occurs when at least one candle closes above the level of 0.236 (for long) or below 0.786 (for short). Also it has massive amout of settings and features more about this below. With good settings, the indicator works great on any market and any time frame! A distinctive feature of this indicator is its backtest panel. With which you can dynamically view the results of setting up a strategy such as profit, what the deposit size is, etc. Please note that the profit is indicated as a percentage of the initial deposit. It is also worth considering that all profit calculations are based on the risk % setting. 🟩 FEATURES First, I want to show you what you see on the chart. And I’ll show you everything closer and in more detail. 1. Position 2. Statistic panel 3. Backtest panel Indicator settings: Let's go in order: 1. Strategies This setting is responsible for loading saved strategies. There are only two preset settings, MANUAL and UNIVERSAL. If you choose any strategy other than MANUAL, then changing the settings for take profits, stop loss, sensitivity will not bring any results. You can also save your customized strategies, this is discussed in a separate paragraph “🟩HOW TO SAVE A STRATEGY” 2. Sensitive Responsible for the time period in bars to create Fibonacci levels 3. Start calculating date This is the time to start backtesting strategies 4. Position group Show checkbox - is responsible for displaying positions Fill checkbox - is responsible for filling positions with background Risk % - is responsible for what percentage of the deposit you are willing to lose if there is a stop loss BE target - here you can choose when you reach which take profit you need to move your stop loss to breakeven Initial deposit- starting deposit for profit calculation 5. Stoploss group Fixed stoploss % checkbox - If choosed: stoploss will be calculated manually depending on the setting below( formula: entry_price * (1 - stoploss percent)) If NOT choosed: stoploss will be ( formula: fibonacci level(0.786/0.236) * (1 + stoploss percent)) 6. Take profit group This group of settings is responsible for how far from the entry point take profits will be and what % of the position to fix 7. RSI Responsible for configuring the built-in RSI. Suitable bars will be highlighted with crosses above or below, depending on overbought/oversold 8. Infopanels group Here I think everything is clear, you can hide or show information panels 9. Developer mode If enabled, all events that occur will be shown, for example, reaching a take profit or stop loss with detailed information about the unfixed balance of the position 🟩 HOW TO USE Very simple. All you need is to wait for the trend to change to long or short, you will immediately see a stop loss and four take profits, and you will also see prices. Like in this picture: 🟩 ALERTS There are 3 types of alerts: 1. Long signal 2. Short signal 3. Any alert() function call - will be send to you json with these fields { "side": "LONG", "entry": "64.454", "tp1": "65.099", "tp2": "65.743", "tp3": "66.388", "tp4": "67.032", "winrate": "35.42%", "strategy": "MANUAL", "beTargetTrigger": "1", "stop": "64.44" } 🟩 HOW TO SAVE A STRATEGY First, you need to make sure that the “MANUAL” strategy is selected in the strategy settings. After this, you can start selecting parameters that will show the largest profit in the statistics panel. I have highlighted what you need to pay attention to when choosing a strategy Let's assume you have set up a strategy. The main question is how to preserve it? Let’s say the strategy turned out with the following parameters: Next we need to find this section of code: // STRATS selector(string strategy_name) => strategy_settings = Strategy_settings.new() switch strategy_name "MANUAL" => strategy_settings.sensitivity := 18 strategy_settings.risk_percent := 1 strategy_settings.break_even_target := "1" strategy_settings.tp1_percent := 1 strategy_settings.tp1_percent_fix := 40 strategy_settings.tp2_percent := 2 strategy_settings.tp2_percent_fix := 30 strategy_settings.tp3_percent := 3 strategy_settings.tp3_percent_fix := 20 strategy_settings.tp4_percent := 4 strategy_settings.tp4_percent_fix := 10 strategy_settings.fixed_stop := false strategy_settings.sl_percent := 0.0 "UNIVERSAL" => strategy_settings.sensitivity := 20 strategy_settings.risk_percent := 1 strategy_settings.break_even_target := "1" strategy_settings.tp1_percent := 1 strategy_settings.tp1_percent_fix := 40 strategy_settings.tp2_percent := 2 strategy_settings.tp2_percent_fix := 30 strategy_settings.tp3_percent := 3 strategy_settings.tp3_percent_fix := 20 strategy_settings.tp4_percent := 4 strategy_settings.tp4_percent_fix := 10 strategy_settings.fixed_stop := false strategy_settings.sl_percent := 0.0 // "NEW STRATEGY" => // strategy_settings.sensitivity := 20 // strategy_settings.risk_percent := 1 // strategy_settings.break_even_target := "1" // strategy_settings.tp1_percent := 1 // strategy_settings.tp1_percent_fix := 40 // strategy_settings.tp2_percent := 2 // strategy_settings.tp2_percent_fix := 30 // strategy_settings.tp3_percent := 3 // strategy_settings.tp3_percent_fix := 20 // strategy_settings.tp4_percent := 4 // strategy_settings.tp4_percent_fix := 10 // strategy_settings.fixed_stop := false // strategy_settings.sl_percent := 0.0 strategy_settings // STRATS Let's uncomment on the latest strategy called "NEW STRATEGY" rename it to "SOL 5m" and change the sensitivity: // STRATS selector(string strategy_name) => strategy_settings = Strategy_settings.new() switch strategy_name "MANUAL" => strategy_settings.sensitivity := 18 strategy_settings.risk_percent := 1 strategy_settings.break_even_target := "1" strategy_settings.tp1_percent := 1 strategy_settings.tp1_percent_fix := 40 strategy_settings.tp2_percent := 2 strategy_settings.tp2_percent_fix := 30 strategy_settings.tp3_percent := 3 strategy_settings.tp3_percent_fix := 20 strategy_settings.tp4_percent := 4 strategy_settings.tp4_percent_fix := 10 strategy_settings.fixed_stop := false strategy_settings.sl_percent := 0.0 "UNIVERSAL" => strategy_settings.sensitivity := 20 strategy_settings.risk_percent := 1 strategy_settings.break_even_target := "1" strategy_settings.tp1_percent := 1 strategy_settings.tp1_percent_fix := 40 strategy_settings.tp2_percent := 2 strategy_settings.tp2_percent_fix := 30 strategy_settings.tp3_percent := 3 strategy_settings.tp3_percent_fix := 20 strategy_settings.tp4_percent := 4 strategy_settings.tp4_percent_fix := 10 strategy_settings.fixed_stop := false strategy_settings.sl_percent := 0.0 "SOL 5m" => strategy_settings.sensitivity := 15 strategy_settings.risk_percent := 1 strategy_settings.break_even_target := "1" strategy_settings.tp1_percent := 1 strategy_settings.tp1_percent_fix := 40 strategy_settings.tp2_percent := 2 strategy_settings.tp2_percent_fix := 30 strategy_settings.tp3_percent := 3 strategy_settings.tp3_percent_fix := 20 strategy_settings.tp4_percent := 4 strategy_settings.tp4_percent_fix := 10 strategy_settings.fixed_stop := false strategy_settings.sl_percent := 0.0 strategy_settings // STRATS Now let's find this code: strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта") And let's add our new strategy there, it turned out like this: strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта") That's all. Our new strategy is now saved! It's simple! Now we can select it in the list of strategies:
Pine Script® göstergesi
IMBA_TRADER tarafından
102
HighLowBox+220MAs[libHTF]HighLowBox+220MAs This is a sample script of libHTF to use HTF values without request.security(). import nazomobile/libHTFwoRS/1 HTF candles are calculated internally using 'GMT+3' from current TF candles by libHTF . To calcurate Higher TF candles, please display many past bars at first. The advantage and disadvantage is that the data can be generated at the current TF granularity. Although the signal can be displayed more sensitively, plots such as MAs are not smooth. In this script, assigned ➊,➋,➌,➍ for htf1,htf2,htf3,htf4. HTF candles Draw candles for HTF1-4 on the right edge of the chart. 2 candles for each HTF. They are updated with every current TF bar update. Left edge of HTF candles is located at the x-postion latest bar_index + offset. DMI HTF ADX/+DI/DI arrows(8lines) are shown each timeframes range. Current TF's is located at left side of the HighLowBox. HTF's are located at HighLowBox of HTF candles. The top of HighLowBox is 100, The bottom of HighLowBox is 0. HighLowBox HTF Enclose in a square high and low range in each timeframe. Shows price range and duration of each box. In current timeframe, shows Fibonacci Scale inside(23.6%, 38.2%, 50.0%, 61.8%, 76.4%)/outside of each box. Outside(161.8%,261.8,361.8%) would be shown as next target, if break top/bottom of each box. In HTF, shows Fibonacci Level of the current price at latest box only. Boxes: 1 for current timeframe. 4 for higher timeframes.(Steps of timeframe: 5, 15, 60, 240, D, W, M, 3M, 6M, Y) HighLowBox TrendLine Draw TrendLine for each HighLow Range. TrendLine is drawn between high and return high(or low and return low) of each HighLowBox. Style of TrendLine is same as each HighLowBox. HighLowBox RSI RSI Signals are shown at the bottom(RSI<=30) or the top(RSI>=70) of HighLowBox in each timeframe. RSI Signal is color coded by RSI9 and RSI14 in each timeframe.(current TF: ●, HTF1-4: ➊➋➌➍) In case of RSI<=30, Location: bottom of the HighLowBox white: only RSI9 is <=30 aqua: RSI9&RSI14; <=30 and RSI9RSI14 green: only RSI14 <=30 In case of RSI>=70, Location: top of the HighLowBox white: only RSI9 is >=70 yellow: RSI9&RSI14; >=70 and RSI9>RSI14 orange: RSI9&RSI14; >=70 and RSI9=70 blue/green and orange/red could be a oversold/overbought sign. 20/200 MAs Shows 20 and 200 MAs in each TFs(tfChart and 4 Higher). TFs: current TF HTF1-4 MAs: 20SMA 20EMA 200SMA 200EMA
Pine Script® göstergesi
nazomobile tarafından
2
Physics CandlesPhysics Candles embed volume and motion physics directly onto price candles or market internals according to the cyclic pattern of financial securities. The indicator works on both real-time “ticks” and historical data using statistical modeling to highlight when these values, like volume or momentum, is unusual or relatively high for some periodic window in time. Each candle is made out of one or more sub-candles that each contain their own information of motion, which converts to the color and transparency, or brightness, of that particular candle segment. The segments extend throughout the entire candle, both body and wicks, and Thick Wicks can be implemented to see the color coding better. This candle segmentation allows you to see if all the volume or energy is evenly distributed throughout the candle or highly contained in one small portion of it, and how intense these values are compared to similar time periods without going to lower time frames. Candle segmentation can also change a trader’s perspective on how valuable the information is. A “low” volume candle, for instance, could signify high value short-term stopping volume if the volume is all concentrated in one segment. The Candles are flexible. The physics information embedded on the candles need not be from the same price security or market internal as the chart when using the Physics Source option, and multiple Candles can be overlayed together. You could embed stock price Candles with market volume, market price Candles with stock momentum, market structure with internal acceleration, stock price with stock force, etc. My particular use case is scalping the SPX futures market (ES), whose price action is also dictated by the volume action in the associated cash market, or SPY, as well as a host of other securities. Physics allows you to embed the ES volume on the SPY price action, or the SPY volume on the ES price action, or you can combine them both by overlaying two Candle streams and increasing the Number of Overlays option to two. That option decreases the transparency levels of your coloring scheme so that overlaying multiple Candles converges toward the same visual color intensity as if you had one. The Candle and Physics Sources allows for both Symbols and Spreads to visualize Candle physics from a single ticker or some mathematical transformation of tickers. Due to certain TradingView programming restrictions, each Candle can only be made out of a maximum of 8 candle segments, or an “8-bit” resolution. Since limits are just an opportunity to go beyond, the user has the option to stack multiple Candle indicators together to further increase the candle resolution. If you don’t want to see the Candles for some particular period of the day, you can hide them, or use the hiding feature to have multiple Candles calibrated to show multiple parts of the trading day. Securities tend to have low volume after hours with sharp spikes at the open or close. Multiple Candles can be used for multiple parts of the trading day to accommodate these different cycles in volume. The Candles do not need be associated with the nominal security listed on the TV chart. The Candle Source allows the user to look at AAPL Candles, for instance, while on a TSLA or SPY chart, each with their respective volume actions integrated into the candles, for instance, to allow the user to see multiple security price and volume correlation on a single chart. The physics information currently embeddable on Candles are volume or time, velocity, momentum, acceleration, force, and kinetic energy. In order to apply equations of motion containing a mass variable to financial securities, some analogous value for mass must be assumed. Traders often regard volume or time as inextricable variables to a securities price that can indicate the direction and strength of a move. Since mass is the inextricable variable to calculating the momentum, force, or kinetic energy of motion, the user has the option to assume either time or volume is analogous to mass. Volume may be a better option for mass as it is not strictly dependent on the speed of a security, whereas time is. Data transformations and outlier statistics are used to color code the intensity of the physics for each candle segment relative to past periodic behavior. A million shares during pre-market or a million shares during noontime may be more intense signals than a typical million shares traded at the open, and should have more intense color signals. To account for a specific cyclic behavior in the market, the user can specify the Window and Cycle Time Frames. The Window Time Frame splits up a Cycle into windows, samples and aggregates the statistics for each window, then compares the current physics values against past values in the same window. Intraday traders may benefit from using a Daily Cycle with a 30-minute Window Time Frame and 1-minute Sample Time Frame. These settings sample and compare the physics of 1-minute candles within the current 30-minute window to the same 30-minute window statistics for all past trading days, up until the data limit imposed by TradingView, or until the Data Collection Start Date specified in the settings. Longer-term traders may benefit from using a Monthly Cycle with a Weekly Time Frame, or a Yearly Cycle with a Quarterly Time Frame. Multiple statistics and data transformation methods are available to convey relative intensity in different ways for different trading signals. Physics Candles allows for both Normal and Log-Normal assumptions in the physics distribution. The data can then be transformed by Linear, Logarithmic, Z-Score, or Power-Law scoring, where scoring simply assigns an intensity to the relative physics value of each candle segment based on some mathematical transformation. Z-scoring often renders adequate detection by scoring the segment value, such as volume or momentum, according to the mean and standard deviation of the data set in each window of the cycle. Logarithmic or power-law transformation with a gamma below 1 decreases the disparity between intensities so more less-important signals will show up, whereas the power-law transformation with gamma values above 1 increases the disparity between intensities, so less more-important signals will show up. These scores are then converted to color and transparency between the Min Score and the Max Score Cutoffs. The Auto-Normalization feature can automatically pick these cutoffs specific to each window based on the mean and standard deviation of the data set, or the user can manually set them. Physics was developed with novices in mind so that most users could calibrate their own settings by plotting the candle segment distributions directly on the chart and fiddling with the settings to see how different cutoffs capture different portions of the distribution and affect the relative color intensities differently. Security distributions are often skewed with fat-tails, known as kurtosis, where high-volume segments for example, have a higher-probabilities than expected for a normal distribution. These distribution are really log-normal, so that taking the logarithm leads to a standard bell-shaped distribution. Taking the Z-score of the Log-Normal distribution could make the most statistical sense, but color sensitivity is a discretionary preference. Background Philosophy This indicator was developed to study and trade the physics of motion in financial securities from a visually intuitive perspective. Newton’s laws of motion are loosely applied to financial motion: “A body remains at rest, or in motion at a constant speed in a straight line, unless acted upon by a force”. Financial securities remain at rest, or in motion at constant speed up or down, unless acted upon by the force of traders exchanging securities. “When a body is acted upon by a force, the time rate of change of its momentum equals the force”. Momentum is the product of mass and velocity, and force is the product of mass and acceleration. Traders render force on the security through the mass of their trading activity and the acceleration of price movement. “If two bodies exert forces on each other, these forces have the same magnitude but opposite directions.” Force arises from the interaction of traders, buyers and sellers. One body of motion, traders’ capitalization, exerts an equal and opposite force on another body of motion, the financial security. A securities movement arises at the expense of a buyer or seller’s capitalization. Volume The premise of this indicator assumes that volume, v, is an analogous means of measuring physical mass, m. This premise allows the application of the equations of motion to the movement of financial securities. We know from E=mc^2 that mass has energy. Energy can be used to create motion as kinetic energy. Taking a simple hypothetical example, the interaction of one short seller looking to cover lower and one buyer looking to sell higher exchange shares in a security at an agreed upon price to create volume or mass, and therefore, potential energy. Eventually the short seller will actively cover and buy the security from the previous buyer, moving the security higher, or the buyer will actively sell to the short seller, moving the security lower. The potential energy inherent in the initial consolidation or trading activity between buy and seller is now converted to kinetic energy on the subsequent trading activity that moves the securities price. The more potential energy that is created in the consolidation, the more kinetic energy there is to move price. This is why point and figure traders are said to give price targets based on the level of volatility or size of a consolidation range, or why Gann traders square price and time, as time is roughly proportional to mass and trading activity. The build-up of potential energy between short sellers and buyers in GME or TSLA led to their explosive moves beyond their standard fundamental valuations. Position Position, p, is simply the price or value of a financial security or market internal. Time Time, t, is another means of measuring mass to discover price behavior beyond the time snapshots that simple candle charts provide. We know from E=mc^2 that time is related to rest mass and energy given the speed of light, c, where time ≈ distance * sqrt(mass/E). This relation can also be derived from F=ma. The more mass there is, the longer it takes to compute the physics of a system. The more energy there is, the shorter it takes to compute the physics of a system. Similarly, more time is required to build a “resting” low-volatility trading consolidation with more mass. More energy added to that trading consolidation by competing buyers and sellers decreases the time it takes to build that same mass. Time is also related to price through velocity. Velocity = (p(t1) – p(t0)) / p(t0) Velocity, v, is the relative percent change of a securities price, p, over a period of time, t0 to t1. The period of time is between subsequent candles, and since time is constant between candles within the same timeframe, it is not used to calculate velocity or acceleration. Price moves faster with higher velocity, and slower with slower velocity, over the same fixed period of time. The product of velocity and mass gives momentum. Momentum = mv This indicator uses physics definition of momentum, not finance’s. In finance, momentum is defined as the amount of change in a securities price, either relative or absolute. This is definition is unfortunate, pun intended, since a one dollar move in a security from a thousand shares traded between a few traders has the exact same “momentum” as a one dollar move from millions of shares traded between hundreds of traders with everything else equal. If momentum is related to the energy of the move, momentum should consider both the level of activity in a price move, and the amount of that price move. If we equate mass to volume to account for the level of trading activity and use physics definition of momentum as the product of mass and velocity, this revised definition now gives a thousand-times more momentum to a one-dollar price move that has a thousand-times more volume behind it. If you want to use finance’s volume-less definition of momentum, use velocity in this indicator. Acceleration = v(t1) – v(t0) Acceleration, a, is the difference between velocities over some period of time, t0 to t1. Positive acceleration is necessary to increase a securities speed in the positive direction, while negative acceleration is necessary to decrease it. Acceleration is related to force by mass. Force = ma Force is required to change the speed of a securities valuation. Price movements with considerable force have considerably more impact on future direction. A change in direction requires force. Kinetic Energy = 0.5mv^2 Kinetic energy is the energy that a financial security gains from the change in its velocity by force. The built-up of potential energy in trading consolidations can be converted to kinetic energy on a breakout from the consolidation. Cycle Theory and Relativity Just as the physics of motion is relative to a point of reference, so too should the physics of financial securities be relative to a point of reference. An object moving at a 100 mph towards another object moving in the same direction at 100 mph will not appear to be moving relative to each other, nor will they collide, but from an outsider observer, the objects are going 100 mph and will collide with significant impact if they run into a stationary object relative to the observer. Similarly, trading with a hundred thousand shares at the open when the average volume is a couple million may have a much smaller impact on the price compared to trading a hundred thousand shares pre-market when the average volume is ten thousand shares. The point of reference used in this indicator is the average statistics collected for a given Window Time Frame for every Cycle Time Frame. The physics values are normalized relative to these statistics. Examples The main chart of this publication shows the Force Candles for the SPY. An intense force candle is observed pre-market that implicates the directional overtone of the day. The assumption that direction should follow force arises from physical observation. If a large object is accelerating intensely in a particular direction, it may be fair to assume that the object continues its direction for the time being unless acted upon by another force. The second example shows a similar Force Candle for the SPY that counters the assumption made in the first example and emphasizes the importance of both motion and context. While it’s fair to assume that a heavy highly accelerating object should continue its course, if that object runs into an obstacle, say a brick wall, it’s course may deviate. This example shows SPY running into the 50% retracement wall from the low of Mar 2020, a significant support level noted in literature. The example also conveys Gann’s idea of “lost motion”, where the SPY penetrated the 50% price but did not break through it. A brick wall is not one atom thick and price support is not one tick thick. An object can penetrate only one layer of a wall and not go through it. The third example shows how Volume Candles can be used to identify scalping opportunities on the SPY and conveys why price behavior is as important as motion and context. It doesn’t take a brick wall to impede direction if you know that the person driving the car tends to forget to feed the cats before they leave. In the chart below, the SPY breaks down to a confluence of the 5-day SMA, 20-day SMA, and an important daily trendline (not shown) after the bullish bounce from the 50% retracement days earlier. High volume candles on the SMA signify stopping volume that reverse price direction. The character of the day changes. Bulls become more aggressive than bears with higher volume on upswings and resistance, whiles bears take on a defensive position with lower volume on downswings and support. High volume stopping candles are seen after rallies, and can tell you when to take profit, get out of a position, or go short. The character change can indicate that its relatively safe to re-enter bullish positions on many major supports, especially given the overarching bullish theme from the large reaction off the 50% retracement level. The last example emphasizes the importance of relativity. The Volume Candles in the chart below are brightest pre-market even though the open has much higher volume since the pre-market activity is much higher compared to past pre-markets than the open is compared to past opens. Pre-market behavior is a good indicator for the character of the day. These bullish Volume Candles are some of the brightest seen since the bounce off the 50% retracement and indicates that bulls are making a relatively greater attempt to bring the SPY higher at the start of the day. Infrequently Asked Questions Where do I start? The default settings are what I use to scalp the SPY throughout most of the extended trading day, on a one-minute chart using SPY volume. I also overlay another Candle set containing ES future volume on the SPY price structure by setting the Physics Source to ES1! and the Number of Overlays setting to 2 for each Candle stream in order to account for pre- and post-market trading activity better. Since the closing volume is exponential-like up until the end of the regular trading day, adding additional Candle streams with a tighter Window Time Frame (e.g., 2-5 minute) in the last 15 minutes of trading can be beneficial. The Hide feature can allow you to set certain intraday timeframes to hide one Candle set in order to show another Candle set during that time. How crazy can you get with this indicator? I hope you can answer this question better. One interesting use case is embedding the velocity of market volume onto an internal market structure. The PCTABOVEVWAP.US is a market statistic that indicates the percent of securities above their VWAP among US stocks and is helpful for determining short term trends in the US market. When securities are rising above their VWAP, the average long is up on the day and a rising PCTABOVEVWAP.US can be viewed as more bullish. When securities are falling below their VWAP, the average short is up on the day and a falling PCTABOVEVWAP.US can be viewed as more bearish. (UPVOL.US - DNVOL.US) / TVOL.US is a “spread” symbol, in TV parlance, that indicates the decimal percent difference between advancing volume and declining volume in the US market, showing the relative flow of volume between stocks that are up on the day, and stocks that are down on the day. Setting PCTABOVEVWAP.US in the Candle Source, (UPVOL.US - DNVOL.US) / TVOL.US in the Physics Source, and selecting the Physics to Velocity will embed the relative velocity of the spread symbol onto the PCTABOVEVWAP.US candles. This can be helpful in seeing short term trends in the US market that have an increasing amount of volume behind them compared to other trends. The chart below shows Volume Candles (top) and these Spread Candles (bottom). The first top at 9:30 and second top at 10:30, the high of the day, break down when the spread candles light up, showing a high velocity volume transfer from up stocks to down stocks. How do I plot the indicator distribution and why should I even care? The distribution is visually helpful in seeing how different normalization settings effect the distribution of candle segments. It is also helpful in seeing what physics intensities you want to ignore or show by segmenting part of the distribution within the Min and Max Cutoff values. The intensity of color is proportional to the physics value between the Min and Max Cutoff values, which correspond to the Min and Max Colors in your color scheme. Any physics value outside these Min and Max Cutoffs will be the same as the Min and Max Colors. Select the Print Windows feature to show the window numbers according to the Cycle Time Frame and Window Time Frame settings. The window numbers are labeled at the start of each window and are candle width in size, so you may need to zoom into to see them. Selecting the Plot Window feature and input the window number of interest to shows the distribution of physics values for that particular window along with some statistics. A log-normal volume distribution of segmented z-scores is shown below for 30-minute opening of the SPY. The Min and Max Cutoff at the top of the graph contain the part of the distribution whose intensities will be linearly color-coded between the Min and Max Colors of the color scheme. The part of the distribution below the Min Cutoff will be treated as lowest quality signals and set to the Min Color, while the few segments above the Max Cutoff will be treated as the highest quality signals and set to the Max Color. What do I do if I don’t see anything? Troubleshooting issues with this indicator can involve checking for error messages shown near the indicator name on the chart or using the Data Validation section to evaluate the statistics and normalization cutoffs. For example, if the Plot Window number is set to a window number that doesn’t exist, an error message will tell you and you won’t see any candles. You can use the Print Windows option to show windows that do exist for you current settings. The auto-normalization cutoff values may be inappropriate for your particular use case and literally cut the candles out of the chart. Try changing the chart time frame to see if they are appropriate for your cycle, sample and window time frames. If you get a “Timeframe passed to the request.security_lower_tf() function must be lower than the timeframe of the main chart” error, this means that the chart timeframe should be increased above the sample time frame. If you get a “Symbol resolve error”, ensure that you have correct symbol or spread in the Candle or Physics Source. How do I see a relative physics values without cycles? Set the Window Time Frame to be equal to the Cycle Time Frame. This will aggregate all the statistics into one bucket and show the physics values, such as volume, relative to all the past volumes that TV will allow. How do I see candles without segmentation? Segmentation can be very helpful in one context or annoying in another. Segmentation can be removed by setting the candle resolution value to 1. Notes I have yet to find a trading platform that consistently provides accurate real-time volume and pricing information, lacking adequate end-user data validation or quality control. I can provide plenty of examples of real-time volume counts or prices provided by TradingView and other platforms that were significantly off from what they should have been when comparing against the exchanges own data, and later retroactively corrected or not corrected at all. Since no indicator can work accurately with inaccurate data, please use at your own discretion. The first version is a beta version. Debugging and validating code in Pine script is difficult without proper unit testing. Please report any bugs with enough information to reproduce them and indicate why they are important. I also encourage you to export the data from TradingView and verify the calculations for your particular use case. The indicator works on real-time updates that occur at a higher frequency than the candle time frame, which TV incorrectly refers to as ticks. They use this terminology inaccurately as updates are really aggregated tick data that can take place at different prices and may not accurately reflect the real tick price action. Consequently, this inaccuracy also impacts the real-time segmentation accuracy to some degree. TV does not provide a means of retaining “tick” information, so the higher granularity of information seen real-time will be lost on a disconnect. TV does not provide time and sales information. The volume and price information collected using the Sample Time Frame is intraday, which provides only part of the picture. Intraday volume is generally 50 to 80% of the end of day volume. Consequently, the daily+ OHLC prices are intraday, and may differ significantly from exchanged settled OHLC prices. The Cycle and Window Time Frames refer to calendar days and time, not trading days or time. For example, the first window week of a monthly cycle is the first seven days of the month, not the first Monday through Friday of trading for the month. Chart Time Frames that are higher than the Window Time Frames average the normalized physics for price action that occurred within a given Candle segment. It does not average price action that did not occur. One of the main performance bottleneck in TradingView’s Pine Script is client-side drawing and plotting. The performance of this indicator can be increased by lowering the resolution (the number of sub-candles this indicator plots), getting a faster computer, or increasing the performance of your computer like plugging your laptop in and eliminating unnecessary processes. The statistical integrity of this indicator relies on the number of samples collected per sample window in a given cycle. Higher sample counts can be obtained by increasing the chart time frame or upgrading the TradingView plan for a higher bar count. While increasing the chart time frame doesn’t increase the visual number of bars plotted on the chart, it does increase the number of bars that can be pulled at a lower time frame, up to 100,000. Due to a limitation in Pine Scripts request_lower_tf() function, using a spread symbol will only work for regular trading hours, not extended trading hours. Ideally, velocity or momentum should be calculated between candle closes. To eliminate the need to deal with price gaps that would lead to an incorrect statistical distributions, momentum is calculated between candle open and closes as a percent change of the price or value, which should not be an issue for most liquid securities.
Pine Script® göstergesi
Techotomy tarafından
32
Relative Volume (rVol), Better Volume, Average Volume ComparisonThis is the best version of relative volume you can find a claim which is based on the logical soundness of its calculation. I have amalgamated various volume analysis into one synergistic script. I wasn't going to opensource it. But, as one of the lucky few winners of TradingClue 2. I felt obligated to give something back to the community. Relative volume traditionally compares current volume to prior bar volume or SMA of volume. This has drawbacks. The question of relative volume is "Volume relative to what?" In the traditional scripts you'll find it displays current volume relative to the last number of bars. But, is that the best way to compare volume. On a daily chart, possibly. On a daily chart this can work because your units of time are uniform. Each day represents a full cycle of volume. However, on an intraday chart? Not so much. Example: If you have a lookback of 9 on an hourly chart in a 24 hour market, you are then comparing the average volume from Midnight - 9 AM to the 9 AM volume. What do you think you'll find? Well at 9:30 when NY exchanges open the volume should be consistently and predictably higher. But though rVol is high relative to the lookback period, its actually just average or maybe even below average compared to prior NY session opens. But prior NY session opens are not included in the lookback and thus ignored. This problem is the most visibly noticed when looking at the volume on a CME futures chart or some equivalent. In a 24 hour market, such as crypto, there are website's like skew can show you the volume disparity from time of day. This led me to believe that the traditional rVol calculation was insufficient. A better way to calculate it would be to compare the 9:30 am 30m bar today to the last week's worth of 9:30 am 30m bars. Then I could know whether today's volume at 9:30 am today is high or low based on prior 9:30 am bars. This seems to be a superior method on an intraday basis and is clearly superior in markets with irregular volume This led me to other problems, such as markets that are open for less than 24 hours and holiday hours on traditional market exchanges. How can I know that the script is accurately looking at the correct prior relevant bars. I've created and/or adapted solutions to all those problems and these calculations and code snippets thus have value that extend beyond this rVol script for other pinecoders. The Script This rVol script looks back at the bars of the same time period on the viewing timeframe. So, as we said, the last 9:30 bars. Averages those, then divides the: . The result is a percentage expressed as x.xxx. Thus 1.0 mean current volume is equal to average volume. Below 1.0 is below the average and above 1.0 is above the average. This information can be viewed on its own. But there are more levels of analysis added to it. Above the bars are signals that correlate to the "Better Volume Indicator" developed by, I believe, the folks at emini-watch and originally adapted to pinescript by LazyBear. The interpretation of these symbols are in a table on the right of the indicator. The volume bars can also be colored. The color is defined by the relationship between the average of the rVol outputs and the current volume. The "Average rVol" so to speak. The color coding is also defined by a legend in the table on the right. These can be researched by you to determine how to best interpret these signals. I originally got these ideas and solid details on how to use the analysis from a fellow out there, PlanTheTrade. I hope you find some value in the code and in the information that the indicator presents. And I'd like to thank the TradingView team for producing the most innovative and user friendly charting package on the market. (p.s. Better Volume is provides better information with a longer lookback value than the default imo) Credit for certain code sections and ideas is due to: LazyBear - Better Volume Grimmolf (From GitHub) - Logic for Loop rVol R4Rocket - The idea for my rVol 1 calculation And I can't find the guy who had the idea for the multiples of volume to the average. Tag him if you know him Final Note: I'd like to leave a couple of clues of my own for fellow seekers of trading infamy. Indicators: indicators are like anemometers (The things that measure windspeed). People talk bad about them all the time because they're "lagging." Well, you can't tell what the windspeed is unless the wind is blowing. anemometers are lagging indicators of wind. But forecasters still rely on them. You would use an indicator, which I would define as a instrument of measure, to tell you the windspeed of the markets. Conversely, when people talk positively about indicators they say "This one is great and this one is terrible." This is like a farmer saying "Shovels are great, but rakes are horrible." There are certain tools that have certain functions and every good tool has a purpose for a specific job. So the next time someone shares their opinion with you about indicators. Just smile and nod, realizing one day they'll learn... hopefully before they go broke. How to forecast: Prediction is accomplished by analyzing the behavior of instruments of measure to aggregate data (using your anemometer). The data is then assembled into a predictive model based on the measurements observed (a trading system). That predictive model is tested against reality for it's veracity (backtesting). If the model is predictive, you can optimize your decision making by creating parameter sets around the prediction that are synergistic with the implications of the prediction (risk, stop loss, target, scaling, pyramiding etc). <3
Pine Script® göstergesi
kurtsmock tarafından
74
How to use Leverage and Margin in PineScriptEn route to being absolutely the best and most complete trading platform out there, TradingView has just closed 2 gaps in their PineScript language. It is now possible to create and backtest a strategy for trading with leverage. Backtester now produces Margin Calls - so recognizes mid-trade drawdown and if it is too big for the broker to maintain your trade, some part of if will be instantly closed. New additions were announced in official blogpost , but it lacked code examples, so I have decided to publish this script. Having said that - this is purely educational stuff. █ LEVERAGE Let's start with the Leverage. I will discuss this assuming we are always entering trades with some percentage of our equity balance (default_qty_type = strategy.percent_of_equity), not fixed order quantity. If you want to trade with 1:1 leverage (so no leverage) and enter a trade with all money in your trading account, then first line of your strategy script must include this parameter: default_qty_value = 100 // which stands for 100% Now, if you want to trade with 30:1 leverage, you need to multipy the quantity by 30x, so you'd get 30 x 100 = 3000: default_qty_value = 3000 // which stands for 3000% And you can play around with this value as you wish, so if you want to enter each trade with 10% equity on 15:1 leverage you'd get default_qty_value = 150. That's easy. Of course you can modify this quantity value not only in the script, but also afterwards in Script Settings popup, "Properties" tab. █ MARGIN Second newly released feature is Margin calculation together with Margin Calls. If the market goes against your trades and your trading account cannot maintain mid-trade drawdown - those trades will be closed in full or partly. Also, if your trading account cannot afford to open more trades (pyramiding those trades), Margin mechanism will prevent them from being entered. I will not go into details about how Margin calculation works, it was all explainged in above mentioned blogpost and documentation . All you need to do is to add two parameters to the opening line of your script: margin_long = 1./30*50, margin_short = 1./30*50 Whereas "30" is a leverage scale as in 30:1, and "50" stands for 50% of Margin required by your broker. Personally the Required Margin number I've met most often is 50%, so I'm using value 50 here, but there are literally 1000+ brokers in this world and this is individual decision by each of them, so you'd better ask yourself. -------------------- Please note, that if you ever encounter a strategy which triggers Margin Call at least once, then it is probably a very bad strategy. Margin Call is a last resort, last security measure - all the risks should be calculated by the strategy algorithm before it is ever hit. So if you see a Margin Call being triggred, then something is wrong with risk management of the strategy. Therefore - don't use it!
Pine Script® stratejisi
Peter_O tarafından
42
Max GainThis indicator is meant to be used for coming up with price targets based on past performances of rallies/selloffs. It shows how much a trade could have made over a 30-day period (or other length of time) in terms of percentage gain. It also show how much could have been lost in terms of percentage loss The green plot shows percentage gain from current high to the low of the previous 30 days. The red plot shows adjusted percentage loss from current low to the high of the previous 30 days. The 30 can be adjusted and the chart can be used on any time interval. Note on max loss adjustment: Max loss percentage is adjusted to be higher to account for the fact that a percentage loss corresponds to a percentage gain of a greater amount. For instance, a loss of 25% can only be recovered with a percentage gain of 33%. A 25% loss looking at the chart from left to right would be a 33% gain looking at the same price action from right to left. In order to compare apples to apples visually and performance wise, max loss percent needs to be adjusted. The actual max loss percent is calculated and plottable but is not plotted by default because it is less useful and adds clutter. There is not a great difference between actual max loss and adjusted max loss under everyday market conditions, but major selloffs (SPY 2020), short squeezes (GME 2021), or other unusually directional moves will display percentage losses that, in absolute terms, should be considered to be fairly incorrect. The adjusted percentages are good indicators of relative performance when comparing the magnitudes to the magnitudes of the max gain percentages and are more visually meaningful than the actual max loss percentages in every situation, so they are plotted despite having incorrect values. Note on bear markets: This indicator was designed for bull markets but should it be used in bear markets the indicators that are and aren't plotted should be swapped using the plot check boxes in the settings dialogue if there is interest in using the loss percentages for actual loss amount calculations while maintaining visual/performance adjustment As can been seen in the example chart a gain of 16.3% to 17.1% appears to be a resistance level. This level was recently broken through and the next resistance is 24.5%. The target is a 24.5% gain from the anticipated 30-day low at the time when the price can be expected to reach a 25.4% gain at the gain rate observed in recent rallies. Previous rallies are shown for reference with their 30-day periods and corresponding gain percentages which are plotted below. A selloff is shown in red for reference as well. It was drawn backward to trick the tool into thinking it was a gain, so as to demonstrate logic behind the adjustment. In reality, this was closer to a 9.5% loss, not 10.55%. I am still experimenting with this indicator to see how to best use it. Ultimately, it helps me do what I was already doing with the percentage gain tools but now I can do those analyses in a more systematic manner and with charting. Please feel free to ask questions.
Pine Script® göstergesi
Skipper86 tarafından
15
Put/Call-Ratio-Buschi English: This script shows the Put/Call-Ratio as seen on the Cboe-Website: www.cboe.com A higher Put/Call-Ratio means a higher trading volume of puts compared to calls, which is a sign of a higher need for protection in the market. For best reflection of the Cboe's data, which is shown in 30 minutes intervals, a 30 min-chart is recommended. 30 min-data as well as end-of-day data are shown. Deutsch: Dieses Skript zeigt das Put/Call Ratio, wie es auf der Cboe-Website angegeben ist: www.cboe.com Ein höheres Put/Call Ratio bedeutet ein höheres Handelsvolumen von Puts gegenüber Calls, was ein Zeichen für Absicherungsbedarf im Markt darstellt. Um die Cboe-Daten bestmöglich wiederzugeben, die in 30 Minuten-Intervallen herausgegeben werden, wird ein 30 min-Chart empfohlen. Es werden sowohl die 30-Minuten-Daten als auch die Tagesenddaten angezeigt.
Pine Script® göstergesi
MagicEins tarafından
13