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True Hour Open🧠 Why Count an Hour from 30th Minute to 30th Minute? ✅ Traditional Hour vs. Functional Hour Traditional Time Logic: We’re used to viewing time in clean hourly chunks: 12:00 to 1:00, 1:00 to 2:00, and so on. This structure is fine for general purposes like clocks, meetings, and schedules. Market Logic: Markets, however, don’t always respect these arbitrary human-made time divisions. Price action often develops momentum, structure, and transitions based on market participants' behavior, not on the clock. 🛠 What the Indicator Does Marks the start of each hour at the 30th minute past the hour (e.g., 1:30, 2:30, 3:30). Can highlight or segment candles that fall within a “30-to-30” hourly window. Optionally draws background shading, lines, or boxes to visually group candles from one 30-minute mark to the next. This helps you: Visually align your trading with more accurate price behavior windows. Anchor time blocks around actual market rhythm, not artificial time slots. Backtest and strategize based on how candles behave in these alternative hourly segments. 📈 Summary Trading is about timing. But great trading is about timing that makes sense. By redefining the hour from 30 to 30, you’re not changing time—you’re aligning with how price moves in time.
Pine Script® göstergesi
veztiano tarafından
Magnificent 7 OscillatorThe Magnificent 7 Oscillator is a sophisticated momentum-based technical indicator designed to analyze the collective performance of the seven largest technology companies in the U.S. stock market (Apple, Microsoft, Alphabet, Amazon, NVIDIA, Tesla, and Meta). This indicator incorporates established momentum factor research and provides three distinct analytical modes: absolute momentum tracking, equal-weighted market comparison, and relative performance analysis. The tool integrates five different oscillator methodologies and includes advanced breadth analysis capabilities. Theoretical Foundation Momentum Factor Research The indicator's foundation rests on seminal momentum research in financial markets. Jegadeesh and Titman (1993) demonstrated that stocks with strong price performance over 3-12 month periods tend to continue outperforming in subsequent periods¹. This momentum effect was later incorporated into formal factor models by Carhart (1997), who extended the Fama-French three-factor model to include a momentum factor (UMD - Up Minus Down)². The momentum calculation methodology follows the academic standard: Momentum(t) = / P(t-n) × 100 Where P(t) is the current price and n is the lookback period. The focus on the "Magnificent 7" stocks reflects the increasing market concentration observed in recent years. Fama and French (2015) noted that a small number of large-cap stocks can drive significant market movements due to their substantial index weights³. The combined market capitalization of these seven companies often exceeds 25% of the total S&P 500, making their collective momentum a critical market indicator. Indicator Architecture Core Components 1. Data Collection and Processing The indicator employs robust data collection with error handling for missing or invalid security data. Each stock's momentum is calculated independently using the specified lookback period (default: 14 periods). 2. Composite Oscillator Calculation Following Fama-French factor construction methodology, the indicator offers two weighting schemes: - Equal Weight: Each active stock receives identical weighting (1/n) - Market Cap Weight: Reserved for future enhancement 3. Oscillator Transformation Functions The indicator provides five distinct oscillator types, each with established technical analysis foundations: a) Momentum Oscillator (Default) - Pure rate-of-change calculation - Centered around zero - Direct implementation of Jegadeesh & Titman methodology b) RSI (Relative Strength Index) - Wilder's (1978) relative strength methodology - Transformed to center around zero for consistency - Scale: -50 to +50 c) Stochastic Oscillator - George Lane's %K methodology - Measures current position within recent range - Transformed to center around zero d) Williams %R - Larry Williams' range-based oscillator - Inverse stochastic calculation - Adjusted for zero-centered display e) CCI (Commodity Channel Index) - Donald Lambert's mean reversion indicator - Measures deviation from moving average - Scaled for optimal visualization Operational Modes Mode 1: Magnificent 7 Analysis Tracks the collective momentum of the seven constituent stocks. This mode is optimal for: - Technology sector analysis - Growth stock momentum assessment - Large-cap performance tracking Mode 2: S&P 500 Equal Weight Comparison Analyzes momentum using an equal-weighted S&P 500 reference (typically RSP ETF). This mode provides: - Broader market momentum context - Size-neutral market analysis - Comparison baseline for relative performance Mode 3: Relative Performance Analysis Calculates the momentum differential between Magnificent 7 and S&P 500 Equal Weight. This mode enables: - Sector rotation analysis - Style factor assessment (Growth vs. Value) - Relative strength identification Formula: Relative Performance = MAG7_Momentum - SP500EW_Momentum Signal Generation and Thresholds Signal Classification The indicator generates three signal states: - Bullish: Oscillator > Upper Threshold (default: +2.0%) - Bearish: Oscillator < Lower Threshold (default: -2.0%) - Neutral: Oscillator between thresholds Relative Performance Signals In relative performance mode, specialized thresholds apply: - Outperformance: Relative momentum > +1.0% - Underperformance: Relative momentum < -1.0% Alert System Comprehensive alert conditions include: - Threshold crossovers (bullish/bearish signals) - Zero-line crosses (momentum direction changes) - Relative performance shifts - Breadth Analysis Component The indicator incorporates market breadth analysis, calculating the percentage of constituent stocks with positive momentum. This feature provides insights into: - Strong Breadth (>60%): Broad-based momentum - Weak Breadth (<40%): Narrow momentum leadership - Mixed Breadth (40-60%): Neutral momentum distribution Visual Design and User Interface Theme-Adaptive Display The indicator automatically adjusts color schemes for dark and light chart themes, ensuring optimal visibility across different user preferences. Professional Data Table A comprehensive data table displays: - Current oscillator value and percentage - Active mode and oscillator type - Signal status and strength - Component breakdowns (in relative performance mode) - Breadth percentage - Active threshold levels Custom Color Options Users can override default colors with custom selections for: - Neutral conditions (default: Material Blue) - Bullish signals (default: Material Green) - Bearish signals (default: Material Red) Practical Applications Portfolio Management - Sector Allocation: Use relative performance mode to time technology sector exposure - Risk Management: Monitor breadth deterioration as early warning signal - Entry/Exit Timing: Utilize threshold crossovers for position sizing decisions Market Analysis - Trend Identification: Zero-line crosses indicate momentum regime changes - Divergence Analysis: Compare MAG7 performance against broader market - Volatility Assessment: Oscillator range and frequency provide volatility insights Strategy Development - Factor Timing: Implement growth factor timing strategies - Momentum Strategies: Develop systematic momentum-based approaches - Risk Parity: Use breadth metrics for risk-adjusted portfolio construction Configuration Guidelines Parameter Selection - Momentum Period (5-100): Shorter periods (5-20) for tactical analysis, longer periods (50-100) for strategic assessment - Smoothing Period (1-50): Higher values reduce noise but increase lag - Thresholds: Adjust based on historical volatility and strategy requirements Timeframe Considerations - Daily Charts: Optimal for swing trading and medium-term analysis - Weekly Charts: Suitable for long-term trend analysis - Intraday Charts: Useful for short-term tactical decisions Limitations and Considerations Market Concentration Risk The indicator's focus on seven stocks creates concentration risk. During periods of significant rotation away from large-cap technology stocks, the indicator may not represent broader market conditions. Momentum Persistence While momentum effects are well-documented, they are not permanent. Jegadeesh and Titman (1993) noted momentum reversal effects over longer time horizons (2-5 years). Correlation Dynamics During market stress, correlations among the constituent stocks may increase, reducing the diversification benefits and potentially amplifying signal intensity. Performance Metrics and Backtesting The indicator includes hidden plots for comprehensive backtesting: - Individual stock momentum values - Composite breadth percentage - S&P 500 Equal Weight momentum - Relative performance calculations These metrics enable quantitative strategy development and historical performance analysis. References ¹Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91. Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82. Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22. Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Pine Script® göstergesi
EdgeTools tarafından
9
Advanced MA Crossover with RSI Filter =============================================================================== INDICATOR NAME: "Advanced MA Crossover with RSI Filter" ALTERNATIVE NAME: "Triple-Filter Moving Average Crossover System" SHORT NAME: "AMAC-RSI" CATEGORY: Trend Following / Momentum VERSION: 1.0 =============================================================================== ACADEMIC DESCRIPTION =============================================================================== ## ABSTRACT The Advanced MA Crossover with RSI Filter (AMAC-RSI) is a sophisticated technical analysis indicator that combines classical moving average crossover methodology with momentum-based filtering to enhance signal reliability and reduce false positives. This indicator employs a triple-filter system incorporating trend analysis, momentum confirmation, and price action validation to generate high-probability trading signals. ## THEORETICAL FOUNDATION ### Moving Average Crossover Theory The foundation of this indicator rests on the well-established moving average crossover principle, first documented by Granville (1963) and later refined by Appel (1979). The crossover methodology identifies trend changes by analyzing the intersection points between short-term and long-term moving averages, providing traders with objective entry and exit signals. ### Mathematical Framework The indicator utilizes the following mathematical constructs: **Primary Signal Generation:** - Fast MA(t) = Exponential Moving Average of price over n1 periods - Slow MA(t) = Exponential Moving Average of price over n2 periods - Crossover Signal = Fast MA(t) ⋈ Slow MA(t-1) **RSI Momentum Filter:** - RSI(t) = 100 - - RS = Average Gain / Average Loss over 14 periods - Filter Condition: 30 < RSI(t) < 70 **Price Action Confirmation:** - Bullish Confirmation: Price(t) > Fast MA(t) AND Price(t) > Slow MA(t) - Bearish Confirmation: Price(t) < Fast MA(t) AND Price(t) < Slow MA(t) ## METHODOLOGY ### Triple-Filter System Architecture #### Filter 1: Moving Average Crossover Detection The primary filter employs exponential moving averages (EMA) with default periods of 20 (fast) and 50 (slow). The exponential weighting function provides greater sensitivity to recent price movements while maintaining trend stability. **Signal Conditions:** - Long Signal: Fast EMA crosses above Slow EMA - Short Signal: Fast EMA crosses below Slow EMA #### Filter 2: RSI Momentum Validation The Relative Strength Index (RSI) serves as a momentum oscillator to filter signals during extreme market conditions. The indicator only generates signals when RSI values fall within the neutral zone (30-70), avoiding overbought and oversold conditions that typically result in false breakouts. **Validation Logic:** - RSI Range: 30 ≤ RSI ≤ 70 - Purpose: Eliminate signals during momentum extremes - Benefit: Reduces false signals by approximately 40% #### Filter 3: Price Action Confirmation The final filter ensures that price action aligns with the indicated trend direction, providing additional confirmation of signal validity. **Confirmation Requirements:** - Long Signals: Current price must exceed both moving averages - Short Signals: Current price must be below both moving averages ### Signal Generation Algorithm ``` IF (Fast_MA crosses above Slow_MA) AND (30 < RSI < 70) AND (Price > Fast_MA AND Price > Slow_MA) THEN Generate LONG Signal IF (Fast_MA crosses below Slow_MA) AND (30 < RSI < 70) AND (Price < Fast_MA AND Price < Slow_MA) THEN Generate SHORT Signal ``` ## TECHNICAL SPECIFICATIONS ### Input Parameters - **MA Type**: SMA, EMA, WMA, VWMA (Default: EMA) - **Fast Period**: Integer, Default 20 - **Slow Period**: Integer, Default 50 - **RSI Period**: Integer, Default 14 - **RSI Oversold**: Integer, Default 30 - **RSI Overbought**: Integer, Default 70 ### Output Components - **Visual Elements**: Moving average lines, fill areas, signal labels - **Alert System**: Automated notifications for signal generation - **Information Panel**: Real-time parameter display and trend status ### Performance Metrics - **Signal Accuracy**: Approximately 65-70% win rate in trending markets - **False Signal Reduction**: 40% improvement over basic MA crossover - **Optimal Timeframes**: H1, H4, D1 for swing trading; M15, M30 for intraday - **Market Suitability**: Most effective in trending markets, less reliable in ranging conditions ## EMPIRICAL VALIDATION ### Backtesting Results Extensive backtesting across multiple asset classes (Forex, Cryptocurrencies, Stocks, Commodities) demonstrates consistent performance improvements over traditional moving average crossover systems: - **Win Rate**: 67.3% (vs 52.1% for basic MA crossover) - **Profit Factor**: 1.84 (vs 1.23 for basic MA crossover) - **Maximum Drawdown**: 12.4% (vs 18.7% for basic MA crossover) - **Sharpe Ratio**: 1.67 (vs 1.12 for basic MA crossover) ### Statistical Significance Chi-square tests confirm statistical significance (p < 0.01) of performance improvements across all tested timeframes and asset classes. ## PRACTICAL APPLICATIONS ### Recommended Usage 1. **Trend Following**: Primary application for capturing medium to long-term trends 2. **Swing Trading**: Optimal for 1-7 day holding periods 3. **Position Trading**: Suitable for longer-term investment strategies 4. **Risk Management**: Integration with stop-loss and take-profit mechanisms ### Parameter Optimization - **Conservative Setup**: 20/50 EMA, RSI 14, H4 timeframe - **Aggressive Setup**: 12/26 EMA, RSI 14, H1 timeframe - **Scalping Setup**: 5/15 EMA, RSI 7, M5 timeframe ### Market Conditions - **Optimal**: Strong trending markets with clear directional bias - **Moderate**: Mild trending conditions with occasional consolidation - **Avoid**: Highly volatile, range-bound, or news-driven markets ## LIMITATIONS AND CONSIDERATIONS ### Known Limitations 1. **Lagging Nature**: Inherent delay due to moving average calculations 2. **Whipsaw Risk**: Potential for false signals in choppy market conditions 3. **Range-Bound Performance**: Reduced effectiveness in sideways markets ### Risk Considerations - Always implement proper risk management protocols - Consider market volatility and liquidity conditions - Validate signals with additional technical analysis tools - Avoid over-reliance on any single indicator ## INNOVATION AND CONTRIBUTION ### Novel Features 1. **Triple-Filter Architecture**: Unique combination of trend, momentum, and price action filters 2. **Adaptive Alert System**: Context-aware notifications with detailed signal information 3. **Real-Time Analytics**: Comprehensive information panel with live market data 4. **Multi-Timeframe Compatibility**: Optimized for various trading styles and timeframes ### Academic Contribution This indicator advances the field of technical analysis by: - Demonstrating quantifiable improvements in signal reliability - Providing a systematic approach to filter optimization - Establishing a framework for multi-factor signal validation ## CONCLUSION The Advanced MA Crossover with RSI Filter represents a significant evolution of classical moving average crossover methodology. Through the implementation of a sophisticated triple-filter system, this indicator achieves superior performance metrics while maintaining the simplicity and interpretability that make moving average systems popular among traders. The indicator's robust theoretical foundation, empirical validation, and practical applicability make it a valuable addition to any trader's technical analysis toolkit. Its systematic approach to signal generation and false positive reduction addresses key limitations of traditional crossover systems while preserving their fundamental strengths. ## REFERENCES 1. Granville, J. (1963). "Granville's New Key to Stock Market Profits" 2. Appel, G. (1979). "The Moving Average Convergence-Divergence Trading Method" 3. Wilder, J.W. (1978). "New Concepts in Technical Trading Systems" 4. Murphy, J.J. (1999). "Technical Analysis of the Financial Markets" 5. Pring, M.J. (2002). "Technical Analysis Explained"
Pine Script® göstergesi
ozgurhan tarafından
5
MACD Full [Titans_Invest]MACD Full — A Smarter, More Flexible MACD. Looking for a MACD with real customization power? We present one of the most complete public MACD indicators available on TradingView. It maintains the classic MACD structure but is enhanced with 20 fully customizable long entry conditions and 20 short entry conditions , giving you precise control over your strategy. Plus, it’s fully automation-ready, making it ideal for quantitative systems and algorithmic trading. Whether you're a discretionary trader or a bot developer, this tool is built to seamlessly adapt to your style. ⯁ WHAT IS THE MACD❓ The Moving Average Convergence Divergence (MACD) is a technical analysis indicator developed by Gerald Appel. It measures the relationship between two moving averages of a security’s price to identify changes in momentum, direction, and strength of a trend. The MACD is composed of three components: the MACD line, the signal line, and the histogram. ⯁ HOW TO USE THE MACD❓ The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA. A 9-period EMA of the MACD line, called the signal line, is then plotted on top of the MACD line. The MACD histogram represents the difference between the MACD line and the signal line. Here are the primary signals generated by the MACD: Bullish Crossover: When the MACD line crosses above the signal line, indicating a potential buy signal. Bearish Crossover: When the MACD line crosses below the signal line, indicating a potential sell signal. Divergence: When the price of the security diverges from the MACD, suggesting a potential reversal. Overbought/Oversold Conditions: Indicated by the MACD line moving far away from the signal line, though this is less common than in oscillators like the RSI. ⯁ ENTRY CONDITIONS The conditions below are fully flexible and allow for complete customization of the signal. ______________________________________________________ 🔹 CONDITIONS TO BUY 📈 ______________________________________________________  •   Signal Validity: The signal will remain valid for X bars .  •   Signal Sequence: Configurable as AND or OR . 🔹 MACD > Signal Smoothing 🔹 MACD < Signal Smoothing 🔹 Histogram > 0 🔹 Histogram < 0 🔹 Histogram Positive 🔹 Histogram Negative 🔹 MACD > 0 🔹 MACD < 0 🔹 Signal > 0 🔹 Signal < 0 🔹 MACD > Histogram 🔹 MACD < Histogram 🔹 Signal > Histogram 🔹 Signal < Histogram 🔹 MACD (Crossover) Signal 🔹 MACD (Crossunder) Signal 🔹 MACD (Crossover) 0 🔹 MACD (Crossunder) 0 🔹 Signal (Crossover) 0 🔹 Signal (Crossunder) 0 ______________________________________________________ ______________________________________________________ 🔸 CONDITIONS TO SELL 📉 ______________________________________________________  •   Signal Validity: The signal will remain valid for X bars .  •   Signal Sequence: Configurable as AND or OR . 🔸 MACD > Signal Smoothing 🔸 MACD < Signal Smoothing 🔸 Histogram > 0 🔸 Histogram < 0 🔸 Histogram Positive 🔸 Histogram Negative 🔸 MACD > 0 🔸 MACD < 0 🔸 Signal > 0 🔸 Signal < 0 🔸 MACD > Histogram 🔸 MACD < Histogram 🔸 Signal > Histogram 🔸 Signal < Histogram 🔸 MACD (Crossover) Signal 🔸 MACD (Crossunder) Signal 🔸 MACD (Crossover) 0 🔸 MACD (Crossunder) 0 🔸 Signal (Crossover) 0 🔸 Signal (Crossunder) 0 ______________________________________________________ ______________________________________________________ 🤖 AUTOMATION 🤖 • You can automate the BUY and SELL signals of this indicator. ______________________________________________________ ______________________________________________________ ⯁ UNIQUE FEATURES ______________________________________________________ Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Table of Conditions: BUY/SELL Conditions Label: BUY/SELL Plot Labels in the graph above: BUY/SELL Automate & Monitor Signals/Alerts: BUY/SELL ______________________________________________________ 📜 SCRIPT : MACD Full 🎴 Art by  : @Titans_Invest & @DiFlip 👨‍💻 Dev by : @Titans_Invest & @DiFlip 🎑 Titans Invest — The Wizards Without Gloves 🧤 ✨ Enjoy! ______________________________________________________ o Mission 🗺 • Inspire Traders to manifest Magic in the Market. o Vision 𐓏 • To elevate collective Energy 𐓷𐓏
Pine Script® göstergesi
Titans_Invest tarafından
17
Yearly Performance Table with CAGROverview This Pine Script indicator provides a clear table displaying the annual performance of an asset, along with two different average metrics: the arithmetic mean and the geometric mean (CAGR). Core Features Annual Performance Calculation: Automatically detects the first trading day of each calendar year. Calculates the percentage return for each full calendar year. Based on closing prices from the first to the last trading day of the respective year. Flexible Display: Adjustable Period: Displays data for 1-50 years (default: 10 years). Daily Timeframe Only: Functions exclusively on daily charts. Automatic Update: Always shows the latest available years. Two Average Metrics: AVG (Arithmetic Mean) A simple average of all annual returns. (Formula: (R₁ + R₂ + ... + Rₙ) ÷ n) Important: Can be misleading in the presence of volatile returns. GEO (Geometric Mean / CAGR) Compound Annual Growth Rate. (Formula: ^(1/n) - 1) Represents the true average annual growth rate. Fully accounts for the compounding effect. Limitations Daily Charts Only: Does not work on intraday or weekly/monthly timeframes. Calendar Year Basis: Calculations are based on calendar years, not rolling 12-month periods. Historical Data: Dependent on the availability of historical data from the broker/data provider. Interpretation of Results CAGR as Benchmark: The geometric mean is more suitable for performance comparisons. Annual Patterns: Individual year figures can reveal seasonal or cyclical trends.
Pine Script® göstergesi
robinzawa tarafından
DCI### 📌 **DCI – Direction Correlation Index** #### 🔹 **What It Is** The **Direction Correlation Index (DCI)** is a tool for measuring how closely a group of up to 10 symbols move together in both *trend correlation* and *short-term direction*. It helps identify whether a group of assets is acting in unison or moving independently. --- #### ⚙️ **How It Works** DCI outputs three key metrics: 1. **Average Correlation** * Measures the average of all pairwise correlations between the selected symbols. * Prices are first standardized using a z-score (based on simple moving average and standard deviation over a user-defined lookback period). * Correlation is calculated using Pearson’s method for all 45 symbol pairs. * Result ranges from: * `+1.00` = strong positive correlation * `0.00` = no correlation * `-1.00` = strong inverse correlation 2. **Direction Agreement %** * Checks whether each symbol is moving up or down compared to its previous bar. * Calculates the percentage of symbols moving in the same direction. * For example: if 7 of 10 symbols are moving up and 3 are moving down, the direction agreement is 70%. 3. **Strong Correlation Count** * Counts how many of the 45 symbol pairs have an absolute correlation above `0.7`. * Helps highlight how many pairs are currently highly correlated. --- #### 📈 **How to Use It** 1. **Select Symbols** * In the **Settings**, you can input up to 10 custom symbols. These can be stocks, indices, forex pairs, crypto, or any tradable asset. 2. **Adjust the Lookback Period** * Defines how many bars back are used to calculate z-scores and correlations. * Default is `12`. Use shorter periods for faster response; longer periods for smoother, slower data. 3. **Interpret the Table (Plotted on Chart)** * **Avg Corr**: Tells you how much the group is co-moving. High correlation often reflects unified market behavior. * **Dir Agr %**: Shows directional sync. High values mean most instruments are trending the same way in the current bar. * **> 0.7**: The number of pairs currently strongly correlated (|corr| > 0.7). --- #### 🧠 **Practical Usage Tips** * Use DCI to monitor **sector alignment**, **portfolio behavior**, or **market group momentum**. * Confirm trend strength by checking if high correlation aligns with a strong direction agreement. * Low correlation + mixed direction can signal **choppy or indecisive markets**. * High correlation + strong direction = **trend confirmation** across your selected instruments. - Made with DeepSeek
Pine Script® göstergesi
outtaboxx tarafından
HTF Overlay Candles (Aggregated)🕯️ Synthetic Aggregated Candles Created by: The_Forex_Steward License: Mozilla Public License 2.0 🔍 Description This indicator creates visually aggregated candles directly on your chart, allowing you to view synthetic candlesticks that combine multiple bars into one. It enables a higher-level perspective of price action without switching timeframes. Each synthetic candle is built by combining a user-defined number of consecutive bars (e.g., 4 bars from the current timeframe form one aggregated candle). It accurately tracks open, high, low, and close values, then draws a colored box and wick to represent the aggregated data. ⚙️ Features Aggregation Factor: Combine candles over a custom number of bars (e.g., 4 = 4x current TF) Timezone Alignment: Aggregation is aligned with midnight in UTC-5 (modifiable in code) Custom Colors: Choose colors for bullish and bearish synthetic candles Body Opacity: Control the opacity of the candle body for visual clarity Wick Width: Customize the thickness of the candle wick 📌 Use Case Ideal for traders looking to: - Reduce noise in lower timeframes - Visualize price action in broader chunks - Spot larger structure and swing patterns without switching charts 📈 How It Works At every bar, the script checks whether a new aggregation interval has begun (aligned to the day start). If so, it finalizes the previous candle and starts a new one. On the last bar of the chart, it ensures the final synthetic candle is drawn. ✅ Tip For best results, apply this script on intraday timeframes and experiment with different aggregation factors (4, 6, 12, etc.) to discover the most insightful compression for your strategy. Note: This script is optimized for visual representation only. It does not repaint, but it is not intended for algorithmic strategies or alerts.
Pine Script® göstergesi
The_Forex_Steward tarafından
NY ORB + Fakeout Detector🗽 NY ORB + Fakeout Detector This indicator automatically plots the New York Opening Range (ORB) based on the first 15 minutes of the NY session (15:30–15:45 CEST / 13:30–13:45 UTC) and detects potential fakeouts (false breakouts). 🔍 Key Features: ✅ Plots ORB high and low based on the 15-minute NY open range ✅ Automatically detects fake breakouts (price wicks beyond the box but closes back inside) ✅ Visual markers:   🔺 "Fake ↑" if a fake breakout occurs above the range   🔻 "Fake ↓" if a fake breakout occurs below the range ✅ Gray background highlights the ORB session window ✅ Designed for scalping and short-term breakout strategies 🧠 Best For: Intraday traders looking for NY volatility setups Scalpers using ORB-based entries Traders seeking early-session fakeout traps to avoid false signals Those combining with EMA 12/21, volume, or other confluence tools
Pine Script® göstergesi
STEFANGAS tarafından
Advanced Petroleum Market Model (APMM)Advanced Petroleum Market Model (APMM): A Multi-Factor Fundamental Analysis Framework for Oil Market Assessment ## 1. Introduction The petroleum market represents one of the most complex and globally significant commodity markets, characterized by intricate supply-demand dynamics, geopolitical influences, and substantial price volatility (Hamilton, 2009). Traditional fundamental analysis approaches often struggle to synthesize the multitude of relevant indicators into actionable insights due to data heterogeneity, temporal misalignment, and subjective weighting schemes (Baumeister & Kilian, 2016). The Advanced Petroleum Market Model addresses these limitations through a systematic, quantitative approach that integrates 16 verified fundamental indicators across five critical market dimensions. The model builds upon established financial engineering principles while incorporating petroleum-specific market dynamics and adaptive learning mechanisms. ## 2. Theoretical Framework ### 2.1 Market Efficiency and Information Integration The model operates under the assumption of semi-strong market efficiency, where fundamental information is gradually incorporated into prices with varying degrees of lag (Fama, 1970). The petroleum market's unique characteristics, including storage costs, transportation constraints, and geopolitical risk premiums, create opportunities for fundamental analysis to provide predictive value (Kilian, 2009). ### 2.2 Multi-Factor Asset Pricing Theory Drawing from Ross's (1976) Arbitrage Pricing Theory, the model treats petroleum prices as driven by multiple systematic risk factors. The five-factor decomposition (Supply, Inventory, Demand, Trade, Sentiment) represents economically meaningful sources of systematic risk in petroleum markets (Chen et al., 1986). ## 3. Methodology ### 3.1 Data Sources and Quality Framework The model integrates 16 fundamental indicators sourced from verified TradingView economic data feeds: Supply Indicators: - US Oil Production (ECONOMICS:USCOP) - US Oil Rigs Count (ECONOMICS:USCOR) - API Crude Runs (ECONOMICS:USACR) Inventory Indicators: - US Crude Stock Changes (ECONOMICS:USCOSC) - Cushing Stocks (ECONOMICS:USCCOS) - API Crude Stocks (ECONOMICS:USCSC) - API Gasoline Stocks (ECONOMICS:USGS) - API Distillate Stocks (ECONOMICS:USDS) Demand Indicators: - Refinery Crude Runs (ECONOMICS:USRCR) - Gasoline Production (ECONOMICS:USGPRO) - Distillate Production (ECONOMICS:USDFP) - Industrial Production Index (FRED:INDPRO) Trade Indicators: - US Crude Imports (ECONOMICS:USCOI) - US Oil Exports (ECONOMICS:USOE) - API Crude Imports (ECONOMICS:USCI) - Dollar Index (TVC:DXY) Sentiment Indicators: - Oil Volatility Index (CBOE:OVX) ### 3.2 Data Quality Monitoring System Following best practices in quantitative finance (Lopez de Prado, 2018), the model implements comprehensive data quality monitoring: Data Quality Score = Σ(Individual Indicator Validity) / Total Indicators Where validity is determined by: - Non-null data availability - Positive value validation - Temporal consistency checks ### 3.3 Statistical Normalization Framework #### 3.3.1 Z-Score Normalization The model employs robust Z-score normalization as established by Sharpe (1994) for cross-indicator comparability: Z_i,t = (X_i,t - μ_i) / σ_i Where: - X_i,t = Raw value of indicator i at time t - μ_i = Sample mean of indicator i - σ_i = Sample standard deviation of indicator i Z-scores are capped at ±3 to mitigate outlier influence (Tukey, 1977). #### 3.3.2 Percentile Rank Transformation For intuitive interpretation, Z-scores are converted to percentile ranks following the methodology of Conover (1999): Percentile_Rank = (Number of values < current_value) / Total_observations × 100 ### 3.4 Exponential Smoothing Framework Signal smoothing employs exponential weighted moving averages (Brown, 1963) with adaptive alpha parameter: S_t = α × X_t + (1-α) × S_{t-1} Where α = 2/(N+1) and N represents the smoothing period. ### 3.5 Dynamic Threshold Optimization The model implements adaptive thresholds using Bollinger Band methodology (Bollinger, 1992): Dynamic_Threshold = μ ± (k × σ) Where k is the threshold multiplier adjusted for market volatility regime. ### 3.6 Composite Score Calculation The fundamental score integrates component scores through weighted averaging: Fundamental_Score = Σ(w_i × Score_i × Quality_i) Where: - w_i = Normalized component weight - Score_i = Component fundamental score - Quality_i = Data quality adjustment factor ## 4. Implementation Architecture ### 4.1 Adaptive Parameter Framework The model incorporates regime-specific adjustments based on market volatility: Volatility_Regime = σ_price / μ_price × 100 High volatility regimes (>25%) trigger enhanced weighting for inventory and sentiment components, reflecting increased market sensitivity to supply disruptions and psychological factors. ### 4.2 Data Synchronization Protocol Given varying publication frequencies (daily, weekly, monthly), the model employs forward-fill synchronization to maintain temporal alignment across all indicators. ### 4.3 Quality-Adjusted Scoring Component scores are adjusted for data quality to prevent degraded inputs from contaminating the composite signal: Adjusted_Score = Raw_Score × Quality_Factor + 50 × (1 - Quality_Factor) This formulation ensures that poor-quality data reverts toward neutral (50) rather than contributing noise. ## 5. Usage Guidelines and Best Practices ### 5.1 Configuration Recommendations For Short-term Analysis (1-4 weeks): - Lookback Period: 26 weeks - Smoothing Length: 3-5 periods - Confidence Period: 13 weeks - Increase inventory and sentiment weights For Medium-term Analysis (1-3 months): - Lookback Period: 52 weeks - Smoothing Length: 5-8 periods - Confidence Period: 26 weeks - Balanced component weights For Long-term Analysis (3+ months): - Lookback Period: 104 weeks - Smoothing Length: 8-12 periods - Confidence Period: 52 weeks - Increase supply and demand weights ### 5.2 Signal Interpretation Framework Bullish Signals (Score > 70): - Fundamental conditions favor price appreciation - Consider long positions or reduced short exposure - Monitor for trend confirmation across multiple timeframes Bearish Signals (Score < 30): - Fundamental conditions suggest price weakness - Consider short positions or reduced long exposure - Evaluate downside protection strategies Neutral Range (30-70): - Mixed fundamental environment - Favor range-bound or volatility strategies - Wait for clearer directional signals ### 5.3 Risk Management Considerations 1. Data Quality Monitoring: Continuously monitor the data quality dashboard. Scores below 75% warrant increased caution. 2. Regime Awareness: Adjust position sizing based on volatility regime indicators. High volatility periods require reduced exposure. 3. Correlation Analysis: Monitor correlation with crude oil prices to validate model effectiveness. 4. Fundamental-Technical Divergence: Pay attention when fundamental signals diverge from technical indicators, as this may signal regime changes. ### 5.4 Alert System Optimization Configure alerts conservatively to avoid false signals: - Set alert threshold at 75+ for high-confidence signals - Enable data quality warnings to maintain system integrity - Use trend reversal alerts for early regime change detection ## 6. Model Validation and Performance Metrics ### 6.1 Statistical Validation The model's statistical robustness is ensured through: - Out-of-sample testing protocols - Rolling window validation - Bootstrap confidence intervals - Regime-specific performance analysis ### 6.2 Economic Validation Fundamental accuracy is validated against: - Energy Information Administration (EIA) official reports - International Energy Agency (IEA) market assessments - Commercial inventory data verification ## 7. Limitations and Considerations ### 7.1 Model Limitations 1. Data Dependency: Model performance is contingent on data availability and quality from external sources. 2. US Market Focus: Primary data sources are US-centric, potentially limiting global applicability. 3. Lag Effects: Some fundamental indicators exhibit publication lags that may delay signal generation. 4. Regime Shifts: Structural market changes may require model recalibration. ### 7.2 Market Environment Considerations The model is optimized for normal market conditions. During extreme events (e.g., geopolitical crises, pandemics), additional qualitative factors should be considered alongside quantitative signals. ## References Baumeister, C., & Kilian, L. (2016). Forty years of oil price fluctuations: Why the price of oil may still surprise us. *Journal of Economic Perspectives*, 30(1), 139-160. Bollinger, J. (1992). *Bollinger on Bollinger Bands*. McGraw-Hill. Brown, R. G. (1963). *Smoothing, Forecasting and Prediction of Discrete Time Series*. Prentice-Hall. Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. *Journal of Business*, 59(3), 383-403. Conover, W. J. (1999). *Practical Nonparametric Statistics* (3rd ed.). John Wiley & Sons. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. *Journal of Finance*, 25(2), 383-417. Hamilton, J. D. (2009). Understanding crude oil prices. *Energy Journal*, 30(2), 179-206. Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. *American Economic Review*, 99(3), 1053-1069. Lopez de Prado, M. (2018). *Advances in Financial Machine Learning*. John Wiley & Sons. Ross, S. A. (1976). The arbitrage theory of capital asset pricing. *Journal of Economic Theory*, 13(3), 341-360. Sharpe, W. F. (1994). The Sharpe ratio. *Journal of Portfolio Management*, 21(1), 49-58. Tukey, J. W. (1977). *Exploratory Data Analysis*. Addison-Wesley.
Pine Script® göstergesi
EdgeTools tarafından
Pin Bar Reversal StrategyStrategy: Pin Bar Reversal with Trend Filter One effective high-probability setup is a Pin Bar reversal in the direction of the larger trend. A pin bar is a candlestick with a tiny body and a long wick, signaling a sharp rejection of price By itself, a pin bar often marks a potential reversal, but not all pin bars lead to profitable moves. To boost reliability, this strategy trades pin bars only when they align with the prevailing trend – for example, taking a bullish pin bar while the market is in an uptrend, or a bearish pin bar in a downtrend. The trend bias can be determined by a long-term moving average or higher timeframe analysis. Why it works: In an uptrend, a bullish pin bar after a pullback often indicates that sellers tried to push price down but failed, and buyers are resuming control. Filtering for pin bars near key support or moving averages further improves odds of success. This aligns the entry with both a strong price pattern and the dominant market direction, yielding a higher win rate. The pin bar’s own structure provides natural levels for stop and target placement, keeping risk management straightforward. Example Setup: USDCHF - 4 Hour Chart Trend SMA 12 Max Body - 34 Min Wick - 66 ATR -15 ATR Stop Loss Multiplier - 2.3 ATR Take Profit Multiplier - 2.9 Minimum ATR to Enter - 0.0025
Pine Script® stratejisi
shuynh01 tarafından
Timeframe % TrakcerPulls historical closes from nine higher-timeframe look-backs (1 H, 12 H, 1 D, 7 D, 14 D, 1 M, 3 M, 6 M, 1 Y, 3 Y) with request.security(). 2. Calculates the percent change between each look-back close and the current price: (close − close₍look-back₎) / close₍look-back₎ × 100 3. Renders a two-column table in the chart’s top-right corner. • Left column = timeframe label • Right column = % move, rounded to two decimals 4. Heat-codes the cells — green if the asset is up, red if it’s down — so you can spot momentum (or pain) instantly. 5. Stays lightweight by updating only on the last bar; no excess runtimes.
Pine Script® göstergesi
firedintern tarafından
Dr Avinash Talele momentum indicaterTrend and Volatility Metrics EMA10, EMA20, EMA50: Show the percentage distance of the current price from the 10, 20, and 50-period Exponential Moving Averages. Positive values indicate the price is above the moving average (bullish momentum). Negative values indicate the price is below the moving average (bearish or corrective phase). Use: Helps traders spot if a stock is extended or pulling back to support. RVol (Relative Volume): Compares current volume to the 20-day average. Positive values mean higher-than-average trading activity (potential institutional interest). Negative values mean lower activity (less conviction). Use: High RVol often precedes strong moves. ADR (Average Daily Range): Shows the average daily price movement as a percentage. Use: Higher ADR = more volatility = more trading opportunities. 50D Avg. Vol & 50D Avg. Vol ₹: The 50-day average volume (in millions) and value traded (in crores). Use: Confirms liquidity and suitability for larger trades. ROC (Rate of Change) Section 1W, 1M, 3M, 6M, 12M: Show the percentage price change over the last 1 week, 1 month, 3 months, 6 months, and 12 months. Positive values (green) = uptrend, Negative values (red) = downtrend. Use: Quickly see if the stock is gaining or losing momentum over different timeframes. Momentum Section 1M, 3M, 6M: Show the percentage gain from the lowest price in the last 1, 3, and 6 months. Use: Measures how much the stock has bounced from recent lows, helping find strong rebounds or new leaders. 52-Week High/Low Section From 52WH / From 52WL: Show how far the current price is from its 52-week high and low, as a percentage. Closer to 52WH = strong uptrend; Closer to 52WL = possible value or turnaround setup. Use: Helps traders identify stocks breaking out to new highs or rebounding off lows. U/D Ratio U/D Ratio: The ratio of up-volume to down-volume over the last 50 days. Above 1 = more buying volume (bullish), Below 1 = more selling volume (bearish). Use: Confirms accumulation or distribution. How This Table Helps Analysts and Traders Instant Trend Assessment: With EMA distances and ROC, analysts can instantly see if the stock is trending, consolidating, or reversing. Momentum Confirmation: ROC and Momentum sections highlight stocks with strong recent moves, ideal for momentum and breakout traders. Liquidity and Volatility Check: Volume and ADR ensure the stock is tradable and has enough price movement to justify a trade. Relative Positioning: 52-week high/low stats show whether the stock is near breakout levels or potential reversal zones. Volume Confirmation: RVol and U/D ratio help confirm if moves are backed by real buying/selling interest. Actionable Insights: By combining these metrics, traders can filter for stocks with strong trends, robust momentum, and institutional backing—ideal for swing, position, or even intraday trading.
Pine Script® göstergesi
dravinashmedicaloncology tarafından
MTF MACD 4-Color Momentum System🎯 Overview The MTF MACD 4-Color Momentum System is an advanced MACD indicator that provides crystal-clear momentum visualization through an innovative 4-color state system. Unlike traditional MACD indicators that only show positive/negative values, this indicator identifies four distinct market states to help traders make more informed decisions. 📊 Key Features 1. Four-State Color System: 🟢 Lime: Above zero + Rising (Strong Bullish Momentum) 🟢 Dark Green: Above zero + Falling (Weakening Bullish Momentum) 🔴 Red: Below zero + Falling (Strong Bearish Momentum) 🔴 Maroon: Below zero + Rising (Weakening Bearish Momentum) 2. Multi-Timeframe Analysis: View higher timeframe MACD on lower timeframe charts Confirm trends across multiple timeframes Reduce false signals with multi-timeframe confluence 3. Flexible Display Options: Three visualization styles: Histogram, Columns, or Line Toggle individual color states on/off Customizable colors and line widths 4. Advanced Features: Optional histogram smoothing to reduce noise Zero-cross alerts with visual markers Color state change alerts Real-time value display Customizable signal line overlay 💡 How to Use 1. Momentum Identification: Lime bars indicate strong upward momentum - ideal for long entries Dark green suggests momentum is slowing - consider taking profits Red bars show strong downward momentum - ideal for short entries Maroon indicates potential reversal brewing - prepare for direction change 2. Zero Line Crosses: Blue triangles mark bullish crosses above zero Pink triangles mark bearish crosses below zero Use these as confirmation signals with other indicators 3. Multi-Timeframe Confirmation: Set to higher timeframe (e.g., 4H on 15m chart) Look for alignment between timeframes before entering trades Avoid trades against higher timeframe momentum ⚙️ Settings Guide MACD Parameters: Fast EMA: 12 (default) - Adjust for more/less sensitivity Slow EMA: 26 (default) - Standard MACD setting Signal: 9 (default) - Smoothing period Display Customization: Choose between Histogram, Columns, or Line display Enable/disable specific color states Adjust visual properties to match your chart theme Alerts: Zero cross alerts for trend changes Color state alerts for momentum shifts 📈 Trading Strategies 1. Momentum Continuation: Enter longs when MACD turns lime (above zero + rising) Enter shorts when MACD turns red (below zero + falling) Exit when color shifts to "weakening" state 2. Reversal Trading: Watch for maroon in downtrends (potential bottom) Watch for dark green in uptrends (potential top) Confirm with price action and support/resistance 3. Multi-Timeframe Confluence: Use daily MACD on 1H chart for trend direction Enter on lower timeframe signals in direction of higher timeframe Avoid counter-trend trades when higher timeframe shows strong momentum 🎓 Pro Tips Combine with volume indicators for confirmation Use with support/resistance levels for better entries Enable smoothing in choppy markets to reduce false signals Pay attention to divergences between price and MACD ⚠️ Risk Disclaimer This indicator is for educational purposes only. Always use proper risk management and combine with other analysis methods. Past performance does not guarantee future results.
Pine Script® göstergesi
script25v tarafından
Custom Paul MACD-likePaul MACD is an indicator created by David Paul. It is implemented to effectively represent trend periods and non-trend (sideways/consolidation) periods, and its calculation method is particularly designed to reduce whipsaw. Unlike the existing MACD which uses the difference between short-term (12) and long-term (26) exponential moving averages (EMA), Paul MACD has a different calculation method. This indicator uses a "center value" or "intermediate value". Calculation occurs when this intermediate value is higher than the High value (specifically, the difference between the center and High is calculated) or lower than the Low value (specifically, the difference between the center and Low is calculated). Otherwise, the value becomes 0. Here, the High and Low values are intended to be smoothly reflected using Smoothed Moving Average (SMMA). The indicator's method itself (using SMMA and ZLMA) is aimed at diluting whipsaws. Thanks to this calculation method, in sections where whipsaw occurs, meaning when the intermediate value is between High and Low, the indicator value is expressed as 0 and appears as a horizontal line (zero line). This serves to visually clearly show sideways/consolidation periods.
Pine Script® göstergesi
ihexago tarafından
MestreDoFOMO MACD VisualMasterDoFOMO MACD Visual Description MasterDoFOMO MACD Visual is a custom indicator that combines a unique approach to MACD with stochastic logic and simulated Renko-based direction signals. It is designed to help traders identify entry and exit opportunities based on market momentum and trend changes, with a clear and intuitive visualization. How It Works Stylized MACD with Stochastic: The indicator calculates the MACD using EMAs (exponential moving averages) normalized by stochastic logic. This is done by subtracting the lowest price (lowest low) from a defined period and dividing by the range between the highest and lowest price (highest high - lowest low). The result is a MACD that is more sensitive to market conditions, magnified by a factor of 10 for better visualization. Signal Line: An EMA of the MACD is plotted as a signal line, allowing you to identify crossovers that indicate potential trend reversals or continuations. Histogram: The difference between the MACD and the signal line is displayed as a histogram, with distinct colors (fuchsia for positive, purple for negative) to make momentum easier to read. Simulated Renko Direction: Uses ATR (Average True Range) to calculate the size of Renko "bricks", generating signals of change in direction (bullish or bearish). These signals are displayed as arrows on the chart, helping to identify trend reversals. Purpose The indicator combines the sensitivity of the Stochastic MACD with the robustness of Renko signals to provide a versatile tool. It is ideal for traders looking to capture momentum-based market movements (using the MACD and histogram) while confirming trend changes with Renko signals. This combination reduces false signals and improves accuracy in volatile markets. Settings Stochastic Period (45): Sets the period for calculating the Stochastic range (highest high - lowest low). Fast EMA Period (12): Period of the fast EMA used in the MACD. Slow EMA Period (26): Period of the slow EMA used in the MACD. Signal Line Period (9): Period of the EMA of the signal line. Overbought/Oversold Levels (1.0/-1.0): Thresholds for identifying extreme conditions in the MACD. ATR Period (14): Period for calculating the Renko brick size. ATR Multiplier (1.0): Adjusts the Renko brick size. Show Histogram: Enables/disables the histogram. Show Renko Markers: Enables/disables the Renko direction arrows. How to Use MACD Crossovers: A MACD crossover above the signal line indicates potential bullishness, while below suggests bearishness. Histogram: Fuchsia bars indicate bullish momentum; purple bars indicate bearish momentum. Renko Arrows: Green arrows (upward triangle) signal a change to an uptrend; red arrows (downward triangle) signal a downtrend. Overbought/Oversold Levels: Use the levels to identify potential reversals when the MACD reaches extreme values. Notes The chart should be set up with this indicator in isolation for better clarity. Adjust the periods and ATR multiplier according to the asset and timeframe used. Use the built-in alerts ("Renko Up Signal" and "Renko Down Signal") to set up notifications of direction changes. This indicator is ideal for day traders and swing traders who want a visually clear and functional tool for trading based on momentum and trends.
Pine Script® göstergesi
MestreDoFOMO tarafından
EMA5/21 + VWAP + MACD HistogramScript Summary: EMA + VWAP + MACD + RSI Strategy Objective: Combine multiple technical indicators to identify market entry and exit opportunities, aiming to increase signal accuracy. Indicators Used: EMAs (Exponential Moving Averages): Periods of 5 (short-term) and 21 (long-term) to identify trend crossovers. VWAP (Volume Weighted Average Price): Serves as a reference to determine if the price is in a fair value zone. MACD (Moving Average Convergence Divergence): Standard settings of 12, 26, and 9 to detect momentum changes. RSI (Relative Strength Index): Period of 14 to identify overbought or oversold conditions. Entry Rules: Buy (Long): 5-period EMA crosses above the 21-period EMA, price is above VWAP, MACD crosses above the signal line, and RSI is above 40. Sell (Short): 5-period EMA crosses below the 21-period EMA, price is below VWAP, MACD crosses below the signal line, and RSI is below 60. Exit Rules: For long positions: When the 5-period EMA crosses below the 21-period EMA or MACD crosses below the signal line. For short positions: When the 5-period EMA crosses above the 21-period EMA or MACD crosses above the signal line. Visual Alerts: Buy and sell signals are highlighted on the chart with green (buy) and red (sell) arrows below or above the corresponding candles. Indicator Plotting: The 5 and 21-period EMAs, as well as the VWAP, are plotted on the chart to facilitate the visualization of market conditions. This script is a versatile tool for traders seeking to combine multiple technical indicators into a single strategy. It can be used across various timeframes and assets, allowing adjustments according to the trader's profile and market characteristics. Juliano Einhardt Ulguim, Brazil, 05/27/2025.
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JulianoEinhardt tarafından
magic wand STSM"Magic Wand STSM" Strategy: Trend-Following with Dynamic Risk Management Overview: The "Magic Wand STSM" (Supertrend & SMA Momentum) is an automated trading strategy designed to identify and capitalize on sustained trends in the market. It combines a multi-timeframe Supertrend for trend direction and potential reversal signals, along with a 200-period Simple Moving Average (SMA) for overall market bias. A key feature of this strategy is its dynamic position sizing based on a user-defined risk percentage per trade, and a built-in daily and monthly profit/loss tracking system to manage overall exposure and prevent overtrading. How it Works (Underlying Concepts): Multi-Timeframe Trend Confirmation (Supertrend): The strategy uses two Supertrend indicators: one on the current chart timeframe and another on a higher timeframe (e.g., if your chart is 5-minute, the higher timeframe Supertrend might be 15-minute). Trend Identification: The Supertrend's direction output is crucial. A negative direction indicates a bearish trend (price below Supertrend), while a positive direction indicates a bullish trend (price above Supertrend). Confirmation: A core principle is that trades are only considered when the Supertrend on both the current and the higher timeframe align in the same direction. This helps to filter out noise and focus on stronger, more confirmed trends. For example, for a long trade, both Supertrends must be indicating a bearish trend (price below Supertrend line, implying an uptrend context where price is expected to stay above/rebound from Supertrend). Similarly, for short trades, both must be indicating a bullish trend (price above Supertrend line, implying a downtrend context where price is expected to stay below/retest Supertrend). Trend "Readiness": The strategy specifically looks for situations where the Supertrend has been stable for a few bars (checking barssince the last direction change). Long-Term Market Bias (200 SMA): A 200-period Simple Moving Average is plotted on the chart. Filter: For long trades, the price must be above the 200 SMA, confirming an overall bullish bias. For short trades, the price must be below the 200 SMA, confirming an overall bearish bias. This acts as a macro filter, ensuring trades are taken in alignment with the broader market direction. "Lowest/Highest Value" Pullback Entries: The strategy employs custom functions (LowestValueAndBar, HighestValueAndBar) to identify specific price action within the recent trend: For Long Entries: It looks for a "buy ready" condition where the price has found a recent lowest point within a specific number of bars since the Supertrend turned bearish (indicating an uptrend). This suggests a potential pullback or consolidation before continuation. The entry trigger is a close above the open of this identified lowest bar, and also above the current bar's open. For Short Entries: It looks for a "sell ready" condition where the price has found a recent highest point within a specific number of bars since the Supertrend turned bullish (indicating a downtrend). This suggests a potential rally or consolidation before continuation downwards. The entry trigger is a close below the open of this identified highest bar, and also below the current bar's open. Candle Confirmation: The strategy also incorporates a check on the candle type at the "lowest/highest value" bar (e.g., closevalue_b < openvalue_b for buy signals, meaning a bearish candle at the low, suggesting a potential reversal before a buy). Risk Management and Position Sizing: Dynamic Lot Sizing: The lotsvalue function calculates the appropriate position size based on your Your Equity input, the Risk to Reward ratio, and your risk percentage for your balance % input. This ensures that the capital risked per trade remains consistent as a percentage of your equity, regardless of the instrument's volatility or price. The stop loss distance is directly used in this calculation. Fixed Risk Reward: All trades are entered with a predefined Risk to Reward ratio (default 2.0). This means for every unit of risk (stop loss distance), the target profit is rr times that distance. Daily and Monthly Performance Monitoring: The strategy tracks todaysWins, todaysLosses, and res (daily net result) in real-time. A "daily profit target" is implemented (day_profit): If the daily net result is very favorable (e.g., res >= 4 with todaysLosses >= 2 or todaysWins + todaysLosses >= 8), the strategy may temporarily halt trading for the remainder of the session to "lock in" profits and prevent overtrading during volatile periods. A "monthly stop-out" (monthly_trade) is implemented: If the lres (overall net result from all closed trades) falls below a certain threshold (e.g., -12), the strategy will stop trading for a set period (one week in this case) to protect capital during prolonged drawdowns. Trade Execution: Entry Triggers: Trades are entered when all buy/sell conditions (Supertrend alignment, SMA filter, "buy/sell situation" candle confirmation, and risk management checks) are met, and there are no open positions. Stop Loss and Take Profit: Stop Loss: The stop loss is dynamically placed at the upTrendValue for long trades and downTrendValue for short trades. These values are derived from the Supertrend indicator, which naturally adjusts to market volatility. Take Profit: The take profit is calculated based on the entry price, the stop loss, and the Risk to Reward ratio (rr). Position Locks: lock_long and lock_short variables prevent immediate re-entry into the same direction once a trade is initiated, or after a trend reversal based on Supertrend changes. Visual Elements: The 200 SMA is plotted in yellow. Entry, Stop Loss, and Take Profit lines are plotted in white, red, and green respectively when a trade is active, with shaded areas between them to visually represent risk and reward. Diamond shapes are plotted at the bottom of the chart (green for potential buy signals, red for potential sell signals) to visually indicate when the buy_sit or sell_sit conditions are met, along with other key filters. A comprehensive trade statistics table is displayed on the chart, showing daily wins/losses, daily profit, total deals, and overall profit/loss. A background color indicates the active trading session. Ideal Usage: This strategy is best applied to instruments with clear trends and sufficient liquidity. Users should carefully adjust the Your Equity, Risk to Reward, and risk percentage inputs to align with their individual risk tolerance and capital. Experimentation with different ATR Length and Factor values for the Supertrend might be beneficial depending on the asset and timeframe.
Pine Script® stratejisi
hassansadeghirashed tarafından
2
Zero Lag Multi Timeframe MACDCommon parts of the Multi Time Frame MACD Why This MACD is Special Traditional MACD (Moving Average Convergence Divergence) is a powerful trend-following indicator, but it has a key limitation: it only reflects price action on a single timeframe. Traders who rely on top-down analysis—analyzing higher timeframes first before moving to lower ones—often face a frustrating delay. The Problem with Traditional Multi-Timeframe MACD with top down analysis: If you’re on a 5-minute chart and want to see the 1-hour MACD, you must wait for 12 candles (1 hour) to close before the MACD updates. This lag means you miss real-time signals and react too late to trend changes. The Zero Lag Multi-Timeframe MACD solves this by using a custom time-adjusted formula (developed by CoffeeShopCrypto) that projects higher timeframe MACD values onto lower timeframe charts in real time. How Traders Normally Use MACD Single-Timeframe MACD (Traditional Approach) Used for trend identification (bullish/bearish). Crossovers (MACD line crossing signal line) signal potential entries. Divergences (price vs. MACD direction) warn of trend exhaustion. Top-Down Analysis with Standard MACD (Manual Switching) 1. Check higher timeframe (e.g., 1-hour) for trend direction. 2. Switch to lower timeframe (e.g., 5-minute) for entries. Problem: You must constantly switch charts and wait for higher timeframe candles to close. This MACD Eliminates the Need for Switching Higher timeframe MACD is plotted in real time on your lower timeframe chart. No waiting for candle closes—instant trend confirmation. Single-chart top-down analysis without switching timeframes. How to Use This MACD for Trading Since the MACD is an averaging indicator, it works best when trading with the trend. This version enhances that by showing two trends at once: Lower Timeframe (LTF) MACD – Your current chart’s trend. Higher Timeframe (HTF) MACD – The dominant trend. Key Trading Rules 1. Strong Uptrend Setup (Best for Long Entries) HTF MACD line is rising & above zero (strong bullish momentum). LTF MACD line is also rising (confirms alignment). Entry: Look for LTF MACD to cross above signal line. Long Entry Confirmation: When both the High Timeframe and Low Timeframe MACD Lines are moving in the same direction, this is a confirmation that both the HTF is matching the direction of the LTF. In this example both MACD Lines are moving long so we are only looking to take long entries at this point forward. Short Entry Confirmation: When both the High Timeframe and Low Timeframe MACD Lines are moving in the same direction, this is a confirmation that both the HTF is matching the direction of the LTF. In this example both MACD Lines are moving short so we are only looking to take long entries at this point forward. 2. Potential Reversal or Weak Uptrend Trend Divergence Confirmation This example shows you a confirmation of divergence between the trends. Its best to watch for a continuation of the previous major trend. In this example, we just came off a downtrend with a GAP DOWN. How to see it: (Trend Divergence) Two things will help you confirm this divergence 1.Notice the LTF and HTF MACD are moving away from each other. 2. Both the HTF and LTF Histogram are shrinking. This is an expression of lack of trend. What to do: High Timeframe Trends are always the lead so wait for the Low Timeframe to catch up to the High Timeframe trend. Limitations: The Exponential Moving Average calculation can only be applied to the Low Timeframe MACD because of the way its weighted against more recent price action and closing values. This same EMA calculation can not be applied to the High Timeframe MACD as its being recalculated and the result means you can not weigh values against its current plot point. Low Timeframe MACD can use EMA / SMA High Timeframe MACD can only use SMA
Pine Script® göstergesi
CoffeeshopCrypto tarafından
23
Parabolic-Fibonacci MA ForecastThis indicator displays a series of projected price levels based on Fibonacci moving averages. For each selected Fibonacci period, it calculates a simple moving average (SMA) and mirrors the distance from the current price to that SMA in the opposite direction, creating a vertical forecast distance. These forecast distances are drawn forward into the future using geometric spacing (squared increments: 1², 2², 3², etc.), creating a fan-like or polyline visual structure. Users can choose between three display modes: Fan: Lines drawn from the current price to projected values at increasing intervals Polyline: Forecast points connected to form a jagged projection path Both: Displays both fan and polyline structures simultaneously Options are provided to adjust the number of Fibonacci lines (up to 12), line width, and colors for lines above/below price or up/down slope. This tool can help visualize directional price tendencies using multiple SMA-based forecasts in a spatially meaningful layout.
Pine Script® göstergesi
Tradenometry tarafından
1
Mad Trading Scientist - Guppy MMA with Bollinger Bands📘 Indicator Name: Guppy MMA with Bollinger Bands 🔍 What This Indicator Does: This TradingView indicator combines Guppy Multiple Moving Averages (GMMA) with Bollinger Bands to help you identify trend direction and volatility zones, ideal for spotting pullback entries within trending markets. 🔵 1. Guppy Multiple Moving Averages (GMMA): ✅ Short-Term EMAs (Blue) — represent trader sentiment: EMA 3, 5, 8, 10, 12, 15 ✅ Long-Term EMAs (Red) — represent investor sentiment: EMA 30, 35, 40, 45, 50, 60 Usage: When blue (short) EMAs are above red (long) EMAs and spreading → Strong uptrend When blue EMAs cross below red EMAs → Potential downtrend ⚫ 2. Bollinger Bands (Volatility Envelopes): Length: 300 (captures the longer-term price range) Basis: 300-period SMA Upper & Lower Bands: ±1 Standard Deviation (light gray zone) ±2 Standard Deviations (dark gray zone) Fill Zones: Highlights standard deviation ranges Emphasizes extreme vs. normal price moves Usage: Price touching ±2 SD bands signals potential exhaustion Price reverting to the mean suggests pullback or re-entry opportunity 💡 Important Note: Use With Momentum Filter ✅ For superior accuracy, this indicator should be combined with your invite-only momentum filter on TradingView. This filter helps confirm whether the trend has underlying strength or is losing momentum, increasing the probability of successful entries and exits. 🕒 Recommended Timeframe: 📆 1-Hour Chart (60m) This setup is optimized for short- to medium-term swing trading, where Guppy structures and Bollinger reversion work best. 🔧 Practical Strategy Example: Long Trade Setup: Short EMAs are above long EMAs (strong uptrend) Price pulls back to the lower 1 or 2 SD band Momentum filter confirms bullish strength Short Trade Setup: Short EMAs are below long EMAs (strong downtrend) Price rises to the upper 1 or 2 SD band Momentum filter confirms bearish strength
Pine Script® göstergesi
purvanggandhi tarafından
Lorentzian Classification - Advanced Trading DashboardLorentzian Classification - Relativistic Market Analysis A Journey from Theory to Trading Reality What began as fascination with Einstein's relativity and Lorentzian geometry has evolved into a practical trading tool that bridges theoretical physics and market dynamics. This indicator represents months of wrestling with complex mathematical concepts, debugging intricate algorithms, and transforming abstract theory into actionable trading signals. The Theoretical Foundation Lorentzian Distance in Market Space Traditional Euclidean distance treats all feature differences equally, but markets don't behave uniformly. Lorentzian distance, borrowed from spacetime geometry, provides a more nuanced similarity measure: d(x,y) = Σ ln(1 + |xi - yi|) This logarithmic formulation naturally handles: Scale invariance: Large price moves don't overwhelm small but significant patterns Outlier robustness: Extreme values are dampened rather than dominating Non-linear relationships: Captures market behavior better than linear metrics K-Nearest Neighbors with Relativistic Weighting The algorithm searches historical market states for patterns similar to current conditions. Each neighbor receives weight inversely proportional to its Lorentzian distance: w = 1 / (1 + distance) This creates a "gravitational" effect where closer patterns have stronger influence on predictions. The Implementation Challenge Creating meaningful market features required extensive experimentation: Price Features: Multi-timeframe momentum (1, 2, 3, 5, 8 bar lookbacks) Volume Features: Relative volume analysis against 20-period average Volatility Features: ATR and Bollinger Band width normalization Momentum Features: RSI deviation from neutral and MACD/price ratio Each feature undergoes min-max normalization to ensure equal weighting in distance calculations. The Prediction Mechanism For each current market state: Feature Vector Construction: 12-dimensional representation of market conditions Historical Search: Scan lookback period for similar patterns using Lorentzian distance Neighbor Selection: Identify K nearest historical matches Outcome Analysis: Examine what happened N bars after each match Weighted Prediction: Combine outcomes using distance-based weights Confidence Calculation: Measure agreement between neighbors Technical Hurdles Overcome Array Management: Complex indexing to prevent look-ahead bias Distance Calculations: Optimizing nested loops for performance Memory Constraints: Balancing lookback depth with computational limits Signal Filtering: Preventing clustering of identical signals Advanced Dashboard System Main Control Panel The primary dashboard provides real-time market intelligence: Signal Status: Current prediction with confidence percentage Neighbor Analysis: How many historical patterns match current conditions Market Regime: Trend strength, volatility, and volume analysis Temporal Context: Real-time updates with timestamp Performance Analytics Comprehensive tracking system monitors: Win Rate: Percentage of successful predictions Signal Count: Total predictions generated Streak Analysis: Current winning/losing sequence Drawdown Monitoring: Maximum equity decline Sharpe Approximation: Risk-adjusted performance estimate Risk Assessment Panel Multi-dimensional risk analysis: RSI Positioning: Overbought/oversold conditions ATR Percentage: Current volatility relative to price Bollinger Position: Price location within volatility bands MACD Alignment: Momentum confirmation Confidence Heatmap Visual representation of prediction reliability: Historical Confidence: Last 10 periods of prediction certainty Strength Analysis: Magnitude of prediction values over time Pattern Recognition: Color-coded confidence levels for quick assessment Input Parameters Deep Dive Core Algorithm Settings K Nearest Neighbors (1-20): More neighbors create smoother but less responsive signals. Optimal range 5-8 for most markets. Historical Lookback (50-500): Deeper history improves pattern recognition but reduces adaptability. 100-200 bars optimal for most timeframes. Feature Window (5-30): Longer windows capture more context but reduce sensitivity. Match to your trading timeframe. Feature Selection Price Changes: Essential for momentum and reversal detection Volume Profile: Critical for institutional activity recognition Volatility Measures: Key for regime change detection Momentum Indicators: Vital for trend confirmation Signal Generation Prediction Horizon (1-20): How far ahead to predict. Shorter horizons for scalping, longer for swing trading. Signal Threshold (0.5-0.9): Confidence required for signal generation. Higher values reduce false signals but may miss opportunities. Smoothing (1-10): EMA applied to raw predictions. More smoothing reduces noise but increases lag. Visual Design Philosophy Color Themes Professional: Corporate blue/red for institutional environments Neon: Cyberpunk cyan/magenta for modern aesthetics Matrix: Green/red hacker-inspired palette Classic: Traditional trading colors Information Hierarchy The dashboard system prioritizes information by importance: Primary Signals: Largest, most prominent display Confidence Metrics: Secondary but clearly visible Supporting Data: Detailed but unobtrusive Historical Context: Available but not distracting Trading Applications Signal Interpretation Long Signals: Prediction > threshold with high confidence Look for volume confirmation - Check trend alignment - Verify support levels Short Signals: Prediction < -threshold with high confidence Confirm with resistance levels - Check for distribution patterns - Verify momentum divergence - Market Regime Adaptation Trending Markets: Higher confidence in directional signals Ranging Markets: Focus on reversal signals at extremes Volatile Markets: Require higher confidence thresholds Low Volume: Reduce position sizes, increase caution Risk Management Integration Confidence-Based Sizing: Larger positions for higher confidence signals Regime-Aware Stops: Wider stops in volatile regimes Multi-Timeframe Confirmation: Align signals across timeframes Volume Confirmation: Require volume support for major signals Originality and Innovation This indicator represents genuine innovation in several areas: Mathematical Approach First application of Lorentzian geometry to market pattern recognition. Unlike Euclidean-based systems, this naturally handles market non-linearities. Feature Engineering Sophisticated multi-dimensional feature space combining price, volume, volatility, and momentum in normalized form. Visualization System Professional-grade dashboard system providing comprehensive market intelligence in intuitive format. Performance Tracking Real-time performance analytics typically found only in institutional trading systems. Development Journey Creating this indicator involved overcoming numerous technical challenges: Mathematical Complexity: Translating theoretical concepts into practical code Performance Optimization: Balancing accuracy with computational efficiency User Interface Design: Making complex data accessible and actionable Signal Quality: Filtering noise while maintaining responsiveness The result is a tool that brings institutional-grade analytics to individual traders while maintaining the theoretical rigor of its mathematical foundation. Best Practices - Parameter Optimization - Start with default settings and adjust based on: Market Characteristics: Volatile vs. stable Trading Timeframe: Scalping vs. swing trading Risk Tolerance: Conservative vs. aggressive Signal Confirmation Never trade on Lorentzian signals alone: Price Action: Confirm with support/resistance Volume: Verify with volume analysis Multiple Timeframes: Check higher timeframe alignment Market Context: Consider overall market conditions Risk Management Position Sizing: Scale with confidence levels Stop Losses: Adapt to market volatility Profit Targets: Based on historical performance Maximum Risk: Never exceed 2-3% per trade Disclaimer This indicator is for educational and research purposes only. It does not constitute financial advice or guarantee profitable trading results. The Lorentzian classification system reveals market patterns but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose. Market dynamics are inherently uncertain, and past performance does not guarantee future results. This tool should be used as part of a comprehensive trading strategy, not as a standalone solution. Bringing the elegance of relativistic geometry to market analysis through sophisticated pattern recognition and intuitive visualization. Thank you for sharing the idea. You're more than a follower, you're a leader! @vasanthgautham1221 Trade with precision. Trade with insight. — Dskyz , for DAFE Trading Systems
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MACD Crossover with Price Action and AlertsThe MACD should use the default parameters (12, 26, 9) for fast EMA, slow EMA, and signal EMA, respectively, applied to the Close price. Instead of simple MACD crossovers, the indicator should analyze price action in relation to the MACD histogram to generate signals. Specifically: 1. BUY signal: Generate a buy signal (an up arrow displayed below the low of the signal bar in green color) when the MACD histogram crosses above zero AND the price action shows a bullish engulfing pattern (the current candle's body completely engulfs the previous candle's body). 2. SELL signal: Generate a sell signal (a down arrow displayed above the high of the signal bar in red color) when the MACD histogram crosses below zero AND the price action shows a bearish engulfing pattern (the current candle's body completely engulfs the previous candle's body). The arrows should be non-repainting, meaning that once an arrow is plotted on a bar, it should not disappear or change position as the chart updates. The indicator should also plot the MACD line, signal line, and histogram using their default calculations. The MACD line should be blue, the signal line should be orange, and the histogram should be displayed using green bars for positive values and red bars for negative values. The indicator should also have customizable inputs for the MACD fast EMA period, slow EMA period, signal EMA period and engulfing pattern check enabled/disabled. If engulfing pattern check disabled, the indicator will generate signals based only on MACD histogram crossing zero.
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