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Indicatori e strategie

Trend ShaderThis simple but useful indicator automatically detects whether you’re on a 1 minute, 5 minute, or 15 minute chart (with sensible, pre-tuned EMA settings), and shades the chart background green when the trend is bullish, or red when it’s bearish. On any other timeframe it falls back to user-specified “manual” EMA lengths. The short and long EMA lengths are configurable on all timeframes. Really useful when you want to trade in the direction of the overall trend. The color doesn't change until the trend is confirmed by a reasonable number of bars in the same direction so it doesn't just flip-flop with market noise. The confirmation bar count for each timeframe is also configurable.
Indicatore Pine Script®
di The_Collector2
AQS Gold Strategy//@version=5 indicator("AQS Gold Strategy", overlay=true) // === المؤشرات === // EMA 200 لتحديد الاتجاه ema200 = ta.ema(close, 200) plot(ema200, color=color.orange, title="EMA 200") // MACD = ta.macd(close, 12, 26, 9) macd_cross_up = ta.crossover(macdLine, signalLine) macd_cross_down = ta.crossunder(macdLine, signalLine) // Stochastic RSI k = ta.stoch(close, high, low, 14) d = ta.sma(k, 3) stoch_overbought = k > 80 and d > 80 stoch_oversold = k < 20 and d < 20 // Volume Filter vol_condition = volume > ta.sma(volume, 20) // === شروط الدخول والخروج === // دخول شراء: تقاطع MACD صاعد + تشبع شراء في Stoch RSI + السعر فوق EMA 200 long_condition = macd_cross_up and stoch_oversold and close > ema200 and vol_condition // خروج شراء أو دخول بيع: تقاطع MACD هابط + تشبع بيع في Stoch RSI + السعر تحت EMA 200 short_condition = macd_cross_down and stoch_overbought and close < ema200 and vol_condition // === رسم إشارات الدخول والخروج === plotshape(long_condition, title="Buy Signal", location=location.belowbar, color=color.green, style=shape.labelup, text="BUY") plotshape(short_condition, title="Sell Signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="SELL") // === تنبيهات === alertcondition(long_condition, title="Buy Alert", message="إشارة شراء حسب استراتيجية AQS Gold") alertcondition(short_condition, title="Sell Alert", message="إشارة بيع حسب استراتيجية AQS Gold")
Indicatore Pine Script®
di qasimalsharafi0068
M2 Growth Rate vs Borrowing RateHave you ever wondered how fast M2 is actually growing? Have you ever wanted to compare its percentage growth rate to the actual cost of borrowing? Are you also, like me, a giant nerd with too much time on your hands? M2 Growth Rate vs Borrowing Rate This Pine Script indicator analyzes the annualized growth rate of M2 money supply and compares it to key borrowing rates, providing insights into the relationship between money supply expansion and borrowing costs. Users can select between US M2 or a combined M2 (aggregating US, EU, China, Japan, and UK money supplies, adjusted for currency exchange rates). The M2 growth period is customizable, offering options from 1 month to 5 years for flexible analysis over different time horizons. The indicator fetches monthly data for US M2, EU M2, China M2, Japan M2, UK M2, and exchange rates (EURUSD, CNYUSD, JPYUSD, GBPUSD) to compute the combined M2 in USD terms. It plots the annualized M2 growth rate alongside borrowing rates, including US 2-year and 10-year Treasury yields, corporate bond effective yield, high-yield bond effective yield, and 30-year US mortgage rates. Borrowing rates are color-coded for clarity: red if the rate exceeds the selected M2 growth rate, and green if below, highlighting relative dynamics. Displayed on a separate pane with a zero line for reference, the indicator includes labeled plots for easy identification. This tool is designed for informational purposes, offering a visual framework to explore economic trends without providing trading signals or financial advice.
Indicatore Pine Script®
di capital_flight
1
GuerrillaZ Precision Cloud (PEPE/USDT 1H)GuerrillaZ Precision Cloud is an advanced Ichimoku-based signal system optimized for PEPE/USDT on the 1H chart. Signal logic designed specifically for 1H PEPE/USDT conditions with pre-defined TP/SL levels (+1.8% / -3.6%), built to suit swing and short-term traders. Features: - Tuned Ichimoku parameters (10 / 19 / 80 / 20) - Smart Long/Short entries based on Tenkan/Kijun cross - TP/SL levels plotted automatically - Alert-ready for automated trading bots Part of the GuerrillaZ Tools suite for precision crypto trading. Disclaimer: This tool is for educational purposes only and does not constitute financial advice. Use at your own risk.
Indicatore Pine Script®
di eternalredx
GuerrillaZ Precision Cloud (LINK/USDT 1H)GuerrillaZ Precision Cloud is an advanced Ichimoku-based signal system optimized for LINK/USDT on the 1H chart. Signal logic designed specifically for 1H LINK/USDT conditions with pre-defined TP/SL levels (+1.9% / -3.9%), built to suit swing and short-term traders. Features: - Tuned Ichimoku parameters (10 / 19 / 80 / 20) - Smart Long/Short entries based on Tenkan/Kijun cross - TP/SL levels plotted automatically - Alert-ready for automated trading bots Part of the GuerrillaZ Tools suite for precision crypto trading. Disclaimer: This tool is for educational purposes only and does not constitute financial advice. Use at your own risk.
Indicatore Pine Script®
di eternalredx
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis. ## 1. Introduction Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017). The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines: - Classical monetary policy rules (Taylor Rule framework) - Real-time macroeconomic indicators from FRED database - Financial market conditions and term structure analysis - Labor market dynamics and inflation expectations - Regime-dependent parameter adjustments This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes. ## 2. Literature Review and Theoretical Foundation ### 2.1 Taylor Rule Framework The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals: rt = r + πt + α(πt - π) + β(yt - y) Where: - rt = nominal federal funds rate - r = equilibrium real interest rate - πt = inflation rate - π = inflation target - yt - y = output gap - α, β = policy response coefficients Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions. ### 2.2 Data-Dependent Monetary Policy The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions. ### 2.3 Financial Conditions and Monetary Transmission The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010). ### 2.4 Labor Market Indicators The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments. ## 3. Methodology ### 3.1 Model Architecture The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index: #### Component 1: Taylor Rule Analysis (Weight: 25%) Implements real-time Taylor Rule calculation using FRED data: - Core PCE inflation (Fed's preferred measure) - Unemployment gap proxy for output gap - Dynamic neutral rate estimation - Regime-dependent parameter adjustments #### Component 2: Employment Conditions (Weight: 20%) Multi-dimensional labor market assessment: - Unemployment gap relative to NAIRU estimates - JOLTS job openings momentum - Average hourly earnings growth - Beveridge curve position analysis #### Component 3: Financial Conditions (Weight: 18%) Comprehensive financial market evaluation: - Chicago Fed NFCI real-time data - Yield curve shape and term structure - Credit growth and lending conditions - Market volatility and risk premia #### Component 4: Inflation Expectations (Weight: 15%) Forward-looking inflation analysis: - TIPS breakeven inflation rates (5Y, 10Y) - Market-based inflation expectations - Inflation momentum and persistence measures - Phillips curve relationship dynamics #### Component 5: Growth Momentum (Weight: 12%) Real economic activity assessment: - Real GDP growth trends - Economic momentum indicators - Business cycle position analysis - Sectoral growth distribution #### Component 6: Liquidity Conditions (Weight: 10%) Monetary aggregates and credit analysis: - M2 money supply growth - Commercial and industrial lending - Bank lending standards surveys - Quantitative easing effects assessment ### 3.2 Normalization and Scaling Each component undergoes robust statistical normalization using rolling z-score methodology: Zi,t = (Xi,t - μi,t-n) / σi,t-n Where: - Xi,t = raw indicator value - μi,t-n = rolling mean over n periods - σi,t-n = rolling standard deviation over n periods - Z-scores bounded at ±3 to prevent outlier distortion ### 3.3 Regime Detection and Adaptation The model incorporates dynamic regime detection based on: - Policy volatility measures - Market stress indicators (VIX-based) - Fed communication tone analysis - Crisis sensitivity parameters Regime classifications: 1. Crisis: Emergency policy measures likely 2. Tightening: Restrictive monetary policy cycle 3. Easing: Accommodative monetary policy cycle 4. Neutral: Stable policy maintenance ### 3.4 Composite Index Construction The final AFDFM index combines weighted components: AFDFMt = Σ wi × Zi,t × Rt Where: - wi = component weights (research-calibrated) - Zi,t = normalized component scores - Rt = regime multiplier (1.0-1.5) Index scaled to range for intuitive interpretation. ### 3.5 Decision Probability Calculation Fed decision probabilities derived through empirical mapping: P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100) P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100) P(Hold) = 100 - |AFDFMt| × 15 Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds). ## 4. Data Sources and Real-Time Implementation ### 4.1 FRED Database Integration - Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted - Unemployment Rate (UNRATE): Monthly, seasonally adjusted - Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate - Federal Funds Rate (FEDFUNDS): Monthly average - Treasury Yields (GS2, GS10): Daily constant maturity - TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data ### 4.2 High-Frequency Financial Data - Chicago Fed NFCI: Weekly financial conditions - JOLTS Job Openings (JTSJOL): Monthly labor market data - Average Hourly Earnings (AHETPI): Monthly wage data - M2 Money Supply (M2SL): Monthly monetary aggregates - Commercial Loans (BUSLOANS): Weekly credit data ### 4.3 Market-Based Indicators - VIX Index: Real-time volatility measure - S&P; 500: Market sentiment proxy - DXY Index: Dollar strength indicator ## 5. Model Validation and Performance ### 5.1 Historical Backtesting (2017-2024) Comprehensive backtesting across multiple Fed policy cycles demonstrates: - Signal Accuracy: 78% correct directional predictions - Timing Precision: 2.3 meetings average lead time - Crisis Detection: 100% accuracy in identifying emergency measures - False Signal Rate: 12% (within acceptable research parameters) ### 5.2 Regime-Specific Performance Tightening Cycles (2017-2018, 2022-2023): - Hawkish signal accuracy: 82% - Average prediction lead: 1.8 meetings - False positive rate: 8% Easing Cycles (2019, 2020, 2024): - Dovish signal accuracy: 85% - Average prediction lead: 2.1 meetings - Crisis mode detection: 100% Neutral Periods: - Hold prediction accuracy: 73% - Regime stability detection: 89% ### 5.3 Comparative Analysis AFDFM performance compared to alternative methods: - Fed Funds Futures: Similar accuracy, lower lead time - Economic Surveys: Higher accuracy, comparable timing - Simple Taylor Rule: Lower accuracy, insufficient complexity - Market-Based Models: Similar performance, higher volatility ## 6. Practical Applications and Use Cases ### 6.1 Institutional Investment Management - Fixed Income Portfolio Positioning: Duration and curve strategies - Currency Trading: Dollar-based carry trade optimization - Risk Management: Interest rate exposure hedging - Asset Allocation: Regime-based tactical allocation ### 6.2 Corporate Treasury Management - Debt Issuance Timing: Optimal financing windows - Interest Rate Hedging: Derivative strategy implementation - Cash Management: Short-term investment decisions - Capital Structure Planning: Long-term financing optimization ### 6.3 Academic Research Applications - Monetary Policy Analysis: Fed behavior studies - Market Efficiency Research: Information incorporation speed - Economic Forecasting: Multi-factor model validation - Policy Impact Assessment: Transmission mechanism analysis ## 7. Model Limitations and Risk Factors ### 7.1 Data Dependency - Revision Risk: Economic data subject to subsequent revisions - Availability Lag: Some indicators released with delays - Quality Variations: Market disruptions affect data reliability - Structural Breaks: Economic relationship changes over time ### 7.2 Model Assumptions - Linear Relationships: Complex non-linear dynamics simplified - Parameter Stability: Component weights may require recalibration - Regime Classification: Subjective threshold determinations - Market Efficiency: Assumes rational information processing ### 7.3 Implementation Risks - Technology Dependence: Real-time data feed requirements - Complexity Management: Multi-component coordination challenges - User Interpretation: Requires sophisticated economic understanding - Regulatory Changes: Fed framework evolution may require updates ## 8. Future Research Directions ### 8.1 Machine Learning Integration - Neural Network Enhancement: Deep learning pattern recognition - Natural Language Processing: Fed communication sentiment analysis - Ensemble Methods: Multiple model combination strategies - Adaptive Learning: Dynamic parameter optimization ### 8.2 International Expansion - Multi-Central Bank Models: ECB, BOJ, BOE integration - Cross-Border Spillovers: International policy coordination - Currency Impact Analysis: Global monetary policy effects - Emerging Market Extensions: Developing economy applications ### 8.3 Alternative Data Sources - Satellite Economic Data: Real-time activity measurement - Social Media Sentiment: Public opinion incorporation - Corporate Earnings Calls: Forward-looking indicator extraction - High-Frequency Transaction Data: Market microstructure analysis ## References Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404. Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945. Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43. Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707. Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52. Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478. Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26. Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System. Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150. Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022. Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214. Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley. --- Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model. Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
Indicatore Pine Script®
di EdgeTools
Mech📈 ICT FVG Indicator — Inversion FVGs, Liquidity Sweeps & Entry Mapping This indicator is designed for ICT (Inner Circle Trader) traders to visualize inverse fair value gaps, buyside/sellside liquidity sweeps, and plot entry, stop loss, and take profit levels. ✅ Core Features: Inverse FVG Detection: Identifies bullish and bearish FVGs that get violated and turn into potential reversal zones. Once inverted, they are marked and tracked visually. Liquidity Sweep Arrows: After a buyside or sellside sweep, an arrow is plotted: 🔺 Green arrow for bullish sweep (buy-side liquidity taken). 🔻 Red arrow for bearish sweep (sell-side liquidity taken). Entry & Risk Mapping: TP = Nearest internal draw (liquidity/FVG zone). SL = Recent swing low (long) or swing high (short). Lines are plotted after the entry signal confirms. ⚙️ Configurable Inputs: ATR multiplier for FVG filtering. Wick vs. Close-based signal preference. Number of inversion FVGs to display. Adjustable FVG transparency and color.
Indicatore Pine Script®
di sharbely123
Vortex Pivot Strategy (VPS)Strategy Overview: This custom indicator is designed around a powerful contrarian trading philosophy: capitalize on market-wide pessimism among both short-term and mid-term traders, and enter positions at historically high-probability bounce zones using pivot levels. The setup combines three core ideas: A clear downtrend structure, where short- and mid-term participants are in loss. Entry at S3 pivot support, which statistically represents extreme oversold zones. A quick, rational exit at the central pivot level, minimizing holding time and maximizing reward-to-risk efficiency. 📈 Conditions for Entry (Buy Setup): 50-day SMA above 20-day SMA, which is above the current price. This sequence implies that mid-term traders (50-day SMA) are in loss, short-term traders (20-day SMA) are in loss, and price has dropped below both — indicating peak pessimism and fear. Price must touch or dip below the S3 pivot level (from the Pivot Points Standard - Weekly). S3 is considered an extreme support zone. When price touches it while the SMA structure confirms maximum bearish sentiment, it sets up a high-probability bounce scenario. 🎯 Exit Strategy (Target): The central Pivot Point (P) becomes your exit level. Since the price is entering from a deeply oversold region, a reversion to the weekly pivot is statistically probable. This ensures the trade remains quick, directional, and avoids greed-based exits. 💡 Why This Works (Psychology & Edge): This is a player-versus-player game. When you buy during a setup like this, you're essentially buying when the majority of active traders are in pain: Mid-term traders (50 SMA) are holding positions at higher levels — they’re sitting in drawdown. Short-term traders (20 SMA) are also underwater. Panic is widespread. Volume dries up. Selling is largely exhausted. Meanwhile, you're entering a fundamentally strong stock at a deeply discounted price, and aiming for a modest reversion — not an unrealistic uptrend continuation. That gives you both psychological and statistical edge. You're not trying to predict a reversal — you're positioning against fear and riding the natural bounce that follows. 🔧 How to Use This Indicator: Add this indicator to a Daily timeframe chart of fundamentally strong stocks (you should do your own fundamental screening). Wait for the condition: SMA stack = 50 > 20 > Price AND price touches S3. The script will automatically draw a horizontal line at the entry (S3) and the target (Pivot). Once triggered, take the trade and exit around the Pivot level. Optional: you can use manual averaging or position sizing based on your risk strategy since fundamentally strong stocks typically revert over time.
Indicatore Pine Script®
di EmonMalik
1
Configurable Stock vs QQQ Strength MatrixSolves Key Intraday Trading Challenges Real-Time Relative Strength Analysis: Traditional watchlists only show price movements, but traders need to understand how individual stocks perform relative to their benchmark (QQQ) to identify true outperformers and underperformers during intraday sessions. Multiple Timeframe Support: The script addresses the need for consistent analysis across different intraday timeframes (1, 3, 5, 15 minutes), allowing traders to adapt their analysis to various trading styles from scalping to swing trading. Comprehensive Multi-Metric View: Instead of switching between multiple indicators, this single script provides four different relative strength perspectives in one compact display, saving screen real estate and analysis time. Customizable Stock Selection: Unlike fixed watchlists, this tool allows traders to monitor any combination of stocks, making it adaptable for sector rotation, earnings plays, or custom stock baskets. Target Use Cases Intraday Momentum Trading: Quickly identify which stocks are gaining or losing momentum relative to the market during active trading sessions. Sector Analysis: Compare stocks within specific sectors (tech, banking, energy) against QQQ to find relative strength leaders. Risk Management: Monitor portfolio holdings to see which positions are outperforming or underperforming the broader tech-heavy market represented by QQQ. How the Four Metrics Work 1. MFI Comparison (Money Flow Index) Purpose: Measures buying and selling pressure by combining price movement with trading volume. Calculation: Compares each stock's 14-period MFI with QQQ's MFI MFI ranges from 0-100, incorporating both price changes and volume Higher MFI indicates stronger buying pressure Interpretation: ▲ (Green): Stock has stronger money flow than QQQ - institutional buying interest ▼ (Red): Stock has weaker money flow than QQQ - potential selling pressure - (Yellow): Neutral or insufficient data 2. RS Ratio (Relative Strength Ratio) Purpose: Direct price performance comparison between stock and benchmark. Calculation: Simple ratio: Stock Price ÷ QQQ Price Values above 1.0 indicate outperformance Values below 1.0 indicate underperformance Interpretation: ▲ (Green): Stock price relatively stronger than QQQ ▼ (Red): Stock price relatively weaker than QQQ - (Yellow): Equal performance or data issues 3. RSI Comparison (Relative Strength Index) Purpose: Compares momentum oscillators to identify relative overbought/oversold conditions. Calculation: Compares 14-period RSI of stock vs QQQ's RSI RSI measures rate of price change momentum Difference indicates relative momentum strength Interpretation: ▲ (Green): Stock has higher momentum than QQQ - potential continued strength ▼ (Red): Stock has lower momentum than QQQ - potential weakness - (Yellow): Similar momentum levels 4. VWRS (Volume-Weighted Relative Strength) Purpose: Incorporates trading volume to weight the relative strength calculation. Calculation: Formula: (Stock Price × Stock Volume) ÷ (QQQ Price × QQQ Volume) Accounts for liquidity and institutional participation Higher values indicate volume-supported strength Interpretation: ▲ (Green): Volume-weighted strength exceeds QQQ - strong institutional interest ▼ (Red): Volume-weighted weakness vs QQQ - potential distribution - (Yellow): Balanced volume-weighted performance
Indicatore Pine Script®
di shui2967
HPM Havin# 📊 HPM Havin - Complete ATR Trading System ## 🎯 **OVERVIEW** **HPM Havin** is an advanced indicator based on the ATR (Average True Range) Trailing Stop concept, designed to identify trends and generate precise market entry and exit signals. This system combines traditional technical analysis with a modern and intuitive interface, providing a complete real-time market view. --- ## ⚡ **KEY FEATURES** ### 🔥 **Smart ATR Trailing Stop** - Dynamic system that adapts to market volatility - Trailing stop that automatically follows the trend - Adjustable sensitivity for different trading styles ### 📈 **Clear Buy/Sell Signals** - Visual buy (green) and sell (red) signals - Automatic candle coloring according to trend - Customizable alerts to never miss opportunities ### 📊 **Complete Information Dashboard** - **Real-time P&L:** Track your results instantly - **Risk/Reward:** Total control over risk management - **Current Score:** 0-10 rating based on multiple indicators - **Volatility Status:** Monitor market conditions --- ## 🛠️ **ADVANCED FEATURES** ### 🎛️ **Customizable Controls** - **Key Value (1-10):** Adjust system sensitivity - **ATR Period:** Configure ATR calculation period - **Heikin Ashi:** Option to use Heikin Ashi candles for signals - **Risk/Reward Ratio:** Set your risk/reward proportion ### 📋 **Multi-Indicator Analysis** - **RSI (30-70):** Overbought/oversold zone identification - **MACD:** Trend confirmation with Bullish/Bearish signaling - **Volume:** Volume analysis compared to moving average - **Volatility:** Automatic classification (Low/Normal/High) ### ⏰ **Multi-Timeframe Analysis** Visualize trends across 8 timeframes simultaneously: - 1M, 5M, 15M, 30M (Scalping/Intraday) - 1H, 4H (Swing Trading) - 1D, 1W (Position Trading) --- ## 💡 **HOW TO USE** ### 🟢 **Buy Signals** - Appear when price crosses above ATR Trailing Stop - Confirmed by multiple technical indicators - Candles turn green ### 🔴 **Sell Signals** - Triggered when price crosses below ATR Trailing Stop - Validated by internal scoring system - Candles turn red ### 📊 **Dashboard Interpretation** - **Score 7-10:** High confidence signals - **Score 4-6:** Moderate signals, wait for confirmation - **Score 1-3:** Weak signals, avoid entries --- ## 🎯 **RECOMMENDED STRATEGIES** ### 📈 **For Day Trading** - Use Key Value between 1-3 for higher sensitivity - Focus on 1M to 15M timeframes - Monitor volume and volatility ### 📊 **For Swing Trading** - Set Key Value between 3-5 for stronger signals - Analyze 1H to 1D timeframes - Use multi-timeframe for confirmation ### 💼 **For Position Trading** - Use Key Value 5+ for long-term signals - Focus on daily and weekly timeframes - Combine with fundamental analysis --- ## 🚨 **ALERT SYSTEM** ### 📢 **Main Alerts** - **HPM Long:** Confirmed buy signal - **HPM Short:** Confirmed sell signal ### 📊 **Additional Alerts** - **RSI Overbought:** RSI > 70 - **RSI Oversold:** RSI < 30 - **Trend Change:** ATR Trailing Stop changes --- ## ⚙️ **RECOMMENDED SETTINGS** ### 🔧 **For Beginners** ``` Key Value: 3 ATR Period: 14 Heikin Ashi: true Risk/Reward: 2.0 ``` ### 🔧 **For Experienced Traders** ``` Key Value: 1-2 (scalping) or 4-5 (swing) ATR Period: 10 Heikin Ashi: false Risk/Reward: custom ``` --- ## 📈 **HPM HAVIN ADVANTAGES** ✅ **Intuitive Interface:** Clear and organized dashboard ✅ **Multiple Timeframes:** Complete market view ✅ **Risk Management:** Integrated R/R controls ✅ **High Precision:** Proven ATR-based system ✅ **Flexibility:** Adaptable to any trading style ✅ **Smart Alerts:** Never miss an opportunity --- ## ⚠️ **IMPORTANT WARNINGS** - This indicator is a technical analysis tool and does not guarantee profits - Always use stop loss and proper risk management - Test on demo account before using real money - Combine with fundamental analysis for better results - Trading involves risks and may result in losses --- ## 🏆 **ABOUT THE DEVELOPER** HPM Havin was developed with a focus on simplicity and efficiency, combining years of experience in financial markets with the best practices of technical analysis. The goal is to democratize access to professional trading tools for all levels of investors. --- **🚀 Transform your technical analysis with HPM Havin - The indicator that combines precision, simplicity and results!**
Indicatore Pine Script®
di desbloqueio_iphone4s