Avg Volatility IndexThis indicator calculates the asset’s logarithmic volatility and overlays a 14-day moving average. It is designed for pair trading to compare the relative volatility of two assets and determine risk-balanced position sizing. Higher volatility implies a smaller recommended position weight.
Indicadores e estratégias
Fhunded's PD LevelsFhunded's PD Levels is a clean visual indicator that plots key price levels like Previous Day High/Low, Weekly High/Low, and Daily/Weekly/Monthly Opens. Designed with a neon theme, extended lines, and large-font labels for maximum clarity during intraday trading.
@Fhunded's Checklist with Grade ScoreFhunded's Checklist is a visual TradingView tool that displays a customizable 10-point trading checklist with dynamic trade scoring and A–F grade output. Designed for clarity and speed, it helps traders evaluate setup quality at a glance directly on the chart.
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
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.
Future Pro Script (RSI, MACD, BOLL, VOL, KDJ)thien is the most handsome man in the world, but he is wanting to find money to have a beautiful gf, so he decided to create this script as a dedicator to describe when he will have a adorable gf
Smart Dynamic S/R OverlaySmart Dynamic S/R Overlay
This indicator generates dynamic support and resistance zones using a trend-following system based on volatility-adjusted EMA logic. It adapts to market conditions by shifting zones based on trend confirmation and higher-timeframe analysis.
Key Features:
Trend Modes: Select from five predefined trend sensitivity levels — Reactive, Balanced, Filtered, Structured, and Macro — to suit your trading timeframe and volatility preferences.
Dynamic Zones: Auto-generates support and resistance bands based on real-time trend strength and direction.
Multi-Timeframe Zones: Displays current timeframe, higher timeframe (HTF) and very high timeframe (HHTF) trend for macro bias alignment. Coloured accordingly to show strength on each timeframe respectively - Blue : Bullish ; Red : Bearish. Useful for seeing trend strength progression across multiple timeframe from current (active) chart.
Trend Confirmation: Adjust how many bars are required to confirm a trend flip using the “Bars to Confirm Trend” input.
Sensitivity Control: Fine-tune how tight or loose the dynamic zone boundaries are via the Support/Resistance Sensitivity input.
Trend Flip Alerts: Get notified instantly when the current timeframe trend flips bullish or bearish.
This tool is designed to help traders identify actionable structure zones that dynamically adapt to market movement while maintaining alignment with higher timeframe bias.
Pocket Pivot Breakoutthis script will show Pivot pocket breakout + institutional buying volume
it will help in identify liquidity rush
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.
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
THE NEXTRON This is Invite only strategy which will produce Buy and Short based on parameter and it is having target and sl
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!**
Hidden Orderblock,HOB,OB,BB,MT,MTF Hidden Order Block & Breaker Block (HOB/BB) Multi-Timeframe Analysis
A powerful tool for Smart Money traders and ICT-style practitioners seeking precision, confluence, and clean visual execution. This indicator identifies institutional price zones such as Hidden Order Blocks (HOB), Breaker Blocks (BB), Partial Hidden Order Blocks (PHOB), and traditional Order Blocks (OB)—all across multiple timeframes with minimal chart clutter.
✅ Key Features
1. Hidden Order Block (HOB) Detection
Identifies non-obvious order blocks often hidden within price action.
Requires the EQ (Equilibrium) of the block to pass through at least one Fair Value Gap (FVG).
Invalidation Rule: If price touches the EQ and then closes beyond it (depending on structure), the HOB is invalidated.
2. Breaker Block (BB) Detection
Highlights zones where price made a liquidity grab followed by a strong reversal.
Useful for anticipating support/resistance flips and high-probability reaction areas.
3. Partial Hidden Order Block (PHOB) Detection
A variation of HOBs where price only partially touches the EQ.
Often acts as an early warning zone for reversals or continuation.
Less strict than HOBs, but still institutionally relevant.
4. Traditional Order Block (OB) Detection
Identifies bullish/bearish OBs based on engulfing patterns and displacement.
Marks only the body of the engulfing candle, with the EQ line acting as a key validation/invalidation level.
Once the EQ is touched, the OB is considered invalidated.
5. Engulfing Filter Engine
Customizable logic for OB qualification.
“Easy Engulfing Mode” simplifies detection for newer traders or faster workflow.
Fine-tune aggressiveness and visual clarity with user-defined settings.
6. Multi-Timeframe (MTF) Visualization
Overlay OBs, HOBs, BBs, and PHOBs from higher timeframes (e.g., 4H, 1D) on lower timeframes (15m, 1H).
Enhances top-down confluence without switching charts.
Keeps the visual experience clean and intuitive.
7. Minimalist Visual Design
Only the zone boundaries and EQ lines are displayed.
No extra noise—perfect for both scalpers and swing traders.
Dynamic label positioning and styling for improved chart aesthetics.
8. Performance-Optimized Code
Lightweight, real-time rendering.
Designed for responsiveness—even on lower timeframes with dense historical data.
⚙️ How It Works (Simplified Logic)
Order Block Detection:
Scans for engulfing candles that show displacement.
Defines the OB as the body of the engulfing candle.
EQ line is marked and projected forward until invalidated.
Hidden Order Block Logic:
Starts from a traditional OB, but requires the EQ to pass through at least one FVG.
Upon a close beyond the EQ in the opposite direction, the HOB is invalidated.
PHOB Logic:
Similar to HOBs, but allows partial touches of the EQ before reacting.
Breaker Block Logic:
Identifies liquidity sweeps followed by impulsive moves.
Marks these zones as BBs for potential reaction areas.
📈 Use Cases
Detect institutional price zones with high precision.
Simplify decision-making with visual EQs and MTF overlays.
Integrate seamlessly into:
Smart Money Concepts (SMC)
ICT-style trading
Wyckoff methodology
Discretionary zone-to-zone strategies
🧠 Definitions Summary
OB (Order Block):
Engulfed candle body; EQ is the midpoint of the body. Invalid once EQ is touched.
HOB (Hidden Order Block):
Like OB, but EQ must pass through at least one FVG.
Invalidated when a candle touches EQ and closes beyond it.
PHOB (Partial Hidden Order Block):
Like HOB, but allows partial touch of the EQ to remain valid.
Gap Open DetectorIndicator Note: Gap Open Detector
What This Indicator Does
This indicator helps you spot significant price gaps at the start of new candles compared to the previous candle’s close. A gap means the current candle’s opening price is noticeably higher or lower than the previous candle’s closing price.
Gap Up: The new candle opens above the previous candle’s close.
Gap Down: The new candle opens below the previous candle’s close.
The indicator highlights these gaps with colored candles:
Green Candle: Gap Up detected.
Red Candle: Gap Down detected.
How to Use the Indicator:
This indicator gives Best Results on Hourly Candles:
This indicator works best on hourly charts (1-hour time frame). It is especially useful for spotting gaps at the start of the next day or after a significant break in trading.
Wait for Confirmation:
After a gap is detected at the open, wait for the candle to form. Ideally, wait for one hour (until the hourly candle is complete) to confirm the candle’s direction and strength before taking any action.
Customize Gap Size:
You can set the minimum gap size using either points or percentage:
Points: Enter the minimum number of points for a gap to be considered significant.
Percentage: Enter the minimum percentage change for a gap to be considered significant.
This flexibility allows you to adjust the indicator to suit different markets and volatility levels.
Trading Logic
If there is a Gap Up and the one hour candle is green:
Buy Option: Consider initiating a buy (long) position.
If there is a Gap Up but the one hour candle is red:
Sell Option: Consider initiating a sell (short) position.
If there is a Gap Down and the one hour closing candle is red:
Sell Option: Consider initiating a sell (short) position.
If there is a Gap Down but the one hour candle is green:
Buy Option: Consider initiating a buy (long) position.
Important Tips
1. Patience Pays: Always wait for the hourly candle to close before making any trading decisions based on the gap.
2. Next Day Open: This strategy is especially effective for catching gaps at the start of a new trading day or after a market break.
3. Visual Cues: The indicator gives you a simple visual cue to spot potential trading opportunities.
4. Flexible Settings: Set your preferred gap size in points or percentage to match your trading style.
EMA Crossover con VWAP, señales Buy/Sell y tabla RSI+MACDindicator("EMA Crossover con VWAP, señales Buy/Sell y tabla RSI+MACD"
1x RVOL Bull/Bear Painter REVERSAL CATCHThis powerful Indicator Paints the candle if there is a Relative Volume of 1.5 or higher.
Notice that if you mark the high of the last Bullish RVOL candle (blue candle) a power reversal begins at that same price.
This does the same thing for the Bearish RVOL Candle. If you mark the low of that candle (purple), a reversal begins at that price.
This can be used on any time frame, but using it on higher time frames catches you HUGE SWINGS
Multi-Timeframe Entry OKThis indicator performs a multi-timeframe trend-and-momentum check across three timeframes—Daily (“D”), 4-hour (“240”), and 1-hour (“60”). For each timeframe it verifies:
• EMA(9) > EMA(20)
• MACD histogram > 0 (MACD parameters 12,26,9)
If all three timeframe conditions are true, it returns 1; otherwise it returns 0.
Use case:
1. Save and publish this script with “Add to Screener” enabled.
2. In Crypto Screener, go to Filters → Custom and add “Multi-Timeframe Entry OK”.
3. Filter for “Multi-Timeframe Entry OK == 1” to automatically extract symbols that satisfy EMA and MACD trend alignment on Daily, 4H, and 1H.
Parameters:
– EMA periods fixed at 9 and 20
– MACD fixed at 12,26,9
– No external inputs required
This automates multi-timeframe AND logic, ensuring only symbols with aligned trend and momentum on longer and shorter timeframes appear in your screener.
💣 Rounded Top Short Signal💣R3KT is your scalp short assassin — locking on to rounded tops and detonating precision sell signals with zero lag. Built to expose weak highs before they collapse, it combines 3-bar swing top detection with a bearish momentum cross, then marks the kill zone with a clean 💣 emoji.
No clutter. No lag. Just surgical entries where the bulls die slow.
🔍 Signal Criteria:
3-bar rounded top structure
Bearish WaveTrend crossunder
Bomb emoji plotted — no background, sniper-ready
🧠 Optimized for Heikin Ashi:
💣R3KT performs with maximum accuracy on Heikin Ashi candles, where smoothed price action enhances signal clarity, trend momentum, and rounded top formation.
Use HA for signal detection, and standard candles for entry and execution.
Scalp sharp. Hit fast. 💣R3KT doesn’t warn — it executes.
Anchored VWAP & STD BandsAnchoring VWAP with optional bands.
Use the settings to adjust the point you want the VWAP to always reset on. This allows you to not have to for example set a VWAP every morning at NY open, it will just be there.
Optional bands are available and configurable to whatever standard deviation you wish to have. Please try to keep them in ascending order if you turn on multiple. Bands can fill between bands, so band 3 will fill between band 2 and 3, but not 1 and 3. If you don't care for the color filling set the transparency on the fill color for the band you want no fill on.
Some cycle examples:
Every day at 6PM:
Year: off
Month: off
Day: off
Hour: 18
Every Month:
Year: off
Month: off
Day: 1
Hour: off
