Fibo RSIThis is a customized Relative Strength Index (RSI) indicator designed to replicate TradingView’s default RSI while adding additional reference levels for deeper market analysis.
🔹 Features:
RSI length set to 8 by default (user adjustable).
Calculates RSI using the standard ta.rsi() function.
Plots the RSI line in a clean, separate panel.
Adds 7 key levels for analysis: 0, 20, 30, 50, 70, 80, 100.
Levels are drawn as thin, solid straight lines for a cleaner look (instead of default dashed).
🔹 Use cases:
Identify momentum shifts with enhanced precision.
Use intermediate levels (20, 30, 50, 70, 80) as potential support/resistance zones.
Ideal for traders who want a Fibonacci-like structure in RSI analysis.
Ortalanmış Osilatörler
Swing Oracle Stock 2.0- Gradient Enhanced# 🌈 Swing Oracle Pro - Advanced Gradient Trading Indicator
**Transform your technical analysis with stunning gradient visualizations that make market trends instantly recognizable.**
## 🚀 **What Makes This Indicator Special?**
The **Swing Oracle Pro** revolutionizes traditional technical analysis by combining advanced NDOS (Normalized Distance from Origin of Source) calculations with a sophisticated gradient color system. This isn't just another indicator—it's a complete visual trading experience that adapts colors based on market strength, making trend identification effortless and intuitive.
## 🎨 **10 Professional Gradient Themes**
Choose from carefully crafted color schemes designed for optimal visual clarity:
- **🌅 Sunset** - Warm oranges and purples for classic elegance
- **🌊 Ocean** - Cool blues and teals for calm analysis
- **🌲 Forest** - Natural greens and browns for organic feel
- **✨ Aurora** - Ethereal greens and magentas for mystique
- **⚡ Neon** - Vibrant electric colors for high-energy trading
- **🌌 Galaxy** - Deep purples and cosmic hues for night sessions
- **🔥 Fire** - Intense reds and golds for volatile markets
- **❄️ Ice** - Cool whites and blues for clear-headed decisions
- **🌈 Rainbow** - Full spectrum for comprehensive analysis
- **⚫ Monochrome** - Professional grays for focused trading
## 📊 **Core Features**
### **Advanced NDOS System**
- Normalized Distance from Origin of Source calculation with 231-period length
- Smoothed with customizable EMA for reduced noise
- Multi-timeframe confirmation with H1 filter option
- Dynamic gradient coloring based on oscillator position
### **Intelligent Visual Feedback**
- **Primary Gradient Line** - Main NDOS plot with dynamic color transitions
- **Gradient Fill Zones** - Beautiful color-coded areas for bullish, neutral, and bearish regions
- **Smart Transparency** - Colors adjust intensity based on market volatility
- **Dynamic Backgrounds** - Subtle gradient backgrounds that respond to market conditions
### **Enhanced EMA Projection System**
- 75/760 period EMA normalization with 50-period lookback
- Gradient-colored projection line for trend forecasting
- Toggleable display with advanced gradient controls
- Price tracking for precise level identification
### **Multi-Timeframe Analysis Table**
- Real-time trend analysis across 6 timeframes (1m, 3m, 5m, 15m, 1H, 4H)
- Gradient-colored cells showing trend strength
- Customizable table size and position
- Professional emoji indicators (🚀 UP, 📉 DOWN, ➡️ FLAT)
### **Signal System**
- **Gradient Buy Signals** - Triangle up arrows with intensity-based coloring
- **Gradient Sell Signals** - Triangle down arrows with strength indicators
- **Alert Conditions** - Built-in alerts for all signal types
- **7-Day Cycle Tracking** - Tuesday-to-Tuesday weekly cycle visualization
## ⚙️ **Customization Controls**
### **🎨 Gradient Controls**
- **Gradient Intensity** - Adjust color vibrancy (0.1-1.0)
- **Gradient Smoothing** - Control color transition smoothness (1-10 periods)
- **Dynamic Background** - Toggle animated background gradients
- **Advanced Gradients** - Enable/disable EMA projection and enhanced features
### **🛠️ Custom Color System**
- **Bullish Colors** - Define custom start/end colors for bull markets
- **Bearish Colors** - Set personalized bear market gradients
- **Full Theme Override** - Create completely custom color schemes
- **Real-time Preview** - See changes instantly on your chart
## 📈 **How to Use**
1. **Choose Your Theme** - Select from 10 professional gradient themes
2. **Configure Levels** - Adjust high/low levels (default 60/40) for your timeframe
3. **Set Smoothing** - Fine-tune gradient smoothing for your trading style
4. **Enable Features** - Toggle background gradients, candlestick coloring, and advanced EMA projection
5. **Monitor Signals** - Watch for gradient buy/sell arrows and multi-timeframe confirmations
## 🎯 **Trading Applications**
- **Swing Trading** - Perfect for identifying medium-term trend changes
- **Scalping** - Multi-timeframe table provides quick trend confirmation
- **Position Sizing** - Gradient intensity shows signal strength for risk management
- **Market Analysis** - Beautiful visualizations make complex data instantly understandable
- **Education** - Ideal for learning market dynamics through visual feedback
## ⚡ **Performance Optimized**
- **Smart Rendering** - Colors update only on significant changes
- **Efficient Calculations** - Optimized algorithms for smooth performance
- **Memory Management** - Minimal resource usage even with complex gradients
- **Real-time Updates** - Responsive to market changes without lag
## 🚨 **Alert System**
Built-in alert conditions notify you when:
- NDOS crosses above high level (Buy Signal)
- NDOS crosses below low level (Sell Signal)
- Multi-timeframe confirmations align
- Customizable alert messages with emoji indicators
## 🔧 **Technical Specifications**
- **PineScript Version**: v6 (Latest)
- **Overlay**: True (plots on main chart)
- **Calculations**: NDOS, EMA normalization, volatility-based transparency
- **Timeframes**: Compatible with all timeframes
- **Markets**: Stocks, Forex, Crypto, Commodities, Indices
## 💡 **Why Choose Swing Oracle Pro?**
This isn't just another technical indicator—it's a complete visual transformation of your trading experience. The gradient system provides instant visual feedback that traditional indicators simply can't match. Whether you're a beginner learning to read market trends or an experienced trader seeking clearer signals, the Swing Oracle Pro delivers professional-grade analysis with unprecedented visual clarity.
**Experience the future of technical analysis. Your charts will never look the same.**
---
*⚠️ Disclaimer: This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always conduct your own research and consider risk management before making trading decisions.*
**🔔 Like this indicator? Please leave a comment and boost! Your feedback helps improve future updates.**
---
**📝 Tags:** #GradientTrading #SwingTrading #NDOS #MultiTimeframe #TechnicalAnalysis #VisualTrading #TrendAnalysis #ColorCoded #ProfessionalCharts #TradingToo
High-and-Tight Impulse + Micro ConsolidationThis indicator detects a specific bullish continuation setup on daily charts:
- An impulse move (X% rise within N bars, mostly green candles)
- Immediately followed by a tight consolidation (small ranges, small bodies)
- Closes holding in the top zone of the impulse
On the chart, signals are plotted as orange dots above bars.
Labels show the last detected setup date, and a counter displays total matches in history.
Useful for backtesting "high-and-tight flag" type momentum patterns or any symbol.
Adjust inputs (impulse % threshold, bars, ATR ratios, top zone %) to make it stricter or looser.
Alerts are included when a new setup is detected.
This tool is not financial advice. For educational and research purposes only.
by fiyatherseydir
AA1 MACD 09.2025this is a learing project i want to share
the script is open for anyone
I combain some ema's mcad and more indicators to help find stocks in momentum
Trend-Strong Candle - 3 EMAs with Filters# Trend-Strong Candle - Professional Trading Indicator
## 📊 What It Does
Identifies high-probability entries by combining triple EMA trend analysis with strong candle detection. Only signals when all conditions align for maximum accuracy.
## 🎯 Core Features
- Triple EMA System: Fast (20) / Medium (50) / Slow (200) for trend confirmation
- Strong Candle Filter: ATR-based sizing ensures genuine momentum
- Advanced Filters: EMA close validation + trend stability checks
- Live Alerts: Instant notifications for real-time signals
- Session Filter: Trade only during active EU/US market hours
## ⚡ Quick Setup
Scalping (1-5min): Default settings + enable session filter
Day Trading (15-60min): Default settings work perfectly
Swing Trading (4H+): Increase ATR multiplier to 0.8-1.0
## 📈 Trading Rules
Long Signals: Green triangle below candle
- Strong bullish candle during confirmed uptrend
- All EMAs properly aligned (Fast > Medium > Slow)
Short Signals: Red triangle above candle
- Strong bearish candle during confirmed downtrend
- All EMAs properly aligned (Fast < Medium < Slow)
## ⚠️ Critical Success Factors
1. Always Verify the Trend Yourself
The indicator helps identify signals, but YOU must confirm the larger trend context. Check higher timeframes and overall market structure before entering.
2. Understand the "Big Players"
Strong candles in trend direction usually come from institutional money (banks, funds, algorithms). These create the momentum that retail traders can follow. The indicator catches these institutional moves.
3. Distance to Next Value Level
NEVER enter if price is too close to major resistance/support levels:
- Check distance to round numbers (1.1000, 1.1050, etc.)
- Ensure at least 20-30 pips room to next key level
- You need space for profit - tight levels = limited upside
4. Risk Management
- Stop Loss: 1-2 ATR from entry
- Take Profit: 2-3 ATR target (minimum 1:2 R/R)
- Position Size: Risk max 1-2% per trade
## 💡 Pro Tips
- Best Sessions: London open (8-12 UTC) and NY open (13-17 UTC)
- Avoid: Major news, low liquidity periods, choppy markets
- Multiple Timeframes: Confirm signals on higher timeframe
- Value Levels: Always check daily/weekly support/resistance before entering
## 🎯 Success Formula
Trend Confirmation + Strong Institutional Candle + Distance to Value Levels = High Probability Trade
*
Remember: The indicator finds the signals, but successful trading requires your analysis of trend context and value level positioning. Trade smart, not just frequent.
Machine Learning Gaussian Mixture Model | AlphaNattMachine Learning Gaussian Mixture Model | AlphaNatt
A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading.
"Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly."
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🤖 THE MACHINE LEARNING
Gaussian Mixture Models (GMM):
Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering :
Models data as coming from multiple Gaussian distributions
Each market regime is a different Gaussian component
Provides probability of belonging to each regime
More sophisticated than simple clustering
Expectation-Maximization Algorithm:
The indicator continuously learns and adapts using the E-M algorithm:
E-step: Calculate probability of current market belonging to each regime
M-step: Update regime parameters based on new data
Continuous learning without repainting
Adapts to changing market conditions
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🎯 THREE MARKET REGIMES
The GMM identifies three distinct market states:
Regime 1 - Low Volatility:
Quiet, ranging markets
Uses RSI-based momentum calculation
Reduces false signals in choppy conditions
Background: Pink tint
Regime 2 - Normal Market:
Standard trending conditions
Uses Rate of Change momentum
Balanced sensitivity
Background: Gray tint
Regime 3 - High Volatility:
Strong trends or volatility events
Uses Z-score based momentum
Captures extreme moves
Background: Cyan tint
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💡 KEY INNOVATIONS
1. Probabilistic Regime Detection:
Instead of binary regime assignment, provides probabilities:
30% Regime 1, 60% Regime 2, 10% Regime 3
Smooth transitions between regimes
No sudden indicator jumps
2. Weighted Momentum Calculation:
Combines three different momentum formulas
Weights based on regime probabilities
Automatically adapts to market conditions
3. Confidence Indicator:
Shows how certain the model is (white line)
High confidence = strong regime identification
Low confidence = transitional market state
Line transparency changes with confidence
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⚙️ PARAMETER OPTIMIZATION
Training Period (50-500):
50-100: Quick adaptation to recent conditions
100: Balanced (default)
200-500: Stable regime identification
Number of Components (2-5):
2: Simple bull/bear regimes
3: Low/Normal/High volatility (default)
4-5: More granular regime detection
Learning Rate (0.1-1.0):
0.1-0.3: Slow, stable learning
0.3: Balanced (default)
0.5-1.0: Fast adaptation
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📊 TRADING STRATEGIES
Visual Signals:
Cyan gradient: Bullish momentum
Magenta gradient: Bearish momentum
Background color: Current regime
Confidence line: Model certainty
1. Regime-Based Trading:
Regime 1 (pink): Expect mean reversion
Regime 2 (gray): Standard trend following
Regime 3 (cyan): Strong momentum trades
2. Confidence-Filtered Signals:
Only trade when confidence > 70%
High confidence = clearer market state
Avoid transitions (low confidence)
3. Adaptive Position Sizing:
Regime 1: Smaller positions (choppy)
Regime 2: Normal positions
Regime 3: Larger positions (trending)
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🚀 ADVANTAGES OVER OTHER ML INDICATORS
vs K-Means Clustering:
Soft clustering (probabilities) vs hard boundaries
Captures uncertainty and transitions
More mathematically robust
vs KNN (K-Nearest Neighbors):
Unsupervised learning (no historical labels needed)
Continuous adaptation
Lower computational complexity
vs Neural Networks:
Interpretable (know what each regime means)
No overfitting issues
Works with limited data
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📈 PERFORMANCE CHARACTERISTICS
Best Market Conditions:
Markets with clear regime shifts
Volatile to trending transitions
Multi-timeframe analysis
Cryptocurrency markets (high regime variation)
Key Strengths:
Automatically adapts to market changes
No manual parameter adjustment needed
Smooth transitions between regimes
Probabilistic confidence measure
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🔬 TECHNICAL BACKGROUND
Gaussian Mixture Models are used extensively in:
Speech recognition (Google Assistant)
Computer vision (facial recognition)
Astronomy (galaxy classification)
Genomics (gene expression analysis)
Finance (risk modeling at investment banks)
The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning.
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💡 PRO TIPS
Watch regime transitions: Best opportunities often occur when regimes change
Combine with volume: High volume + regime change = strong signal
Use confidence filter: Avoid low confidence periods
Multi-timeframe: Compare regimes across timeframes
Adjust position size: Scale based on identified regime
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⚠️ IMPORTANT NOTES
Machine learning adapts but doesn't predict the future
Best used with other confirmation indicators
Allow time for model to learn (100+ bars)
Not financial advice - educational purposes
Backtest thoroughly on your instruments
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🏆 CONCLUSION
The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides:
Automatic adaptation to market conditions
Clear regime identification with confidence levels
Smooth, professional signal generation
True unsupervised machine learning
This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in:
Google's speech recognition
Tesla's computer vision
NASA's data analysis
Wall Street risk models
"Let the machine learn the market regimes. Trade with statistical confidence."
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Developed by AlphaNatt | Machine Learning Trading Systems
Version: 1.0
Algorithm: Gaussian Mixture Model with E-M
Classification: Unsupervised Learning Oscillator
Not financial advice. Always DYOR.
Stockbee Reversal BullishCustom indicator for identifying stocks that meet the Stockbee's Reversal Bullish criteria. This can be used as a standalone indicator or use it to screen for stocks in Pine Screener.
AI-Weighted RSI (Zeiierman)█ Overview
AI-Weighted RSI (Zeiierman) is an adaptive oscillator that enhances classic RSI by applying a correlation-weighted prediction layer. Instead of looking only at RSI values directly, this indicator continuously evaluates how other price- and volume-based features (returns, volatility, volume shifts) correlate with RSI, and then weights them accordingly to project the next RSI state.
The result is a smoother, forward-looking RSI framework that adapts to market conditions in real time.
By leveraging feature correlation instead of static formulas, AI-Weighted RSI behaves like a lightweight learning model, adjusting its emphasis depending on which features are most aligned with RSI behavior during the current regime.
█ How It Works
⚪ Feature Extraction
Each bar, the script computes features: log returns, RSI itself, ATR% (volatility), volume, and volume log-change.
⚪ Correlation Screening
Over a rolling learning window, it measures the correlation of each feature against RSI. The strongest relationships are ranked and selected.
⚪ Adaptive Weighting
Features are standardized (z-scored), then combined using their signed correlations as weights, building a rolling, adaptive prediction of RSI.
⚪ Prediction to RSI Weight
The predicted RSI is mapped back into a “weight” scale (±2 by default). Above 0 = bullish bias, below 0 = bearish bias, with color-graded fills to visualize overbought/oversold pressure.
⚪ Signal Line
A smoothing option (signal length) overlays a moving average of the AI-Weighted RSI for clearer trend confirmation.
█ Why AI-Weighted RSI
⚪ Adaptive to Market Regime
Because the model re-evaluates correlations continuously, it naturally shifts which features dominate, sometimes volatility explains RSI best, sometimes volume, sometimes returns.
⚪ Forward-Looking Bias
Instead of simply reflecting RSI, the model provides a projection, helping anticipate shifts in momentum before RSI itself flips.
█ How to Use
⚪ Directional Bias
Read the RSI relative to 0. Above = bullish momentum bias, below = bearish.
⚪ Overbought / Oversold Zones
Shaded fills beyond +0.5 or -0.5 highlight extremes where RSI pressure often exhausts.
⚪ Divergences
When price makes new highs/lows but AI-Weighted RSI fails to confirm, it often signals weakening momentum.
█ Settings
RSI Length: Lookback for the core RSI calculation.
Signal Length: Smoothing applied to the AI-Weighted RSI output.
Learning Window: Bars used for correlation learning and z-scoring.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
عكفة الماكد المتقدمة - أبو فارس ©// 🔒 Advanced MACD Curve © 2025
// 💡 Idea & Creativity: Engineer Abu Elias
// 🛠️ Development & Implementation: Abu Fares
// 📜 All intellectual rights reserved - Copying, modifying, or redistributing is not permitted
// 🚫 Any attempt to tamper with this code or violate intellectual property rights is legally prohibited
// 📧 For inquiries and licensing: Please contact the developer, Abu Fares
عكفة الماكد المتقدمة - أبو فارس ©// 🔒 عكفة الماكد المتقدمة © 2025
// 💡 فكرة وإبداع: المهندس أبو الياس
// 🛠️ تطوير وتنفيذ: أبو فارس
// 📜 جميع الحقوق الفكرية محفوظة - لا يُسمح بالنسخ أو التعديل أو إعادة التوزيع
// 🚫 أي محاولة للعبث بهذا الكود أو انتهاك الحقوق الفكرية مرفوضة قانونياً
// 📧 للاستفسارات والتراخيص: يرجى التواصل مع المطور أبو فارس
// 🔒 Advanced MACD Curve © 2025
// 💡 Idea & Creativity: Engineer Abu Elias
// 🛠️ Development & Implementation: Abu Fares
// 📜 All intellectual rights reserved - Copying, modifying, or redistributing is not permitted
// 🚫 Any attempt to tamper with this code or violate intellectual property rights is legally prohibited
// 📧 For inquiries and licensing: Please contact the developer, Abu Fares
Hurst Momentum Oscillator | AlphaNattHurst Momentum Oscillator | AlphaNatt
An adaptive oscillator that combines the Hurst Exponent - which identifies whether markets are trending or mean-reverting - with momentum analysis to create signals that automatically adjust to market regime.
"The Hurst Exponent reveals a hidden truth: markets aren't always trending. This oscillator knows when to ride momentum and when to fade it."
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📐 THE MATHEMATICS
Hurst Exponent (H):
Measures the long-term memory of time series:
H > 0.5: Trending (persistent) behavior
H = 0.5: Random walk
H < 0.5: Mean-reverting behavior
Originally developed for analyzing Nile river flooding patterns, now used in:
Fractal market analysis
Network traffic prediction
Climate modeling
Financial markets
The Innovation:
This oscillator multiplies momentum by the Hurst coefficient:
When trending (H > 0.5): Momentum is amplified
When mean-reverting (H < 0.5): Momentum is reduced
Result: Adaptive signals based on market regime
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💎 KEY ADVANTAGES
Regime Adaptive: Automatically adjusts to trending vs ranging markets
False Signal Reduction: Reduces momentum signals in mean-reverting markets
Trend Amplification: Stronger signals when trends are persistent
Mathematical Edge: Based on fractal dimension analysis
No Repainting: All calculations on historical data
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📊 TRADING SIGNALS
Visual Interpretation:
Cyan zones: Bullish momentum in trending market
Magenta zones: Bearish momentum or mean reversion
Background tint: Blue = trending, Pink = mean-reverting
Gradient intensity: Signal strength
Trading Strategies:
1. Trend Following:
Trade momentum signals when background is blue (trending)
2. Mean Reversion:
Fade extreme readings when background is pink
3. Regime Transition:
Watch for background color changes as early warning
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🎯 OPTIMAL USAGE
Best Conditions:
Strong trending markets (crypto bull runs)
Clear ranging markets (forex sessions)
Regime transitions
Multi-timeframe analysis
Market Applications:
Crypto: Excellent for identifying trend persistence
Forex: Detects when pairs are ranging
Stocks: Identifies momentum stocks
Commodities: Catches persistent trends
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Developed by AlphaNatt | Fractal Market Analysis
Version: 1.0
Classification: Adaptive Regime Oscillator
Not financial advice. Always DYOR.
Ark FCI OscillatorFinancial Conditions Index Oscillator
This indicator tracks week-over-week changes in the National Financial Conditions Index (NFCI), providing a dynamic view of evolving financial conditions in the United States.
Overview
The National Financial Conditions Index (NFCI) is a comprehensive weekly composite index published by the Federal Reserve Bank of Chicago. It measures financial conditions across U.S. money markets, debt and equity markets, and the traditional and shadow banking systems.
Interpretation
Positive values indicate improving financial conditions
Negative values signal deteriorating financial conditions
Risk assets demonstrate particular sensitivity to changes in financial conditions, making this oscillator valuable for market timing and risk assessment.
Alternative Data Source
Users can modify the source to FRED:NFCIRISK to focus specifically on risk dynamics. The NFCIRISK subindex isolates volatility and funding risk measures within the financial sector, capturing market volatility indicators and liquidity shortage probabilities while excluding broader credit and leverage conditions.
Fisher Volume Transform | AlphaNattFisher Volume Transform | AlphaNatt
A powerful oscillator that applies the Fisher Transform - converting price into a Gaussian normal distribution - while incorporating volume weighting to identify high-probability reversal points with institutional participation.
"The Fisher Transform reveals what statistics professors have known for decades: when you transform market data into a normal distribution, turning points become crystal clear."
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🎲 THE MATHEMATICS
Fisher Transform Formula:
The Fisher Transform converts any bounded dataset into a Gaussian distribution:
y = 0.5 × ln((1 + x) / (1 - x))
Where x is normalized price (-1 to 1 range)
Why This Matters:
Market extremes become statistically identifiable
Turning points are amplified and clarified
Removes the skew from price distributions
Creates nearly instantaneous signals at reversals
Volume Integration:
Unlike standard Fisher Transform, this version weights price by relative volume:
High volume moves get more weight
Low volume moves get filtered out
Identifies institutional participation
Reduces false signals from retail chop
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💎 KEY ADVANTAGES
Statistical Edge: Transforms price into normal distribution where extremes are mathematically defined
Volume Confirmation: Only signals with volume support
Early Reversal Detection: Fisher Transform amplifies turning points
Clean Signals: Gaussian distribution reduces noise
No Lag: Mathematical transformation, not averaging
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⚙️ SETTINGS OPTIMIZATION
Fisher Period (5-30):
5-9: Very sensitive, many signals
10: Default - balanced sensitivity
15-20: Moderate smoothing
25-30: Major reversals only
Volume Weight (0.1-1.0):
0.1-0.3: Minimal volume influence
0.5-0.7: Balanced price/volume
0.7: Default - strong volume weight
0.8-1.0: Volume dominant
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📊 TRADING SIGNALS
Primary Signals:
Zero Cross Up: Bullish momentum shift
Zero Cross Down: Bearish momentum shift
Signal Line Cross: Early reversal warning
Extreme Readings (±75): Potential reversal zones
Visual Interpretation:
Cyan zones: Bullish momentum
Magenta zones: Bearish momentum
Gradient intensity: Strength of move
Histogram: Raw momentum power
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🎯 OPTIMAL USAGE
Best Market Conditions:
Range-bound markets (reversals clear)
High volume periods
Major support/resistance levels
Divergence hunting
Trading Strategies:
1. Extreme Reversal:
Enter when oscillator exceeds ±75 and reverses
2. Zero Line Momentum:
Trade crosses of zero line with volume confirmation
3. Signal Line Strategy:
Early entry on signal line crosses
4. Divergence Trading:
Price makes new high/low but Fisher doesn't
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Developed by AlphaNatt | Quantitative Trading Systems
Version: 1.0
Classification: Statistical Transform Oscillator
Not financial advice. Always DYOR.
Momentum+This script provides a colored histogram of recent price action with the price derivative method for finding momentum.
buy sell ultra systemWhat it is
EMA-POC Momentum System Ultra combines a proven trend stack (EMA 20/50/238), a price-of-control layer (POC via Bar-POC or VWAP alternative), and a momentum trigger (RSI) to surface higher-quality entries only when multiple, independent conditions align. This is not a cosmetic mashup; each component gates the others.
How components work together
Trend (EMA 20/50/238): Defines short/medium/long bias and filters counter-trend signals.
POC (Bar-POC or Alt-POC/VWAP): Locates the most-traded/weighted price area; a neutral band around POC helps avoid chop.
Control background: Above POC → buyers likely in control; below → sellers.
Momentum (RSI): Entry arrows print only when RSI confirms with trend and price location vs POC; optional “cross 50” requirement reduces noise.
Optional HTF trend: Confluence with a higher-timeframe EMA stack for stricter filtering.
Why it’s original/useful
Signals require confluence of (1) EMA trend stack, (2) POC location and neutral-zone filtering, (3) momentum confirmation, (4) optional slope and distance-to-POC checks, and (5) optional HTF trend. This reduces false positives compared with using any layer in isolation.
How to use
Markets/TFs: Built for XAUUSD (Gold) and US30. Works 1m–1h for intraday; 2h–4h for swing.
Entries:
Long: EMA stack bullish, price above POC, not in neutral band, RSI condition true → “Buy” arrow.
Short: Opposite conditions → “Sell” arrow.
Stops/Targets (suggested):
Initial stop beyond POC/neutral band or recent swing.
First target around 1R; trail with EMA20/50 or structure breaks.
Settings to tune:
POC Mode: Bar-POC (highest-volume bar’s close over lookback) or Alt-POC (VWAP).
Neutral Band %: 0.10–0.35 typical intraday.
Min distance from POC: 0.10–0.50% helps avoid low-RR entries right at POC.
RSI: Choose “cross 50” for stricter triggers or simple >/< 50 for more signals.
HTF trend: Turn on for extra confluence.
Alerts:
Buy Signal and Sell Signal (separate), or one Combined Buy/Sell alert.
Set to “Once per bar close” if you want only confirmed arrows.
Repainting / limitations
Shapes can move until bar close (standard Pine behavior) when using intrabar conditions; final confirmation at close. No system guarantees profitability—forward test and adapt to your market/instrument.
Clean chart
The published chart contains only this script so outputs are easy to identify.
Versions / updates
Use Publish → Update for minor changes; do not create new publications for small tweaks. If you fork to preserve older behavior, explain why and how your fork differs.
Changelog
v1.1 – Tuning for Gold/US30, neutral-band & distance filters, optional HTF trend, combined alert.
v1.0 – Initial public release (EMA stack + POC modes + RSI + alerts).
License & credits
Open-source for learning and improvement. Please credit on forks and explain modifications in your description.
Momentum Index [BigBeluga]The Momentum Index is an innovative indicator designed to measure the momentum of price action by analyzing the distribution of positive and negative momentum values over a defined period. By incorporating delta-based calculations and smoothing techniques, it provides traders with a clear and actionable representation of market momentum dynamics.
🔵 Key Features:
Delta-Based Momentum Analysis:
Calculates the momentum of price by comparing its current state to its value from a defined number of bars back.
Inside a loop, it evaluates whether momentum values are above or below zero, producing a delta value that reflects the net momentum direction and intensity.
Double EMA Smoothing:
Smooths the raw delta-based momentum values with a double EMA filter, reducing noise and providing a clearer trend signal.
tmi(len) =>
sum = 0.0
sum1 = 0.0
above = 0.0
below = 0.0
src_ = src - src
for i = 0 to len
sum := sum + (src_ > nz(src_ ) ? 1 : -1)
sum1 := sum1 + (sum > 0 ? 1 : -1)
sum1 := emaEma(sum1, 10)
for i = 1 to len
above := above + (sum1 > 0 ? 1 : 0)
below := below + (sum1 > 0 ? 0 : 1)
Directional Momentum Signals:
Generates momentum shift signals and displays them on both the oscillator and the main chart:
- △ Aqua Triangles: Represent upward momentum shifts.
- ▽ Red Triangles: Represent downward momentum shifts.
Dynamic Gradient Display:
Highlights momentum zones with gradient fills:
- Aqua shades for positive momentum (above zero).
- Red shades for negative momentum (below zero).
Dashboard Display:
A dashboard summarizing the count of momentum values above and below zero for the defined period (Sentiment Length e.g. 100), helping traders assess market sentiment at a glance.
🔵 How It Works:
The indicator takes price momentum as its source and evaluates the number of momentum values above and below zero within a defined period.
The delta calculation aggregates this information, providing a net representation of the prevailing market momentum.
A double EMA filter is applied to the delta values, smoothing the momentum line and enhancing signal clarity.
Momentum shifts are highlighted with visual signals on the oscillator and price chart, while the gradient display provides a visual representation of intensity.
🔵 Use Cases:
Momentum Tracking: Identify whether market momentum is predominantly bullish or bearish.
Signal Confirmation: Use chart-based signals to confirm potential trend reversals or continuation.
Analyze Market Strength: Leverage the dashboard to quickly assess the distribution of momentum over the chosen period.
Overbought/Oversold Conditions: Utilize gradient zones to detect areas of momentum extremes and possible price exhaustion.
Momentum Index offers a refined approach to analyzing momentum dynamics, combining delta-based calculations with smoothing techniques and intuitive visuals, making it an essential tool for traders looking to anticipate market movements effectively.
Logit Transform -EasyNeuro-Logit Transform
This script implements a novel indicator inspired by the Fisher Transform, replacing its core arctanh-based mapping with the logit transform. It is designed to highlight extreme values in bounded inputs from a probabilistic and statistical perspective.
Background: Fisher Transform
The Fisher Transform, introduced by John Ehlers , is a statistical technique that maps a bounded variable x (between a and b) to a variable approximately following a Gaussian distribution. The standard form for a normalized input y (between -1 and 1) is F(y) = 0.5 * ln((1 + y)/(1 - y)) = arctanh(y).
This transformation has the following properties:
Linearization of extremes:
Small deviations around the mean are smooth, while movements near the boundaries are sharply amplified.
Gaussian approximation:
After transformation, the variable approximates a normal distribution, enabling analytical techniques that assume normality.
Probabilistic interpretation:
The Fisher Transform can be linked to likelihood ratio tests, where the transform emphasizes deviations from median or expected values in a statistically meaningful way.
In technical analysis, this allows traders to detect turning points or extreme market conditions more clearly than raw oscillators alone.
Logit Transform as a Generalization
The logit function is defined for p between 0 and 1 as logit(p) = ln(p / (1 - p)).
Key properties of the logit transform:
Maps probabilities in (0, 1) to the entire real line, similar to the Fisher Transform.
Emphasizes values near 0 and 1, providing sharp differentiation of extreme states.
Directly interpretable in terms of odds and likelihood ratios: logit(p) = ln(odds).
From a statistical viewpoint, the logit transform corresponds to the canonical link function in binomial generalized linear models (GLMs). This provides a natural interpretation of the transformed variable as the logarithm of the likelihood ratio between success and failure states, giving a rigorous probabilistic framework for extreme value detection.
Theoretical Advantages
Distributional linearization:
For inputs that can be interpreted as probabilities, the logit transform creates a variable approximately linear in log-odds, similar to Fisher’s goal of Gaussianization but with a probabilistic foundation.
Extreme sensitivity:
By amplifying small differences near 0 or 1, it allows for sharper detection of market extremes or overbought/oversold conditions.
Statistical interpretability:
Provides a link to statistical hypothesis testing via likelihood ratios, enabling integration with probabilistic models or risk metrics.
Applications in Technical Analysis
Oscillator enhancement:
Apply to RSI, Stochastic Oscillators, or other bounded indicators to accentuate extreme values with a well-defined probabilistic interpretation.
Comparative study:
Use alongside the Fisher Transform to analyze the effect of different nonlinear mappings on market signals, helping to uncover subtle nonlinearity in price behavior.
Probabilistic risk assessment:
Transforming input series into log-odds allows incorporation into statistical risk models or volatility estimation frameworks.
Practical Considerations
The logit diverges near 0 and 1, requiring careful scaling or smoothing to avoid numerical instability. As with the Fisher Transform, this indicator is not a standalone trading signal and should be combined with complementary technical or statistical indicators.
In summary, the Logit Transform builds upon the Fisher Transform’s theoretical foundation while introducing a probabilistically rigorous mapping. By connecting extreme-value detection to odds ratios and likelihood principles, it provides traders and analysts with a mathematically grounded tool for examining market dynamics.
X-Scalp by LogicatX-Scalp by Logicat — Clean-Range MTF Scalper
Turn noisy intraday action into clear, actionable scalps. X-Scalp builds “Clean Range” zones only when three timeframes agree (default: M30/M15/M5), then waits for a single, high-quality M5 confirmation to print a BUY/SELL label. It’s fast, simple, and ruthlessly focused on precision.
What it does
Clean Range zones: Drawn from the last completed M30 candle only when M30/M15/M5 align (all green or all red).
Size filter (pips): Ignore tiny, low-value ranges with a configurable minimum height (auto-pip detection included).
Extend-until-mitigated: Zones stretch right and “freeze” on first mitigation (close inside or close beyond, your choice). Optional fade when mitigated.
Laser M5 entries (one per box):
Red M5 bar inside a green zone → SELL
Green M5 bar inside a red zone → BUY
Prints once per zone on the closed M5 candle—no spam.
Quality of life: Keep latest N zones, customizable colors, optional H4 reference lines, alert conditions for both zone creation and entries.
Why traders love it
Clarity: Filters chop; you see only aligned zones and one clean trigger.
Speed: Designed for scalpers on FX, XAU/USD, indices, and more.
Control: Tune lookback, pip threshold, mitigation logic, and visuals to fit your playbook.
Tips
Use on liquid sessions for best results.
Combine with your risk model (fixed R, partials at mid/edge, etc.).
Backtest different pip filters per symbol.
Disclaimer: No indicator guarantees profits. Trade responsibly and manage risk.
POC Migration Velocity (POC-MV) [PhenLabs]📊POC Migration Velocity (POC-MV)
Version: PineScript™v6
📌Description
The POC Migration Velocity indicator revolutionizes market structure analysis by tracking the movement, speed, and acceleration of Point of Control (POC) levels in real-time. This tool combines sophisticated volume distribution estimation with velocity calculations to reveal hidden market dynamics that conventional indicators miss.
POC-MV provides traders with unprecedented insight into volume-based price movement patterns, enabling the early identification of continuation and exhaustion signals before they become apparent to the broader market. By measuring how quickly and consistently the POC migrates across price levels, traders gain early warning signals for significant market shifts and can position themselves advantageously.
The indicator employs advanced algorithms to estimate intra-bar volume distribution without requiring lower timeframe data, making it accessible across all chart timeframes while maintaining sophisticated analytical capabilities.
🚀Points of Innovation
Micro-POC calculation using advanced OHLC-based volume distribution estimation
Real-time velocity and acceleration tracking normalized by ATR for cross-market consistency
Persistence scoring system that quantifies directional consistency over multiple periods
Multi-signal detection combining continuation patterns, exhaustion signals, and gap alerts
Dynamic color-coded visualization system with intensity-based feedback
Comprehensive customization options for resolution, periods, and thresholds
🔧Core Components
POC Calculation Engine: Estimates volume distribution within each bar using configurable price bands and sophisticated weighting algorithms
Velocity Measurement System: Tracks the rate of POC movement over customizable lookback periods with ATR normalization
Acceleration Calculator: Measures the rate of change of velocity to identify momentum shifts in POC migration
Persistence Analyzer: Quantifies how consistently POC moves in the same direction using exponential weighting
Signal Detection Framework: Combines trend analysis, velocity thresholds, and persistence requirements for signal generation
Visual Rendering System: Provides dynamic color-coded lines and heat ribbons based on velocity and price-POC relationships
🔥Key Features
Real-time POC calculation with 10-100 configurable price bands for optimal precision
Velocity tracking with customizable lookback periods from 5 to 50 bars
Acceleration measurement for detecting momentum changes in POC movement
Persistence scoring to validate signal strength and filter false signals
Dynamic visual feedback with blue/orange color scheme indicating bullish/bearish conditions
Comprehensive alert system for continuation patterns, exhaustion signals, and POC gaps
Adjustable information table displaying real-time metrics and current signals
Heat ribbon visualization showing price-POC relationship intensity
Multiple threshold settings for customizing signal sensitivity
Export capability for use with separate panel indicators
🎨Visualization
POC Connecting Lines: Color-coded lines showing POC levels with intensity based on velocity magnitude
Heat Ribbon: Dynamic colored ribbon around price showing POC-price basis intensity
Signal Markers: Clear exhaustion top/bottom signals with labeled shapes
Information Table: Real-time display of POC value, velocity, acceleration, basis, persistence, and current signal status
Color Gradients: Blue gradients for bullish conditions, orange gradients for bearish conditions
📖Usage Guidelines
POC Calculation Settings
POC Resolution (Price Bands): Default 20, Range 10-100. Controls the number of price bands used to estimate volume distribution within each bar
Volume Weight Factor: Default 0.7, Range 0.1-1.0. Adjusts the influence of volume in POC calculation
POC Smoothing: Default 3, Range 1-10. EMA smoothing period applied to the calculated POC to reduce noise
Velocity Settings
Velocity Lookback Period: Default 14, Range 5-50. Number of bars used to calculate POC velocity
Acceleration Period: Default 7, Range 3-20. Period for calculating POC acceleration
Velocity Significance Threshold: Default 0.5, Range 0.1-2.0. Minimum normalized velocity for continuation signals
Persistence Settings
Persistence Lookback: Default 5, Range 3-20. Number of bars examined for persistence score calculation
Persistence Threshold: Default 0.7, Range 0.5-1.0. Minimum persistence score required for continuation signals
Visual Settings
Show POC Connecting Lines: Toggle display of colored lines connecting POC levels
Show Heat Ribbon: Toggle display of colored ribbon showing POC-price relationship
Ribbon Transparency: Default 70, Range 0-100. Controls transparency level of heat ribbon
Alert Settings
Enable Continuation Alerts: Toggle alerts for continuation pattern detection
Enable Exhaustion Alerts: Toggle alerts for exhaustion pattern detection
Enable POC Gap Alerts: Toggle alerts for significant POC gaps
Gap Threshold: Default 2.0 ATR, Range 0.5-5.0. Minimum gap size to trigger alerts
✅Best Use Cases
Identifying trend continuation opportunities when POC velocity aligns with price direction
Spotting potential reversal points through exhaustion pattern detection
Confirming breakout validity by monitoring POC gap behavior
Adding volume-based context to traditional technical analysis
Managing position sizing based on POC-price basis strength
⚠️Limitations
POC calculations are estimations based on OHLC data, not true tick-by-tick volume distribution
Effectiveness may vary in low-volume or highly volatile market conditions
Requires complementary analysis tools for complete trading decisions
Signal frequency may be lower in ranging markets compared to trending conditions
Performance optimization needed for very short timeframes below 1-minute
💡What Makes This Unique
Advanced Estimation Algorithm: Sophisticated method for calculating POC without requiring lower timeframe data
Velocity-Based Analysis: Focus on POC movement dynamics rather than static levels
Comprehensive Signal Framework: Integration of continuation, exhaustion, and gap detection in one indicator
Dynamic Visual Feedback: Intensity-based color coding that adapts to market conditions
Persistence Validation: Unique scoring system to filter signals based on directional consistency
🔬How It Works
Volume Distribution Estimation:
Divides each bar into configurable price bands for volume analysis
Applies sophisticated weighting based on OHLC relationships and proximity to close
Identifies the price level with maximum estimated volume as the POC
Velocity and Acceleration Calculation:
Measures POC rate of change over specified lookback periods
Normalizes values using ATR for consistent cross-market performance
Calculates acceleration as the rate of change of velocity
Signal Generation Process:
Combines trend direction analysis using EMA crossovers
Applies velocity and persistence thresholds to filter signals
Generates continuation, exhaustion, and gap alerts based on specific criteria
💡Note:
This indicator provides estimated POC calculations based on available OHLC data and should be used in conjunction with other analysis methods. The velocity-based approach offers unique insights into market structure dynamics but requires proper risk management and complementary analysis for optimal trading decisions.
Adaptive Convergence Divergence### Adaptive Convergence Divergence (ACD)
By Gurjit Singh
The Adaptive Convergence Divergence (ACD) reimagines the classic MACD by replacing fixed moving averages with adaptive moving averages. Instead of a static smoothing factor, it dynamically adjusts sensitivity based on price momentum, relative strength, volatility, fractal roughness, or volume pressure. This makes the oscillator more responsive in trending markets while filtering noise in choppy ranges.
#### 📌 Key Features
1. Dual Adaptive Structure: The oscillator uses two adaptive moving averages to form its convergence-divergence line, with EMA/RMA as signal line:
* Primary Adaptive (MA): Fast line, reacts quickly to changes.
* Following Adaptive (FAMA): Slow line, with half-alpha smoothing for confirmation.
2. Adaptive MA Types
* ACMO: Adaptive CMO (momentum)
* ARSI: Adaptive RSI (relative strength)
* FRMA: Fractal Roughness (volatility + fractal dimension)
* VOLA: Volume adaptive (volume pressure)
3. PPO Option: Switch between classic MACD or Percentage Price Oscillator (PPO) style calculation.
4. Signal Smoothing: Choose between EMA or Wilder’s RMA.
5. Visuals: Colored oscillator, signal line, histogram with adaptive transparency.
6. Alerts: Bullish/Bearish crossovers built-in.
#### 🔑 How to Use
1. Add to chart: Works on any timeframe and asset.
2. Choose MA Type: Experiment with ACMO, ARSI, FRMA, or VOLA depending on market regime.
3. Crossovers:
* Bullish (🐂): Oscillator crosses above signal → potential long entry.
* Bearish (🐻): Oscillator crosses below signal → potential short entry.
4. Histogram: expansion = strengthening trend; contraction = weakening trend.
5. Divergences:
* Bullish (hidden strength): Price pushes lower, but ACD turns higher = potential upward reversal.
* Bearish (hidden weakness): Price pushes higher, but ACD turns lower = potential downward reversal.
6. Customize: Adjust lengths, smoothing type, and PPO/MACD mode to match your style.
7. Set Alerts:
* Enable Bullish or Bearish crossover alerts to catch momentum shifts in real time.
#### 💡 Tips
* PPO mode normalizes values across assets, useful for cross-asset analysis.
* Wilder’s smoothing is gentler than EMA, reducing whipsaws in sideways conditions.
* Adaptive smoothing helps reduce false divergence signals by filtering noise in choppy ranges.
