ML Compressor Enhanced Trading Indicator# 🤖 ML Enhanced Trading Indicator - Advanced Market Analysis
## 📊 Overview
This is a comprehensive Machine Learning Enhanced Trading Indicator that combines multiple advanced analytical techniques to provide high-probability trading signals. The indicator uses artificial intelligence, pattern recognition, anomaly detection, and traditional technical analysis to identify optimal entry and exit points in the market.
## 🚀 Key Features
### 🧠 **Machine Learning Core**
- **Advanced Pattern Recognition**: Uses cosine similarity, Pearson correlation, and Spearman rank correlation to identify historical patterns
- **AI-Powered Predictions**: Implements multiple correlation methods to forecast price movements
- **Anomaly Detection**: Z-score based detection system for unusual market activities
- **Signal Confidence Scoring**: Reliability assessment for each trading signal
### 📈 **Technical Analysis Integration**
- **Multi-Timeframe RSI Analysis**: 14 and 21-period RSI with oversold/overbought detection
- **MACD Momentum**: Enhanced MACD histogram analysis for trend confirmation
- **Bollinger Bands Position**: Dynamic position tracking within BB channels
- **Volume Analysis**: Spike and dry volume detection with ratio calculations
- **Trend Strength Measurement**: EMA-based trend power analysis
### 🎯 **Perfect Zone Detection**
- **Ideal Buy Zone**: Identifies perfect buying opportunities when 7 conditions align:
- ML Score ≥ 0.60
- Bottom proximity detection
- RSI in 20-35 range
- Volume spike confirmation
- Positive price anomaly
- Bullish pattern match
- Positive MACD momentum
### 📊 **Comprehensive Display Table**
- **Real-time ML Analysis**: Complete breakdown of all indicators
- **Perfect Buy Conditions Tracker**: Visual checklist with completion percentage
- **Performance Metrics**: Win rate tracking and P&L analysis
- **Signal Strength Indicators**: Confidence levels for each signal
## 🔧 **Customizable Parameters**
### **ML Settings**
- **ML Lookback Period**: 20-500 bars (default: 100)
- **Anomaly Threshold**: 1.0-5.0 sensitivity (default: 2.0)
- **Pattern Similarity**: 0.5-0.99 matching threshold (default: 0.80)
- **AI Lookback Period**: 20-200 bars (default: 50)
### **AI Prediction Models**
- **Correlation Methods**: Spearman, Pearson, Cosine Similarity
- **Forecast Length**: 15-250 bars (default: 50)
- **Similarity Type**: Price or %Change analysis
### **Visual Options**
- **Table Position**: Top/Bottom Left/Right positioning
- **Table Size**: Small, Normal, Large options
- **Signal Display**: Toggle buy/sell signals on/off
- **AI Visualization**: Optional prediction paths and ZigZag
## 📋 **How to Use**
### **For Beginners**
1. Add the indicator to your chart
2. Look for "PERFECT BUY" signals in the table
3. Wait for completion percentage ≥ 85% for highest probability trades
4. Use the background color changes as visual confirmation
### **For Advanced Traders**
1. Analyze individual ML components in the detailed table
2. Monitor anomaly detection for unusual market conditions
3. Use pattern confidence levels for trade timing
4. Combine with your existing strategy for confirmation
### **Signal Interpretation**
- **🟢 PERFECT BUY**: All 7 conditions met - highest probability reversal
- **🟡 NEAR BOTTOM**: Close to ideal conditions - monitor closely
- **🔴 NOT READY**: Wait for better setup
- **Strong Buy/Sell Signals**: ML score-based entries with high confidence
## ⚠️ **Important Notes**
### **Risk Management**
- This indicator provides analysis and signals, not guaranteed outcomes
- Always use proper risk management and position sizing
- Consider market conditions and fundamental factors
- Backtest the strategy on your preferred timeframes and assets
### **Best Practices**
- Use multiple timeframe analysis for confirmation
- Combine with support/resistance levels
- Monitor volume confirmation for all signals
- Set appropriate stop-losses and profit targets
### **Performance Tracking**
- The indicator tracks its own performance with win rate calculations
- Monitor the "AI Prediction" accuracy percentage
- Use the P&L tracking to assess signal quality over time
## 🔄 **Updates and Improvements**
This indicator is continuously evolving with:
- Enhanced machine learning algorithms
- Improved pattern recognition capabilities
- Additional correlation methods for better accuracy
- Performance optimization for faster calculations
- New visualization features based on user feedback
## 📚 **Technical Details**
### **Machine Learning Implementation**
- **Pattern Matching**: 20-bar normalized price patterns with historical comparison
- **Correlation Analysis**: Mathematical similarity scoring between current and historical patterns
- **Anomaly Detection**: Statistical Z-score analysis across price, volume, and RSI
- **Signal Weighting**: Multi-factor scoring system with optimized weights
### **Algorithm Components**
1. **Feature Extraction**: Price, volume, momentum, volatility, and trend features
2. **Pattern Recognition**: Historical pattern database with similarity matching
3. **Anomaly Detection**: Multi-dimensional Z-score threshold analysis
4. **Signal Generation**: Weighted scoring system with confidence intervals
5. **Performance Tracking**: Real-time win rate and accuracy monitoring
### **Calculation Methods**
- **Trend Strength**: (EMA8 - EMA21) / EMA21 * 100
- **Volume Ratio**: Current Volume / 20-period SMA Volume
- **BB Position**: (Close - BB_Lower) / (BB_Upper - BB_Lower)
- **Anomaly Score**: Average of normalized Z-scores for price, volume, and RSI
## 🎨 **Visual Elements**
### **Background Colors**
- **Light Green**: Perfect buy zone detected
- **Light Red**: Perfect sell zone detected
- **Light Blue**: Near bottom proximity
- **Green/Red Transparency**: Price anomaly detection
### **Signal Shapes**
- **Green Triangle Up**: Strong buy signal
- **Red Triangle Down**: Strong sell signal
- **Aqua Diamond**: Perfect buy zone entry
- **Purple Diamond**: Perfect sell zone entry
### **Table Information**
- **ML Complete Analysis**: 16 comprehensive metrics
- **Perfect Buy Conditions**: 7-point checklist with status indicators
- **Real-time Values**: Live updating of all calculations
- **Color-coded Status**: Green (good), Yellow (moderate), Red (caution)
## 🔍 **Troubleshooting**
### **Common Issues**
- **Table Not Showing**: Enable "Show ML Table" in settings
- **No Signals Appearing**: Check "Show Buy/Sell Signals" option
- **Performance Issues**: Reduce ML Lookback Period for faster calculation
- **Too Many/Few Signals**: Adjust Anomaly Threshold sensitivity
### **Optimization Tips**
- **For Day Trading**: Use lower timeframes (1m, 5m, 15m) with reduced lookback periods
- **For Swing Trading**: Use higher timeframes (1h, 4h, 1D) with standard settings
- **For Scalping**: Enable only strong signals and reduce pattern similarity threshold
- **For Long-term**: Increase all lookback periods and use daily/weekly timeframes
## 📖 **Disclaimer**
This indicator is for educational and informational purposes only. It should not be considered as financial advice. Trading involves substantial risk of loss and is not suitable for all investors. Past performance does not guarantee future results.
### **Risk Warning**
- All trading involves risk of substantial losses
- Never risk more than you can afford to lose
- This indicator does not guarantee profitable trades
- Always use proper risk management techniques
- Consider consulting with a financial advisor
### **Liability**
The creator of this indicator is not responsible for any losses incurred from its use. Users should thoroughly test and understand the indicator before using it with real money.
### **Feature Requests**
- Suggest improvements through TradingView comments
- Report bugs with detailed descriptions
- Share successful strategies using the indicator
- Contribute to community discussions
## 🏆 **Credits and Acknowledgments**
This indicator builds upon various open-source libraries and mathematical concepts:
- TradingView ZigZag library for visualization
- Statistical correlation methods from academic research
- Machine learning concepts adapted for financial markets
- Community feedback and testing contributions
## 📈 **Performance Metrics**
The indicator includes built-in performance tracking:
- **Win Rate Calculation**: Percentage of profitable signals
- **Signal Accuracy**: ML prediction vs actual price movement
- **Drawdown Tracking**: Current unrealized P&L from last signal
- **Completion Percentage**: How many perfect conditions are met
## 🔬 **Mathematical Foundation**
### **Correlation Calculations**
- **Pearson**: Measures linear correlation between patterns
- **Spearman**: Rank-based correlation for non-linear relationships
- **Cosine Similarity**: Vector-based similarity for pattern matching
### **Statistical Methods**
- **Z-Score**: (Value - Mean) / Standard Deviation
- **Pattern Normalization**: Price / Price
- **Volatility Percentile**: Historical ranking of current volatility
- **Momentum Calculation**: Price change over multiple periods
## 🎯 **Trading Strategies**
### **Conservative Approach**
- Wait for Perfect Buy Zone (85%+ completion)
- Use higher timeframes for confirmation
- Set stop-loss at recent swing low
- Take profits at resistance levels
### **Aggressive Approach**
- Trade on Strong Buy/Sell signals
- Use lower completion thresholds (70%+)
- Tighter stop-losses with faster exits
- Higher position sizes with confirmed trends
### **Hybrid Strategy**
- Combine with other indicators for confirmation
- Use different settings for different market conditions
- Scale in/out based on signal strength
- Adjust parameters based on market volatility
스크립트에서 "ai"에 대해 찾기
AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend)The AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend) is a cutting-edge indicator that combines advanced mathematical modeling, AI-driven analytics, and segment-based pattern recognition to forecast price movements with precision. This tool is designed to provide traders with deep insights into market dynamics by leveraging multivariate pattern detection and sophisticated predictive algorithms.
👽 Core Features
Segment-Based Pattern Recognition
At its heart, the indicator divides price data into discrete segments, capturing key elements like candle bodies, high-low ranges, and wicks. These segments are normalized using ATR-based volatility adjustments to ensure robustness across varying market conditions.
AI-Powered k-Nearest Neighbors (kNN) Prediction
The predictive engine uses the kNN algorithm to identify the closest historical patterns in a multivariate dictionary. By calculating the distance between current and historical segments, the algorithm determines the most likely outcomes, weighting predictions based on either proximity (distance) or averages.
Dynamic Dictionary of Historical Patterns
The indicator maintains a rolling dictionary of historical patterns, storing multivariate data for:
Candle body ranges, High-low ranges, Wick highs and lows.
This dynamic approach ensures the model adapts continuously to evolving market conditions.
Volatility-Normalized Forecasting
Using ATR bands, the indicator normalizes patterns, reducing noise and enhancing the reliability of predictions in high-volatility environments.
AI-Driven Trend Detection
The indicator not only predicts price levels but also identifies market regimes by comparing current conditions to historically significant highs, lows, and midpoints. This allows for clear visualizations of trend shifts and momentum changes.
👽 Deep Dive into the Core Mathematics
👾 Segment-Based Multivariate Pattern Analysis
The indicator analyzes price data by dividing each bar into distinct segments, isolating key components such as:
Body Ranges: Differences between the open and close prices.
High-Low Ranges: Capturing the full volatility of a bar.
Wick Extremes: Quantifying deviations beyond the body, both above and below.
Each segment contributes uniquely to the predictive model, ensuring a rich, multidimensional understanding of price action. These segments are stored in a rolling dictionary of patterns, enabling the indicator to reference historical behavior dynamically.
👾 Volatility Normalization Using ATR
To ensure robustness across varying market conditions, the indicator normalizes patterns using Average True Range (ATR). This process scales each component to account for the prevailing market volatility, allowing the algorithm to compare patterns on a level playing field regardless of differing price scales or fluctuations.
👾 k-Nearest Neighbors (kNN) Algorithm
The AI core employs the kNN algorithm, a machine-learning technique that evaluates the similarity between the current pattern and a library of historical patterns.
Euclidean Distance Calculation:
The indicator computes the multivariate distance across four distinct dimensions: body range, high-low range, wick low, and wick high. This ensures a comprehensive and precise comparison between patterns.
Weighting Schemes: The contribution of each pattern to the forecast is either weighted by its proximity (distance) or averaged, based on user settings.
👾 Prediction Horizon and Refinement
The indicator forecasts future price movements (Y_hat) by predicting logarithmic changes in the price and projecting them forward using exponential scaling. This forecast is smoothed using a user-defined EMA filter to reduce noise and enhance actionable clarity.
👽 AI-Driven Pattern Recognition
Dynamic Dictionary of Patterns: The indicator maintains a rolling dictionary of N multivariate patterns, continuously updated to reflect the latest market data. This ensures it adapts seamlessly to changing market conditions.
Nearest Neighbor Matching: At each bar, the algorithm identifies the most similar historical pattern. The prediction is based on the aggregated outcomes of the closest neighbors, providing confidence levels and directional bias.
Multivariate Synthesis: By combining multiple dimensions of price action into a unified prediction, the indicator achieves a level of depth and accuracy unattainable by single-variable models.
Visual Outputs
Forecast Line (Y_hat_line):
A smoothed projection of the expected price trend, based on the weighted contribution of similar historical patterns.
Trend Regime Bands:
Dynamic high, low, and midlines highlight the current market regime, providing actionable insights into momentum and range.
Historical Pattern Matching:
The nearest historical pattern is displayed, allowing traders to visualize similarities
👽 Applications
Trend Identification:
Detect and follow emerging trends early using dynamic trend regime analysis.
Reversal Signals:
Anticipate market reversals with high-confidence predictions based on historically similar scenarios.
Range and Momentum Trading:
Leverage multivariate analysis to understand price ranges and momentum, making it suitable for both breakout and mean-reversion strategies.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
Elite Trading Network | HQ: Quantum Edge V2Elite Trading Network HQ: Quantum Edge V2 is a sophisticated market structure analysis tool designed to help traders make informed decisions based on a deep understanding of market conditions. This script blends structural trend analysis with AI-based predictive models to provide dynamic, real-time insights into market behavior. Here is what makes Quantum Edge V2 unique:
Key Features:
Adaptive Market Structure Analysis:
The script uses a multi-level algorithm to identify key market structures, such as swing highs and swing lows, to help traders understand the underlying strength or weakness of the current market trend. It dynamically tracks critical market boundaries using historical price action and recalculates trend levels as new data emerges.
Range and Trend Condition Detection:
Quantum Edge V2 detects whether the market is trending or ranging by analyzing historical structure breaks. This detection helps identify moments of consolidation (yellow zones) or periods of trend continuation. By calculating average structural break durations, the indicator alerts users to conditions that may require caution, such as ranging markets.
Predictive AI Analysis for Entry Optimization:
An AI-powered module evaluates volume thresholds and ATR (Average True Range) to provide users with an understanding of the current market risk. The ATR is calculated based on a user-defined timeframe, giving flexibility in how users approach different market conditions. This feature also determines the risk per trade and calculates the optimal position size, ensuring that users can tailor their risk according to their trading plan.
Real-Time Alerts and Visual Indicators:
The indicator includes alerts for key conditions:
Green Condition: Signals optimal market entry conditions.
Yellow Condition: Indicates a cautionary ranging market, alerting traders to the potential lack of strong trends.
Red Condition: Identifies unsuitable market conditions for entry due to insufficient volume or unfavorable metrics.
Color-coded background visuals provide instant clarity regarding market conditions—red, yellow, or green—allowing traders to make quick, informed decisions.
Dynamic Multi-Timeframe Analysis:
The user can select a custom entry timeframe, while the script internally calculates and adapts to a higher timeframe for deep trend analysis. This approach gives traders a complete view of both the short-term (entry) and higher timeframe (overall trend) dynamics.
How to Use:
Identify Trend Conditions: The indicator visually plots key market structures (green and red structural lines) to help users determine where the market may find support or resistance. The background changes color to indicate trending (green), ranging (yellow), or high-risk (red) conditions.
Make Informed Entries: Use the real-time alerts and label information to get insights into current market conditions. If the background is green and metrics align, the indicator suggests an optimal time for entry.
Position Sizing and Risk Management: The calculated risk per trade and position size (displayed on-screen) assist users in managing risk effectively. Users can utilize this data to adjust trade sizes and maximize profit potential while adhering to their risk tolerance.
What Sets Quantum Edge V2 Apart:
Unlike other indicators that solely provide trend direction, Quantum Edge V2 offers an integrated understanding of market structure, volume analysis, and predictive AI models.
The ranging market detection (yellow zones) is particularly valuable for traders looking to avoid low-probability trades during periods of market indecision.
The use of ATR-based risk calculation ensures the position sizing is always aligned with market volatility, adding an extra layer of protection for capital.
Important Notes:
Educational Value: This script does not just tell you when to enter or exit. It provides deep insights into market dynamics, giving traders a tool to learn and improve their market understanding. The ability to view market structure across different timeframes and visualize areas of caution is crucial for long-term growth as a trader.
No Guaranteed Results: This indicator is a powerful tool for analysis, but like all trading strategies, it does not guarantee profits. Always practice proper risk management.
Why It's Worth Using: This indicator combines multi-timeframe structure analysis, volume metrics, and predictive AI modeling—an approach typically reserved for professional trading systems. Traders looking to incorporate a systematic approach to risk, ranging markets, and trend detection will find Quantum Edge V2 invaluable.
Closed-source Explanation: The script uses proprietary algorithms and unique concepts for trend detection and volume-based analysis that ensure high levels of accuracy in defining market structure and determining entry signals. Because of its complexity and the unique blend of tools, it remains closed-source.
Feedback and Support:
If you have questions or suggestions about this script, feel free to comment or reach out. We value your input as we strive to improve and provide traders with cutting-edge tools.
TradingIQ - Impulse IQIntroducing "Impulse IQ" by TradingIQ
Impulse IQ is an exclusive trading algorithm developed by TradingIQ, designed to trade breakouts and established trends. By integrating artificial intelligence and IQ Technology, Impulse IQ analyzes historical and real-time price data to construct a dynamic trading system adaptable to various asset and timeframe combinations.
Philosophy of Impulse IQ
Impulse IQ combines IQ Technology (AI) with the classic principles of trend and breakout trading. Recognizing that markets inherently follow trends that need to persist for significant price movements to unfold, Impulse IQ eliminates the need for rigid settings or manual intervention.
Instead, it dynamically develops, adapts, and executes trend-based trading strategies, enabling a more responsive approach to capturing meaningful market opportunities.
Impulse IQ is designed to work straight out of the box. In fact, its simplicity requires just one user setting, making it incredibly straightforward to manage.
Strategy type is the only setting that controls Impulse IQ’s functionality.
Traders don’t have to spend hours adjusting settings and trying to find what works best - Impulse IQ handles this on its own.
Key Features of Impulse IQ
Self-Learning Breakout Detection
Employs IQ Technology to identify breakouts.
AI-Generated Trading Signals
Provides breakout trading signals derived from self-learning algorithms.
Comprehensive Trading System
Offers clear entry and exit labels.
AI-Determined Trailing Profit Target and Stop Loss
Position exit levels are clearly defined and calculated by the AI once the trade is entered.
Performance Tracking
Records and presents trading performance data, easily accessible for user analysis.
Long and Short Trading Capabilities
Supports both long and short positions to trade various market conditions.
IQ Meter
The IQ Meter details where price is trading relative to a higher timeframe trend and lower timeframe trend. Fibonacci levels are interlaced along the meter, offering unique insights on trend retracement opportunities.
Self Learning, Multi Timeframe IQ Zig Zags
The Zig Zag IQ is a self-learning, multi-timeframe indicator that adapts to market volatility, providing a clearer representation of market movements than traditional zig zag indicators.
Dual Strategy Execution
Impulse IQ integrates two distinct strategy types: Breakout and Cheap (details explained later).
How It Works
Before diving deeper into Impulse IQ, it's essential to understand the core terminology:
Zig Zag IQ : A self-learning trend and breakout identification mechanism that serves as the foundation for Impulse IQ. Although it belongs to the “Zig Zag” class of technical indicators, it's powered by IQ Technology.
Impulse IQ : A self-learning trading strategy that executes trades based on Zig Zag IQ. Zig Zag IQ identifies market trends, while Impulse IQ adapts, learns, and executes trades based on these trend characterizations.
Impulse IQ operates on a simple heuristic: go long during upside volatility and go short during downside volatility, essentially capturing price breakouts.
The definition of a “price breakout” is determined by IQ Technology, TradingIQ's exclusive AI algorithm. In Impulse IQ, the algorithm utilizes two IQ Zig Zags (self-learning, multi-timeframe zig zags) to analyze and learn from market trends.
It identifies breakout opportunities by recognizing violations of established price levels marked by the IQ Zig Zags. Impulse IQ then adapts and evolves to trade similar future violations in a recurring and dynamic manner.
Put simply, IQ Zig Zags continuously learn from both historical and real-time price updates to adjust themselves for an "optimal fit" to price data. The aim is to adapt so that the marked price tops and bottoms, when violated, reveal potential breakout opportunities.
The strategy layer of IQ Zig Zags, known as Impulse IQ, incorporates an additional level of self-learning with IQ Technology. It learns from breakout signals generated by the IQ Zig Zags, enabling it to dynamically identify and signal tradable breakouts. Moreover, Impulse IQ learns from historical price data to manage trade exits.
All positions start with an initial fixed stop loss and a trailing stop target. Once the trailing stop target is reached, the fixed stop loss converts into a trailing stop, allowing Impulse IQ to remain in the breakout/trend until the trailing stop is triggered.
What Classifies as a Breakout, Price Top, and Price Bottom?
For Impulse IQ:
Price tops are considered the highest price achieved before a price bottom forms.
Price bottoms are the lowest price reached before a price top forms.
For price tops, the highest price continues to be calculated until a significant downside price move occurs. Similarly, for price bottoms, the lowest price is calculated until a significant upside price move happens.
What distinguishes Zig Zag IQ from other zig zag indicators is its unique mechanism for determining a "significant counter-trend price move." Zig Zag IQ evaluates multiple fits to identify what best suits the current market conditions. Consequently, a "significant counter-trend price move" in one market might differ in magnitude from what’s considered "significant" in another, allowing it to adapt to varying market dynamics.
For example, a 1% price move in the opposite direction might be substantial in one market but not in another, and Zig Zag IQ figures this out internally.
The image above illustrates the IQ Zig Zags in action. The solid Zig Zag IQ lines represent the most recent price move being calculated, while the dotted, shaded lines display historical price moves previously analyzed by IQ Zig Zag.
Notice how the green zig zag aligns with a larger trend, while the purple zig zag follows a smaller trend. This mechanism is crucial for generating breakout signals in Impulse IQ: for a position to be entered, the breakout of the smaller trend must occur in the same direction as the larger trend.
The image above depicts the IQ Meters—an exclusive TradingIQ tool designed to help traders evaluate trend strength and retracement opportunities.
When the lower timeframe Zig Zag IQ and the higher timeframe Zig Zag IQ are out of sync (i.e., one is uptrending while the other is downtrending, with no active positions), the meters display a neutral color, as shown in the image.
The key to using these meters is to identify trend unison and pinpoint key trend retracement entry opportunities. Fibonacci retracement levels for the current trend are interlaced along each meter, and the current price is converted to a retracement ratio of the trend.
These meters can mathematically determine where price stands relative to the larger and smaller trends, aiding in identifying entry opportunities.
The top of each meter indicates the highest price achieved during the current price move.
The bottom of each meter indicates the lowest price achieved during the current price move.
When both the larger and smaller trends are in sync and uptrending, or when a long position is active, the IQ meters turn green, indicating uptrend strength.
When both trends are in sync and downtrending, or when a short position is active, the IQ meters turn red, indicating downtrend strength.
The image above shows the Point of Change for both the larger and smaller Zig Zag IQ trends. A distinctive feature of Zig Zag IQ is its ability to calculate these turning points in advance—unlike most traditional zig zag indicators that lack predetermined turning points and often lag behind price movements. In contrast, Zig Zag IQ offers a minimal-lag trend detection capability, providing a more responsive representation of market trends.
Simply put, once the market Zig Zag anchors are touched, the corresponding Zig Zag IQ will change direction.
Trade Signals
Impulse IQ can trade in one of two ways: Entering breakouts as soon as they happen (Breakout Strategy Type) or entering the pullback of a price breakout (Cheap Strategy Type).
Generally, the Breakout Strategy type will take a greater number of trades and enter a breakout quicker. The Cheap Strategy type will usually take less trades, but potentially enter at a better time/price point, prior to the next leg up of a break up, or the next leg down of a break down.
Entry signals are given when price breaks out to the upside or downside for the "Breakout" strategy type, or for the "Cheap" strategy type, when price retraces to the level it broke out from!
Breakout Strategy Example
The image above demonstrates a long position entered and exited using the Breakout strategy. The price breakout level is marked by the dotted, horizontal green line, representing a previously established price high identified by IQ Zig Zag. Once the price breaks and closes above this level, a long position is initiated.
After entering a long position, Impulse IQ immediately displays the initial fixed stop price. As the price moves favorably for the long position, the trailing stop conversion level is reached, and the indicator switches to a trailing stop, as shown in the image. Impulse IQ continues to "ride the trend" for as long as it persists, exiting only when the trailing stop is triggered.
Cheap Strategy Example
The image above shows a short entry executed using the Cheap strategy. The aim of the Cheap strategy is to enter on a pullback before the breakout occurs. While this results in fewer trades if price doesn’t pull back before the breakout, it typically allows for a better entry time and price point when a pullback does happen.
The image above illustrates the remainder of the trade until the trailing stop was hit.
Green Arrow = Long Entry
Red Arrow = Short Entry
Blue Arrow = Trade Exit
Impulse IQ calculates the initial stop price and trailing stop distance before any entry signals are triggered. This means users don’t need to constantly tweak these settings to improve performance—Impulse IQ handles this process internally.
Verifying Impulse IQ’s Effectiveness
Impulse IQ automatically tracks its performance and displays the profit factor for both its long and short strategies, visible in a table located in the top-right corner of your chart.
The image above shows the profit factor for both the long and short strategies used by Impulse IQ.
A profit factor greater than 1 indicates that the strategy was profitable when trading historical price data.
A profit factor less than 1 indicates that the strategy was unprofitable when trading historical price data.
A profit factor equal to 1 indicates that the strategy neither gained nor lost money on historical price data.
Using Impulse IQ
While Impulse IQ functions as a comprehensive trading system with its own entry and exit signals, it was designed for the manual trader to take its trading signals and analysis indications to greater heights - offering numerous applications beyond its built-in trading system.
The standout feature of Impulse IQ is its ability to characterize and capitalize on trends. Keeping a close eye on “Breakout” labels and making use of the IQ meter is the best way to use Impulse IQ.
The IQ Meters can be used to:
Find entry points during trend retracements
Assess trend alignment across higher and lower timeframes
Evaluate overall trend strength, indicating where the price lies on both IQ Meters.
Additionally, "Break Up" and "Break Down" labels can be identified for anticipating breakouts. Impulse IQ self-learns to capture breakouts optimally, making these labels dynamic signals for predicting a breakout.
The Zig Zag IQ indicators are instrumental in characterizing the market's current state. As a self-learning tool, Zig Zag IQ constantly adapts to improve the representation of current price action. The price tops and bottoms identified by Zig Zag IQ can be treated as support/resistance and breakout levels.
Of course, you can set alerts for all Impulse IQ entry and exit signals, effectively following along its systematic conquest of price movement.
GG Short & Long IndicatorGG Short & Long Indicator is a powerful signal indicator with AI
How do indicator signals work?
The main purpose of the indicator is to give a signal that is most likely to bring profit based on historical data. This ORIGINAL trend algorithm gives SHORT and LONG signals when several conditions coincide: 1) Breakout of the average value of the modernized VWAP (this VWAP takes data only from certain time periods and trading sessions, as a result, its breakout most often coincides with the beginning of a strong trend); 2) The previous condition must be confirmed by volumes. I noticed that on some crypto exchanges, depending on whether the breakout is false or true, the volumes are different relative to each other. I applied this knowledge for additional filtering of signals (this point works only on crypto assets, on other assets the algorithm works without taking it into account, maybe later I will refine it); 3) When some of my original formulas to determine overbought (similar in principle to RSI, but more designed to work with the trader algorithm), should not show overbought - so that the entry into the transaction was not at too unfavorable values. To summarize, the algorithm tries to find a balance to determine a true breakout, during which the price will not go too far (for an acceptable RR).
But the most important thing is that the parameters to customize the algorithm are governed by our original AI algorithm. It can adjust the indicator in two modes: 1) Settings are selected based on the most profitable historical settings. 2) The settings are selected based not only on historical profitability, but also on winrate, frequency of trades, and a few other items that we will not disclose (so the code is closed) - we consider this approach as a priority, because according to our observations, it gives the highest performance compared to manual tuning. In addition, AI simply simplifies the work with the indicator - you do not need to adjust the settings manually for different trading pairs or timeframes, AI will do it all by itself and immediately give the ready result (backtest) on the table.
How to trade?
After the signal is issued, the indicator determines the recommended levels to close the trade (green dots). Stop loss should be placed behind the corresponding gray SL mark. Levels for closing a deal (TP) and the level of stop loss setting (SL) are also determined automatically for the selected pair and TF, based on volatility and selected indicator settings
To make a trade, you can also use the built-in “Support and Resistance Zones” tool, which displays ranges on the chart based on the modernized ATR, from which the price is more likely to rebound (here I also used my own approach, where in addition to the classic ATR formula, I also used volumes from certain crypto exchanges to determine more accurate price rebound zones)
These zones are also adjusted by AI - the algorithm compares several dozens of variations of these zones (with different settings) and chooses the one that best fits the current settings of the signal algorithm. For example, if the indicator is set up for frequent trades - the zones will be updated faster and will be less deep than if the indicator is set up for medium-term trading
If desired, you can customize the indicator manually using the corresponding section of the settings. Each paramater has a tooltip describing how and what it affects.
Statistisc panel
The panel can be divided into 2 conditional parts:
1) Statistics for each individual TP for the selected strategy. It shows the winrate and gross profit, if you fix a trade on a single target completely
2) Total trading result, if you trade clearly according to the strategy and fix the position by equal hours on 4 TPs. The total trading result is displayed for the current indicator settings, it also shows the best, worst and optimal of the possible indicator settings and the trading result of these settings on the side.
How do setup the indicator?
The indicator has preset settings for several major pairs and timeframes. These are fixed settings specifically selected for individual pairs and timeframes. You can use these presets, or you can choose one of the adaptive settings, which will AUTOMATICALLY select the best/optimal indicator settings.
I recommend choosing the “Adaptive Optimal” preset, as it uses more data to determine the optimal indicator settings and according to my observations this method works better in comparison to manual indicator settings or the “Adaptive Best” preset
Or you can use the manual settings, as mentioned earlier.
RSI Phi PhiSống để cho đi.
Phương pháp của sư phụ
Sống trong đời sống cần có một tấm lòng
Để làm gì, em biết không?
Để gió cuốn đi
Để gió cuốn đi
Gió cuốn đi cho mây qua dòng sông
Ngày vừa lên hay đêm xuống mênh mông
Ôi trái tim đang bay theo thời gian
Làm chiếc bóng đi rao lời dối gian
Những khi chiều tới, cần có một tiếng cười
Để ngậm ngùi theo lá bay
Rồi nước cuốn trôi
Rồi nước cuốn trôi
Hãy nghiêng đời xuống, nhìn suốt một mối tình
Chỉ lặng nhìn không nói năng
Để buốt trái tim
Để buốt trái tim
Trong trái tim con chim đau nằm yên
Ngủ dài lâu mang theo vết thương sâu
Một sớm mai, chim bay đi triền miên
Và tiếng hót tan trong trời gió lên
Hãy yêu ngày tới dù quá mệt kiếp người
Còn cuộc đời, ta cứ vui
Dù vắng bóng ai
Dù vắng bóng ai
Dù vắng bóng ai
Dù vắng bóng ai
Dù vắng bóng ai
Fibonacci Momentum CascadeThe Foundation: FMC Indicator:
The Fibonacci Momentum Cascade (FMC) is an AI-enhanced technical indicator that automates Fibonacci analysis, removing the guesswork and doubt that plagues manual drawing. Instead of relying on subjective human input, the FMC uses a proprietary Momentum Cascade Engine™ that constantly analyzes market strength to detect significant shifts in buying and selling pressure. When confirmed, it automatically identifies the most relevant market trend, cascades fresh Fibonacci levels, and grades potential technical setups. It features 100% automated swings, adaptive real-time analysis, and professional setup grading with Primary Setups (▲P / ▼P) for A+ formations and Secondary Setups (▲S / ▼S) for supplementary patterns.
The Adaptive Edge: AI Co-Pilot:
Our AI analyzes multiple data sources including market sentiment, technical patterns, fundamental factors, and news events to generate comprehensive market insights. It also fine-tunes the FMC indicator inputs for today's market, outputting personalized settings optimized for multiple timeframes (1d, 4h, 1h, 15m, 5m) — removing guesswork and maximizing precision for your asset.
The Force Multiplier: The Hub:
The Hub is our community intelligence platform where users share market analyses and insights. When you or others request AI analyses, they become available in The Hub for everyone to access without using credits. This creates a growing library of market insights across all asset classes. You can browse community analyses, discover trending assets, and benefit from the collective wisdom of experienced traders—essentially getting free analyses beyond your monthly credits.
Neural Network Buy and Sell SignalsTrend Architect Suite Lite - Neural Network Buy and Sell Signals
Advanced AI-Powered Signal Scoring
This indicator provides neural network market analysis on buy and sell signals designed for scalpers and day traders who use 30s to 5m charts. Signals are generated based on an ATR system and then filtered and scored using an advanced AI-driven system.
Features
Neural Network Signal Engine
5-Layer Deep Learning analysis combining market structure, momentum, and market state detection
AI-based Letter Grade Scoring (A+ through F) for instant signal quality assessment
Normalized Input Processing with Z-score standardization and outlier clipping
Real-time Signal Evaluation using 5 market dimensions
Advanced Candle Types
Standard Candlesticks - Raw price action
Heikin Ashi - Trend smoothing and noise reduction
Linear Regression - Mathematical trend visualization
Independent Signal vs Display - Calculate signals on one type, display another
Key Settings
Signal Configuration
- Signal Trigger Sensitivity (Default: 1.7) - Controls signal frequency vs quality
- Stop Loss ATR Multiplier (Default: 1.5) - Risk management sizing
- Signal Candle Type (Default: Candlesticks) - Data source for signal calculations
- Display Candle Type (Default: Linear Regression) - Visual candle display
Display Options
- Signal Distance (Default: 1.35 ATR) - Label positioning from price
- Label Size (Default: Medium) - Optimal readability
Trading Applications
Scalping
- Fast pace signal detection with quality filtering
- ATR-based stop management prevents signal overlap
- Neural network attempts to reduces false signals in choppy markets
Day Trading
- Multi-timeframe compatible with adaptation settings
- Clear trend visualization with Linear Regression candles
- Support/resistance integration for better entries/exits
Signal Filtering
- Use A+/A grades for highest probability setups
- B grades for confirmation in trending markets
- C-F grades help identify market uncertainty
Why Choose Trend Architect Lite?
No Lag - Real-time neural network processing
No Repainting - Signals appear and stay fixed
Clean Charts - Focus on price action, not indicators
Smart Filtering - AI reduces noise and false signals
Flexible and customizable - Works across all timeframes and instruments
Compatibility
- All Timeframes - 1m to Monthly charts
- All Instruments - Forex, Crypto, Stocks, Futures, Indices
Risk Disclaimer
This indicator is a tool for technical analysis and should not be used as the sole basis for trading decisions. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
Institutional Analyst LLM📊 Institutional Analyst Board LLM – Smart Money Confluence Scanner for XAUUSD, Forex, Crypto 🔍 Overview The Institutional Analyst Board is a complete multi-timeframe smart money toolkit designed for traders who demand clarity, confluence, and precision. It brings together institutional-grade metrics—Order Blocks (OB), Fair Value Gaps (FVG), Liquidity Sweeps, MACD/RSI...
PTS Ultimate Analysis Board (Flexible Position + Ticker)
GoldenTradeClub
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PTS Ultimate Analysis Board (Flexible Position + Ticker) Version: Pine v5 Description: This indicator builds a fully customizable, multi-timeframe dashboard table that surfaces 19 key metrics for any ticker (current chart TF, 1 h, 4 h). You can position the table at the top-right or bottom-right of your chart and toggle each metric on or off. Key...
Trading Engine AI Light
GoldenTradeClub
GoldenTradeClub
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The Trading Engine includes the best and most effective technical analysis tools. It has 27 different Buy Signal parameters and 26 different Sell Signal parameters. Furthermore, it also has 9 Stop Loss triggers for Long Positions and 8 Stop Loss triggers for Short Positions. Many of the Buy or Sell Signal parameters function as Take Profit and Stop Loss signals...
Elliott Wave Complete
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💀⚡ PTS WIZARD 666™ ULTIMATE SUPREME V5.0 - COMPLETE FIXED ⚡💀
GoldenTradeClub
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🔥 PTS TRADE 666™ ULTIMATE BOOKMAP + QUANTUM ENGINE
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🔥 PTS TRADE 666™ - ULTIMATE INSTITUTIONAL TOOL 🔥
GoldenTradeClub
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GoldenTradeClub
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🧙♂ PTS WIZARD V3.0 - FINAL EDITION
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1. Indicator Presentation Name: 🧙♂ PTS WIZARD V3.0 – FINAL EDITION Short Title: PTS-WIZARD-V3-FINAL Type: Overlay trading dashboard for TradingView Purpose: A comprehensive multi-module indicator that blends classic cipher momentum signals, Elliott Wave pattern detection, advanced statistical analyses (Z-Score, Benford’s Law, Ehlers SNR), footprint-style volume...
🧙♂ PTS WIZARD V3.0 + FOOTPRINT ULTIMATE
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Name: PTS WIZARD V3.0 + FOOTPRINT ULTIMATE Type: Overlay trading dashboard for TradingView Purpose: Combines classic cipher-style momentum signals with an advanced footprint volume profile, multi-timeframe bias, statistical filters, and a fusion-score system—displayed in a customizable on-chart dashboard. Core Modules Cipher Momentum Signals WaveTrend...
🧙♂ PTS WIZARD V3.0 - BASIC
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PTS WIZARD V3.0 Basic – Ultimate Multi-Tool Trading Dashboard An all-in-one overlay combining classic cipher signals, Elliott Wave pattern detection, volume analytics, divergence spotting, and smart-entry timing—backed by advanced statistical filters and a live dashboard. Key Features Cipher Signals WaveTrend with overbought/oversold zones & cross signals RSI...
Trading Engine vCD AI
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Trading Engine vCD
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GoldenTradeClub
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Trading Engine v13
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Trading Engine B2B
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Trading Engine B2B FX V9
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Updated 3 hours ago
Institutional Analyst Board
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Jul 19
📊 Institutional Analyst Board – Smart Money Confluence Scanner for XAUUSD, Forex, Crypto
🔍 Overview
The Institutional Analyst Board is a complete multi-timeframe smart money toolkit designed for traders who demand clarity, confluence, and precision. It brings together institutional-grade metrics—Order Blocks (OB), Fair Value Gaps (FVG), Liquidity Sweeps, MACD/RSI bias, VWAP positioning, and Break of Structure (BoS)—into a single powerful visual dashboard.
This indicator is especially optimized for Gold (XAUUSD) but is also compatible with Crypto and Forex assets.
🧠 Key Features
✅ Multi-Timeframe Dashboard (5M / 15M / 1H)
✅ Order Block Detection with dynamic zones that extend until broken
✅ Fair Value Gap Detection with clear zone shading and border distinction
✅ MACD + RSI Confluence for momentum and bias alignment
✅ VWAP Positioning to identify premium/discount zones
✅ Liquidity Sweeps (internal/external range breaks)
✅ Killzone Highlighting (Asia / London / New York)
✅ Break of Structure (BoS) with advanced confluence filters
✅ Gold Bias Flags across timeframes (BUY / SELL / NEUTRAL)
✅ Dynamic Price Watermark with real-time data
✅ Fully customizable colors, transparencies, and text labels
🧠 How It Works
The Board uses institutional logic to analyze the chart in real time:
Metric Purpose
OB Zones Highlight potential smart money footprints where price is likely to react.
FVG Zones Identify imbalance areas between buyers and sellers—ideal for mean reversion entries.
MACD/RSI Confirm momentum direction and relative strength confluence.
VWAP Determine whether price is trading at a premium or discount.
Liquidity Sweeps Detect manipulative moves before major reversals.
BoS Mark potential trend reversals, filtered by institutional confluence.
Each signal is computed across 3 timeframes and visualized in a clean board that updates live. You’ll also see labels, alerts, and session overlays for maximum clarity.
📌 Ideal Use Case
This tool is perfect for:
Funded Challenge Traders (FTMO, MyForexFunds, etc.)
Gold scalpers and intraday traders
Crypto price action traders using BTC, ETH, SOL, etc.
Smart Money Concept (SMC) and ICT followers
⚙️ Customization Options
Toggle each module (OB, FVG, VWAP, MACD/RSI, etc.)
Set transparency and color for each zone type
Adjust Killzone timing (Asia, London, NY)
Control board position (Top/Bottom) and metric visibility
📈 Compatible Assets
✅ XAUUSD (optimized)
✅ Forex majors/minors
✅ Crypto pairs (BTC, ETH, SOL, etc.)
✅ Indices (GER40, NASDAQ, SPX with minor adaptation)
🛠️ Requirements
Use on TradingView v5
Set chart time to UTC+0 or UTC+3 for optimal Killzone accuracy
For crypto, redefine Killzone hours if needed (24/7 market)
🧠 Pro Tip
Pair this indicator with volume profile tools, CVD/Delta Flow, or Footprint overlays to build high-confidence trade setups with clear institutional confluence.
Linear Regression Channel Screener [Daveatt]Hello traders
First and foremost, I want to extend a huge thank you to @LonesomeTheBlue for his exceptional Linear Regression Channel indicator that served as the foundation for this screener.
Original work can be found here:
Overview
This project demonstrates how to transform any open-source indicator into a powerful multi-asset screener.
The principles shown here can be applied to virtually any indicator you find interesting.
How to Transform an Indicator into a Screener
Step 1: Identify the Core Logic
First, identify the main calculations of the indicator.
In our case, it's the Linear Regression
Channel calculation:
get_channel(src, len) =>
mid = math.sum(src, len) / len
slope = ta.linreg(src, len, 0) - ta.linreg(src, len, 1)
intercept = mid - slope * math.floor(len / 2) + (1 - len % 2) / 2 * slope
endy = intercept + slope * (len - 1)
dev = 0.0
for x = 0 to len - 1 by 1
dev := dev + math.pow(src - (slope * (len - x) + intercept), 2)
dev
dev := math.sqrt(dev / len)
Step 2: Use request.security()
Pass the function to request.security() to analyze multiple assets:
= request.security(sym, timeframe.period, get_channel(src, len))
Step 3: Scale to Multiple Assets
PineScript allows up to 40 request.security() calls, letting you monitor up to 40 assets simultaneously.
Features of This Screener
The screener provides real-time trend detection for each monitored asset, giving you instant insights into market movements.
It displays each asset's position relative to its middle regression line, helping you understand price momentum.
The data is presented in a clean, organized table with color-coded trends for easy interpretation.
At its core, the screener performs trend detection based on regression slope calculations, clearly indicating whether an asset is in a bullish or bearish trend.
Each asset's price is tracked relative to its middle regression line, providing additional context about trend strength.
The color-coded visual feedback makes it easy to spot changes at a glance.
Built-in alerts notify you instantly when any asset experiences a trend change, ensuring you never miss important market moves.
Customization Tips
You can easily expand the screener by adding more symbols to the symbols array, adapting it to your watchlist.
The regression parameters can be adjusted to match your preferred trading timeframes and sensitivity.
The alert system is already configured to notify you of trend changes, but you can customize the alert messages and conditions to your needs.
Limitations
While powerful, the screener is bound by PineScript's limitation of 40 security calls, capping the maximum number of monitored assets.
Using AI to Help With Conversion
An interesting tip:
You can use AI tools to help convert single-asset indicators to screeners.
Simply provide the original code and ask for assistance in transforming it into a screener format. While the AI output might need some syntax adjustments, it can handle much of the heavy lifting in the conversion process.
Prompt (example) : " Please make a pinescript version 5 screener out of this indicator below or in attachment to scan 20 instruments "
I prefer Claude AI (Opus model) over ChatGPT for pinescript.
Conclusion
This screener transformation technique opens up endless possibilities for market analysis.
By following these steps, you can convert any indicator into a powerful multi-asset scanner, enhancing your trading toolkit significantly.
Remember: The power of a screener lies not just in monitoring multiple assets, but in applying consistent analysis across your entire watchlist in real-time.
Feel free to fork and modify this screener for your own needs.
Happy trading! 🚀📈
Daveatt
PCA-Risk IndicatorOBJECTIVE:
The objective of this indicator is to synthesize, via PCA (Principal Component Analysis), several of the most used indicators with in order to simplify the reading of any asset on any timeframe.
It is based on my Bitcoin Risk Long Term indicator, and is the evolution of another indicator that I have not published 'Average Risk Indicator'.
The idea of this indicator is to use statistics, in this case the PCA, to reduce the number of dimensions (indicator) to aggregate them in some synthetic indicators (PCX)
I invite you to dig deeper into the PCA, but that is to try to keep as much information as possible from the raw data. The signal minus the noise.
I realized this indicator a year ago, but I publish it now because I do not see the interest to keep it private.
USAGE:
Unlike the Bitcoin Risk Long Term indicator, it does not make sense to change or disable the input indicators unless you use the 'Average Indicator' function. Because each input is weighted to generate the outputs, the PCX.
I extracted several courses (Bitcoin, Gold, S&P, CAC40) on several timeframes (W, D, 4h, 1h) of Trading view and use the Excel generated for the data on which I played the PCA analysis.
The results:
explained_variance_ratio: 0.55540809 / 0.13021972 / 0.07303142 / 0.03760925
explained_variance: 11.6639671 / 2.73470717 / 1.53371209 / 0.7898212
Interpretation:
Simply put, 55% of the information contained in each indicator can be represented with PC1, +13% with PC2, +7% with PC3, +3% with PC4.
What is important to understand is that PC1, which serves as a thermometer in a way, gives a simple indication of over-buying or over-selling area better than any other indicator.
PC2, difficult to interpret, is more reactive because precedes PC1, but can give false signals.
PC3 and PC4 do not seem relevant to me.
The way I use it is to take PC1 for Risk indicator, and display PC2 with 'Area'. When PC2 turns around and PC1 arrives on extremes, it can be good points to act.
NOTES :
- It is surprising that a simple average of all the indicators gives a fairly relevant result
- With Average indicator as Risk indicator, you can combine the indicators of your choice and see the predictive power with the staining of bars.
- You can add alerts on the levels of your choice on the Risk Indicator
- If you have any idea of adding an indicator, modification, criticism, bug found: share them, it’s appreciated!
---- FR ----
OBJECTIF :
L'objectif de cet indicateur est de synthétiser, via l'ACP (Analyse en Composantes Principales), plusieurs indicateurs parmi les plus utilisés avec afin de simplifier la lecture de n'importe quel actif sur n'importe quel timeframe.
Il est inspiré de mon indicateur 'Bitcoin Risk Long Term indicator', et est l'évolution d'un autre indicateur que je n'ai pas publié 'Average Risk Indicator'.
L'idée de cet indicateur est d'utiliser les statistiques, en l'occurence l'ACP, pour réduire le nombre de dimensions (indicateur) pour les agréger dans quelques indicateurs synthétiques (PCX)
Je vous invite à creuser l'ACP, mais c'est chercher à conserver un maximum d'informations à partir de la donnée brute. Le signal moins le bruit.
J'ai réalisé cet indicateur il y a un an, mais je le publie maintenant car je ne vois pas l'intérêt de le garder privé.
UTILISATION :
Contrairement à 'Bitcoin Risk Long Term indicator', il ne fait pas sens de modifier ou désactiver les indicateurs inputs, sauf si vous utiliser la fonction 'Average Indicator'. Car chaque input est pondéré pour générer les outputs, les PCX.
J'ai extrait plusieurs cours (Bitcoin, Gold, S&P, CAC40) sur plusieurs timeframes (W, D, 4h, 1h) de Trading view et utiliser les Excel généré pour la data sur laquelle j'ai joué l'analyse ACP.
Les résultats :
explained_variance_ratio : 0.55540809 / 0.13021972 / 0.07303142 / 0.03760925
explained_variance : 11.6639671 / 2.73470717 / 1.53371209 / 0.7898212
Interprétation :
Pour faire simple, 55% de l'information contenu dans chaque indicateur peut être représenté avec PC1, +13% avec PC2, +7% avec PC3, +3% avec PC4.
Ce qui faut y comprendre c'est que le PC1, qui sert de thermomètre en quelque sorte, donne une indication simple de zone de sur-achat ou sur-vente mieux que n'importe quel autre indicateur.
PC2, difficile à interpréter, est plus réactif car précède PC1, mais peut donner des faux signaux.
PC3 et PC4 ne me semble pas pertinent.
La manière dont je m'en sert c'est de prendre PC1 pour Risk indicator, et d'afficher PC2 avec 'Region'. Lorsque PC2 se retourne et que PC1 arrive sur des extrêmes, cela peut être des bons points pour agir.
NOTES :
- Il est étonnant de constater qu'une simple moyenne de tous les indicateurs donne un résultat assez pertinent
- Avec Average indicator comme Risk indicator, vous pouvez combiner les indicateurs de vos choix et voir la force prédictive avec la coloration des bars.
- Vous pouvez ajouter des alertes sur les niveaux de votre choix sur le Risk Indicator
- Si vous avez la moindre idée d'ajout d'indicateur, modification, critique, bug trouvé : partagez-les, c'est apprécié !
Endpointed SSA of Price [Loxx]The Endpointed SSA of Price: A Comprehensive Tool for Market Analysis and Decision-Making
The financial markets present sophisticated challenges for traders and investors as they navigate the complexities of market behavior. To effectively interpret and capitalize on these complexities, it is crucial to employ powerful analytical tools that can reveal hidden patterns and trends. One such tool is the Endpointed SSA of Price, which combines the strengths of Caterpillar Singular Spectrum Analysis, a sophisticated time series decomposition method, with insights from the fields of economics, artificial intelligence, and machine learning.
The Endpointed SSA of Price has its roots in the interdisciplinary fusion of mathematical techniques, economic understanding, and advancements in artificial intelligence. This unique combination allows for a versatile and reliable tool that can aid traders and investors in making informed decisions based on comprehensive market analysis.
The Endpointed SSA of Price is not only valuable for experienced traders but also serves as a useful resource for those new to the financial markets. By providing a deeper understanding of market forces, this innovative indicator equips users with the knowledge and confidence to better assess risks and opportunities in their financial pursuits.
█ Exploring Caterpillar SSA: Applications in AI, Machine Learning, and Finance
Caterpillar SSA (Singular Spectrum Analysis) is a non-parametric method for time series analysis and signal processing. It is based on a combination of principles from classical time series analysis, multivariate statistics, and the theory of random processes. The method was initially developed in the early 1990s by a group of Russian mathematicians, including Golyandina, Nekrutkin, and Zhigljavsky.
Background Information:
SSA is an advanced technique for decomposing time series data into a sum of interpretable components, such as trend, seasonality, and noise. This decomposition allows for a better understanding of the underlying structure of the data and facilitates forecasting, smoothing, and anomaly detection. Caterpillar SSA is a particular implementation of SSA that has proven to be computationally efficient and effective for handling large datasets.
Uses in AI and Machine Learning:
In recent years, Caterpillar SSA has found applications in various fields of artificial intelligence (AI) and machine learning. Some of these applications include:
1. Feature extraction: Caterpillar SSA can be used to extract meaningful features from time series data, which can then serve as inputs for machine learning models. These features can help improve the performance of various models, such as regression, classification, and clustering algorithms.
2. Dimensionality reduction: Caterpillar SSA can be employed as a dimensionality reduction technique, similar to Principal Component Analysis (PCA). It helps identify the most significant components of a high-dimensional dataset, reducing the computational complexity and mitigating the "curse of dimensionality" in machine learning tasks.
3. Anomaly detection: The decomposition of a time series into interpretable components through Caterpillar SSA can help in identifying unusual patterns or outliers in the data. Machine learning models trained on these decomposed components can detect anomalies more effectively, as the noise component is separated from the signal.
4. Forecasting: Caterpillar SSA has been used in combination with machine learning techniques, such as neural networks, to improve forecasting accuracy. By decomposing a time series into its underlying components, machine learning models can better capture the trends and seasonality in the data, resulting in more accurate predictions.
Application in Financial Markets and Economics:
Caterpillar SSA has been employed in various domains within financial markets and economics. Some notable applications include:
1. Stock price analysis: Caterpillar SSA can be used to analyze and forecast stock prices by decomposing them into trend, seasonal, and noise components. This decomposition can help traders and investors better understand market dynamics, detect potential turning points, and make more informed decisions.
2. Economic indicators: Caterpillar SSA has been used to analyze and forecast economic indicators, such as GDP, inflation, and unemployment rates. By decomposing these time series, researchers can better understand the underlying factors driving economic fluctuations and develop more accurate forecasting models.
3. Portfolio optimization: By applying Caterpillar SSA to financial time series data, portfolio managers can better understand the relationships between different assets and make more informed decisions regarding asset allocation and risk management.
Application in the Indicator:
In the given indicator, Caterpillar SSA is applied to a financial time series (price data) to smooth the series and detect significant trends or turning points. The method is used to decompose the price data into a set number of components, which are then combined to generate a smoothed signal. This signal can help traders and investors identify potential entry and exit points for their trades.
The indicator applies the Caterpillar SSA method by first constructing the trajectory matrix using the price data, then computing the singular value decomposition (SVD) of the matrix, and finally reconstructing the time series using a selected number of components. The reconstructed series serves as a smoothed version of the original price data, highlighting significant trends and turning points. The indicator can be customized by adjusting the lag, number of computations, and number of components used in the reconstruction process. By fine-tuning these parameters, traders and investors can optimize the indicator to better match their specific trading style and risk tolerance.
Caterpillar SSA is versatile and can be applied to various types of financial instruments, such as stocks, bonds, commodities, and currencies. It can also be combined with other technical analysis tools or indicators to create a comprehensive trading system. For example, a trader might use Caterpillar SSA to identify the primary trend in a market and then employ additional indicators, such as moving averages or RSI, to confirm the trend and generate trading signals.
In summary, Caterpillar SSA is a powerful time series analysis technique that has found applications in AI and machine learning, as well as financial markets and economics. By decomposing a time series into interpretable components, Caterpillar SSA enables better understanding of the underlying structure of the data, facilitating forecasting, smoothing, and anomaly detection. In the context of financial trading, the technique is used to analyze price data, detect significant trends or turning points, and inform trading decisions.
█ Input Parameters
This indicator takes several inputs that affect its signal output. These inputs can be classified into three categories: Basic Settings, UI Options, and Computation Parameters.
Source: This input represents the source of price data, which is typically the closing price of an asset. The user can select other price data, such as opening price, high price, or low price. The selected price data is then utilized in the Caterpillar SSA calculation process.
Lag: The lag input determines the window size used for the time series decomposition. A higher lag value implies that the SSA algorithm will consider a longer range of historical data when extracting the underlying trend and components. This parameter is crucial, as it directly impacts the resulting smoothed series and the quality of extracted components.
Number of Computations: This input, denoted as 'ncomp,' specifies the number of eigencomponents to be considered in the reconstruction of the time series. A smaller value results in a smoother output signal, while a higher value retains more details in the series, potentially capturing short-term fluctuations.
SSA Period Normalization: This input is used to normalize the SSA period, which adjusts the significance of each eigencomponent to the overall signal. It helps in making the algorithm adaptive to different timeframes and market conditions.
Number of Bars: This input specifies the number of bars to be processed by the algorithm. It controls the range of data used for calculations and directly affects the computation time and the output signal.
Number of Bars to Render: This input sets the number of bars to be plotted on the chart. A higher value slows down the computation but provides a more comprehensive view of the indicator's performance over a longer period. This value controls how far back the indicator is rendered.
Color bars: This boolean input determines whether the bars should be colored according to the signal's direction. If set to true, the bars are colored using the defined colors, which visually indicate the trend direction.
Show signals: This boolean input controls the display of buy and sell signals on the chart. If set to true, the indicator plots shapes (triangles) to represent long and short trade signals.
Static Computation Parameters:
The indicator also includes several internal parameters that affect the Caterpillar SSA algorithm, such as Maxncomp, MaxLag, and MaxArrayLength. These parameters set the maximum allowed values for the number of computations, the lag, and the array length, ensuring that the calculations remain within reasonable limits and do not consume excessive computational resources.
█ A Note on Endpionted, Non-repainting Indicators
An endpointed indicator is one that does not recalculate or repaint its past values based on new incoming data. In other words, the indicator's previous signals remain the same even as new price data is added. This is an important feature because it ensures that the signals generated by the indicator are reliable and accurate, even after the fact.
When an indicator is non-repainting or endpointed, it means that the trader can have confidence in the signals being generated, knowing that they will not change as new data comes in. This allows traders to make informed decisions based on historical signals, without the fear of the signals being invalidated in the future.
In the case of the Endpointed SSA of Price, this non-repainting property is particularly valuable because it allows traders to identify trend changes and reversals with a high degree of accuracy, which can be used to inform trading decisions. This can be especially important in volatile markets where quick decisions need to be made.
Intelligent Exponential Moving Average Private AccessView the full documentation on this indicator here: www.kenzing.com
Note: This indicator is now intended for those who have been granted private access and may be more frequently updated than the previous versions.
Introduction
This indicator uses machine learning (Artificial Intelligence) to solve a real human problem.
The Exponential Moving Average ( EMA ) is one of the most used indicators on the planet, yet no one really knows what pair of exponential moving average lengths works best in combination with each other.
A reason for this is because no two EMA lengths are always going to be the best on every instrument, time-frame, and at any given point in time.
The " Intelligent Exponential Moving Average " solves the moving average problem by adapting the period length to match the most profitable combination of exponential moving averages in real time.
How does the Intelligent Exponential Moving Average work?
The artificial intelligence that operates these moving average lengths was created by an algorithm that tests every single combination across the entire chart history of an instrument for maximum profitability in real-time.
No matter what happens, the combination of these exponential moving averages will be the most profitable.
Can we learn from the Intelligent Moving Average?
There are many lessons to be learned from the Intelligent EMA . Most will come with time as it is still a new concept. Adopting the usefulness of this AI will change how we perceive moving averages to work.
Limitations
Ultimately, there are no limiting factors within the range of combinations that has been programmed. The exponential moving averages will operate normally, but may change lengths in unexpected ways - maybe it knows something we don't?
Thresholds
The range of exponential moving average lengths is between 5 to 40.
Additional coverage resulted in TradingView server errors.
Future Updates!
Soon, I will be publishing tools to test the AI and visualise what moving average combination the AI is currently using.
Follow and like for more content!
RSI or MACD + Tendance Kijun LTThis script is an update of my previous script "RSI + Tendance Kijun LT", I will not explain it here, if needed have a look at it :
I made a new script (and not update the previous one) because some people may not be interested by MACD and for performance perspective they may be interested to only have RSI (since you can't have both but only switch from RSI and MACD)
So now, you can choose to have MACD instead of RSI with long term trend based on Kijun still dispaying. Why am I adding MACD even if most of the time I never use it ? It's for Elliott Wave purpose and principaly for triangle. With MACD, you can easily identify if you're forming a triangle or not in an Elliott Wave perspective (I'm not speaking about chartist triangle).
As an example, you can see ETHUSD in daily and something looking similar to a triangle. We can trade it with many possibility (breakout, support/resistance) but I'm interested in to identify if it's a triangle with an EW count (not chartist) and if it's the case I will consider different scenario (triangle are most of the time wave 4 so we could have one more push leg down on this ticker)
So, in my daytrading I'm still always using RSI except when I want to verify if we have a triangle :) and I need to switch to MACD for that to check the following things :
- am I able to draw a triangle as the price did
- may I able to join A to C and B to D and still have a triangle on MACD
- If Yes, I will take care of E point because it's the start of the 5th wave (E point may be a truncated wave of the triangle and not join the line of points A to C)
By adding this in my strategy, I can anticipate different scenarios and invalidate them if I didn't get the triangle on MACD (by having a D point on MACD not respecting the triangle form). Don't forget, we can decide to don't trade a triangle as an EW count but still trade it as a chartist form (breakout or anything else).
In summary for the next days/weeks, on ETHUSD in daily time unit I will therefore wait and see if the price goes up to point D by being validated on the MACD. If so, then I will look at the possible formation of point E around prices 141$ and 156$, if at these prices I have short signals then it will be interesting to go back in short position
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Ce script est une mise à jour de mon script précédent "RSI + Tendance Kijun LT", je ne l'expliquerai pas ici, si besoin jetez-y un oeil :
J'ai fait un nouveau script (sans forcer la mise à jour du précédent) parce que certaines personnes peuvent ne pas être intéressées par MACD et pour des raisons de performance, elles peuvent être intéressées à n'avoir que RSI (puisque vous ne pouvez pas avoir les deux en même temps mais seulement passer de RSI et MACD)
Donc maintenant, vous pouvez choisir d'avoir MACD au lieu de RSI avec une tendance à long terme basée sur Kijun qui s'affiche toujours. Pourquoi est-ce que j'ajoute MACD même si la plupart du temps je ne l'utilise jamais ? C'est pour Elliott Wave et surtout pour le triangle. Avec MACD, vous pouvez facilement identifier si vous formez un triangle ou non dans une perspective Elliott Wave (je ne parle pas de triangle chartiste).
Par exemple, vous pouvez voir que sur ETHUSD en unité de temps journalière nous avons quelque chose qui ressemble à un triangle. Nous pouvons faire des trade de pleins de manières différentes (cassure d'oblique, support/résistance) mais je suis intéressé d'identifier si c'est un triangle avec un compte EW (pas chartiste) et si c'est le cas, je vais envisager un scénario différent (sachant que les triangles sont la plupart du temps la vague 4 et donc envisager à la sortie une dernière poussée baissière type vague 5 sur ETHUSD)
Donc, dans mon daytrading, j'utilise toujours RSI sauf quand je veux vérifier si nous avons un triangle :) et j'ai besoin de passer à MACD afin de vérifier les éléments suivantes :
- Suis-je capable de dessiner un triangle comme le prix le dessine
- puis-je joindre A à C et B à D et toujours avoir un triangle sur MACD ?
- Si oui, je m'occupe du point E car c'est le début de la 5ème vague (le point E peut être une onde tronquée du triangle et ne pas joindre la ligne des points A à C).
En ajoutant cela dans ma stratégie, je peux anticiper différents scénarios et les invalider si je n'ai pas obtenu le triangle sur MACD (en ayant un point D sur MACD ne respectant pas la forme du triangle). N'oubliez pas, nous pouvons décider de ne pas le trade comme un triangle d'un décompte EW mais de le trade simplement comme un triangle chartiste (breakout ou autre chose).
En résumé pour les prochains jours/semaines, sur ETHUSD en unité de temps journalière je vais donc patienter et voir si le prix va jusqu'au point D en étant validé sur le MACD. Si oui, alors je regarderai l'éventuelle formation du point E autour des prix 141$ et 156$, si à ces prix j'ai des signaux vendeurs alors il sera intéressant de rentrer en position short
London Midpoint Raid [Plazo Sullivan Roche Capital]London Midpoint BOS AI™ – User Manual
By Plazo Sullivan Roche Capital
Core Strategy in a Nutshell
The London Midpoint BOS AI™ is a precision intraday tool built on ICT and Smart Money Concepts (SMC) principles. It identifies London session reversal-to-continuation setups that align with higher-timeframe (HTF) bias and true market intent.
In essence:
When the Daily and 4H structure is bullish, the market often dips below equilibrium during London’s early volatility to grab liquidity before resuming upward.
Conversely, in a bearish structure, it typically spikes above equilibrium before continuing downward.
The tool automatically detects:
HTF Bias (Daily + H4) via EMA or structure logic
Yesterday’s mid-range (equilibrium)
Intraday Break of Structure (BOS) on your 2–5-minute chart
Volume expansion, confirming institutional displacement
Optional VWAP confluence for extra precision
When all filters align, the script marks BUY or SELL signals during the London Killzone (02:30–04:30 NY time) — when 70% of the day’s institutional liquidity is set.
What’s in It for You
Benefit Description
🎯 Ultra-High Precision Entries
Trades only when price sweeps the prior day’s equilibrium and confirms BOS with real volume expansion.
🧩 Institutional Logic, Simplified
Combines ICT, SMC, and Goldbach bias confirmation without clutter — showing only signals that matter.
⚙️ Adaptive Multi-Timeframe Bias
Auto-syncs with your Daily & H4 direction, ensuring you only trade with macro momentum.
🔔 Alert-Ready for Automation
BUY and SELL alert conditions are pre-built for webhook integration with cTrader or brokers.
📊 Clean Dashboard Interface
Real-time HTF bias panel keeps you aligned with the larger market context.
⏱ Session-Specific Smart Filtering
Restricts signals to the London Killzone for maximum precision and volatility efficiency.
Best Usage Guide
✅ Recommended Chart & Assets
Chart timeframe 2-minute to 5-minute
Higher timeframes monitored 4H and Daily
Pairs & Assets EURUSD, GBPUSD, XAUUSD (Gold), DXY, NAS100
Session London Killzone – 02:30 to 04:30 New York time
Ideal Market Conditions
Asian session forms a narrow, defined range (low volatility).
Price sweeps below or above yesterday’s midpoint during early London volatility.
HTF bias is clear and unconflicted (both Daily and 4H agree).
A strong BOS candle with volume expansion appears immediately after sweep.
VWAP alignment supports the intended direction.
Avoid trading:
Mixed HTF signals (Daily bullish, H4 bearish).
Large fundamental days (CPI, NFP, FOMC).
Markets already heavily trending with no retracement.
Tool Settings Breakdown
Session Control
Limit to London Killzone Filters signals only between 02:30–04:30 NY time.
HTF Bias Method
EMA or Structure Choose how Daily/H4 bias is determined.
Midpoint Logic
Require Sweep of Yesterday’s Midpoint Only triggers signals after liquidity sweep around yesterday’s mid-level.
Volume Confirmation
Volume SMA Length, Volume Expansion ≥ Confirms BOS with a spike in relative volume.
VWAP Confluence
Require VWAP alignment Adds institutional volume reference for more accurate trades.
Display Options
Show Dashboard, Show Midpoint, Show Labels Customize visibility of components for clarity.
How to Interpret Signals
BUY Signal (Bullish Setup)
HTF (Daily & H4) bias = Bullish
Price sweeps below yesterday’s midpoint
A BOS up forms on the 2–5m chart
Volume expansion confirms displacement
Optional VWAP confluence: Price above VWAP
deal Entry:
Buy on retracement to the BOS candle midpoint or a micro Fair Value Gap (FVG).
Target:
First partial at 1R or prior high
Final target near London session high or daily liquidity level
SELL Signal (Bearish Setup)
HTF (Daily & H4) bias = Bearish
Price sweeps above yesterday’s midpoint
A BOS down forms on the 2–5m chart
Volume expansion confirms displacement
Optinal VWAP confluence: Price below VWAP
Ideal Entry:
Sell on retracement to BOS candle midpoint or micro FVG fill.
🎯 Target:
First partial at 1R or session equilibrium
Final target at London low or key liquidity pocket
Best Setup Configuration
Parameter Recommended Value
Timeframe 2-minute or 3-minute
HTF Bias Method EMA (20)
Require Sweep of Midpoint ✅ Enabled
Volume Expansion ≥ 1.5x to 2.0x average
VWAP Filter ✅ Enabled
Session Limit ✅ London Killzone (02:30–04:30 NY)
Display Dashboard ON, Midpoint ON, Labels ON
This configuration yields an excellent balance of signal clarity, precision, and frequency — typically 2–4 valid trades per week per pair, with average R:R of 2.5–4.0.
Pro Tips for Maximum Edge
Bias Confirmation: Always double-check that Daily and H4 structure are aligned before entering.
Session Timing: Wait for the London open (02:30–03:00 NY). Avoid early pre-London signals.
Volume Clues: The best trades come when BOS candles show clear displacement — wide-range, high-volume bars.
Liquidity Targets: Focus on previous day’s high/low, session highs/lows, or obvious liquidity pools.
Psychological Precision: Don’t chase; let the tool print the signal after the sweep, then wait for confirmation.
🔔 Alerts & Automation
Pre-built alert conditions:
BUY: London Midpoint BOS
SELL: London Midpoint BOS
Use them for:
Webhook connections (e.g., cTrader, MT5, or Discord alerts).
External trade execution bots or journaling tools.
🏁 Summary
The London Midpoint BOS AI™ distills institutional concepts into a clean, actionable framework for traders who want to:
Trade only high-probability London setups
Filter out noise and fake reversals
Align entries with HTF direction and real liquidity intent
It’s your daily edge to capture the most profitable 90-minute window in global forex — the London Killzone, where precision beats volume every time.
Aggregation Index SmoothedAggregation Index Smoothed (AIS) - Multi-Method Trend Consensus Oscillator
What This Indicator Does
The Aggregation Index Smoothed combines four independent trend-detection methodologies into a unified momentum oscillator that operates across multiple timeframes simultaneously. Unlike traditional single-method indicators that can produce conflicting or false signals during market transitions, AIS requires consensus agreement across all four calculation methods before confirming trend direction.
Technical Methodology
Four-Component Loop System
Each component analyzes 16 different lookback periods (default range: 5-20 bars), creating a multi-timeframe perspective within a single calculation:
1. Price Change Analysis
Measures directional price movement across all periods. Each period scores +1 for positive change or -1 for negative change. Results are averaged and scaled to ±100.
2. RSI Multi-Period Analysis
Evaluates Relative Strength Index values across the same 16 periods. Scores +1 when RSI > 50 (momentum favoring bulls) or -1 when RSI < 50 (momentum favoring bears). This captures overbought/oversold conditions across multiple timeframes.
3. EMA Trend Position
Compares current price against Exponential Moving Averages of varying lengths (5-20 periods). Scores +1 when price trades above EMA (uptrend) or -1 when below (downtrend). This identifies trend alignment across short, medium, and longer-term moving averages.
4. Momentum Rate-of-Change
Calculates price momentum across all periods using the mom() function. Scores +1 for positive momentum or -1 for negative momentum, detecting acceleration and deceleration patterns.
Aggregation Process
Each of the four indicators independently calculates scores across all 16 periods
Individual indicator scores are averaged (range: -100 to +100)
All four indicator averages are combined using arithmetic mean
The resulting index undergoes EMA smoothing (default: 20 periods)
Optional double-smoothing applies a second EMA pass for maximum noise reduction
Why This Approach Is Unique
Problem Solved: Traditional oscillators often conflict - RSI might be bullish while MACD is bearish, or stochastic shows oversold while price trend is clearly down. Traders waste time reconciling these contradictions.
Solution: AIS eliminates conflicts by design. A bullish signal (+10 threshold) means all four methods across all 16 timeframes agree on upward momentum. This consensus approach dramatically reduces whipsaws and false signals compared to using any single method.
Technical Advantage: The for-loop methodology validates each signal across a spectrum of timeframes (5 bars through 20 bars), ensuring the trend is confirmed in both immediate-term and intermediate-term contexts. This is mathematically equivalent to running 64 separate indicators (4 methods × 16 periods) and requiring majority agreement.
Signal Generation
Long Signal (Bullish): Index crosses above +10 threshold
Indicates all four methods confirm upward momentum across multiple timeframes
Sustained readings above +10 suggest strong trend continuation
Short Signal (Bearish): Index crosses below -10 threshold
Indicates all four methods confirm downward momentum across multiple timeframes
Sustained readings below -10 suggest strong downtrend
Neutral Zone (-10 to +10): Mixed signals or consolidation
Methods disagree on direction, suggesting choppy or range-bound conditions
Avoid trend-following strategies in this zone
How to Use This Indicator
Best Practices
Timeframe Selection:
Most effective on 4-hour charts and higher (Daily, Weekly)
Lower timeframes (1H, 15m) may produce excessive signals despite smoothing
The 16-period loop range is optimized for swing trading timeframes
Entry Strategy:
Wait for index to cross threshold levels (±10)
Confirm with price action (breakout, support/resistance levels)
Consider entering on first pullback after threshold cross for better risk/reward
Parameter Adjustment:
Volatile instruments (crypto, small-caps): Increase thresholds to ±15 or ±20 to filter noise
Stable instruments (large-cap stocks, indices): Reduce thresholds to ±5 for earlier signals
Smoothing Length: Increase to 30+ for cleaner signals; decrease to 10-15 for faster response
Double Smoothing: Keep enabled for trend following; disable for more reactive signals
Risk Management:
Exit longs when index drops back into neutral zone (below +10)
Exit shorts when index rises into neutral zone (above -10)
Use index slope as trend strength indicator (steeper = stronger)
Interpretation Guidelines
Strong Trending Conditions:
Index sustained above +50 or below -50 indicates powerful directional move
All four methods showing extreme agreement across all timeframes
High probability of trend continuation
Trend Exhaustion Signals:
Index reaches extreme levels (+80 to +100 or -80 to -100)
Potential reversal zone; watch for divergence with price
Consider taking partial profits on existing positions
Divergence Detection:
Price makes new highs while index fails to confirm = bearish divergence
Price makes new lows while index shows higher lows = bullish divergence
Divergences on 4H+ timeframes carry significant weight
Limitations and Considerations
Not Suitable For:
Scalping or very short-term trading (under 1-hour timeframes)
Range-bound markets with no clear trend (index oscillates in neutral zone)
Instruments with erratic, news-driven price action
Known Lag:
Double smoothing introduces 40+ bar delay in signal generation
Designed for trend confirmation, not early trend detection
Fast market reversals may produce late exit signals
Complementary Tools:
Combine with support/resistance levels for entry precision
Use with volume analysis to confirm signal strength
Pair with volatility indicators (ATR) for position sizing
Technical Implementation Notes
The indicator pre-calculates all RSI and EMA values for lengths 5-20 to comply with Pine Script's requirement for constant-length parameters in ta.rsi() and ta.ema() functions. This workaround allows dynamic loop-based analysis while maintaining calculation consistency on every bar.
The scoring methodology uses binary classification (+1/-1) rather than normalized percentage values to ensure equal weighting across all four methods, preventing any single indicator from dominating the aggregate signal.
Summary: The Aggregation Index Smoothed provides trend confirmation through multi-method consensus across variable timeframes. Its primary value is eliminating the confusion of conflicting indicator signals by requiring agreement from four independent trend calculations before generating actionable signals. Best suited for swing traders and position traders on 4-hour and higher timeframes seeking high-probability trend-following entries with reduced false signals.
VIP PRO Pulse – Liquidity • Momentum • OI • RSI💠 VipPro Pulse – Liquidity • Momentum • OI
“Catch the heartbeat of market momentum.”
Developed exclusively for members of Crypto Arab Academy, VipPro Pulse is the first Arabic-engineered professional indicator that merges liquidity, momentum, and open interest into one intelligent real-time system — designed for traders who want clarity, precision, and confidence in every decision.
At the top, a dynamic color-coded dashboard clearly shows the market’s bias:
🟢 Green = Bullish momentum
🔴 Red = Bearish pressure
⚫ Gray = Neutral or corrective phase
Below, the wave engine automatically paints the cycles — green during bullish trends and red during bearish moves — so you can understand the market’s direction at a glance.
For scalpers, an orange line has been added — a precision tool built for Forex and Gold traders.
Buy when price moves above the orange line.
Sell when price moves below it.
This line alone can turn short-term volatility into smart, consistent profits.
💡 What you’ll find inside this indicator represents years of trading experience, countless hours of chart watching, and massive research and cost — all distilled into one masterpiece, now delivered to you on a golden plate.
No more need for multiple indicators:
✅ Supports all markets: Crypto (Spot & Futures), Forex, Stocks, Gold, Oil.
✅ Real-time liquidity flow analysis
✅ Momentum + OI confirmation
✅ Smart overbought/oversold alerts
✅ VWAP and Fibonacci integration (1.27 / 1.61 / 2.61 targets)
📸 Bonus AI Integration:
Take a screenshot of your chart showing the VipPro Pulse dashboard and wave section, then send it to our AI bot 👇
👉 VipPro Realtime AI Bot
The bot will instantly generate:
Full technical, wave & time analysis
Entry and retracement levels for safe liquidity management
Fibonacci-based targets ready for execution
VipPro Pulse isn’t just an indicator — it’s your silent partner that transforms complex market data into confident, profitable action.
Rejects unsafe trades when sell signals or weak momentum appear.
🔒 This indicator is invite-only. Its code cannot be accessed or copied.
📩 To get your copy or request a free trial, contact us:
👉 t.me
👨💻 Developer: Amr El Mehrezy
🔗 linktr.ee
📊 More info:
chatgpt.com
x.com
📊 Availability
VipPro Realtime is available exclusively to members of Crypto Arab Academy, the largest Arabic community specialized in intelligent trading.
💎 Annual membership: $1000
🎁 Good news! Exclusive offers and limited-time discounts are always available here:
👉 t.me
ML-Enhanced Multi-Indicator Composite Signal🤖 ML-Enhanced Multi-Indicator Composite Signal
Revolutionary AI-Powered Trading Indicator with Adaptive Learning
Transform your trading with cutting-edge machine learning technology that automatically optimizes indicator weights based on real market performance!
🎯 What Makes This Indicator Special?
This isn't just another composite indicator. It's an intelligent trading system that learns from market data and continuously adapts to improve signal accuracy. Unlike static indicators with fixed weights, this AI-powered tool dynamically adjusts the importance of each technical indicator based on their actual prediction success rates.
⚡ Key Features
🤖 Adaptive Machine Learning Engine
Automatically tracks prediction accuracy for each indicator
Dynamically adjusts weights based on performance
Continuous learning and adaptation to market conditions
Configurable learning parameters for fine-tuning
📊 Multi-Indicator Fusion
SuperTrend: Trend direction and momentum
Moving Averages: Price trend confirmation (SMA/EMA/WMA/RMA)
VWAP: Volume-weighted price levels
Linear Regression: Mathematical trend analysis
🎯 Intelligent Signal Generation
Strong Buy/Buy/Sell/Strong Sell signals
Configurable threshold levels
Signal smoothing to reduce noise
Smart signal timing to avoid repetitive alerts
📈 Performance Analytics Dashboard
Real-time accuracy tracking for each indicator
Weight distribution visualization
ML vs. Equal weights comparison
Learning progress monitoring
🚀 How It Works
1. Data Collection Phase
The indicator continuously monitors the performance of each technical component, storing predictions and actual market outcomes.
2. Learning Phase
Using configurable learning periods (20-500 bars), the ML engine calculates accuracy rates for each indicator's predictions.
3. Weight Optimization
Based on performance data, the system automatically adjusts weights using a configurable learning rate, ensuring better-performing indicators have more influence.
4. Signal Generation
The optimized composite signal triggers buy/sell alerts when crossing predefined thresholds, with visual signals and background coloring.
⚙️ Customization Options
Machine Learning Parameters
Learning Period: 20-500 bars (default: 100)
Prediction Horizon: 1-20 bars (default: 5)
Learning Rate: 0.01-1.0 (default: 0.1)
Minimum Weight: Prevents any indicator from becoming irrelevant
Performance Smoothing: Reduces noise in accuracy calculations
Traditional Settings
SuperTrend: Period and multiplier adjustment
Moving Average: Type selection and length
VWAP: Source customization
Linear Regression: Length and source options
Signal Configuration
Threshold Levels: Customizable buy/sell trigger points
Signal Smoothing: Reduces false signals
Manual Override: Option to use fixed weights instead of ML
📱 Visual Features
Signal Line Display
Dynamic color coding based on signal strength
Threshold level lines for clear entry/exit points
Background coloring for quick market sentiment assessment
Performance Table
Real-time accuracy metrics for each indicator
Current weight distribution showing ML optimization
Performance comparison between ML and equal weights
Learning progress indicator
Signal Shapes
🚀 Strong Buy: Large green triangle with text
📈 Buy: Standard green triangle
📉 Sell: Standard red triangle
💥 Strong Sell: Large red triangle with text
🎓 Best Practices & Usage Tips
For Beginners
Start with default ML settings
Allow 100+ bars for proper learning
Focus on Strong Buy/Sell signals initially
Monitor the performance table to understand ML adaptation
For Advanced Traders
Adjust learning rate based on market volatility
Customize prediction horizon for your trading timeframe
Fine-tune threshold levels for your risk tolerance
Combine with additional confirmation indicators
Recommended Settings by Timeframe
Scalping (1m-5m): Learning Period: 50, Prediction Horizon: 3
Day Trading (15m-1h): Learning Period: 100, Prediction Horizon: 5
Swing Trading (4h-1D): Learning Period: 200, Prediction Horizon: 10
🔔 Alert System
Set up comprehensive alerts for:
Strong Buy/Sell signals with maximum consensus
Regular Buy/Sell signals for standard entries
Custom message templates with price and signal strength
Email, SMS, and webhook compatibility
⚠️ Important Notes
Learning Period: Allow sufficient data for ML optimization (minimum 50 bars recommended)
Market Conditions: Performance may vary during high volatility or trending vs. ranging markets
Backtesting: Test thoroughly on historical data before live trading
Risk Management: Always use proper position sizing and stop losses
🏆 Why Choose This Indicator?
✅ Adaptive Intelligence: Unlike static indicators, this tool evolves with market conditions
✅ Transparent Performance: See exactly how well each component is performing
✅ Comprehensive Analytics: Make informed decisions with detailed performance metrics
✅ Professional Grade: Developed by experienced traders for serious market participants
✅ Continuous Innovation: Regular updates and improvements based on user feedback
📊 Performance Tracking
The indicator provides unprecedented transparency into its decision-making process:
Individual indicator accuracy rates
Weight evolution over time
Improvement metrics vs. baseline
Learning curve visualization
Transform your trading with the power of adaptive machine learning. Let the market data guide your strategy optimization automatically!
Tags: Machine Learning, AI Trading, Composite Signal, Multi-Indicator, Adaptive Algorithm, Signal Generation, Trading Automation, Technical Analysis
Category: Trend Following, Oscillators, Signal Generators
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Aethix Cipher Pro2Aethix Cipher Pro: AI-Enhanced Crypto Signal Indicator grok Ai made signal created for aethix users.
Unlock the future of crypto trading with Aethix Cipher Pro—a powerhouse indicator inspired by Market Cipher A, turbocharged for Aethix.io users! Built on WaveTrend Oscillator, 8-EMA Ribbon, RSI+MFI, and custom enhancements like Grok AI confidence levels (70-100%), on-chain whale volume thresholds, and fun meme alerts ("To the moon! 🌕").
Key Features: no whale tabs
WaveTrend Signals: Spot overbought/oversold with levels at ±53/60/100—crosses trigger red diamonds, blood diamonds, yellow X's for high-prob buy/sell entries.
Neon Teal EMA Ribbon: Dynamic 5-34 EMA gradient (bullish teal/bearish red) for trend direction—crossovers plot green/red circles, blue triangles.
RSI+MFI Fusion: Overbought (70+)/oversold (30-) with long snippets for sentiment edges.
Aethix Cipher Pro2Aethix Cipher Pro: AI-Enhanced Crypto Signal Indicator grok Ai made signal created for aethix users.
Unlock the future of crypto trading with Aethix Cipher Pro—a powerhouse indicator inspired by Market Cipher A, turbocharged for Aethix.io users! Built on WaveTrend Oscillator, 8-EMA Ribbon, RSI+MFI, and custom enhancements like Grok AI confidence levels (70-100%), on-chain whale volume thresholds, and fun meme alerts ("To the moon! 🌕").
Key Features:
WaveTrend Signals: Spot overbought/oversold with levels at ±53/60/100—crosses trigger red diamonds, blood diamonds, yellow X's for high-prob buy/sell entries.
Neon Teal EMA Ribbon: Dynamic 5-34 EMA gradient (bullish teal/bearish red) for trend direction—crossovers plot green/red circles, blue triangles.
RSI+MFI Fusion: Overbought (70+)/oversold (30-) with long snippets for sentiment edges.
Aethix Cipher ProAethix Cipher Pro: AI-Enhanced Crypto Signal Indicator grok Ai made signal created for aethix users.
Unlock the future of crypto trading with Aethix Cipher Pro—a powerhouse indicator inspired by Market Cipher A, turbocharged for Aethix.io users! Built on WaveTrend Oscillator, 8-EMA Ribbon, RSI+MFI, and custom enhancements like Grok AI confidence levels (70-100%), on-chain whale volume thresholds, and fun meme alerts ("To the moon! 🌕").
Key Features:
WaveTrend Signals: Spot overbought/oversold with levels at ±53/60/100—crosses trigger red diamonds, blood diamonds, yellow X's for high-prob buy/sell entries.
Neon Teal EMA Ribbon: Dynamic 5-34 EMA gradient (bullish teal/bearish red) for trend direction—crossovers plot green/red circles, blue triangles.
RSI+MFI Fusion: Overbought (70+)/oversold (30-) with long snippets for sentiment edges.
Crypto Narratives Performance [SwissAlgo]Crypto Narratives Performance Index
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What this indicator is
This script displays a relative performance index that compares the market capitalization trends of various crypto categories (narratives) against a selected 'Base asset' (BTC, ETH, or SOL) over a configurable rolling time window (default: 14-day).
It’s designed to help users observe sector rotation dynamics across the crypto ecosystem — such as whether DeFi is outperforming ETH, or if AI coins are underperforming relative to BTC.
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What it does
This indicator measures the percentage change in total market cap of a selected crypto sector over a user-defined lookback period, and compares it to the percentage change in market cap of a chosen base asset over the same period. The result is expressed as a ratio and transformed into a z-score, normalized over the last 180 bars. This allows the user to easily identify whether the sector is outperforming or underperforming the base asset in relative terms.
It also includes a smoothed signal line, a performance table, and marked background zones (levels of standard deviations) to help interpret potential extremes in sector outperformance or underperformance.
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How it works
It retrieves daily market capitalization data for both the selected base asset and sector from TradingView's CRYPTOCAP: data feed.
It computes the percent change in $ market cap over one of the following selectable periods: 1, 3, 7, 14, 30, or 60 days (14-day is the default).
The percentage change of the base is subtracted from the percentage change of the sector, producing a raw relative performance differential.
This differential is then normalized into a Z-Score, using a 180-day rolling mean and standard deviation.
The Z-Score is smoothed using an exponential moving average (EMA), and plotted against a secondary EMA signal line (to track potential performance trend changes).
A visual table compares the performance of all listed sectors against the selected base, ranked and annotated with basic symbols (stars for performance, alerts for underperformance vs. the selected 'Base Asset', i.e. BTC or ETH or SOL).
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Visual Features
* Color-coded plot line: Turns green, yellow, orange, or red based on zone and momentum.
* Signal line: Gray EMA of the z-score for trend comparison.
* Background fill zones:
±3 = "Extreme" outperform/underperform
±2 to ±3 = "Strong" zone
±1 to ±2 = Mild over/underperformance
±1 to -1 = Neutral performance range
* Dynamic Table:
Displays all sector vs. base performance differences.
Highlights the selected comparison sector.
Uses emojis (⭐/⚠️) for relative status at a glance.
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Who may benefit
This script may assist:
Crypto analysts tracking capital rotation across narratives.
Swing traders looking to spot momentum trends in crypto sectors.
Portfolio allocators observing which groups are leading or lagging relative to majors (BTC, ETH, SOL).
Developers or researchers evaluating sentiment shifts across categories (e.g., AI tokens rising vs. DeFi).
It is not a buy/sell signal tool — it's a sector/crypto narratives -relative monitor.
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Key Applications
Detect sector rotation (e.g., when Layer 1s start to outperform ETH, or BTC/SOL).
Monitor if certain categories are experiencing sustained interest or fading momentum.
Compare the strength of emerging narratives like DePIN, RWA, or World Liberty vs. majors.
Identify possible "mean-reversion" setups when a sector is excessively stretched relative to its historical norm.
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Limitations
Data dependency: All calculations rely on TradingView’s CRYPTOCAP: market cap feeds.
Normalization window: The z-score normalization is static at 180 bars; in choppy markets this may over-smooth or underreact.
Asset inclusion: The sectors reflect predefined index aggregates. Not all coins in a category may be equally weighted or relevant.
Lag: EMA smoothing introduces delay in reactive plotting.
No intra-day support: Works best on daily timeframes, as CRYPTOCAP: feeds are daily-only.
Not predictive: This script reflects past capital flows. It does not forecast future price moves.
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Customization
Users can adjust the following:
Base asset: BTC, ETH, SOL
Crypto sector (comparison): Choose from 11+ sectors, including DeFi, AI, Memes, Layer 1, etc.
Rolling performance period: Choose between 1–60 days.
Smoothing settings: Length of the EMA for the ratio and signal line.
Show/hide info table: Useful for screen space management.
Special Notes:
Please set the chart timeframe at 1-day in line with CRYPTOCAP data availability.
Please select the dark color scheme to view table and colors properly.
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Risk Disclaimer
This indicator is for informational and educational purposes only. It does not constitute financial advice, trading advice, or an invitation to engage in any financial strategy. Always conduct your own due diligence before making investment decisions. Use at your own risk.
Market conditions may shift rapidly, and past sector performance is not necessarily indicative of future outcomes. This tool is best used as part of a broader analytical framework, not in isolation.
Protected script: source code is hidden to preserve logic integrity and prevent tampering.
If you need clarification or encounter unexpected behavior with data feeds, please check the TradingView Help Center or post in the "Indicators and Strategies" section of the TradingView community.
