YaS-IN Multi-Timeframe RSI AnalyzerYAS-IN MULTI-TIMEFRAME RSI ANALYZER
📊 OVERVIEW
YaS-IN (Yield and Signal Indicator) is an advanced RSI-based trading tool that analyzes multiple timeframe RSI data (14, 25, 100 periods) to identify 5 key market scenarios with confirmation from volume, MACD, and ATR indicators.
🎯 KEY FEATURES
1. MULTI-TIMEFRAME RSI ANALYSIS
RSI 14: Short-term momentum
RSI 25: Medium-term trend
RSI 100: Long-term structural trend
2. 5 MARKET SCENARIOS
Trend Start (New trend confirmation)
Trend Continuation (Healthy uptrend)
Trend End (Overbought, reversal imminent)
Dip Buy Opportunity (Oversold, bounce expected)
Structural Turn (Major trend change)
3. CONFIRMATION SYSTEM
Volume: Above/below average confirmation
MACD: Momentum and crossover confirmation
ATR: Volatility confirmation
4. VISUAL TABLE DISPLAY
Real-time color-coded table showing:
Current RSI values
Active scenarios
Confirmation status
Scenario colors
🔧 HOW IT WORKS
SCENARIO DETECTION
The indicator analyzes RSI values against predefined thresholds to identify which market scenario is currently active.
CONFIRMATION STATUS
Each scenario is validated against three confirmation indicators:
✅ CONFIRMED: 2+ indicators confirm
🔶 PARTIAL: 1 indicator confirms
⚠️ WARNING: 1 indicator contradicts
⚠️ DIVERGENT: 2+ indicators contradict
➖ NEUTRAL: No clear signal
TABLE COLORS
Green: Active bullish scenario
Blue: Active continuation scenario
Red: Active bearish scenario
Orange: Active dip buy scenario
Purple: Active structural turn
Gray: Inactive scenario
⚙️ CUSTOMIZATION OPTIONS
1. RSI PERIODS
Adjust RSI calculation periods (14, 25, 100 default)
2. CONFIRMATION INDICATORS
Toggle Volume/MACD/ATR confirmation on/off
Adjust volume threshold multiplier
Set ATR change percentage
3. TABLE SETTINGS
Position: 6 different screen positions
Size: Small/Medium/Large text
Colors: Custom text and background
Opacity: Background transparency
4. VISUAL OPTIONS
Show/hide chart label
Customize text colors
Adjust table transparency
📈 OPTIMAL TIMEFRAMES
BEST PERFORMANCE
1-Hour: Optimal balance for most traders
4-Hour: Excellent for swing trading
Daily: Good for position trading
GOOD PERFORMANCE
30-Minute: Short-term swing trading
15-Minute: Precise entry timing
Weekly: Long-term analysis
NOT RECOMMENDED
1-5 Minute: Too much noise
Monthly: Too slow for active trading
🎮 USAGE GUIDE
FOR BEGINNERS
Add indicator to 4-hour chart
Watch table for 1-2 days
Trade only "✅ CONFIRMED" scenarios
Use 1-hour chart for entry confirmation
FOR INTERMEDIATE TRADERS
Use multi-timeframe analysis:
4-hour: Main trend direction
1-hour: Confirmation signals
30-minute: Entry timing
Look for scenario consistency across timeframes
Use divergence warnings for risk management
FOR ADVANCED TRADERS
Combine with other technical analysis
Adjust parameters for specific markets
Use alerts for automated notifications
Backtest different parameter combinations
📊 INTERPRETING RESULTS
STRONG SIGNALS
Multiple "✅ CONFIRMED" scenarios
Consistent signals across timeframes
High volume + MACD confirmation
WEAK SIGNALS
"🔶 PARTIAL" or "➖ NEUTRAL" status
Contradictory indicators
Low volume during signals
WARNING SIGNALS
"⚠️ WARNING" or "⚠️ DIVERGENT" status
Indicator divergence
ATR showing low volatility during moves
🔔 ALERT SYSTEM
4 TYPES OF ALERTS
Divergence Detected: Indicators contradict scenarios
Strong Confirmation: Multiple indicators confirm
Confirmed Trend End: Trend reversal with confirmation
Confirmed Dip Buy: Oversold bounce with confirmation
💡 TRADING STRATEGIES
TREND FOLLOWING
Enter on "Trend Start ✅ CONFIRMED"
Add on "Trend Continuation ✅ CONFIRMED"
Exit on "Trend End ✅ CONFIRMED"
MEAN REVERSION
Enter on "Dip Buy ✅ CONFIRMED"
Exit on RSI returning to normal levels
Use ATR for stop loss placement
BREAKOUT TRADING
Watch for "Structural Turn ✅ CONFIRMED"
Enter on confirmation of new trend
Use volume confirmation for validity
⚠️ RISK MANAGEMENT
POSITION SIZING
"✅ CONFIRMED": Full position
"🔶 PARTIAL": Half position
"⚠️ WARNING": Quarter position or avoid
"⚠️ DIVERGENT": No position
STOP LOSS SUGGESTIONS
Based on ATR value (2x ATR recommended)
Adjust for timeframe (tighter on lower TFs)
Consider scenario type (wider for structural turns)
📚 EDUCATIONAL VALUE
LEARN MARKET CYCLES
Understand different market phases
Recognize trend transitions
Identify overbought/oversold conditions
IMPROVE TIMING
Better entry/exit points
Reduced false signals
Improved risk/reward ratios
🚀 BENEFITS
Clear Visualization: All data in one table
Multi-Indicator Confirmation: Reduces false signals
Customizable: Adapt to any trading style
Educational: Helps understand market dynamics
Versatile: Works across multiple timeframes
📝 PUBLISHING NOTES
When publishing this indicator:
Name: YaS-IN Multi-Timeframe RSI Analyzer
Category: Momentum/Volume Indicators
Access Type: Open Source
Tags: RSI, Multi-Timeframe, Volume, MACD, ATR, Scanner
Description: Include this complete documentation
Preview Images: Show table on different charts
Video Tutorial: Demonstrate multi-timeframe usage
🔄 UPDATES & SUPPORT
For updates, improvements, or support:
Check TradingView script page
Join community discussions
Share backtest results
Suggest new features
Happy Trading with YaS-IN! 🚀
This response is AI-generated, for reference only.
스크립트에서 "ai"에 대해 찾기
Intraday Fibonacci Retracement Golden pocket for scalping# Intraday Fibonacci Retracement Golden pocket for scalping
## Overview
This advanced Pine Script indicator provides dynamic Fibonacci retracement levels specifically designed for intraday trading. Using proprietary AI-powered algorithms, the script automatically identifies optimal high and low reference points to generate precise Fibonacci levels that adapt in real-time throughout the trading day.
## Key Features
### 🎯 Dynamic Level Generation
- **Intelligent Auto-Detection**: Advanced algorithm automatically identifies key price levels using machine learning-based pattern recognition
- **Real-Time Updates**: Fibonacci levels dynamically adjust as new highs or lows are established during the session
- **Seven Core Levels**: 0% (LOD), 23.6%, 38.2%, 50%, 61.8%, 78.6%, and 100% (HOD)
### 📊 Visual Customization
- **Individual Level Control**: Show or hide any Fibonacci level independently
- **Custom Color Schemes**: Assign unique colors to each retracement level for easy identification
- **Adjustable Line Width**: Choose line thickness from 1-5 pixels for optimal chart clarity
- **Professional Labeling**: Each level displays both percentage and exact price value
### 🏆 Golden Zone Highlighting
- **Automated Zone Detection**: Automatically highlights the critical 50%-61.8% retracement zone
- **Visual Emphasis**: Shaded area between these key levels for quick visual reference
- **Customizable Transparency**: Adjust the golden zone color and opacity to match your chart theme
### 🔧 Flexible Configuration Options
#### Label Management
- **Master Toggle**: Instantly show or hide all labels with a single switch
- **Individual Label Control**: Selective visibility for each Fibonacci level label
- **Custom Label Colors**: Choose distinct colors for each label to match your trading style
- **Price Display Format**: Labels show percentage and corresponding price level
#### Level Visibility
Independent toggles for each retracement level:
- 0% (Low of Day)
- 23.6% Retracement
- 38.2% Retracement
- 50% Retracement (Midpoint)
- 61.8% Retracement (Golden Ratio)
- 78.6% Retracement
- 100% (High of Day)
### 📈 Trading Applications
**Support & Resistance**
- Identify potential reversal zones
- Spot key support and resistance levels
- Plan entry and exit points
**Price Targets**
- Set realistic profit targets based on Fibonacci extensions
- Identify potential pullback levels in trending markets
**Risk Management**
- Place stop losses at strategic Fibonacci levels
- Calculate risk-to-reward ratios using multiple levels
**Golden Zone Strategy**
- Focus on the 50%-61.8% zone for high-probability trade setups
- The golden ratio area often acts as a strong confluence zone
### 🔔 Built-in Alert System
Pre-configured alert conditions for critical price level crossings:
- 38.2% level cross
- 50% level cross (equilibrium)
- 61.8% level cross (golden ratio)
### 💡 Best Practices
**Optimal Usage**
- Works on all intraday timeframes (1min, 5min, 15min, 30min, 1hour)
- Most effective during active trading sessions
- Combine with volume analysis for confirmation
- Use alongside other technical indicators for confluence
**Chart Setup Tips**
- Adjust colors to ensure levels are visible against your chart background
- Use thicker lines on higher timeframes for better visibility
- Enable only the levels most relevant to your trading strategy
- Customize label colors to differentiate between key levels quickly
## Technical Specifications
**Performance Features**
- Maximum 500 lines supported for extensive historical analysis
- Maximum 500 labels for comprehensive price level identification
- Optimized calculations for minimal chart lag
- Real-time updates with every price tick
**Compatibility**
- Pine Script Version 6
- Compatible with all TradingView chart types
- Works across all markets (Stocks, Forex, Crypto, Futures, Options)
- Supports all timeframes from 1-minute to daily
## Installation & Setup
1. Copy the script code into TradingView Pine Editor
2. Click "Add to Chart" to apply the indicator
3. Access settings via the indicator's gear icon
4. Customize colors, labels, and visibility options to your preference
5. Save your configuration as a default template for future use
## Advanced Configuration
**For Clean Charts**
- Disable labels for a minimalist view
- Show only 50% and 61.8% levels for focused trading
- Use muted colors with higher transparency
**For Detailed Analysis**
- Enable all levels and labels
- Use high-contrast colors for each level
- Increase line width for emphasis
**For Specific Strategies**
- Mean reversion traders: Focus on 38.2%, 50%, 61.8%
- Breakout traders: Monitor 0% and 100% levels closely
- Scalpers: Use golden zone exclusively with tight stops
## Algorithm Intelligence
The indicator employs sophisticated algorithms to:
- Automatically calculate optimal reference points
- Adapt to changing market conditions
- Filter out noise and false signals
- Provide consistent, reliable level placement
This ensures that traders receive accurate, actionable Fibonacci levels without manual intervention or subjective placement decisions.
🎁 Free Trial Access
Interested in trying this indicator?
I'm offering a ONE MONTH FREE TRIAL to help you experience the power of dynamic Fibonacci levels in your trading.
To request your trial access:
Send me a Direct Message (DM) on TradingView
Include "Fib Trial Request" in your message
I'll respond with access instructions within 24 hours
This trial includes:
✅ Full access to all indicator features
✅ All customization options unlocked
✅ Priority support during trial period
✅ Setup assistance and configuration help
Don't miss this opportunity to enhance your intraday trading with professional-grade Fibonacci analysis!
📞 Author's Notes
For questions, feedback, or trial access requests, feel free to reach out via DM. I'm committed to helping traders succeed and continuously improving this tool based on user feedback.
Happy Trading!
---
**Disclaimer**: This indicator is a technical analysis tool. Past performance does not guarantee future results. Always use proper risk management and combine with other forms of analysis for trading decisions.
Jericho AI ScalperThis indicator is designed for use on Nifty and Sensex Options 1-minute chart.
A trade entry is valid only if the very next candle breaks above the high of the signal candle.
If the next candle fails to break that high, the setup becomes invalid and no trade should be taken.
Based on historical observations, a 1:1 risk-reward ratio is recommended; however, market conditions can change, and results may vary.
This indicator is intended strictly for educational and research purposes, helping traders understand market structure and candle-based momentum behavior.
It does not offer financial advice or guarantee profits. Please conduct your own analysis and consult a licensed financial professional when required.
ADX Forecast Colorful [DiFlip]ADX Forecast Colorful
Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis.
⯁ Real-Time ADX Forecasting
For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions.
⯁ Highly Customizable
The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system.
⯁ Innovative and Science-Based
This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted value (e.g., future ADX)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept
β₁ = slope (rate of change)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error, the regression coefficients are calculated as:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ = summation
x̄ and ȳ = means of x and y
i ranges from 1 to n (number of data points)
These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool.
⯁ Visual Interpretation
Imagine an ADX time series like this:
Time →
ADX →
The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals.
⯁ Summary of Scientific Concepts
Linear Regression: Models variable relationships with a straight line.
Least Squares: Minimizes prediction errors for best fit.
Time-Series Forecasting: Predicts future values using historical data.
Supervised Learning: Trains models to predict outcomes from inputs.
Statistical Smoothing: Reduces noise and highlights underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Based on rigorous statistical theory.
Unprecedented: First public ADX using least-squares forecast modeling.
Smart: Uses machine learning logic.
Forward-Looking: Generates predictive, not just reactive, signals.
Customizable: Flexible for any strategy or timeframe.
⯁ Conclusion
By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the ADX?
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ How to use the ADX?
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
Strong Trend: When the ADX is above 25, indicating a strong trend.
Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
Neutral Zone: Between 20 and 25, where the trend strength is unclear.
⯁ Entry Conditions
Each condition below is fully configurable and can be combined to build precise trading logic.
📈 BUY
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
📉 SELL
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
🤖 Automation
All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
RSI Forecast Colorful [DiFlip]RSI Forecast Colorful
Introducing one of the most complete RSI indicators available — a highly customizable analytical tool that integrates advanced prediction capabilities. RSI Forecast Colorful is an evolution of the classic RSI, designed to anticipate potential future RSI movements using linear regression. Instead of simply reacting to historical data, this indicator provides a statistical projection of the RSI’s future behavior, offering a forward-looking view of market conditions.
⯁ Real-Time RSI Forecasting
For the first time, a public RSI indicator integrates linear regression (least squares method) to forecast the RSI’s future behavior. This innovative approach allows traders to anticipate market movements based on historical trends. By applying Linear Regression to the RSI, the indicator displays a projected trendline n periods ahead, helping traders make more informed buy or sell decisions.
⯁ Highly Customizable
The indicator is fully adaptable to any trading style. Dozens of parameters can be optimized to match your system. All 28 long and short entry conditions are selectable and configurable, allowing the construction of quantitative, statistical, and automated trading models. Full control over signals ensures precise alignment with your strategy.
⯁ Innovative and Science-Based
This is the first public RSI indicator to apply least-squares predictive modeling to RSI calculations. Technically, it incorporates machine-learning logic into a classic indicator. Using Linear Regression embeds strong statistical foundations into RSI forecasting, making this tool especially valuable for traders seeking quantitative and analytical advantages.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental statistical method that models the relationship between a dependent variable y and one or more independent variables x. The general formula for simple linear regression is:
y = β₀ + β₁x + ε
where:
y = predicted variable (e.g., future RSI value)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept (value of y when x = 0)
β₁ = slope (rate of change of y relative to x)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the RSI projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error between predicted and observed values, we use the formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational component of supervised learning. Its simplicity and precision in numerical prediction make it essential in AI, predictive algorithms, and time-series forecasting. Applying regression to RSI is akin to embedding artificial intelligence inside a classic indicator, adding a new analytical dimension.
⯁ Visual Interpretation
Imagine a time series of RSI values like this:
Time →
RSI →
The regression line smooths these historical values and projects itself n periods forward, creating a predictive trajectory. This projected RSI line can cross the actual RSI, generating sophisticated entry and exit signals. In summary, the RSI Forecast Colorful indicator provides both the current RSI and the forecasted RSI, allowing comparison between past and future trend behavior.
⯁ Summary of Scientific Concepts Used
Linear Regression: Models relationships between variables using a straight line.
Least Squares: Minimizes squared prediction errors for optimal fit.
Time-Series Forecasting: Predicts future values from historical patterns.
Supervised Learning: Predictive modeling based on known output values.
Statistical Smoothing: Reduces noise to highlight underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Built on statistical and mathematical theory.
First of its kind: The first public RSI with least-squares predictive modeling.
Intelligent: Incorporates machine-learning logic into RSI interpretation.
Forward-looking: Generates predictive, not just reactive, signals.
Customizable: Exceptionally flexible for any strategic framework.
⯁ Conclusion
By combining RSI and linear regression, the RSI Forecast Colorful allows traders to predict market momentum rather than simply follow it. It's not just another indicator: it's a scientific advancement in technical analysis technology. Offering 28 configurable entry conditions and advanced signals, this open-source indicator paves the way for innovative quantitative systems.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What Is RSI?
The RSI (Relative Strength Index) is a technical indicator developed by J. Welles Wilder. It measures the velocity and magnitude of recent price movements to identify overbought and oversold conditions. The RSI ranges from 0 to 100 and is commonly used to identify potential reversals and evaluate trend strength.
⯁ How RSI Works
RSI is calculated from average gains and losses over a set period (commonly 14 bars) and plotted on a 0–100 scale. It consists of three key zones:
Overbought: RSI above 70 may signal an overbought market.
Oversold: RSI below 30 may signal an oversold market.
Neutral Zone: RSI between 30 and 70, indicating no extreme condition.
These zones help identify potential price reversals and confirm trend strength.
⯁ Entry Conditions
All conditions below are fully customizable and allow detailed control over entry signal creation.
📈 BUY
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
📉 SELL
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
🤖 Automation
All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
XAU DOMINION AI This script is a technical analysis tool that helps traders visualize market structure and signals.
It should be used with proper risk management.
This script does not guarantee accuracy or profit, and is only for educational use.
Buy/Sell Signals [WynTrader]Hello dear Friend
Here is a new version ( B-S_251121_wt ) of my Buy/Sell Signals indicator.
Some calculation updates and useful enhancements have been applied.
Concepts
This Buy/Sell Signals indicator generates Buy/Sell signals as accurately as possible, identifying trend changes. Compared to other tools that detect trend shifts, this one is simple, easy to use, and demonstrates its efficiency on its own.
- Its features are carefully designed to minimize false signals while ensuring optimal signal placement.
- The Table results allow you to quickly evaluate signal performance, both on their own and compared to a Buy & Hold strategy.
- The Table calculations are fully synchronized with the visible chart (WYSIWYG – What You See Is What You Get). You can also scroll the chart across different date ranges to see how a stock or product performs under various market conditions.
- Seeing Buy/Sell signals on a chart is appealing, but assessing their performance in a Table makes it even more convincing. And without running a full backtest, you can get a clear overview of overall performance immediately.
Features
This indicator generates Buy/Sell signals using:
- Fast and Slow Moving Averages (adjustable).
- Bollinger Bands (adjustable).
- Filters (optional, adjustable) to refine signals, including : Bollinger Bands Lookback Trend Filter; High-Low vs Candle Range Threshold %; Distance from Fast and Slow MAs Threshold %.
- Results are displayed in a Table on the chart, based on the currently visible start and end dates.
Functionality
- The indicator aims to confirm trend changes through timely Buy/Sell signals.
- It uses two Moving Averages and Bollinger Bands, combined with filters such as BB Lookback, -- The variable settings have been tested with a mix of manual and AI testing to find the optimal configuration. You can adjust the variables to suit your goals.
- The design is simple, with clear parameters and instant readability of Buy/Sell Signals on the chart and in the Table results, without complex interpretation needed.
- It works effectively by requiring both trend confirmation and volatility control management.
- Signals are timed to be as accurate as possible, avoiding futile weak or false ones.
- A Table shows the effectiveness of the signals on the current visible chart, providing immediate, realistic feedback. The Buy & Hold strategy results are also included for comparison with the Buy/Sell swing strategy. The Buy & Hold results start from the first Buy signal to ensure a fair comparison.
- Changing the parameters instantly updates the Table, giving a quick, at-a-glance performance check.
Caution
- No technical tool is perfect; it cannot predict disasters, wars, or the actions of large fund managers or short sellers.
- After testing thousands of TradingView indicators over 24 years, I’ve found none to be 100% accurate all the time.
- This Buy/Sell Signals indicator may outperform some others but is still not perfect.
So, just be aware, and don’t be fooled by this tool.
Flux-Tensor Singularity [ML/RL PRO]Flux-Tensor Singularity
This version of the Flux-Tensor Singularity (FTS) represents a paradigm shift in technical analysis by treating price movement as a physical system governed by volume-weighted forces and volatility dynamics. Unlike traditional indicators that measure price change or momentum in isolation, FTS quantifies the complete energetic state of the market by fusing three fundamental dimensions: price displacement (delta_P), volume intensity (V), and local-to-global volatility ratio (gamma).
The Physics-Inspired Foundation:
The tensor calculation draws inspiration from general relativity and fluid dynamics, where massive objects (large volume) create curvature in spacetime (price action). The core formula:
Raw Singularity = (ΔPrice × ln(Volume)) × γ²
Where:
• ΔPrice = close - close (directional force)
• ln(Volume) = logarithmic volume compression (prevents extreme outliers)
• γ (Gamma) = (ATR_local / ATR_global)² (volatility expansion coefficient)
This raw value is then normalized to 0-100 range using the lookback period's extremes, creating a bounded oscillator that identifies critical density points—"singularities" where normal market behavior breaks down and explosive moves become probable.
The Compression Factor (Epsilon ε):
A unique sensitivity control compresses the normalized tensor toward neutral (50) using the formula:
Tensor_final = 50 + (Tensor_normalized - 50) / ε
Higher epsilon values (1.5-3.0) make threshold breaches rare and significant, while lower values (0.3-0.7) increase signal frequency. This mathematical compression mimics how black holes compress matter—the higher the compression, the more energy required to escape the event horizon (reach signal thresholds).
Singularity Detection:
When the smoothed tensor crosses above the upper threshold (default 90) or below the lower threshold (100-90=10), a singularity event is detected. These represent moments of extreme market density where:
• Buying/selling pressure has reached unsustainable levels
• Volatility is expanding relative to historical norms
• Volume confirms the directional bias
• Mean-reversion or continuation breakout becomes highly probable
The system doesn't predict direction—it identifies critical energy states where probability distributions shift dramatically in favor of the trader.
🤖 ML/RL ENHANCEMENT SYSTEM: THOMPSON SAMPLING + CONTEXTUAL BANDITS
The FTS-PRO² incorporates genuine machine learning and reinforcement learning algorithms that adapt strategy selection based on performance feedback. This isn't cosmetic—it's a functional implementation of advanced AI concepts coded natively in Pine Script.
Multi-Armed Bandit Framework:
The system treats strategy selection as a multi-armed bandit problem with three "arms" (strategies):
ARM 0 - TREND FOLLOWING:
• Prefers signals aligned with regime direction
• Bullish signals in uptrend regimes (STRONG↗, WEAK↗)
• Bearish signals in downtrend regimes (STRONG↘, WEAK↘)
• Confidence boost: +15% when aligned, -10% when misaligned
ARM 1 - MEAN REVERSION:
• Prefers signals in ranging markets near extremes
• Buys when tensor < 30 in RANGE⚡ or RANGE~ regimes
• Sells when tensor > 70 in ranging conditions
• Confidence boost: +15% in range with counter-trend setup
ARM 2 - VOLATILITY BREAKOUT:
• Prefers signals with high gamma (>1.5) and extreme tensor (>85 or <15)
• Captures explosive moves with expanding volatility
• Confidence boost: +20% when both conditions met
Thompson Sampling Algorithm:
For each signal, the system uses true Beta distribution sampling to select the optimal arm:
1. Each arm maintains Alpha (successes) and Beta (failures) parameters per regime
2. Three random samples drawn: one from Beta(α₀,β₀), Beta(α₁,β₁), Beta(α₂,β₂)
3. Highest sample wins and that arm's strategy applies
4. After trade outcome:
- Win → Alpha += 1.0, reward += 1.0
- Loss → Beta += 1.0, reward -= 0.5
This naturally balances exploration (trying less-proven arms) with exploitation (using best-performing arms), converging toward optimal strategy selection over time.
Alternative Algorithms:
Users can select UCB1 (deterministic confidence bounds) or Epsilon-Greedy (random exploration) if they prefer different exploration/exploitation tradeoffs. UCB1 provides more predictable behavior, while Epsilon-Greedy is simple but less adaptive.
Regime Detection (6 States):
The contextual bandit framework requires accurate regime classification. The system identifies:
• STRONG↗ : Uptrend with slope >3% and high ADX (strong trending)
• WEAK↗ : Uptrend with slope >1% but lower conviction
• STRONG↘ : Downtrend with slope <-3% and high ADX
• WEAK↘ : Downtrend with slope <-1% but lower conviction
• RANGE⚡ : High volatility consolidation (vol > 1.2× average)
• RANGE~ : Low volatility consolidation (default/stable)
Each regime maintains separate performance statistics for all three arms, creating an 18-element matrix (3 arms × 6 regimes) of Alpha/Beta parameters. This allows the system to learn which strategy works best in each market environment.
🧠 DUAL MEMORY ARCHITECTURE
The indicator implements two complementary memory systems that work together to recognize profitable patterns and avoid repeating losses.
Working Memory (Recent Signal Buffer):
Stores the last N signals (default 30) with complete context:
• Tensor value at signal
• Gamma (volatility ratio)
• Volume ratio
• Market regime
• Signal direction (long/short)
• Trade outcome (win/loss)
• Age (bars since occurrence)
This short-term memory allows pattern matching against recent history and tracks whether the system is "hot" (winning streak) or "cold" (no signals for long period).
Pattern Memory (Statistical Abstractions):
Maintains exponentially-weighted running averages of winning and losing setups:
Winning Pattern Means:
• pm_win_tensor_mean (average tensor of wins)
• pm_win_gamma_mean (average gamma of wins)
• pm_win_vol_mean (average volume ratio of wins)
Losing Pattern Means:
• pm_lose_tensor_mean (average tensor of losses)
• pm_lose_gamma_mean (average gamma of losses)
• pm_lose_vol_mean (average volume ratio of losses)
When a new signal forms, the system calculates:
Win Similarity Score:
Weighted distance from current setup to winning pattern mean (closer = higher score)
Lose Dissimilarity Score:
Weighted distance from current setup to losing pattern mean (farther = higher score)
Final Pattern Score = (Win_Similarity + Lose_Dissimilarity) / 2
This score (0.0 to 1.0) feeds into ML confidence calculation with 15% weight. The system actively seeks setups that "look like" past winners and "don't look like" past losers.
Memory Decay:
Pattern means update exponentially with decay rate (default 0.95):
New_Mean = Old_Mean × 0.95 + New_Value × 0.05
This allows the system to adapt to changing market character while maintaining stability. Faster decay (0.80-0.90) adapts quickly but may overfit to recent noise. Slower decay (0.95-0.99) provides stability but adapts slowly to regime changes.
🎓 ADAPTIVE FEATURE WEIGHTS: ONLINE LEARNING
The ML confidence score combines seven features, each with a learnable weight that adjusts based on predictive accuracy.
The Seven Features:
1. Overall Win Rate (15% initial) : System-wide historical performance
2. Regime Win Rate (20% initial) : Performance in current market regime
3. Score Strength (15% initial) : Bull vs bear score differential
4. Volume Strength (15% initial) : Volume ratio normalized to 0-1
5. Pattern Memory (15% initial) : Similarity to winning patterns
6. MTF Confluence (10% initial) : Higher timeframe alignment
7. Divergence Score (10% initial) : Price-tensor divergence presence
Adaptive Weight Update:
After each trade, the system uses gradient descent with momentum to adjust weights:
prediction_error = actual_outcome - predicted_confidence
gradient = momentum × old_gradient + learning_rate × error × feature_value
weight = max(0.05, weight + gradient × 0.01)
Then weights are normalized to sum to 1.0.
Features that consistently predict winning trades get upweighted over time, while features that fail to distinguish winners from losers get downweighted. The momentum term (default 0.9) smooths the gradient to prevent oscillation and overfitting.
This is true online learning—the system improves its internal model with every trade without requiring retraining or optimization. Over hundreds of trades, the confidence score becomes increasingly accurate at predicting which signals will succeed.
⚡ SIGNAL GENERATION: MULTI-LAYER CONFIRMATION
A signal only fires when ALL layers of the confirmation stack agree:
LAYER 1 - Singularity Event:
• Tensor crosses above upper threshold (90) OR below lower threshold (10)
• This is the "critical mass" moment requiring investigation
LAYER 2 - Directional Bias:
• Bull Score > Bear Score (for buys) or Bear Score > Bull Score (for sells)
• Bull/Bear scores aggregate: price direction, momentum, trend alignment, acceleration
• Volume confirmation multiplies scores by 1.5x
LAYER 3 - Optional Confirmations (Toggle On/Off):
Price Confirmation:
• Buy signals require green candle (close > open)
• Sell signals require red candle (close < open)
• Filters false signals in choppy consolidation
Volume Confirmation:
• Requires volume > SMA(volume, lookback)
• Validates conviction behind the move
• Critical for avoiding thin-volume fakeouts
Momentum Filter:
• Buy requires close > close (default 5 bars)
• Sell requires close < close
• Confirms directional momentum alignment
LAYER 4 - ML Approval:
If ML/RL system is enabled:
• Calculate 7-feature confidence score with adaptive weights
• Apply arm-specific modifier (+20% to -10%) based on Thompson Sampling selection
• Apply freshness modifier (+5% if hot streak, -5% if cold system)
• Compare final confidence to dynamic threshold (typically 55-65%)
• Signal fires ONLY if confidence ≥ threshold
If ML disabled, signals fire after Layer 3 confirmation.
Signal Types:
• Standard Signal (▲/▼): Passed all filters, ML confidence 55-70%
• ML Boosted Signal (⭐): Passed all filters, ML confidence >70%
• Blocked Signal (not displayed): Failed ML confidence threshold
The dashboard shows blocked signals in the state indicator, allowing users to see when a potential setup was rejected by the ML system for low confidence.
📊 MULTI-TIMEFRAME CONFLUENCE
The system calculates a parallel tensor on a higher timeframe (user-selected, default 60m) to provide trend context.
HTF Tensor Calculation:
Uses identical formula but applied to HTF candle data:
• HTF_Tensor = Normalized((ΔPrice_HTF × ln(Vol_HTF)) × γ²_HTF)
• Smoothed with same EMA period for consistency
Directional Bias:
• HTF_Tensor > 50 → Bullish higher timeframe
• HTF_Tensor < 50 → Bearish higher timeframe
Strength Measurement:
• HTF_Strength = |HTF_Tensor - 50| / 50
• Ranges from 0.0 (neutral) to 1.0 (extreme)
Confidence Adjustment:
When a signal forms:
• Aligned with HTF : Confidence += MTF_Weight × HTF_Strength
(Default: +20% × strength, max boost ~+20%)
• Against HTF : Confidence -= MTF_Weight × HTF_Strength × 0.6
(Default: -20% × strength × 0.6, max penalty ~-12%)
This creates a directional bias toward the higher timeframe trend. A buy signal with strong bullish HTF tensor (>80) receives maximum boost, while a buy signal with strong bearish HTF tensor (<20) receives maximum penalty.
Recommended HTF Settings:
• Chart: 1m-5m → HTF: 15m-30m
• Chart: 15m-30m → HTF: 1h-4h
• Chart: 1h-4h → HTF: 4h-D
• Chart: Daily → HTF: Weekly
General rule: HTF should be 3-5x the chart timeframe for optimal confluence without excessive lag.
🔀 DIVERGENCE DETECTION: EARLY REVERSAL WARNINGS
The system tracks pivots in both price and tensor independently to identify disagreements that precede reversals.
Pivot Detection:
Uses standard pivot functions with configurable lookback (default 14 bars):
• Price pivots: ta.pivothigh(high) and ta.pivotlow(low)
• Tensor pivots: ta.pivothigh(tensor) and ta.pivotlow(tensor)
A pivot requires the lookback number of bars on EACH side to confirm, introducing inherent lag of (lookback) bars.
Bearish Divergence:
• Price makes higher high
• Tensor makes lower high
• Interpretation: Buying pressure weakening despite price advance
• Effect: Boosts SELL signal confidence by divergence_weight (default 15%)
Bullish Divergence:
• Price makes lower low
• Tensor makes higher low
• Interpretation: Selling pressure weakening despite price decline
• Effect: Boosts BUY signal confidence by divergence_weight (default 15%)
Divergence Persistence:
Once detected, divergence remains "active" for 2× the pivot lookback period (default 28 bars), providing a detection window rather than single-bar event. This accounts for the fact that reversals often take several bars to materialize after divergence forms.
Confidence Integration:
When calculating ML confidence, the divergence score component:
• 0.8 if buy signal with recent bullish divergence (or sell with bearish div)
• 0.2 if buy signal with recent bearish divergence (opposing signal)
• 0.5 if no divergence detected (neutral)
Divergences are leading indicators—they form BEFORE reversals complete, making them valuable for early positioning.
⏱️ SIGNAL FRESHNESS TRACKING: HOT/COLD SYSTEM
The indicator tracks temporal dynamics of signal generation to adjust confidence based on system state.
Bars Since Last Signal Counter:
Increments every bar, resets to 0 when a signal fires. This metric reveals whether the system is actively finding setups or lying dormant.
Cold System State:
Triggered when: bars_since_signal > cold_threshold (default 50 bars)
Effects:
• System has gone "cold" - no quality setups found in 50+ bars
• Applies confidence penalty: -5%
• Interpretation: Market conditions may not favor current parameters
• Requires higher-quality setup to break the dry spell
This prevents forcing trades during unsuitable market conditions.
Hot Streak State:
Triggered when: recent_signals ≥ 3 AND recent_wins ≥ 2
Effects:
• System is "hot" - finding and winning trades recently
• Applies confidence bonus: +5% (default hot_streak_bonus)
• Interpretation: Current market conditions favor the system
• Momentum of success suggests next signal also likely profitable
This capitalizes on periods when market structure aligns with the indicator's logic.
Recent Signal Tracking:
Working memory stores outcomes of last 5 signals. When 3+ winners occur in this window, hot streak activates. After 5 signals, the counter resets and tracking restarts. This creates rolling evaluation of recent performance.
The freshness system adds temporal intelligence—recognizing that signal reliability varies with market conditions and recent performance patterns.
💼 SHADOW PORTFOLIO: GROUND TRUTH PERFORMANCE TRACKING
To provide genuine ML learning, the system runs a complete shadow portfolio that simulates trades from every signal, generating real P&L; outcomes for the learning algorithms.
Shadow Portfolio Mechanics:
Starts with initial capital (default $10,000) and tracks:
• Current equity (increases/decreases with trade outcomes)
• Position state (0=flat, 1=long, -1=short)
• Entry price, stop loss, target
• Trade history and statistics
Position Sizing:
Base sizing: equity × risk_per_trade% (default 2.0%)
With dynamic sizing enabled:
• Size multiplier = 0.5 + ML_confidence
• High confidence (0.80) → 1.3× base size
• Low confidence (0.55) → 1.05× base size
Example: $10,000 equity, 2% risk, 80% confidence:
• Impact: $10,000 × 2% × 1.3 = $260 position impact
Stop Loss & Target Placement:
Adaptive based on ML confidence and regime:
High Confidence Signals (ML >0.7):
• Tighter stops: 1.5× ATR
• Larger targets: 4.0× ATR
• Assumes higher probability of success
Standard Confidence Signals (ML 0.55-0.7):
• Standard stops: 2.0× ATR
• Standard targets: 3.0× ATR
Ranging Regimes (RANGE⚡/RANGE~):
• Tighter setup: 1.5× ATR stop, 2.0× ATR target
• Ranging markets offer smaller moves
Trending Regimes (STRONG↗/STRONG↘):
• Wider setup: 2.5× ATR stop, 5.0× ATR target
• Trending markets offer larger moves
Trade Execution:
Entry: At close price when signal fires
Exit: First to hit either stop loss OR target
On exit:
• Calculate P&L; percentage
• Update shadow equity
• Increment total trades counter
• Update winning trades counter if profitable
• Update Thompson Sampling Alpha/Beta parameters
• Update regime win/loss counters
• Update arm win/loss counters
• Update pattern memory means (exponential weighted average)
• Store complete trade context in working memory
• Update adaptive feature weights (if enabled)
• Calculate running Sharpe and Sortino ratios
• Track maximum equity and drawdown
This complete feedback loop provides the ground truth data required for genuine machine learning.
📈 COMPREHENSIVE PERFORMANCE METRICS
The dashboard displays real-time performance statistics calculated from shadow portfolio results:
Core Metrics:
• Win Rate : Winning_Trades / Total_Trades × 100%
Visual color coding: Green (>55%), Yellow (45-55%), Red (<45%)
• ROI : (Current_Equity - Initial_Capital) / Initial_Capital × 100%
Shows total return on initial capital
• Sharpe Ratio : (Avg_Return / StdDev_Returns) × √252
Risk-adjusted return, annualized
Good: >1.5, Acceptable: >0.5, Poor: <0.5
• Sortino Ratio : (Avg_Return / Downside_Deviation) × √252
Similar to Sharpe but only penalizes downside volatility
Generally higher than Sharpe (only cares about losses)
• Maximum Drawdown : Max((Peak_Equity - Current_Equity) / Peak_Equity) × 100%
Worst peak-to-trough decline experienced
Critical risk metric for position sizing and stop-out protection
Segmented Performance:
• Base Signal Win Rate : Performance of standard confidence signals (55-70%)
• ML Boosted Win Rate : Performance of high confidence signals (>70%)
• Per-Regime Win Rates : Separate tracking for all 6 regime types
• Per-Arm Win Rates : Separate tracking for all 3 bandit arms
This segmentation reveals which strategies work best and in what conditions, guiding parameter optimization and trading decisions.
🎨 VISUAL SYSTEM: THE ACCRETION DISK & FIELD THEORY
The indicator uses sophisticated visual metaphors to make the mathematical complexity intuitive.
Accretion Disk (Background Glow):
Three concentric layers that intensify as the tensor approaches critical values:
Outer Disk (Always Visible):
• Intensity: |Tensor - 50| / 50
• Color: Cyan (bullish) or Red (bearish)
• Transparency: 85%+ (subtle glow)
• Represents: General market bias
Inner Disk (Tensor >70 or <30):
• Intensity: (Tensor - 70)/30 or (30 - Tensor)/30
• Color: Strengthens outer disk color
• Transparency: Decreases with intensity (70-80%)
• Represents: Approaching event horizon
Core (Tensor >85 or <15):
• Intensity: (Tensor - 85)/15 or (15 - Tensor)/15
• Color: Maximum intensity bullish/bearish
• Transparency: Lowest (60-70%)
• Represents: Critical mass achieved
The accretion disk visually communicates market density state without requiring dashboard inspection.
Gravitational Field Lines (EMAs):
Two EMAs plotted as field lines:
• Local Field : EMA(10) - fast trend, cyan color
• Global Field : EMA(30) - slow trend, red color
Interpretation:
• Local above Global = Bullish gravitational field (price attracted upward)
• Local below Global = Bearish gravitational field (price attracted downward)
• Crosses = Field reversals (marked with small circles)
This borrows the concept that price moves through a field created by moving averages, like a particle following spacetime curvature.
Singularity Diamonds:
Small diamond markers when tensor crosses thresholds BUT full signal doesn't fire:
• Gold/yellow diamonds above/below bar
• Indicates: "Near miss" - singularity detected but missing confirmation
• Useful for: Understanding why signals didn't fire, seeing potential setups
Energy Particles:
Tiny dots when volume >2× average:
• Represents: "Matter ejection" from high volume events
• Position: Below bar if bullish candle, above if bearish
• Indicates: High energy events that may drive future moves
Event Horizon Flash:
Background flash in gold when ANY singularity event occurs:
• Alerts to critical density point reached
• Appears even without full signal confirmation
• Creates visual alert to monitor closely
Signal Background Flash:
Background flash in signal color when confirmed signal fires:
• Cyan for BUY signals
• Red for SELL signals
• Maximum visual emphasis for actual entry points
🎯 SIGNAL DISPLAY & TOOLTIPS
Confirmed signals display with rich information:
Standard Signals (55-70% confidence):
• BUY : ▲ symbol below bar in cyan
• SELL : ▼ symbol above bar in red
ML Boosted Signals (>70% confidence):
• BUY : ⭐ symbol below bar in bright green
• SELL : ⭐ symbol above bar in bright green
• Distinct appearance signals high-conviction trades
Tooltip Content (hover to view):
• ML Confidence: XX%
• Arm: T (Trend) / M (Mean Revert) / V (Vol Breakout)
• Regime: Current market regime
• TS Samples (if Thompson Sampling): Shows all three arm samples that led to selection
Signal positioning uses offset percentages to avoid overlapping with price bars while maintaining clean chart appearance.
Divergence Markers:
• Small lime triangle below bar: Bullish divergence detected
• Small red triangle above bar: Bearish divergence detected
• Separate from main signals, purely informational
📊 REAL-TIME DASHBOARD SECTIONS
The comprehensive dashboard provides system state and performance in multiple panels:
SECTION 1: CORE FTS METRICS
• TENSOR : Current value with visual indicator
- 🔥 Fire emoji if >threshold (critical bullish)
- ❄️ Snowflake if 2.0× (extreme volatility)
- ⚠ Warning if >1.0× (elevated volatility)
- ○ Circle if normal
• VOLUME : Current volume ratio
- ● Solid circle if >2.0× average (heavy)
- ◐ Half circle if >1.0× average (above average)
- ○ Empty circle if below average
SECTION 2: BULL/BEAR SCORE BARS
Visual bars showing current bull vs bear score:
• BULL : Horizontal bar of █ characters (cyan if winning)
• BEAR : Horizontal bar of █ characters (red if winning)
• Score values shown numerically
• Winner highlighted with full color, loser de-emphasized
SECTION 3: SYSTEM STATE
Current operational state:
• EJECT 🚀 : Buy signal active (cyan)
• COLLAPSE 💥 : Sell signal active (red)
• CRITICAL ⚠ : Singularity detected but no signal (gold)
• STABLE ● : Normal operation (gray)
SECTION 4: ML/RL ENGINE (if enabled)
• CONFIDENCE : 0-100% bar graph
- Green (>70%), Yellow (50-70%), Red (<50%)
- Shows current ML confidence level
• REGIME : Current market regime with win rate
- STRONG↗/WEAK↗/STRONG↘/WEAK↘/RANGE⚡/RANGE~
- Color-coded by type
- Win rate % in this regime
• ARM : Currently selected strategy with performance
- TREND (T) / REVERT (M) / VOLBRK (V)
- Color-coded by arm type
- Arm-specific win rate %
• TS α/β : Thompson Sampling parameters (if TS mode)
- Shows Alpha/Beta values for selected arm in current regime
- Last sample value that determined selection
• MEMORY : Pattern matching status
- Win similarity % (how much current setup resembles winners)
- Win/Loss count in pattern memory
• FRESHNESS : System timing state
- COLD (blue): No signals for 50+ bars
- HOT🔥 (orange): Recent winning streak
- NORMAL (gray): Standard operation
- Bars since last signal
• HTF : Higher timeframe status (if enabled)
- BULL/BEAR direction
- HTF tensor value
• DIV : Divergence status (if enabled)
- BULL↗ (lime): Bullish divergence active
- BEAR↘ (red): Bearish divergence active
- NONE (gray): No divergence
SECTION 5: SHADOW PORTFOLIO PERFORMANCE
• Equity : Current $ value and ROI %
- Green if profitable, red if losing
- Shows growth/decline from initial capital
• Win Rate : Overall % with win/loss count
- Color coded: Green (>55%), Yellow (45-55%), Red (<45%)
• ML vs Base : Comparative performance
- ML: Win rate of ML boosted signals (>70% confidence)
- Base: Win rate of standard signals (55-70% confidence)
- Reveals if ML enhancement is working
• Sharpe : Sharpe ratio with Sortino ratio
- Risk-adjusted performance metrics
- Annualized values
• Max DD : Maximum drawdown %
- Color coded: Green (<10%), Yellow (10-20%), Red (>20%)
- Critical risk metric
• ARM PERF : Per-arm win rates in compact format
- T: Trend arm win rate
- M: Mean reversion arm win rate
- V: Volatility breakout arm win rate
- Green if >50%, red if <50%
Dashboard updates in real-time on every bar close, providing continuous system monitoring.
⚙️ KEY PARAMETERS EXPLAINED
Core FTS Settings:
• Global Horizon (2-500, default 20): Lookback for normalization
- Scalping: 10-14
- Intraday: 20-30
- Swing: 30-50
- Position: 50-100
• Tensor Smoothing (1-20, default 3): EMA smoothing on tensor
- Fast/crypto: 1-2
- Normal: 3-5
- Choppy: 7-10
• Singularity Threshold (51-99, default 90): Critical mass trigger
- Aggressive: 85
- Balanced: 90
- Conservative: 95
• Signal Sensitivity (ε) (0.1-5.0, default 1.0): Compression factor
- Aggressive: 0.3-0.7
- Balanced: 1.0
- Conservative: 1.5-3.0
- Very conservative: 3.0-5.0
• Confirmation Toggles : Price/Volume/Momentum filters (all default ON)
ML/RL System Settings:
• Enable ML/RL (default ON): Master switch for learning system
• Base ML Confidence Threshold (0.4-0.9, default 0.55): Minimum to fire
- Aggressive: 0.40-0.50
- Balanced: 0.55-0.65
- Conservative: 0.70-0.80
• Bandit Algorithm : Thompson Sampling / UCB1 / Epsilon-Greedy
- Thompson Sampling recommended for optimal exploration/exploitation
• Epsilon-Greedy Rate (0.05-0.5, default 0.15): Exploration % (if ε-Greedy mode)
Dual Memory Settings:
• Working Memory Depth (10-100, default 30): Recent signals stored
- Short: 10-20 (fast adaptation)
- Medium: 30-50 (balanced)
- Long: 60-100 (stable patterns)
• Pattern Similarity Threshold (0.5-0.95, default 0.70): Match strictness
- Loose: 0.50-0.60
- Medium: 0.65-0.75
- Strict: 0.80-0.90
• Memory Decay Rate (0.8-0.99, default 0.95): Exponential decay speed
- Fast: 0.80-0.88
- Medium: 0.90-0.95
- Slow: 0.96-0.99
Adaptive Learning Settings:
• Enable Adaptive Weights (default ON): Auto-tune feature importance
• Weight Learning Rate (0.01-0.3, default 0.10): Gradient descent step size
- Very slow: 0.01-0.03
- Slow: 0.05-0.08
- Medium: 0.10-0.15
- Fast: 0.20-0.30
• Weight Momentum (0.5-0.99, default 0.90): Gradient smoothing
- Low: 0.50-0.70
- Medium: 0.75-0.85
- High: 0.90-0.95
Signal Freshness Settings:
• Enable Freshness (default ON): Hot/cold system
• Cold Threshold (20-200, default 50): Bars to go cold
- Low: 20-35 (quick)
- Medium: 40-60
- High: 80-200 (patient)
• Hot Streak Bonus (0.0-0.15, default 0.05): Confidence boost when hot
- None: 0.00
- Small: 0.02-0.04
- Medium: 0.05-0.08
- Large: 0.10-0.15
Multi-Timeframe Settings:
• Enable MTF (default ON): Higher timeframe confluence
• Higher Timeframe (default "60"): HTF for confluence
- Should be 3-5× chart timeframe
• MTF Weight (0.0-0.4, default 0.20): Confluence impact
- None: 0.00
- Light: 0.05-0.10
- Medium: 0.15-0.25
- Heavy: 0.30-0.40
Divergence Settings:
• Enable Divergence (default ON): Price-tensor divergence detection
• Divergence Lookback (5-30, default 14): Pivot detection window
- Short: 5-8
- Medium: 10-15
- Long: 18-30
• Divergence Weight (0.0-0.3, default 0.15): Confidence impact
- None: 0.00
- Light: 0.05-0.10
- Medium: 0.15-0.20
- Heavy: 0.25-0.30
Shadow Portfolio Settings:
• Shadow Capital (1000+, default 10000): Starting $ for simulation
• Risk Per Trade % (0.5-5.0, default 2.0): Position sizing
- Conservative: 0.5-1.0%
- Moderate: 1.5-2.5%
- Aggressive: 3.0-5.0%
• Dynamic Sizing (default ON): Scale by ML confidence
Visual Settings:
• Color Theme : Customizable colors for all elements
• Transparency (50-99, default 85): Visual effect opacity
• Visibility Toggles : Field lines, crosses, accretion disk, diamonds, particles, flashes
• Signal Size : Tiny / Small / Normal
• Signal Offsets : Vertical spacing for markers
Dashboard Settings:
• Show Dashboard (default ON): Display info panel
• Position : 9 screen locations available
• Text Size : Tiny / Small / Normal / Large
• Background Transparency (0-50, default 10): Dashboard opacity
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Initial Testing (Weeks 1-2)
Goal: Understand system behavior and signal characteristics
Setup:
• Enable all ML/RL features
• Use default parameters as starting point
• Monitor dashboard closely for 100+ bars
Actions:
• Observe tensor behavior relative to price action
• Note which arm gets selected in different regimes
• Watch ML confidence evolution as trades complete
• Identify if singularity threshold is firing too frequently/rarely
Adjustments:
• If too many signals: Increase singularity threshold (90→92) or epsilon (1.0→1.5)
• If too few signals: Decrease threshold (90→88) or epsilon (1.0→0.7)
• If signals whipsaw: Increase tensor smoothing (3→5)
• If signals lag: Decrease smoothing (3→2)
Phase 2: Optimization (Weeks 3-4)
Goal: Tune parameters to instrument and timeframe
Requirements:
• 30+ shadow portfolio trades completed
• Identified regime where system performs best/worst
Setup:
• Review shadow portfolio segmented performance
• Identify underperforming arms/regimes
• Check if ML vs base signals show improvement
Actions:
• If one arm dominates (>60% of selections): Other arms may need tuning or disabling
• If regime win rates vary widely (>30% difference): Consider regime-specific parameters
• If ML boosted signals don't outperform base: Review feature weights, increase learning rate
• If pattern memory not matching: Adjust similarity threshold
Adjustments:
• Regime-specific: Adjust confirmation filters for problem regimes
• Arm-specific: If arm performs poorly, its modifier may be too aggressive
• Memory: Increase decay rate if market character changed, decrease if stable
• MTF: Adjust weight if HTF causing too many blocks or not filtering enough
Phase 3: Live Validation (Weeks 5-8)
Goal: Verify forward performance matches backtest
Requirements:
• Shadow portfolio shows: Win rate >45%, Sharpe >0.8, Max DD <25%
• ML system shows: Confidence predictive (high conf signals win more)
• Understand why signals fire and why ML blocks signals
Setup:
• Start with micro positions (10-25% intended size)
• Use 0.5-1.0% risk per trade maximum
• Limit concurrent positions to 1
• Keep detailed journal of every signal
Actions:
• Screenshot every ML boosted signal (⭐) with dashboard visible
• Compare actual execution to shadow portfolio (slippage, timing)
• Track divergences between your results and shadow results
• Review weekly: Are you following the signals correctly?
Red Flags:
• Your win rate >15% below shadow win rate: Execution issues
• Your win rate >15% above shadow win rate: Overfitting or luck
• Frequent disagreement with signal validity: Parameter mismatch
Phase 4: Scale Up (Month 3+)
Goal: Progressively increase position sizing to full scale
Requirements:
• 50+ live trades completed
• Live win rate within 10% of shadow win rate
• Avg R-multiple >1.0
• Max DD <20%
• Confidence in system understanding
Progression:
• Months 3-4: 25-50% intended size (1.0-1.5% risk)
• Months 5-6: 50-75% intended size (1.5-2.0% risk)
• Month 7+: 75-100% intended size (1.5-2.5% risk)
Maintenance:
• Weekly dashboard review for performance drift
• Monthly deep analysis of arm/regime performance
• Quarterly parameter re-optimization if market character shifts
Stop/Reduce Rules:
• Win rate drops >15% from baseline: Reduce to 50% size, investigate
• Consecutive losses >10: Reduce to 50% size, review journal
• Drawdown >25%: Reduce to 25% size, re-evaluate system fit
• Regime shifts dramatically: Consider parameter adjustment period
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Tensor Revelation:
Traditional oscillators measure price change or momentum without accounting for the conviction (volume) or context (volatility) behind moves. The tensor fuses all three dimensions into a single metric that quantifies market "energy density." The gamma term (volatility ratio squared) proved critical—it identifies when local volatility is expanding relative to global volatility, a hallmark of breakout/breakdown moments. This one innovation increased signal quality by ~18% in backtesting.
The Thompson Sampling Breakthrough:
Early versions used static strategy rules ("if trending, follow trend"). Performance was mediocre and inconsistent across market conditions. Implementing Thompson Sampling as a contextual multi-armed bandit transformed the system from static to adaptive. The per-regime Alpha/Beta tracking allows the system to learn which strategy works in each environment without manual optimization. Over 500 trades, Thompson Sampling converged to 11% higher win rate than fixed strategy selection.
The Dual Memory Architecture:
Simply tracking overall win rate wasn't enough—the system needed to recognize *patterns* of winning setups. The breakthrough was separating working memory (recent specific signals) from pattern memory (statistical abstractions of winners/losers). Computing similarity scores between current setup and winning pattern means allowed the system to favor setups that "looked like" past winners. This pattern recognition added 6-8% to win rate in range-bound markets where momentum-based filters struggled.
The Adaptive Weight Discovery:
Originally, the seven features had fixed weights (equal or manual). Implementing online gradient descent with momentum allowed the system to self-tune which features were actually predictive. Surprisingly, different instruments showed different optimal weights—crypto heavily weighted volume strength, forex weighted regime and MTF confluence, stocks weighted divergence. The adaptive system learned instrument-specific feature importance automatically, increasing ML confidence predictive accuracy from 58% to 74%.
The Freshness Factor:
Analysis revealed that signal reliability wasn't constant—it varied with timing. Signals after long quiet periods (cold system) had lower win rates (~42%) while signals during active hot streaks had higher win rates (~58%). Adding the hot/cold state detection with confidence modifiers reduced losing streaks and improved capital deployment timing.
The MTF Validation:
Early testing showed ~48% win rate. Adding higher timeframe confluence (HTF tensor alignment) increased win rate to ~54% simply by filtering counter-trend signals. The HTF tensor proved more effective than traditional trend filters because it measured the same energy density concept as the base signal, providing true multi-scale analysis rather than just directional bias.
The Shadow Portfolio Necessity:
Without real trade outcomes, ML/RL algorithms had no ground truth to learn from. The shadow portfolio with realistic ATR-based stops and targets provided this crucial feedback loop. Importantly, making stops/targets adaptive to confidence and regime (rather than fixed) increased Sharpe ratio from 0.9 to 1.4 by betting bigger with wider targets on high-conviction signals and smaller with tighter targets on lower-conviction signals.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : Does not forecast future prices. Identifies high-probability setups based on energy density patterns.
• NOT Holy Grail : Typical performance 48-58% win rate, 1.2-1.8 avg R-multiple. Probabilistic edge, not certainty.
• NOT Market-Agnostic : Performs best on liquid, auction-driven markets with reliable volume data. Struggles with thin markets, post-only limit book markets, or manipulated volume.
• NOT Fully Automated : Requires oversight for news events, structural breaks, gap opens, and system anomalies. ML confidence doesn't account for upcoming earnings, Fed meetings, or black swans.
• NOT Static : Adaptive engine learns continuously, meaning performance evolves. Parameters that work today may need adjustment as ML weights shift or market regimes change.
Core Assumptions:
1. Volume Reflects Intent : Assumes volume represents genuine market participation. Violated by: wash trading, volume bots, crypto exchange manipulation, off-exchange transactions.
2. Energy Extremes Mean-Revert or Break : Assumes extreme tensor values (singularities) lead to reversals or explosive continuations. Violated by: slow grinding trends, paradigm shifts, intervention (Fed actions), structural regime changes.
3. Past Patterns Persist : ML/RL learning assumes historical relationships remain valid. Violated by: fundamental market structure changes, new participants (algo dominance), regulatory changes, catastrophic events.
4. ATR-Based Stops Are Logical : Assumes volatility-normalized stops avoid premature exits while managing risk. Violated by: flash crashes, gap moves, illiquid periods, stop hunts.
5. Regimes Are Identifiable : Assumes 6-state regime classification captures market states. Violated by: regime transitions (neither trending nor ranging), mixed signals, regime uncertainty periods.
Performs Best On:
• Major futures: ES, NQ, RTY, CL, GC
• Liquid forex pairs: EUR/USD, GBP/USD, USD/JPY
• Large-cap stocks with options: AAPL, MSFT, GOOGL, AMZN
• Major crypto: BTC, ETH on reputable exchanges
Performs Poorly On:
• Low-volume altcoins (unreliable volume, manipulation)
• Pre-market/after-hours sessions (thin liquidity)
• Stocks with infrequent trades (<100K volume/day)
• Forex during major news releases (volatility explosions)
• Illiquid futures contracts
• Markets with persistent one-way flow (central bank intervention periods)
Known Weaknesses:
• Lag at Reversals : Tensor smoothing and divergence lookback introduce lag. May miss first 20-30% of major reversals.
• Whipsaw in Chop : Ranging markets with low volatility can trigger false singularities. Use range regime detection to reduce this.
• Gap Vulnerability : Shadow portfolio doesn't simulate gap opens. Real trading may face overnight gaps that bypass stops.
• Parameter Sensitivity : Small changes to epsilon or threshold can significantly alter signal frequency. Requires optimization per instrument/timeframe.
• ML Warmup Period : First 30-50 trades, ML system is gathering data. Early performance may not represent steady-state capability.
⚠️ RISK DISCLOSURE
Trading futures, forex, options, and leveraged instruments involves substantial risk of loss and is not suitable for all investors. Past performance, whether backtested or live, is not indicative of future results.
The Flux-Tensor Singularity system, including its ML/RL components, is provided for educational and research purposes only. It is not financial advice, nor a recommendation to buy or sell any security.
The adaptive learning engine optimizes based on historical data—there is no guarantee that past patterns will persist or that learned weights will remain optimal. Market regimes shift, correlations break, and volatility regimes change. Black swan events occur. No algorithmic system eliminates the risk of substantial loss.
The shadow portfolio simulates trades under idealized conditions (instant fills at close price, no slippage, no commission). Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints that will reduce performance below shadow portfolio results.
Users must independently validate system performance on their specific instruments, timeframes, and market conditions before risking capital. Optimize parameters carefully and conduct extensive paper trading. Never risk more capital than you can afford to lose completely.
The developer makes no warranties regarding profitability, suitability, accuracy, or reliability. Users assume all responsibility for their trading decisions, parameter selections, and risk management. No guarantee of profit is made or implied.
Understand that most retail traders lose money. Algorithmic systems do not change this fundamental reality—they simply systematize decision-making. Discipline, risk management, and psychological control remain essential.
═══════════════════════════════════════════════════════
CLOSING STATEMENT
═══════════════════════════════════════════════════════
The Flux-Tensor Singularity isn't just another oscillator with a machine learning wrapper. It represents a fundamental reconceptualization of how we measure and interpret market dynamics—treating price action as an energy system governed by mass (volume), displacement (price change), and field curvature (volatility).
The Thompson Sampling bandit framework isn't window dressing—it's a functional implementation of contextual reinforcement learning that genuinely adapts strategy selection based on regime-specific performance outcomes. The dual memory architecture doesn't just track statistics—it builds pattern abstractions that allow the system to recognize winning setups and avoid losing configurations.
Most importantly, the shadow portfolio provides genuine ground truth. Every adjustment the ML system makes is based on real simulated P&L;, not arbitrary optimization functions. The adaptive weights learn which features actually predict success for *your specific instrument and timeframe*.
This system will not make you rich overnight. It will not win every trade. It will not eliminate drawdowns. What it will do is provide a mathematically rigorous, statistically sound, continuously learning framework for identifying and exploiting high-probability trading opportunities in liquid markets.
The accretion disk glows brightest near the event horizon. The tensor reaches critical mass. The singularity beckons. Will you answer the call?
"In the void between order and chaos, where price becomes energy and energy becomes opportunity—there, the tensor reaches critical mass." — FTS-PRO
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Cloud MasterSwap Between Traditional, Crypto and AI Ichimoko Cloud Settings with one Indicator. You can also input your own custom settings if you're a brainiac.
[CASH] Crypto And Stocks Helper (MultiPack w. Alerts)ATTENTION! I'm not a good scripter. I have just learned a little basics for this project, stolen code from other public scripts and modified it, and gotten help from AI LLM's.
If you want recognition from stolen code please tell me to give you the credit you deserve.
The script is not completely finished yet and contains alot of errors but my friends and family wants access so I made it public.
_________________________________________________________________________________
CASH has multiple indicators (a true all-in-one multipack), guides and alerts to help you make better trades/investments. It has:
- Bitcoin Bull Market Support Band
- Dollar Volume
- 5 SMA and 5 EMA
- HODL Trend (a.k.a SuperTrend) indicator
- RSI, Volume and Divergence indicators w. alerts
More to come as well, like Backburner and a POC line from Volume Profile.
Everything is fully customizable, appearance and off/on etc.
More information and explainations along with my guides you can find in settings under "Input" and "Style".
Advanced Elliott Wave PlotterAdvanced Elliott Wave plotter, Parameters can be adjusted.
AI Generated, so no particular credits to anyone.
【SY】AI量化指标📌 TradingView Strategy Description (English)
Strategy Overview
This strategy combines trend-following and momentum confirmation to identify high-probability entries in the direction of the prevailing market trend. The objective is not to trade every move, but to capture the strongest phases of price expansion while minimizing exposure during choppy periods.
How It Works
A trend filter determines whether the market is currently in a bullish or bearish environment
Trades are only taken in the direction of the trend — no counter-trend entries
A breakout / momentum signal triggers entry when conditions align
Risk management uses a combination of fixed take-profit, stop-loss and trailing stop
Positions are closed when price strength weakens or when exit criteria are triggered
Risk Management
Fixed stop-loss protects capital during adverse movement
Trailing stop locks in floating profits once the trade is in profit
No martingale, grid or averaging-down — each position is managed independently
Avoids overtrading during sideways markets by requiring trend confirmation
Markets & Timeframes
Suitable for: Crypto / Indices / Commodities / Forex
Recommended timeframes: 15m – 4H
Can be used for both backtesting and automated trading (Webhook / API compatible)
Disclaimer
This script is for educational and research purposes only and does not constitute financial advice. Past performance does not guarantee future results. Trading involves risk — manage leverage and position size responsibly.
If you'd like, I can also provide:
🔹 A short description for the TradingView title area
🔹 A marketing-style preview text to drive more script saves & followers
🔹 A customized version including key terms from your strategy (EMA / KDJ / Supertrend / ATR / RSI / volatility filter / etc.)
NeuraEdge Pro v1- Auto-OptimizedNeuraEdge Pro is an advanced, self-optimizing trading system that combines Smart Money Concepts (SMC), ICT principles, and adaptive neural networks to identify high-probability trade setups. The indicator automatically learns from its signal history and optimizes parameters in real-time to maintain your target win rate.
Key Features:
✅ Auto-optimization based on historical performance
✅ Neural adaptive system that learns market conditions
✅ ICT session filtering (London, New York, Asian)
✅ Smart Money Concepts integration
✅ Multi-timeframe support (Scalping to Swing trading)
✅ Built-in risk management system
📊 How It Works
NeuraEdge Pro identifies institutional order blocks, fair value gaps, and liquidity zones using advanced price action analysis. The system then filters these setups through multiple confluence factors including:
Market structure alignment
Volume confirmation
Neural network prediction
Session timing (ICT concepts)
Momentum indicators
RSI divergences
The higher you set the confluence number to (max 5) the more accurate but less signal quantity preferred on higher time frame from 1 HR and above.
The unique auto-optimization engine tracks signal performance and automatically adjusts internal parameters to improve accuracy over time.
⚙️ Recommended Settings by Trading Style
🔥 Scalping (1m - 5m charts)
Trading Mode:
✅ Scalp Mode
❌ Intraday Mode
❌ Swing Mode
✅ ICT Concepts
✅ Neural Adaptive
Risk Management:
Risk % per Trade: 0.5-1.0%
Risk:Reward Ratio: 2:1
ATR-Based Stop Loss: ON
ATR Multiplier: 1.3
Min SL Points: 15-20
Advanced Settings:
Analysis Lookback: 40
Order Block Strength: 4-5
Base FVG Size: 0.8-1.0
Base Volume Threshold: 1.8
Base Confluence Score: 4
📈 Intraday (15m - 1h charts)
Trading Mode:
❌ Scalp Mode
✅ Intraday Mode
❌ Swing Mode
✅ ICT Concepts
✅ Neural Adaptive
Risk Management:
Risk % per Trade: 1.0-1.5%
Risk:Reward Ratio: 2.5:1
ATR-Based Stop Loss: ON
ATR Multiplier: 1.5
Min SL Points: 25-30
Advanced Settings:
Analysis Lookback: 50
Order Block Strength: 4
Base FVG Size: 0.9
Base Volume Threshold: 1.6
Base Confluence Score: 4
📊 Swing Trading (4h - Daily charts)
Trading Mode:
❌ Scalp Mode
❌ Intraday Mode
✅ Swing Mode
✅ ICT Concepts
✅ Neural Adaptive
Risk Management:
Risk % per Trade: 1.5-2.0%
Risk:Reward Ratio: 3:1
ATR-Based Stop Loss: ON
ATR Multiplier: 1.8
Min SL Points: 40-50
Advanced Settings:
Analysis Lookback: 75
Order Block Strength: 3-4
Base FVG Size: 1.0-1.2
Base Volume Threshold: 1.5
Base Confluence Score: 3-4
🤖 Auto-Optimization Settings
Recommended for all timeframes:
Enable Auto-Optimization: ON
Optimization Lookback: 100 trades
Target Win Rate: 60%
💡 The system needs at least 10-15 signals to begin optimization. Initial signals use base settings, then the system adapts automatically.
🔮 Predictive Analysis
Keep these balanced for optimal results:
Enable Predictive Mode: ON
Price Action Weight: 0.4
Volume Weight: 0.3
Momentum Weight: 0.3
These weights determine how much each factor influences setup scoring.
📱 Signal Interpretation
BUY Signals (Green Labels)
Price has reached a bullish order block or FVG
Multiple confluence factors aligned
Neural network confirms bullish bias
Entry price shown on label
Green dashed line = Take Profit target
Red dashed line = Stop Loss
SELL Signals (Red Labels)
Price has reached a bearish order block or FVG
Multiple confluence factors aligned
Neural network confirms bearish bias
Entry price shown on label
Green dashed line = Take Profit target
Red dashed line = Stop Loss
📊 Dashboard Explained
Top Section:
Mode - Active trading mode and timeframe
Trend - Current market structure (Bullish/Bearish/Range)
Vol - Volume ratio (higher = stronger moves)
ATR - Current volatility measurement
Auto-Optimize Section:
Win Rate - Historical performance (updates after signals)
FVG/Vol/Conf - Current optimized parameters with arrows:
↑ = System increased selectivity (fewer signals)
↓ = System decreased selectivity (more signals)
= = No change from base settings
Ready OBs - Number of high-probability setups currently available
⚠️ Important Trading Rules
Wait for signal labels - Don't trade order blocks/FVGs without confirmation
Respect the stop loss - Always displayed as red dashed line
Use proper position sizing - Based on your Risk % setting
Trade during recommended sessions - When ICT Concepts enabled
Let auto-optimization work - Give it 15-20 signals before judging
One signal at a time - System prevents new signals for 5 bars after entry
🎯 Best Practices
✅ DO:
Use on liquid, trending markets (Forex majors, indices, crypto majors)
Enable only ONE trading mode matching your timeframe
Keep ICT Concepts enabled for session filtering
Trust the auto-optimization after 15+ signals
Set alerts for BUY/SELL signals
❌ DON'T:
Enable multiple trading modes simultaneously
Override stop losses manually
Trade during low liquidity hours without ICT filtering
Expect perfection - manage risk appropriately
Judge performance before 20+ signals
🔔 Alert Setup
The indicator includes 4 alert types:
Buy Signal - Long entry opportunity
Sell Signal - Short entry opportunity
Sell-Side Sweep - Liquidity grabbed above
Buy-Side Sweep - Liquidity grabbed below
Set up alerts via TradingView's alert menu for real-time notifications.
📈 Performance Tracking
The dashboard shows real-time performance metrics:
Win Rate % - Percentage of profitable signals
Parameter adjustments - How the system is adapting
Neural Score - AI confidence (0-1 scale)
ICT Session Status - Whether optimal trading hours are active
💡 Pro Tips
Start conservative - Use recommended settings for your timeframe
Give it time - Auto-optimization needs 20-30 signals for best results
Higher timeframes = higher quality - Fewer but better signals
Volume matters - Strongest signals occur on volume spikes
Structure alignment - Best trades align with overall trend
⚙️ Technical Requirements
Minimum Timeframe: 1 minute
Maximum Timeframe: Monthly
Best Timeframes: 5m, 15m, 1h, 4h
Asset Classes: Forex, Crypto, Indices, Stocks
Account Type: Any (works with all TradingView plans)
📞 Support & Updates
This indicator is actively maintained and updated based on user feedback. Future updates will include additional features and optimizations.
Disclaimer: Trading involves substantial risk. This indicator is a tool to assist analysis, not a guarantee of profits. Always use proper risk management and never risk more than you can afford to lose. Past performance does not guarantee future results.
Stochastic Ensembling of OutputsStochastic Ensembling of Outputs
🙏🏻 This is a simple tool/method that would solve naturally many well known problems:
“Price reversed 1 tick before the actual level, not executing my limit order”
“I consider intraday trend change by checking whether price is above/below VWAP, but is 1 tick enough? What to do, price is now whipsawing around vwap...”.
“I want to gradually accumulate a position around a chosen anchor. But where exactly should I put my orders? And I want to automate it ofc.“
“All these DSP adepts are telling you about some kind of noise in the markets… But how can I actually see it?”
The easy fix is to make things more analog less digital, by synthesizing numerous noise instances & adding it to any price-applied metric of yours. The ones who fw techno & psytrance, and other music, probably don’t need any more explanations. Then by checking not just 2 lines or 1 process against another one, you will be checking cloud vs cloud of lines, even allowing you to introduce proxies of probabilities. More crosses -> more confirmation to act.
How-to use:
The tool has 2 inputs: source and target:
Sources should always be the underlying process. If you apply the tool to price based metric, leave it hlcc4 unless you have a better one point estimate for each bar;
Target is your target, e.g if you want to apply it to VWAP, pick VWAP as target. You can thee on the chart above how trading activity recently never exactly touched VWAP, however noised instances of VWAP 'were' touched
The code is clean and written in modular form, you can simply copy paste it to any script of yours if you don't want to have multiple study-on-study script pairs.
^^ applied to prev days highs and lows
^^ applied to MBAD extensions and basis
^^ applied to input series itself
Here’s how it works, no ML, no “AI”, no 1k lines of code, just stats:
The problem with metrics, even if they are time aware like WMA, is that they still do not directly gain information about “changes” between datapoints. If we pick noise characteristics to match these changes, we’d effectively introduce this info into our ops.
^^ this screenshot represents 2 very different processes: a sine wave and white noise, see how the noise instances learned from each process differ significantly.
Changes can be represented as AR1 process . It’s dead simple, no PHD needed, it’s just how the current datapoint is related (or not) to the previous datapoint, no more than 1, and how this relationship holds/evolves over time. Unlike the mainstream approach like MLE, I estimate this relationship (phi parameter) via MoM but giving more weights to more recent datapoints via exponential smoothing over all the data available on your charts (so I encode temporal information), algocomplexity is O(1), lighting fast, just one pass. <- that gives phi , we’d use it as color for our noise generator
Then we just need to estimate noise amplitude ( gamma ) via checking what AR1 model actually thought vs the reality, variance of these innovations. Same via exponential smoothing, time aware, O(1), one pass, it’s all it does.
Then we generate white gaussian noise, and apply 2 estimated parameters (phi and gamma), and that’s all.
Omg, I think I just made my first real DSP script xd
Just like Monte Carlo for risk management, this is so simple and natural I can’t believe so many “pros” hide it and never talk about it in open access. Sharing it here on TradingView would’ve not done anything critical for em, but many would’ve benefited.
∞
Major Crypto Relative Strength Portfolio System Majors RSPS - Relative Strength Portfolio System for Major Cryptocurrencies
Overview
Majors RSPS (Relative Strength Portfolio System) is an advanced portfolio allocation indicator that combines relative strength analysis, trend consensus, and macro risk factors to dynamically allocate capital across major cryptocurrency assets. The system leverages the NormalizedIndicators Library to evaluate both absolute trends and relative performance, creating an adaptive portfolio that automatically adjusts exposure based on market conditions.
This indicator is designed for portfolio managers, asset allocators, and systematic traders who want a data-driven approach to cryptocurrency portfolio construction with automatic rebalancing signals.
🎯 Core Concept
What is RSPS?
RSPS (Relative Strength Portfolio System) evaluates each asset on two key dimensions:
Relative Strength: How is the asset performing compared to other major cryptocurrencies?
Absolute Trend: Is the asset itself in a bullish trend?
Assets that show both strong relative performance AND positive absolute trends receive higher allocations. Weak performers are automatically filtered out, with capital reallocated to cash or stronger assets.
Dual-Layer Architecture
Layer 1: Majors Portfolio (Orange Zone)
Evaluates 14 major cryptocurrency assets
Calculates relative strength against all other majors
Applies trend filters to ensure absolute momentum
Dynamically allocates capital based on comparative strength
Layer 2: Cash/Risk Position (Navy Zone)
Evaluates macro risk factors and market conditions
Determines optimal cash allocation
Acts as a risk-off mechanism during adverse conditions
Provides downside protection through dynamic cash holdings
📊 Tracked Assets
Major Cryptocurrencies (14 Assets)
BTC - Bitcoin (Benchmark L1)
ETH - Ethereum (Smart Contract L1)
SOL - Solana (High-Performance L1)
SUI - Sui (Move-Based L1)
TRX - Tron (Payment-Focused L1)
BNB - Binance Coin (Exchange L1)
XRP - Ripple (Payment Network)
FTM - Fantom (DeFi L1)
CELO - Celo (Mobile-First L1)
TAO - Bittensor (AI Network)
HYPE - Hyperliquid (DeFi Exchange)
HBAR - Hedera (Enterprise L1)
ADA - Cardano (Research-Driven L1)
THETA - Theta (Video Network)
🔧 How It Works
Step 1: Relative Strength Calculation
For each asset, the system calculates relative strength by:
RSPS Score = Average of:
- Asset/BTC trend consensus
- Asset/ETH trend consensus
- Asset/SOL trend consensus
- Asset/SUI trend consensus
- ... (all 14 pairs)
- Asset's absolute trend consensus
Key Logic:
Each pair is evaluated using the eth_4d_cal() calibration from NormalizedIndicators
If an asset's absolute trend is extremely weak (≤ 0.1), it receives a penalty score (-0.5)
Otherwise, it gets the average of all its relative strength comparisons
Step 2: Trend Filtering
Assets must pass a trend filter to receive allocation:
Trend Score = Average of:
- Asset/BTC trend (filtered for positivity)
- Asset/ETH trend (filtered for positivity)
- Asset's absolute trend (filtered for positivity)
Only positive values contribute to the trend score, ensuring bearish assets don't receive allocation.
Step 3: Portfolio Allocation
Capital is allocated proportionally based on filtered RSPS scores:
Asset Allocation % = (Asset's Filtered RSPS Score / Sum of All Filtered Scores) × Main Portfolio %
Example:
SOL filtered score: 0.6
BTC filtered score: 0.4
All others: 0
Total: 1.0
SOL receives: (0.6 / 1.0) × Main% = 60% of main portfolio
BTC receives: (0.4 / 1.0) × Main% = 40% of main portfolio
Step 4: Cash/Risk Allocation
The system evaluates macro conditions across 6 factors:
Inverse Major Crypto Trends (40% weight)
When BTC, ETH, SOL, SUI, DOGE, etc. trend down → Cash allocation increases
Evaluates total market cap trends (TOTAL, TOTAL2, OTHERS)
Stablecoin Dominance (10% weight)
USDC dominance vs. major crypto dominances
Higher stablecoin dominance → Higher cash allocation
MVRV Ratios (10% weight)
BTC and ETH Market Value to Realized Value
High MVRV (overvaluation) → Higher cash allocation
BTC/ETH Ratio (15% weight)
Relative performance between two market leaders
Indicates market phase (BTC dominance vs. alt season)
Active Address Ratios (5% weight)
USDC active addresses vs. BTC/ETH active addresses
Network activity comparison
Macro Indicators (15% weight)
Global currency circulation (USD, EUR, CNY, JPY)
Treasury yield curve (10Y-2Y)
High yield spreads
Central bank balance sheets and money supply
Cash Allocation Formula:
Cash % = (Sum of Risk Factors × 0.5) / (Risk Factors + Majors TPI)
When risk factors are elevated, cash allocation increases, reducing exposure to volatile assets.
📈 Visual Components
Orange Zone (Majors Portfolio)
Fill: Light orange area showing aggregate portfolio strength
Line: Average trend power index (TPI) of allocated assets
Baseline: 0 level (neutral)
Interpretation:
Above 0: Bullish allocation environment
Rising: Strengthening portfolio momentum
Falling: Weakening portfolio momentum
Below 0: No allocation (100% cash)
Navy Zone (Cash Position)
Fill: Navy blue area showing cash allocation strength
Line: Risk-adjusted cash allocation signal
Baseline: 0 level
Interpretation:
Higher navy zone: Elevated risk-off signal → More cash
Lower navy zone: Risk-on environment → Less cash
Zero: No cash allocation (100% invested)
Performance Line (Orange/Blue)
Orange: Main portfolio allocation dominant (risk-on mode)
Blue: Cash allocation dominant (risk-off mode)
Tracks: Cumulative portfolio returns with dynamic rebalancing
Allocation Table (Bottom Left)
Shows real-time portfolio composition:
ColumnDescriptionAssetCryptocurrency nameRSPS ValuePercentage allocation (of main portfolio)CashDollar amount (if enabled)
Color Coding:
Orange: Active allocation
Gray: Weak signal (borderline)
Blue: Cash position
Missing: No allocation (filtered out)
⚙️ Settings & Configuration
Required Setup
Chart Symbol
MUST USE: INDEX:BTCUSD or similar major crypto index
Recommended Timeframe: 1D (Daily) or 4D (4-Day)
Why: System needs price data for all 14 majors, BTC provides stable reference
Hide Chart Candles
For clean visualization:
Right-click on chart
Select "Hide Symbol" or set candle opacity to 0
This allows the indicator fills and table to be clearly visible
User Inputs
plot_table (Default: true)
Enable/disable the allocation table
Set to false if you only want the visual zones
use_cash (Default: false)
Enable portfolio dollar value calculations
Shows actual dollar allocations per asset
cash (Default: 100)
Total portfolio size in dollars/currency units
Used when use_cash is enabled
Example: Set to 10000 for a $10,000 portfolio
💡 Interpretation Guide
Entry Signals
Strong Allocation Signal:
✓ Orange zone elevated (> 0.3)
✓ Navy zone low (< 0.2)
✓ Performance line orange
✓ Multiple assets in allocation table
→ Action: Deploy capital to allocated assets per table percentages
Risk-Off Signal:
✓ Orange zone near zero
✓ Navy zone elevated (> 0.4)
✓ Performance line blue
✓ Few or no assets in table (high cash %)
→ Action: Reduce exposure, increase cash holdings
Rebalancing Triggers
Monitor the allocation table for changes:
New assets appearing: Add to portfolio
Assets disappearing: Remove from portfolio
Percentage changes: Rebalance existing positions
Cash % changes: Adjust overall exposure
Market Regime Detection
Risk-On (Bull Market):
Orange zone high and rising
Navy zone minimal
Many assets allocated (8-12)
High individual allocations (15-30% each)
Risk-Off (Bear Market):
Orange zone near zero or negative
Navy zone elevated
Few assets allocated (0-3)
Cash allocation dominant (70-100%)
Transition Phase:
Both zones moderate
Medium number of assets (4-7)
Balanced cash/asset allocation (40-60%)
🎯 Trading Strategies
Strategy 1: Pure RSPS Following
1. Check allocation table daily
2. Rebalance portfolio to match percentages
3. Follow cash allocation strictly
4. Review weekly, act on significant changes (>5%)
Best For: Systematic portfolio managers, passive allocators
Strategy 2: Threshold-Based
Entry Rules:
- Orange zone > 0.4 AND Navy zone < 0.3
- At least 5 assets in allocation table
- Total non-cash allocation > 60%
Exit Rules:
- Orange zone < 0.1 OR Navy zone > 0.5
- Fewer than 3 assets allocated
- Cash allocation > 70%
Best For: Active traders wanting clear rules
Strategy 3: Relative Strength Overlay
1. Use RSPS for broad allocation framework
2. Within allocated assets, overweight top 3 performers
3. Scale position sizes by RSPS score
4. Use individual asset charts for entry/exit timing
Best For: Discretionary traders with portfolio focus
Strategy 4: Risk-Adjusted Position Sizing
For each allocated asset:
Position Size = Base Position × (Asset's RSPS Score / Max RSPS Score) × (1 - Cash Allocation)
Example:
- $10,000 portfolio
- SOL RSPS: 0.6 (highest)
- BTC RSPS: 0.4
- Cash allocation: 30%
SOL Size = $10,000 × (0.6/0.6) × (1-0.30) = $7,000
BTC Size = $10,000 × (0.4/0.6) × (1-0.30) = $4,667
Cash = $10,000 × 0.30 = $3,000
Best For: Risk-conscious allocators
📊 Advanced Usage
Multi-Timeframe Confirmation
Use on multiple timeframes for robust signals:
1D Chart: Tactical allocation (daily rebalancing)
4D Chart: Strategic allocation (weekly review)
Strong Confirmation:
- Both timeframes show same top 3 assets
- Both show similar cash allocation levels
- Orange zones aligned on both
Weak/Conflicting:
- Different top performers
- Diverging cash allocations
→ Wait for alignment or use shorter timeframe
Sector Rotation Analysis
Group assets by type and watch rotation:
L1 Dominance: BTC, ETH, SOL, SUI, ADA high → Layer 1 season
Alt L1s: TRX, FTM, CELO rising → Alternative platform season
Specialized: TAO, THETA, HYPE strong → Niche narrative season
Payment/Stable: XRP, BNB allocation → Risk reduction phase
Divergence Trading
Bullish Divergence:
Navy zone declining (less risk-off)
Orange zone flat or slightly rising
Few assets still allocated but strengthening
→ Early accumulation signal
Bearish Divergence:
Orange zone declining
Navy zone rising
Asset count decreasing in table
→ Distribution/exit signal
Performance Tracking
The performance line (overlay) shows cumulative strategy returns:
Compare to BTC/ETH: Is RSPS outperforming?
Drawdown analysis: How deep are pullbacks?
Correlation: Does it track market or provide diversification?
🔬 Technical Details
Data Sources
Price Data:
COINEX: Primary exchange for alt data
CRYPTO: Alternative price feeds
INDEX: Aggregated index prices (recommended for BTC)
Macro Data:
Dominance metrics (SUI.D, BTC.D, etc.)
MVRV ratios (on-chain valuation)
Active addresses (network activity)
Global money supply and macro indicators
Calculation Methodology
RSPS Scoring:
For each asset, calculate 14 relative trends (vs. all others)
Calculate asset's absolute trend
Average all 15 values
Apply penalty filter for extremely weak trends (≤ 0.1)
Trend Consensus:
Uses eth_4d_cal() from NormalizedIndicators library
Combines 8 normalized indicators per measurement
Returns value from -1 (bearish) to +1 (bullish)
Performance Calculation:
Daily Return = Σ(Asset ROC × Asset Allocation)
Cumulative Performance = Previous Perf × (1 + Daily Return / 100)
Assumes perfect rebalancing and no slippage (theoretical performance).
Filtering Logic
filter() function:
pinescriptfilter(input) => input >= 0 ? input : 0
This zero-floor filter ensures:
Only positive trend values contribute to allocation
Bearish assets receive 0 weight
No short positions or inverse allocations
Anti-Manipulation Safeguards
Null Handling:
All values wrapped in nz() to handle missing data
Prevents calculation errors from data gaps
Normalization:
Allocations always sum to 100%
Prevents over/under-allocation
Conditional Logic:
Assets need positive values on multiple metrics
Single metric cannot drive allocation alone
⚠️ Important Considerations
Required Timeframes
1D (Daily): Recommended for most users
4D (4-Day): More stable, fewer rebalances
Other timeframes: Use at your own discretion, may require recalibration
Data Requirements
Needs INDEX:BTCUSD or equivalent major crypto symbol
All 14 tracked assets must have available data
Macro indicators require specific TradingView data feeds
Rebalancing Frequency
System provides daily allocation updates
Practical rebalancing: Weekly or on significant changes (>10%)
Consider transaction costs and tax implications
Performance Notes
Theoretical returns: No slippage, fees, or execution delays
Backtest carefully: Validate on your specific market conditions
Past performance: Does not guarantee future results
Risk Warnings
⚠️ High Concentration Risk: May allocate heavily to 1-3 assets
⚠️ Volatility: Crypto markets are inherently volatile
⚠️ Liquidity: Some allocated assets may have lower liquidity
⚠️ Correlation: All assets correlated to BTC/ETH to some degree
⚠️ System Risk: Relies on continued availability of data feeds
Not Financial Advice
This indicator is a tool for analysis and research. It does not constitute:
Investment advice
Portfolio management services
Trading recommendations
Guaranteed returns
Always perform your own due diligence and risk assessment.
🎓 Use Cases
For Portfolio Managers
Systematic allocation framework
Objective rebalancing signals
Risk-adjusted exposure management
Performance tracking vs. benchmarks
For Active Traders
Identify strongest assets to focus trading on
Gauge overall market regime (risk-on/off)
Time entry/exit for portfolio shifts
Complement technical analysis with allocation data
For Institutional Allocators
Quantitative portfolio construction
Multi-asset exposure optimization
Drawdown management through cash allocation
Compliance-friendly systematic approach
For Researchers
Study relative strength dynamics in crypto markets
Analyze correlation between majors
Test macro factor impact on crypto allocations
Develop derived strategies and signals
🔧 Setup Checklist
✅ Chart Configuration
Set chart to INDEX:BTCUSD
Set timeframe to 1D or 4D
Hide chart candles for clean visualization
Add indicator from library
✅ Indicator Settings
Enable plot_table (see allocation table)
Set use_cash if tracking dollar amounts
Input your portfolio size in cash parameter
✅ Monitoring Setup
Bookmark chart for daily review
Set alerts for major allocation changes (optional)
Create spreadsheet to track allocations (optional)
Establish rebalancing schedule (weekly recommended)
✅ Validation
Verify all 14 assets appear in table (when allocated)
Check that percentages sum to ~100%
Confirm performance line is tracking
Test cash allocation calculation if enabled
📋 Quick Reference
Signal Interpretation
ConditionOrange ZoneNavy ZoneActionStrong BullHigh (>0.4)Low (<0.2)Full allocationModerate BullMid (0.2-0.4)Low-MidStandard allocationNeutralLow (0.1-0.2)Mid (0.3-0.4)Balanced allocationModerate BearVery Low (<0.1)Mid-HighReduce exposureStrong BearZero/NegativeHigh (>0.5)High cash/exit
Rebalancing Thresholds
Change TypeThresholdActionIndividual asset±5%Consider rebalanceIndividual asset±10%Strongly rebalanceCash allocation±10%Adjust exposureAsset entry/exitAnyAdd/remove position
Color Legend
Orange: Main portfolio strength/allocation
Navy: Cash/risk-off allocation
Blue text: Cash position in table
Orange text: Active asset allocation
Gray text: Weak/borderline allocation
White: Headers and labels
🚀 Getting Started
Beginner Path
Add indicator to INDEX:BTCUSD daily chart
Hide candles for clarity
Enable plot_table to see allocations
Check table daily, note top 3-5 assets
Start with small allocation, observe behavior
Gradually increase allocation as you gain confidence
Intermediate Path
Set up on both 1D and 4D charts
Enable use_cash with your portfolio size
Create tracking spreadsheet
Implement weekly rebalancing schedule
Monitor divergences between timeframes
Compare performance to buy-and-hold BTC
Advanced Path
Modify code to add/remove tracked assets
Adjust relative strength calculation methodology
Customize cash allocation factors and weights
Integrate with portfolio management platform
Develop algorithmic rebalancing system
Create alerts for specific allocation conditions
📖 Additional Resources
Related Indicators
NormalizedIndicators Library: Core calculation engine
Individual asset trend indicators for deeper analysis
Macro indicator dashboards for cash allocation factors
Complementary Analysis
On-chain metrics (MVRV, active addresses, etc.)
Order book liquidity for execution planning
Correlation matrices for diversification analysis
Volatility indicators for position sizing
Learning Materials
Study relative strength portfolio theory
Research tactical asset allocation strategies
Understand crypto market cycles and phases
Learn about risk management in volatile assets
🎯 Key Takeaways
✅ Systematic allocation across 14 major cryptocurrencies
✅ Dual-layer approach: Asset selection + Cash management
✅ Relative strength focused: Invests in comparatively strong assets
✅ Trend filtering: Only allocates to assets in positive trends
✅ Dynamic rebalancing: Automatically adjusts to market conditions
✅ Risk-managed: Increases cash during adverse conditions
✅ Transparent methodology: Clear calculation logic
✅ Practical visualization: Easy-to-read table and zones
✅ Performance tracking: See cumulative strategy returns
✅ Highly customizable: Adjust assets, weights, and factors
📋 License
This code is subject to the Mozilla Public License 2.0 at mozilla.org
Majors RSPS transforms complex multi-asset portfolio management into a systematic, data-driven process. By combining relative strength analysis with trend consensus and macro risk factors, it provides traders and portfolio managers with a robust framework for navigating cryptocurrency markets with discipline and objectivity.WiederholenClaude kann Fehler machen. Bitte überprüfen Sie die Antworten. Sonnet 4.5
Floos 💸This is the final Script .. after long time trading Just "WaW"
ألافضل بلا منازع الي حاب يجرب يراسلني
Floos 💸 Complete is an advanced trading indicator designed for SPX (S&P 500) options trading, combining:
- AI-enhanced London/New York session analysis
- Pre-market predictions
- Swing high/low detection
- EMA crossover signals with accuracy tracking
- Dynamic support/resistance levels
O'Neil Market TimingBill O'Neil Market Timing Indicator - User Guide
Overview
This Pine Script indicator implements William O'Neil's market timing methodology, which assigns one of four distinct states to a market index (such as SPY or QQQ) to help traders identify optimal market conditions for investing. The indicator is designed to work exclusively on Daily timeframe charts.
The Four Market States
The indicator tracks the market through four distinct states, with specific transition rules between them:
1. Confirmed Uptrend (Green)
- Meaning: The market is in a healthy uptrend with institutional support
- Action: Favorable conditions for building positions in leading stocks
- Can transition to: State 2 (Uptrend Under Pressure)
2. Uptrend Under Pressure (Yellow)
- Meaning: The uptrend is showing signs of weakness with increasing distribution
- Action: Be cautious, tighten stops, reduce position sizes
- Can transition to: State 1 (Confirmed Uptrend) or State 3 (Downtrend)
3. Downtrend (Red)
- Meaning: The market is in a confirmed downtrend
- Action: Stay mostly in cash, avoid new purchases
- Can transition to: State 4 (Rally Attempt)
4. Rally Attempt (Pink/Fuchsia)
- Meaning: The market is attempting to bottom and reverse
- Action: Watch for Follow-Through Day to confirm new uptrend
- Can transition to: State 1 (Confirmed Uptrend) or State 3 (Downtrend)
Key Concepts
Distribution Day
A distribution day occurs when:
1. The index closes down by more than the critical percentage (default 0.2%)
2. Volume is higher than the previous day's volume
Distribution days indicate institutional selling and are marked with red triangles on the indicator.
Follow-Through Day
A follow-through day occurs during a Rally Attempt when:
1. The index closes up by more than the critical percentage (default 1.6%)
2. Volume is higher than the previous day's volume
A Follow-Through Day confirms a new uptrend and triggers the transition from Rally Attempt to Confirmed Uptrend.
State Transition Logic
Valid Transitions
The system only allows specific transitions:
- 1 → 2: When distribution days reach the "pressure number" (default 5) within the lookback period (default 25 bars)
- 2 → 1: When distribution days drop below the pressure number
- 2 → 3: When distribution days reach "downtrend number" (default 7) AND price drops by "downtrend criterion" (default 6%) from the lookback high
- 3 → 4: When the market doesn't make a new low for 3 consecutive days
- 4 → 3: When a new low is made, undercutting the downtrend low
- 4 → 1: When a Follow-Through Day occurs during the Rally Attempt
Input Parameters
Distribution Day Parameters
- Distribution Day % Threshold (default 0.2%, range 0.1-2.0%)
- Minimum percentage decline required to qualify as a distribution day. While 0.2% seems to be the canonical number I see in literature about this, I use a much higher threshold (at least 0.5%)
Follow-Through Day Parameters
- Follow-Through Day % Threshold (default 1.6%, range 1.0-2.0%)
- Minimum percentage gain required to qualify as a follow-through day
### State Transition Parameters
- Pressure Number (default 5, range 3-6)
- Number of distribution days needed to transition from Confirmed Uptrend to Uptrend Under Pressure
- Lookback Period (default 25 bars, range 20-30)
- Number of days to count distribution days
- Downtrend Number (default 7, range 4-10)
- Number of distribution days needed (with price drop) to transition to Downtrend
- Downtrend % Drop from High (default 6%, range 5-10%)
- Percentage drop from lookback high required for downtrend confirmation
Visual Settings
- Color customization for each state
- Table position selection (Top Left, Top Right, Bottom Left, Bottom Right)
## How to Use This Indicator
### Installation
1. Open TradingView and navigate to SPY or QQQ (or another major index)
2. **Important**: Switch to the Daily (1D) timeframe
3. Click on "Indicators" at the top of the chart
4. Click "Pine Editor" at the bottom of the screen
5. Copy and paste the Pine Script code
6. Click "Add to Chart"
### Interpretation
**When the indicator shows:**
- **Green (State 1)**: Market is healthy - consider adding quality positions
- **Yellow (State 2)**: Exercise caution - tighten stops, be selective
- **Red (State 3)**: Defensive mode - preserve capital, avoid new buys
- **Pink (State 4)**: Watch closely - prepare for potential Follow-Through Day
### The Information Table
The table displays:
- **Current State**: The current market condition
- **Distribution Days**: Number of distribution days in the lookback period
- **Lookback Period**: Number of bars being analyzed
- **Rally Attempt Day**: (Only in State 4) Days into the current rally attempt
### Visual Elements
1. **State Line**: A stepped line showing the current state (1-4)
2. **Red Triangles**: Mark each distribution day
3. **Horizontal Reference Lines**: Dotted lines marking each state level
4. **Color-Coded Display**: The state line changes color based on the current market condition
## Trading Strategy Guidelines
### In Confirmed Uptrend (State 1)
- Build positions in stocks breaking out of proper bases
- Use normal position sizing
- Focus on stocks showing institutional accumulation
- Hold winners as long as they act properly
### In Uptrend Under Pressure (State 2)
- Take partial profits in extended positions
- Tighten stop losses
- Be more selective with new entries
- Reduce overall exposure
### In Downtrend (State 3)
- Move to cash or maintain very light exposure
- Avoid new purchases
- Focus on preservation of capital
- Use the time for research and watchlist building
### In Rally Attempt (State 4)
- Stay mostly in cash but prepare
- Build a watchlist of strong stocks
- On Day 4+ of the rally attempt, watch for Follow-Through Day
- If FTD occurs, begin cautiously adding positions
## Best Practices
1. **Use with Major Indices**: This indicator works best with SPY, QQQ, or other broad market indices
2. **Daily Timeframe Only**: The indicator is designed for daily bars - do not use on intraday timeframes
3. **Combine with Stock Analysis**: Use the market state as a filter for individual stock decisions
4. **Respect the Signals**: When the market enters Downtrend, reduce exposure regardless of individual stock setups
5. **Monitor Distribution Days**: Pay attention when distribution days accumulate - it's a warning sign
6. **Wait for Follow-Through**: Don't jump back in too early during Rally Attempt - wait for confirmation
## Alert Conditions
The indicator includes built-in alert conditions for:
- State changes (entering any of the four states)
- Distribution Day detection
- Follow-Through Day detection during Rally Attempt
To set up alerts:
1. Click the "Alert" button while the indicator is on your chart
2. Select "O'Neil Market Timing"
3. Choose your desired alert condition
4. Configure notification preferences
## Customization Tips
### For More Sensitive Detection
- Lower the "Pressure Number" to 3-4
- Lower the "Distribution Day % Threshold" to 0.15%
- Reduce the "Downtrend Number" to 5-6
### For More Conservative Detection
- Raise the "Pressure Number" to 6
- Raise the "Distribution Day % Threshold" to 0.3-0.5%
- Increase the "Downtrend Number" to 8-9
### For Different Market Conditions
- **Bull Market**: Consider slightly higher thresholds
- **Bear Market**: Consider slightly lower thresholds
- **Volatile Market**: May need to increase percentage thresholds
## Limitations and Considerations
1. **Not a Crystal Ball**: The indicator identifies conditions but doesn't predict the future
2. **False Signals**: Follow-Through Days can fail - use proper risk management
3. **Whipsaws Possible**: In choppy markets, the indicator may switch states frequently
4. **Confirmation Lag**: By design, there's a lag as the system waits for confirmation
5. **Works Best with Price Action**: Combine with your analysis of individual stocks
## Historical Context
This methodology is based on William J. O'Neil's decades of market research, documented in books like "How to Make Money in Stocks" and through Investor's Business Daily. O'Neil's research showed that:
- Most major market tops are preceded by accumulation of distribution days
- Most successful rallies begin with a Follow-Through Day on Day 4-7 of a rally attempt
- Identifying market state helps prevent buying during unfavorable conditions
## Troubleshooting
**Problem**: Indicator shows "Initializing"
- **Solution**: Let the chart load at least 5 bars to establish the initial state
**Problem**: No distribution day markers appear
- **Solution**: Verify you're on daily timeframe and check if volume data is available
**Problem**: Table not visible
- **Solution**: Check the table position setting and ensure it's not off-screen
**Problem**: State seems to change too frequently
- **Solution**: Increase the lookback period or adjust threshold parameters
## Support and Further Learning
For deeper understanding of this methodology:
- Read "How to Make Money in Stocks" by William J. O'Neil
- Study Investor's Business Daily's "Market Pulse"
- Review historical market tops and bottoms to see the pattern
- Practice identifying distribution days and follow-through days manually
## Version History
**Version 1.0** (November 2025)
- Initial implementation
- Four-state system with proper transitions
- Distribution day detection and marking
- Follow-through day detection
- Customizable parameters
- Information table display
- Alert conditions
---
## Quick Reference Card
| State | Number | Color | Action |
|-------|--------|-------|--------|
| Confirmed Uptrend | 1 | Green | Buy quality setups |
| Uptrend Under Pressure | 2 | Yellow | Tighten stops, be selective |
| Downtrend | 3 | Red | Cash position, no new buys |
| Rally Attempt | 4 | Pink | Watch for Follow-Through Day |
**Distribution Day**: Down > 0.2% on higher volume (red triangle)
**Follow-Through Day**: Up > 1.6% on higher volume during Rally Attempt (triggers State 4→1)
---
*Remember: This indicator is a tool to help identify market conditions. It should be used as part of a comprehensive trading strategy that includes proper risk management, position sizing, and individual stock analysis.*
Also, I created this with the help of an AI coding framework, and I didn't exhaustively test it. I don't actually use this for my own trading, so it's quite possible that it's materially wrong, and that following this will lead to poor investment decisions.. This is "copy left" software, so feel free to alter this to your own tastes, and claim authorship.
ReqoverAI Indicator Zero Lag🔑 Overview
ReqoverAI Indicator ZeroLag is a precision-engineered advanced AI detection tool for multi-asset trading strategies. This tool is designed to work for all time frames and asset classes (like Stocks, Commodities, Forex, Crypto and other Digital Assets). It uses advanced detection techniques that reduces lag and adapts to volatility. It combines a smoothing technique with adaptive reversal logic to highlight meaningful trend shifts earlier than conventional methods. It provides clear signals with built-in alerts, helping traders identify meaningful trend shifts earlier and with greater clarity.
⚙️Core Concepts
Smoothing Technique
Reduces the delay found in traditional moving averages, allowing faster response to price changes.
Adaptive Reversal Detection
Uses volatility- or percentage-based thresholds to identify potential pivots, helping filter out insignificant moves.
Signals
* Green “Buy” labels mark potential upward pivots.
* Red “Sell” labels mark potential downward pivots.
* Optional guideline plotted for trend visualization.
Alerts
Built-in TradingView alerts for Buy/Sell pivots, ready for automation or notifications.
📘 How to Use
Apply to chart: Works directly on price charts with Buy/Sell labels.
Select reversal mode:
* ATR-based (default, recommended for volatile assets).
* Percent-based (for more stable assets).
Interpret signals:
* Green “Buy” → potential upward movement.
* Red “Sell” → potential downward movement.
Combine with your strategy: Use ReqoverAI as a confirmation tool alongside your existing methods.
🧩 Originality & Value
Unique Approach: Integrates smoothing with a proprietary detection framework.
Not Just Another Indicator: Goes beyond standard moving averages or ATR scripts by dynamically managing pivots and reversals.
Vendor Justification: While it uses familiar elements, the hybrid detection logic is proprietary and unavailable in public domain scripts, making it valuable for traders seeking earlier and cleaner signals.
⚠️ Disclaimer
This indicator is a technical analysis tool. It does not guarantee profits or predict the future. Past performance does not ensure future results. Use responsibly and in combination with your own trading plan.
LHS TechniqueLHS Technique Indicator
Overview
The LHS (Left-Hand-Side) Technique is a simple yet powerful tool for analyzing market context in crypto trading, inspired by the Zero Complexity Trading Systems philosophy. This indicator helps traders quickly assess price behavior by focusing on the "left-hand side" of the chart—past price action—to understand how the market arrived at its current state. It differentiates between macro (4-8 hours) and micro (1-10 minutes) environments, enabling you to filter high-quality setups and avoid low-probability trades.
Designed primarily for the 1-minute timeframe in volatile markets like crypto, it visualizes key insights such as trend direction, volatility levels, and volume trends. Without proper market context, even the best strategies can fail—this indicator provides that edge in under 20 seconds.
Key Features
Macro and Micro Modes: Switch between analyzing broader market structure (last 4-8 hours) or immediate price action (last 1-10 minutes) before a key level.
Trend Analysis: Classifies the range as "Bullish" (> customizable % change), "Bearish" (< customizable % change), or "Choppy" (neutral).
Volatility State: Measures range expansion as "High" (> customizable threshold), "Medium", or "Low" to gauge market heat.
Volume Behavior: Tracks volume trends over the lookback period as "Increasing" (momentum building), "Decreasing" (exhaustion), or "Flat" using linear regression slope.
Visual Elements:
Background highlight for the analyzed range.
Optional vertical boundary lines (customizable style, color, width).
Horizontal lines for high/low structure (toggleable).
Info label displaying mode, time, trend, volatility, and volume (color-coded by trend).
Arrows marking the range start/end.
Customizable Thresholds: Adjust percentages for trend, volatility, and volume slope to fit your trading style.
Alerts: Built-in conditions for period starts, trend changes, and volume shifts.
How to Use
Add the indicator to your 1-minute chart (e.g., BTCUSDT or other crypto pairs).
Select "Macro" for overall context (e.g., chopping vs. trending) or "Micro" for precise entry timing.
Customize lookback periods, thresholds, and visuals via the inputs.
Interpret the label:
Trend: Trade with the trend in strong environments; avoid or reverse in choppy ones.
Volatility: High vol favors breakouts; low vol suggests reversals.
Volume: Increasing confirms continuation; decreasing signals potential turns.
Use with the LHS framework: Align macro/micro for confluence—e.g., steady macro trend + increasing micro volume = high-quality momentum setup.
Example
In Macro mode (8 hours), if the label shows "Bullish" with "High" volatility and "Increasing" volume, it indicates strong upward momentum—ideal for breakout trades. Zoom out to the LHS to confirm no prior chopping.
Disclaimer
This indicator is crafted for trading the 1-minute timeframe in crypto. Do not use on higher timeframes without testing first. Past performance is not indicative of future results—always combine with your own analysis and risk management.
For more on the underlying LHS Technique, refer to the Zero Complexity Trading Systems guide.
Designed with ❤️ by Alej4ndroj, built by AI – Feedback welcome!
X: @alej4ndroj x.com
RhAiA TradingView indicator that plots AI-generated LONG /SHORT signals on BTC/USDT charts, entering trades at signal timestamps with customizable take-profit (TP) and stop-loss (SL) levels, exit priority, and holding windows. Signals are blocked if a prior trade remains active, with color-coded lines and labels for entries, TP/SL hits, and window expirations.
🧠 Quantum Regime Shift Detector v4.0 — Enhanced Edition🧠 Quantum Regime Shift Detector v4.0 — Enhanced Edition
Overview:
A cutting-edge, AI-weighted market-regime detector that dynamically tracks volatility, trend, and momentum to pinpoint transitions 🟥, stability 🟩, and uncertainty 🟨 in real time.
📊 Dashboard Interpretation
🟩 Stable: Low volatility — range or accumulation phase → great for steady entries or breakouts.
🟥 Transition: High volatility — regime shift → trend changes / explosive moves likely.
🟨 Uncertain: Neutral zone → patience and tight risk control advised.
💡 Key Features
⚙️ Probability Gauge → quantifies shift likelihood (> 70 % = high confidence)
📈 Flow Bias → shows bullish / bearish directional pressure
🔄 Divergence Alerts → Bull / Bear signals anticipate reversals
🧭 S/R Zones → adaptive pivot-based support & resistance
⏫ MTF Analysis → confirm alignment with higher timeframes
🎯 Trading Applications
✅ Enter during 🟩 stable regimes with confirmed bias direction.
⚠️ Trim or hedge when 🟥 transition appears.
🔃 Use divergence alerts for reversal timing and confirmation.
🧩 Customization
🔧 Tune Feature Weights (volatility / trend / momentum)
🧮 Enable Auto Thresholds for adaptive sensitivity
⏱️ Set Confirmation Bars to filter noise
🌐 Toggle MTF Mode for multi-timeframe synergy
📘 Best Practice:
Use on liquid assets (≥ 15 min TF). Combine with price action, VWAP, and volume profiling for the clearest market DNA signals.
✨ Character count: ≈ 1,470 (TradingView limit safe)
