Price Change Sentiment Index [tradeviZion]Price Change Sentiment Index
A technical indicator that measures price changes relative to the day's range.
Indicator Overview
Normalizes price changes on a 0-100 scale
Uses a smoothing period for signal clarity
Shows potential overbought/oversold conditions
Inputs
Smoothing Period (default: 3)
Show Background Colors (on/off)
Overbought Level (default: 75)
Oversold Level (default: 25)
Reading the Indicator
Values above 75: Price change showing strong upward movement
Values below 25: Price change showing strong downward movement
Around 50: Neutral price movement
Technical Details
// Core calculation
changePct = (currClose - prevClose) / (high - low)
normalized = 50 + (changePct * 50)
smoothedNormalized = ta.sma(normalizedClamped, smoothingPeriod)
Usage Notes
Best used with other technical analysis tools
Adjustable smoothing period affects signal sensitivity
Background colors highlight extreme readings
Works on any timeframe
Settings Guide
Smoothing Period:
- Lower values (1-3): More responsive
- Higher values (5-10): Smoother output
Visual Settings: Toggle background colors
Levels: Adjust overbought/oversold thresholds
This indicator is a technical analysis tool. Please conduct your own research and testing before use.
스크립트에서 "technical"에 대해 찾기
OA - PowerZones Support And ResistancePowerZones - Dynamic Support/Resistance Identifier
Overview
PowerZones is an advanced technical analysis tool that automatically detects significant support and resistance zones using volume data and pivot points. This indicator pulls data from higher timeframes (weekly by default) to help you identify strong and meaningful levels that are filtered from short-term "noise."
Features
Multi-Timeframe Analysis: Create support/resistance levels from daily, weekly, or monthly data
Volume Filtering: Detect high-volume pivot points to identify more reliable levels
Dynamic Threshold: Volume filter that automatically adjusts to market conditions
Visual Clarity: Support/resistance zones are displayed as boxes with adjustable transparency
Optimal Level Selection: Filter out close levels to focus on the most significant support/resistance points
Use Cases
Entry/Exit Points: Identify trading opportunities at important support and resistance levels
Stop-Loss Placement: Use natural support levels to set more effective stop-losses
Target Setting: Use potential resistance levels as profit-taking targets
Understanding Market Structure: Detect long-term support/resistance zones to better interpret price movement
Input Parameters
Lookback Period: The period used to determine pivot points
Box Width : Adjusts the width of support/resistance zones
Relative Volume Period: The period used for relative volume calculation
Maximum Number of Boxes: Maximum number of support/resistance zones to display on the chart
Box Transparency: Transparency value for the boxes
Timeframe: Timeframe to use for support/resistance detection (Daily, Weekly, Monthly)
How It Works
PowerZones identifies pivot highs and lows in the selected timeframe. It filters these points using volume data to show only meaningful and strong levels. The indicator also consolidates nearby levels, allowing you to focus only on the most important zones on the chart.
Best Practices
Weekly timeframe setting is ideal for identifying long-term important support/resistance levels
Working with weekly levels on a daily chart allows you to combine long-term levels with short-term trades
ATR-based box width creates support/resistance zones that adapt to market volatility
Use the indicator along with other technical indicators such as RSI, MACD, or moving averages to confirm trading signals
Note: Like all technical indicators, this indicator does not guarantee 100% accuracy. Always apply risk management principles and use it in conjunction with other analysis methods to achieve the best results.
If you like the PowerZones indicator, please show your support by giving it a star and leaving a comment!
RSI Full Forecast [Titans_Invest]RSI Full Forecast
Get ready to experience the ultimate evolution of RSI-based indicators – the RSI Full Forecast, a boosted and even smarter version of the already powerful: RSI Forecast
Now featuring over 40 additional entry conditions (forecasts), this indicator redefines the way you view the market.
AI-Powered RSI Forecasting:
Using advanced linear regression with the least squares method – a solid foundation for machine learning - the RSI Full Forecast enables you to predict future RSI behavior with impressive accuracy.
But that’s not all: this new version also lets you monitor future crossovers between the RSI and the MA RSI, delivering early and strategic signals that go far beyond traditional analysis.
You’ll be able to monitor future crossovers up to 20 bars ahead, giving you an even broader and more precise view of market movements.
See the Future, Now:
• Track upcoming RSI & RSI MA crossovers in advance.
• Identify potential reversal zones before price reacts.
• Uncover statistical behavior patterns that would normally go unnoticed.
40+ Intelligent Conditions:
The new layer of conditions is designed to detect multiple high-probability scenarios based on historical patterns and predictive modeling. Each additional forecast is a window into the price's future, powered by robust mathematics and advanced algorithmic logic.
Full Customization:
All parameters can be tailored to fit your strategy – from smoothing periods to prediction sensitivity. You have complete control to turn raw data into smart decisions.
Innovative, Accurate, Unique:
This isn’t just an upgrade. It’s a quantum leap in technical analysis.
RSI Full Forecast is the first of its kind: an indicator that blends statistical analysis, machine learning, and visual design to create a true real-time predictive system.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an RSI time series like this:
Time →
RSI →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public RSI with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
RSI Full Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
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🔮 Linear Regression: PineScript Technical Parameters 🔮
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Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
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⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
• Overbought: When the RSI is above 70, indicating that the asset may be overbought.
• Oversold: When the RSI is below 30, indicating that the asset may be oversold.
• Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
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⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 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 Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
📈 RSI Forecast:
🔹 RSI (Crossover) MA Forecast
🔹 RSI (Crossunder) MA Forecast
🔹 RSI Forecast 1 > MA Forecast 1
🔹 RSI Forecast 1 < MA Forecast 1
🔹 RSI Forecast 2 > MA Forecast 2
🔹 RSI Forecast 2 < MA Forecast 2
🔹 RSI Forecast 3 > MA Forecast 3
🔹 RSI Forecast 3 < MA Forecast 3
🔹 RSI Forecast 4 > MA Forecast 4
🔹 RSI Forecast 4 < MA Forecast 4
🔹 RSI Forecast 5 > MA Forecast 5
🔹 RSI Forecast 5 < MA Forecast 5
🔹 RSI Forecast 6 > MA Forecast 6
🔹 RSI Forecast 6 < MA Forecast 6
🔹 RSI Forecast 7 > MA Forecast 7
🔹 RSI Forecast 7 < MA Forecast 7
🔹 RSI Forecast 8 > MA Forecast 8
🔹 RSI Forecast 8 < MA Forecast 8
🔹 RSI Forecast 9 > MA Forecast 9
🔹 RSI Forecast 9 < MA Forecast 9
🔹 RSI Forecast 10 > MA Forecast 10
🔹 RSI Forecast 10 < MA Forecast 10
🔹 RSI Forecast 11 > MA Forecast 11
🔹 RSI Forecast 11 < MA Forecast 11
🔹 RSI Forecast 12 > MA Forecast 12
🔹 RSI Forecast 12 < MA Forecast 12
🔹 RSI Forecast 13 > MA Forecast 13
🔹 RSI Forecast 13 < MA Forecast 13
🔹 RSI Forecast 14 > MA Forecast 14
🔹 RSI Forecast 14 < MA Forecast 14
🔹 RSI Forecast 15 > MA Forecast 15
🔹 RSI Forecast 15 < MA Forecast 15
🔹 RSI Forecast 16 > MA Forecast 16
🔹 RSI Forecast 16 < MA Forecast 16
🔹 RSI Forecast 17 > MA Forecast 17
🔹 RSI Forecast 17 < MA Forecast 17
🔹 RSI Forecast 18 > MA Forecast 18
🔹 RSI Forecast 18 < MA Forecast 18
🔹 RSI Forecast 19 > MA Forecast 19
🔹 RSI Forecast 19 < MA Forecast 19
🔹 RSI Forecast 20 > MA Forecast 20
🔹 RSI Forecast 20 < MA Forecast 20
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 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 Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
📉 RSI Forecast:
🔸 RSI (Crossover) MA Forecast
🔸 RSI (Crossunder) MA Forecast
🔸 RSI Forecast 1 > MA Forecast 1
🔸 RSI Forecast 1 < MA Forecast 1
🔸 RSI Forecast 2 > MA Forecast 2
🔸 RSI Forecast 2 < MA Forecast 2
🔸 RSI Forecast 3 > MA Forecast 3
🔸 RSI Forecast 3 < MA Forecast 3
🔸 RSI Forecast 4 > MA Forecast 4
🔸 RSI Forecast 4 < MA Forecast 4
🔸 RSI Forecast 5 > MA Forecast 5
🔸 RSI Forecast 5 < MA Forecast 5
🔸 RSI Forecast 6 > MA Forecast 6
🔸 RSI Forecast 6 < MA Forecast 6
🔸 RSI Forecast 7 > MA Forecast 7
🔸 RSI Forecast 7 < MA Forecast 7
🔸 RSI Forecast 8 > MA Forecast 8
🔸 RSI Forecast 8 < MA Forecast 8
🔸 RSI Forecast 9 > MA Forecast 9
🔸 RSI Forecast 9 < MA Forecast 9
🔸 RSI Forecast 10 > MA Forecast 10
🔸 RSI Forecast 10 < MA Forecast 10
🔸 RSI Forecast 11 > MA Forecast 11
🔸 RSI Forecast 11 < MA Forecast 11
🔸 RSI Forecast 12 > MA Forecast 12
🔸 RSI Forecast 12 < MA Forecast 12
🔸 RSI Forecast 13 > MA Forecast 13
🔸 RSI Forecast 13 < MA Forecast 13
🔸 RSI Forecast 14 > MA Forecast 14
🔸 RSI Forecast 14 < MA Forecast 14
🔸 RSI Forecast 15 > MA Forecast 15
🔸 RSI Forecast 15 < MA Forecast 15
🔸 RSI Forecast 16 > MA Forecast 16
🔸 RSI Forecast 16 < MA Forecast 16
🔸 RSI Forecast 17 > MA Forecast 17
🔸 RSI Forecast 17 < MA Forecast 17
🔸 RSI Forecast 18 > MA Forecast 18
🔸 RSI Forecast 18 < MA Forecast 18
🔸 RSI Forecast 19 > MA Forecast 19
🔸 RSI Forecast 19 < MA Forecast 19
🔸 RSI Forecast 20 > MA Forecast 20
🔸 RSI Forecast 20 < MA Forecast 20
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
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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
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
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📜 SCRIPT : RSI Full Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
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o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
Market Structure Confluence [AlgoAlpha]OVERVIEW
This script is called "Market Structure Confluence" and it combines classic market structure analysis with a dynamic volatility-based band system to detect shifts in trend and momentum more reliably. It tracks key swing points (higher highs, higher lows, lower highs, lower lows) to define the trend, then overlays a basis and ATR-smoothed volatility bands to catch rejection signals and highlight potential inflection points in the market.
CONCEPTS
Market structure is the foundation of price action trading, focusing on the relationship between successive highs and lows to understand trend conditions. Break of Structure (BOS) and Change of Character (CHoCH) events are important because they signal when a market might be shifting direction. This script enhances traditional structure by integrating volatility bands, which act like dynamic support/resistance zones based on ATR, allowing it to capture momentum surges and rejections beyond just structural shifts.
FEATURES
Swing Detection: It detects and labels Higher Highs (HH), Higher Lows (HL), Lower Highs (LH), and Lower Lows (LL) based on user-defined time horizons, helping traders quickly spot the trend direction.
BOS and CHoCH Lines: When a previous swing point is broken, the script automatically plots a Break of Structure (BOS) line. If the break represents a major trend reversal (a CHoCH), it is marked differently to separate simple breakouts from real trend changes.
Rejection Signals: Special arrows plot when price pierces a band and then pulls back, suggesting a potential trap move or rejection signal in the direction of the new structure.
Alerts: Built-in alerts for structure breaks, CHoCHs, swing points, rejections at bands, and trend flips make it easy to automate setups without manually watching the chart.
USAGE
Set your preferred swing detection size depending on your timeframe and trading style — smaller numbers for intraday, larger numbers for swing trading. Choose whether you want BOS/CHoCH confirmed by candle closes or by wick breaks. Use the volatility band settings to fine-tune how tightly or loosely the bands hug the price, adjusting sensitivity based on market conditions. When a BOS or CHoCH occurs, or when a rejection happens at the bands, the script will highlight it clearly and optionally trigger alerts. Watch for combinations where both structure breaks and volatility band rejections happen together — those are high-quality trade signals. This setup works best when used with basic trend filtering and higher timeframe confirmation.
Moving Average ToolkitMoving Average Toolkit - Advanced MA Analysis with Flexible Source Input
A powerful and versatile moving average indicator designed for maximum flexibility. Its unique source input feature allows you to analyze moving averages of ANY indicator or price data, making it perfect for creating custom combinations with RSI, Volume, OBV, or any other technical indicator.
Key Features:
• Universal Source Input:
- Analyze moving averages of any data: Price, Volume, RSI, MACD, Custom Indicators
- Perfect for creating advanced technical setups
- Identify trends in any technical data
• 13 Moving Average Types:
- Traditional: SMA, EMA, WMA, RMA, VWMA
- Advanced: HMA, T3, DEMA, TEMA, KAMA, ZLEMA, McGinley, EPMA
• Dual MA System:
- Compare two different moving averages
- Independent settings for each MA
- Perfect for multiple timeframe analysis
• Visual Offset Analysis:
- Dynamic color changes based on momentum
- Fill between current and offset values
- Clear visualization of trend strength
Usage Examples:
• Price Trend: Traditional MA analysis using price data
• Volume Trend: Apply MA to volume for volume trend analysis
• RSI Trend: Smooth RSI movements for clearer signals
• Custom: Apply to any indicator output for unique insights
Settings:
• Fully customizable colors for bull/bear conditions
• Adjustable offset periods
• Independent length settings
• Optional second MA for comparison
Perfect for:
• Advanced technical analysts
• Multi-indicator strategy developers
• Custom indicator creators
• Traders seeking flexible analysis tools
This versatile toolkit goes beyond traditional moving averages by allowing you to apply sophisticated MA analysis to any technical data, creating endless possibilities for custom technical analysis strategies.
[blackcat] L2 FiboKAMA Adaptive TrendOVERVIEW
The L2 FiboKAMA Adaptive Trend indicator leverages advanced technical analysis techniques by integrating Fibonacci principles with the Kaufman Adaptive Moving Average (KAMA). This combination creates a dynamic and responsive tool designed to adapt seamlessly to changing market conditions. By providing clear buy and sell signals based on adaptive momentum, this indicator helps traders identify potential entry and exit points effectively. Its intuitive design and robust features make it a valuable addition to any trader’s arsenal 📊💹.
According to the principle of Kaufman's Adaptive Moving Average (KAMA), it is a type of moving average line specifically designed for markets with high volatility. Unlike traditional moving averages, KAMA can automatically adjust its period based on market conditions to improve accuracy and responsiveness. This makes it particularly useful for capturing market trends and reducing false signals in varying market environments.
The use of Fibonacci magic numbers (3, 8, 13) enhances the performance and accuracy of KAMA. These numbers have special mathematical properties that align well with the changing trends of KAMA moving averages. Combining them with KAMA can significantly boost its effectiveness, making it a popular choice among traders seeking reliable signals.
This fusion not only smoothens price fluctuations but also ensures quick responses to market changes, offering dependable entry and exit points. Thanks to the flexibility and precision of KAMA combined with Fibonacci magic numbers, traders can better manage risks and aim for higher returns.
FEATURES
Enhanced Kaufman Adaptive Moving Average (KAMA): Incorporates Fibonacci principles for improved adaptability:
Source Price: Allows customization of the price series used for calculation (default: HLCC4).
Fast Length: Determines the period for quicker adjustments to recent price changes.
Slow Length: Sets the period for smoother transitions over longer-term trends.
Dynamic Lines:
KAMA Line: A yellow line representing the primary adaptive moving average, which adapts quickly to new trends.
Trigger Line: A fuchsia line serving as a reference point for detecting crossovers and generating signals.
Visual Cues:
Buy Signals: Green 'B' labels indicating potential buying opportunities.
Sell Signals: Red 'S' labels signaling possible selling points.
Fill Areas: Colored regions between the KAMA and Trigger lines to visually represent trend directions and strength.
Alert Functionality: Generates real-time alerts for both buy and sell signals, ensuring timely notifications for actionable insights 🔔.
Customizable Parameters: Offers flexibility through adjustable inputs, allowing users to tailor the indicator to their specific trading strategies and preferences.
HOW TO USE
Adding the Indicator:
Open your TradingView chart and navigate to the indicators list.
Select L2 FiboKAMA Adaptive Trend and add it to your chart.
Configuring Parameters:
Adjust the Source Price to choose the desired price series (e.g., close, open, high, low).
Set the Fast Length to define how quickly the indicator responds to recent price movements.
Configure the Slow Length to determine the smoothness of long-term trend adaptations.
Interpreting Signals:
Monitor the chart for green 'B' labels indicating buy signals and red 'S' labels for sell signals.
Observe the colored fill areas between the KAMA and Trigger lines to gauge trend strength and direction.
Setting Up Alerts:
Enable alerts within the indicator settings to receive notifications whenever buy or sell signals are triggered.
Customize alert messages and frequencies according to your trading plan.
Combining with Other Tools:
Integrate this indicator with additional technical analysis tools and fundamental research for comprehensive decision-making.
Confirm signals using other indicators like RSI, MACD, or Bollinger Bands for increased reliability.
Optimizing Performance:
Backtest the indicator across various assets and timeframes to understand its behavior under different market conditions.
Fine-tune parameters based on historical performance and current market dynamics.
Integrating Magic Numbers:
Understand the basic principles of KAMA to find suitable entry points for Fibonacci magic numbers.
Utilize the efficiency ratio to measure market volatility and adjust moving average parameters accordingly.
Apply Fibonacci magic numbers (3, 8, 13) to enhance the responsiveness and accuracy of KAMA.
LIMITATIONS
Market Volatility: May produce false signals during periods of extreme volatility or sideways movement.
Parameter Sensitivity: Requires careful tuning of fast and slow lengths to balance responsiveness and stability.
Asset-Specific Behavior: Effectiveness can vary significantly across different financial instruments and time horizons.
Complementary Analysis: Should be used alongside other analytical methods to enhance accuracy and reduce risk.
NOTES
Historical Data: Ensure adequate historical data availability for precise calculations and backtesting.
Demo Testing: Thoroughly test the indicator on demo accounts before deploying it in live trading environments.
Continuous Learning: Stay updated with market trends and continuously refine your strategy incorporating feedback from the indicator's performance.
Risk Management: Always implement proper risk management practices regardless of the signals provided by the indicator.
ADVANCED USAGE TIPS
Multi-Timeframe Analysis: Apply the indicator across multiple timeframes to gain deeper insights into underlying trends.
Divergence Strategy: Look for divergences between price action and the KAMA line to spot potential reversals early.
Volume Integration: Combine volume analysis with the indicator to confirm the strength of identified trends.
Custom Scripting: Modify the script to include additional filters or conditions tailored to your unique trading approach.
IMPROVING KAMA PERFORMANCE
Increase Length: Extend the KAMA length to consider more historical data, reducing the impact of short-term price fluctuations.
Adjust Fast and Slow Lengths: Make KAMA smoother by increasing the fast length and decreasing the slow length.
Use Smoothing Factor: Apply a smoothing factor to control the level of smoothness; typical values range from 0 to 1.
Combine with Other Indicators: Pair KAMA with other smoothing indicators like EMA or SMA for more reliable signals.
Filter Noise: Use filters or other technical analysis tools to eliminate price noise, enhancing KAMA's effectiveness.
ADR% Extension Levels from SMA 50I created this indicator inspired by RealSimpleAriel (a swing trader I recommend following on X) who does not buy stocks extended beyond 4 ADR% from the 50 SMA and uses extensions from the 50 SMA at 7-8-9-10-11-12-13 ADR% to take profits with a 20% position trimming.
RealSimpleAriel's strategy (as I understood it):
-> Focuses on leading stocks from leading groups and industries, i.e., those that have grown the most in the last 1-3-6 months (see on Finviz groups and then select sector-industry).
-> Targets stocks with the best technical setup for a breakout, above the 200 SMA in a bear market and above both the 50 SMA and 200 SMA in a bull market, selecting those with growing Earnings and Sales.
-> Buys stocks on breakout with a stop loss set at the day's low of the breakout and ensures they are not extended beyond 4 ADR% from the 50 SMA.
-> 3-5 day momentum burst: After a breakout, takes profits by selling 1/2 or 1/3 of the position after a 3-5 day upward move.
-> 20% trimming on extension from the 50 SMA: At 7 ADR% (ADR% calculated over 20 days) extension from the 50 SMA, takes profits by selling 20% of the remaining position. Continues to trim 20% of the remaining position based on the stock price extension from the 50 SMA, calculated using the 20-period ADR%, thus trimming 20% at 8-9-10-11 ADR% extension from the 50 SMA. Upon reaching 12-13 ADR% extension from the 50 SMA, considers the stock overextended, closes the remaining position, and evaluates a short.
-> Trailing stop with ascending SMA: Uses a chosen SMA (10, 20, or 50) as the definitive stop loss for the position, depending on the stock's movement speed (preferring larger SMAs for slower-moving stocks or for long-term theses). If the stock's closing price falls below the chosen SMA, the entire position is closed.
In summary:
-->Buy a breakout using the day's low of the breakout as the stop loss (this stop loss is the most critical).
--> Do not buy stocks extended beyond 4 ADR% from the 50 SMA.
--> Sell 1/2 or 1/3 of the position after 3-5 days of upward movement.
--> Trim 20% of the position at each 7-8-9-10-11-12-13 ADR% extension from the 50 SMA.
--> Close the entire position if the breakout fails and the day's low of the breakout is reached.
--> Close the entire position if the price, during the rise, falls below a chosen SMA (10, 20, or 50, depending on your preference).
--> Definitively close the position if it reaches 12-13 ADR% extension from the 50 SMA.
I used Grok from X to create this indicator. I am not a programmer, but based on the ADR% I use, it works.
Below is Grok from X's description of the indicator:
Script Description
The script is a custom indicator for TradingView that displays extension levels based on ADR% relative to the 50-period Simple Moving Average (SMA). Below is a detailed description of its features, structure, and behavior:
1. Purpose of the Indicator
Name: "ADR% Extension Levels from SMA 50".
Objective: Draw horizontal blue lines above and below the 50-period SMA, corresponding to specific ADR% multiples (4, 7, 8, 9, 10, 11, 12, 13). These levels represent potential price extension zones based on the average daily percentage volatility.
Overlay: The indicator is overlaid on the price chart (overlay=true), so the lines and SMA appear directly on the price graph.
2. Configurable Inputs
The indicator allows users to customize parameters through TradingView settings:
SMA Length (smaLength):
Default: 50 periods.
Description: Specifies the number of periods for calculating the Simple Moving Average (SMA). The 50-period SMA serves as the reference point for extension levels.
Constraint: Minimum 1 period.
ADR% Length (adrLength):
Default: 20 periods.
Description: Specifies the number of days to calculate the moving average of the daily high/low ratio, used to determine ADR%.
Constraint: Minimum 1 period.
Scale Factor (scaleFactor):
Default: 1.0.
Description: An optional multiplier to adjust the distance of extension levels from the SMA. Useful if levels are too close or too far due to an overly small or large ADR%.
Constraint: Minimum 0.1, increments of 0.1.
Tooltip: "Adjust if levels are too close or far from SMA".
3. Main Calculations
50-period SMA:
Calculated with ta.sma(close, smaLength) using the closing price (close).
Serves as the central line around which extension levels are drawn.
ADR% (Average Daily Range Percentage):
Formula: 100 * (ta.sma(dhigh / dlow, adrLength) - 1).
Details:
dhigh and dlow are the daily high and low prices, obtained via request.security(syminfo.tickerid, "D", high/low) to ensure data is daily-based, regardless of the chart's timeframe.
The dhigh / dlow ratio represents the daily percentage change.
The simple moving average (ta.sma) of this ratio over 20 days (adrLength) is subtracted by 1 and multiplied by 100 to obtain ADR% as a percentage.
The result is multiplied by scaleFactor for manual adjustments.
Extension Levels:
Defined as ADR% multiples: 4, 7, 8, 9, 10, 11, 12, 13.
Stored in an array (levels) for easy iteration.
For each level, prices above and below the SMA are calculated as:
Above: sma50 * (1 + (level * adrPercent / 100))
Below: sma50 * (1 - (level * adrPercent / 100))
These represent price levels corresponding to a percentage change from the SMA equal to level * ADR%.
4. Visualization
Horizontal Blue Lines:
For each level (4, 7, 8, 9, 10, 11, 12, 13 ADR%), two lines are drawn:
One above the SMA (e.g., +4 ADR%).
One below the SMA (e.g., -4 ADR%).
Color: Blue (color.blue).
Style: Solid (style=line.style_solid).
Management:
Each level has dedicated variables for upper and lower lines (e.g., upperLine1, lowerLine1 for 4 ADR%).
Previous lines are deleted with line.delete before drawing new ones to avoid overlaps.
Lines are updated at each bar with line.new(bar_index , level, bar_index, level), covering the range from the previous bar to the current one.
Labels:
Displayed only on the last bar (barstate.islast) to avoid clutter.
For each level, two labels:
Above: E.g., "4 ADR%", positioned above the upper line (style=label.style_label_down).
Below: E.g., "-4 ADR%", positioned below the lower line (style=label.style_label_up).
Color: Blue background, white text.
50-period SMA:
Drawn as a gray line (color.gray) for visual reference.
Diagnostics:
ADR% Plot: ADR% is plotted in the status line (orange, histogram style) to verify the value.
ADR% Label: A label on the last bar near the SMA shows the exact ADR% value (e.g., "ADR%: 2.34%"), with a gray background and white text.
5. Behavior
Dynamic Updating:
Lines update with each new bar to reflect new SMA 50 and ADR% values.
Since ADR% uses daily data ("D"), it remains constant within the same day but changes day-to-day.
Visibility Across All Bars:
Lines are drawn on every bar, not just the last one, ensuring visibility on historical data as well.
Adaptability:
The scaleFactor allows level adjustments if ADR% is too small (e.g., for low-volatility symbols) or too large (e.g., for cryptocurrencies).
Compatibility:
Works on any timeframe since ADR% is calculated from daily data.
Suitable for symbols with varying volatility (e.g., stocks, forex, cryptocurrencies).
6. Intended Use
Technical Analysis: Extension levels represent significant price zones based on average daily volatility. They can be used to:
Identify potential price targets (e.g., take profit at +7 ADR%).
Assess support/resistance zones (e.g., -4 ADR% as support).
Measure price extension relative to the 50 SMA.
Trading: Useful for strategies based on breakouts or mean reversion, where ADR% levels indicate reversal or continuation points.
Debugging: Labels and ADR% plot help verify that values align with the symbol’s volatility.
7. Limitations
Dependence on Daily Data: ADR% is based on daily dhigh/dlow, so it may not reflect intraday volatility on short timeframes (e.g., 1 minute).
Extreme ADR% Values: For low-volatility symbols (e.g., bonds) or high-volatility symbols (e.g., meme stocks), ADR% may require adjustments via scaleFactor.
Graphical Load: Drawing 16 lines (8 upper, 8 lower) on every bar may slow the chart for very long historical periods, though line management is optimized.
ADR% Formula: The formula 100 * (sma(dhigh/dlow, Length) - 1) may produce different values compared to other ADR% definitions (e.g., (high - low) / close * 100), so users should be aware of the context.
8. Visual Example
On a chart of a stock like TSLA (daily timeframe):
The 50 SMA is a gray line tracking the average trend.
Assuming an ADR% of 3%:
At +4 ADR% (12%), a blue line appears at sma50 * 1.12.
At -4 ADR% (-12%), a blue line appears at sma50 * 0.88.
Other lines appear at ±7, ±8, ±9, ±10, ±11, ±12, ±13 ADR%.
On the last bar, labels show "4 ADR%", "-4 ADR%", etc., and a gray label shows "ADR%: 3.00%".
ADR% is visible in the status line as an orange histogram.
9. Code: Technical Structure
Language: Pine Script @version=5.
Inputs: Three configurable parameters (smaLength, adrLength, scaleFactor).
Calculations:
SMA: ta.sma(close, smaLength).
ADR%: 100 * (ta.sma(dhigh / dlow, adrLength) - 1) * scaleFactor.
Levels: sma50 * (1 ± (level * adrPercent / 100)).
Graphics:
Lines: Created with line.new, deleted with line.delete to avoid overlaps.
Labels: Created with label.new only on the last bar.
Plots: plot(sma50) for the SMA, plot(adrPercent) for debugging.
Optimization: Uses dedicated variables for each line (e.g., upperLine1, lowerLine1) for clear management and to respect TradingView’s graphical object limits.
10. Possible Improvements
Option to show lines only on the last bar: Would reduce visual clutter.
Customizable line styles: Allow users to choose color or style (e.g., dashed).
Alert for anomalous ADR%: A message if ADR% is too small or large.
Dynamic levels: Allow users to specify ADR% multiples via input.
Optimization for short timeframes: Adapt ADR% for intraday timeframes.
Conclusion
The script creates a visual indicator that helps traders identify price extension levels based on daily volatility (ADR%) relative to the 50 SMA. It is robust, configurable, and includes debugging tools (ADR% plot and labels) to verify values. The ADR% formula based on dhigh/dlow
[blackcat] L2 MTF Heikin-Ashi SR LevelsOVERVIEW
The L2 MTF Heikin-Ashi SR Levels indicator is a sophisticated tool designed to help traders identify critical support and resistance levels across multiple timeframes. This script employs Heikin-Ashi candles, which provide a smoothed representation of price action, making it easier to spot trends and reversals. By integrating multi-timeframe analysis, this indicator offers a comprehensive view of market dynamics, enabling traders to make more informed decisions 📊✅.
This indicator not only calculates essential support and resistance levels but also visually represents them on the chart with gradient colors based on Relative Strength Index (RSI) values. Additionally, it features customizable alerts and labels to enhance user experience and ensure timely execution of trades.
FEATURES
Advanced Trend Identification:
Uses Heikin-Ashi candles for smoother price action analysis.
Helps filter out noise and focus on significant trends.
Ideal for both short-term and long-term trading strategies.
Multi-Timeframe Analysis:
Allows users to select different resolutions for deeper insights.
Ensures compatibility with various trading styles and preferences.
Comprehensive Support and Resistance Calculation:
Computes four distinct levels: Support Level 1, Support Level 2, Resistance Level 1, and Resistance Level 2.
Each level serves as a reference point for potential price reversals or continuations.
Gradient Color Visualization:
Employs a spectrum of colors derived from RSI values to represent support and resistance lines.
Enhances readability and helps traders quickly assess market sentiment 🎨.
Dynamic Labels and Alerts:
Automatically generates buy ('Buy') and sell ('Sell') labels when price crosses key levels.
Provides real-time alerts for crossing events, ensuring traders never miss important signals 🔔.
Customizable Parameters:
Offers adjustable Length and Resolution inputs for tailored performance.
Allows traders to fine-tune the indicator according to their unique needs and strategies.
HOW TO USE
Adding the Indicator:
Open your TradingView chart and navigate to the indicators list.
Search for ' L2 MTF Heikin-Ashi SR Levels' and add it to your chart.
Configuring Settings:
Adjust the Length parameter to determine the period over which calculations are made.
A shorter length increases sensitivity, while a longer length smoothens the output.
Choose a specific Resolution to analyze different timeframes simultaneously.
For example, set it to 'D' for daily charts or 'W' for weekly charts.
Interpreting the Chart:
Observe the plotted support and resistance lines on the chart.
Look for price interactions with these levels to identify potential entry and exit points.
Pay attention to the gradient colors, which reflect underlying market momentum.
Setting Up Alerts:
Configure alerts based on the generated signals to receive instant notifications.
Customize alert messages and conditions to suit your trading plan.
Utilizing Labels:
Use the automatically placed buy and sell labels as quick references for decision-making.
Combine these labels with other technical analyses for confirmation.
Backtesting and Optimization:
Thoroughly test the indicator on historical data to evaluate its performance.
Optimize settings and refine your strategy based on backtest results.
Live Trading:
Apply the indicator to live charts and monitor real-time price movements.
Execute trades based on the generated signals and adjust positions accordingly.
Combining with Other Tools:
Integrate this indicator with other technical tools and fundamental analyses for a holistic approach.
Consider using moving averages, oscillators, or volume indicators alongside L2 MTF Heikin-Ashi SR Levels.
LIMITATIONS
Market Volatility:
In highly volatile or ranging markets, the indicator might produce false signals due to erratic price movements 🌪️.
Traders should exercise caution during such periods and consider additional confirmations.
Timeframe Dependency:
The effectiveness of the indicator can vary significantly depending on the chosen timeframe and asset.
Always validate the indicator's performance across different contexts before relying solely on it.
Over-reliance Risk:
While powerful, no single indicator guarantees success in all market conditions.
Combining this tool with other analytical methods enhances reliability and reduces risk.
NOTES
Data Requirements:
Ensure your chart has enough historical data to perform accurate calculations.
Insufficient data may lead to inaccurate or incomplete results.
Demo Testing:
Before deploying the indicator in live trading, conduct extensive testing on demo accounts.
Familiarize yourself with how the indicator behaves under various market scenarios.
Parameter Tuning:
Experiment with different Length and Resolution settings to find what works best for your trading style.
Regularly review and update parameters as market conditions evolve.
Continuous Learning:
Stay updated with the latest developments in technical analysis and trading strategies.
Adapt your use of the indicator based on new insights and experiences.
THANKS
Additionally, gratitude goes to the broader TradingView community for fostering collaboration and knowledge-sharing among traders worldwide. Together, we strive to elevate our understanding and application of financial markets 🌍💡.
Trend Matrix Multi-Timeframe Dashboard(TechnoBlooms)Trend Matrix Multi-Timeframe Dashboard is a Minimalist Multi-Timeframe Trend Analyzer with Smart Indicator Integration. Trend Matrix MTF Dashboard is a clean, efficient, and visually intuitive trend analyzer built for traders who value simplicity without compromising on technical depth.
This dashboard empowers you to track trend direction across multiple timeframes using a curated set of powerful technical indicators—all from one compact visual panel. The design philosophy is simple: eliminate clutter, highlight trend clarity, and accelerate your decision-making process.
Key Features
✅ Minimalist Design with Maximum Insight
A compact dashboard view designed for clean charts and focused trading
Optimized layout shows everything you need—nothing you don’t
✅ Multi-Timeframe Access at a Glance
Instantly read the trend direction of selected indicators on multiple timeframes (e.g., 15m, 1h, 4h, 1D)
Customize the timeframe stack to fit scalping, intraday, swing, or positional strategies
✅ Robust Technical Indicators Built In
Each one is hand-picked for trend reliability:
MACD – Momentum and crossover confirmation
RSI – Overbought/oversold and directional shift
EMA – Dynamic support/resistance and trend bias
Bollinger Bands – Volatility structure and trend containment
PVT – Volume-Weighted Trend Confirmation
Supertrend – Price-following trend tracker
✅ Live Updates & Lightweight Performance
Built to update efficiently on every bar close
Minimal performance impact even with multiple timeframes active
By offering multi-timeframe (MTF) access to proven trend-following indicators, Trend Matrix helps you confidently align with the market’s dominant direction—without jumping between charts or analyzing indicators one by one.
This indicator offers customizable settings. The trader can choose the input parameters timeframes as per the choice.
Trend Matrix Multi-Timeframe Dashboard helps traders to identify trend based on technical indications. Trader can refer this while taking trading decisions.
🧠 Ideal For
Scalpers who need higher timeframe confirmation
Swing traders identifying clean entries aligned with the macro trend
Trend followers seeking clarity before committing capital
Price action & SMC traders validating market structure setups
Beginners who want a high-level trend guide without messy indicators
[blackcat] L3 Trendmaster XOVERVIEW
The L3 Trendmaster X is an advanced trend-following indicator meticulously crafted to assist traders in identifying and capitalizing on market trends. This sophisticated tool integrates multiple technical factors, including Average True Range (ATR), volume dynamics, and price spreads, to deliver precise buy and sell signals. By plotting dynamic trend bands directly onto the chart, it offers a comprehensive visualization of potential trend directions, enabling traders to make informed decisions swiftly and confidently 📊↗️.
FEATURES
Customizable Input Parameters: Tailor the indicator to match your specific trading needs with adjustable settings:
Trendmaster X Multiplier: Controls the sensitivity of the ATR-based levels.
Trendmaster X Period: Defines the period over which the ATR is calculated.
Window Length: Specifies the length of the moving window for standard deviation calculations.
Volume Averaging Length: Determines how many periods are considered for averaging volume.
Volatility Factor: Adjusts the impact of volatility on the trend bands.
Core Technical Metrics:
Dynamic Range: Measures the range between high and low prices within each bar.
Candle Body Size: Evaluates the difference between open and close prices.
Volume Average: Assesses the cumulative On-Balance Volume relative to the dynamic range.
Price Spread: Computes the standard deviation of the price ranges over a specified window.
Volatility Factor: Incorporates volatility into the calculation of trend bands.
Advanced Trend Bands Calculation:
Upper Level: Represents potential resistance levels derived from the ATR multiplier.
Lower Level: Indicates possible support levels using the same ATR multiplier.
High Band and Low Band: Dynamically adjust to reflect current trend directions, offering a clear view of market sentiment.
Visual Representation:
Plots distinct green and red trend lines representing bullish and bearish trends respectively.
Fills the area between these trend lines and the middle line for enhanced visibility.
Displays clear buy ('B') and sell ('S') labels on the chart for immediate recognition of trading opportunities 🏷️.
Alert System:
Generates real-time alerts when buy or sell conditions are triggered, ensuring timely action.
Allows customization of alert messages and frequencies to align with individual trading strategies 🔔.
HOW TO USE
Adding the Indicator:
Open your TradingView platform and navigate to the "Indicators" section.
Search for " L3 Trendmaster X" and add it to your chart.
Adjusting Settings:
Fine-tune the input parameters according to your preferences and trading style.
For example, increase the Trendmaster X Multiplier for higher sensitivity during volatile markets.
Decrease the Window Length for shorter-term trend analysis.
Monitoring Trends:
Observe the plotted trend bands and labels on the chart.
Look for buy ('B') labels at potential support levels and sell ('S') labels at resistance levels.
Setting Up Alerts:
Configure alerts based on the generated buy and sell signals.
Choose notification methods (e.g., email, SMS) and set alert frequencies to stay updated without constant monitoring 📲.
Combining with Other Tools:
Integrate the Trendmaster X with other technical indicators like Moving Averages or RSI for confirmation.
Utilize fundamental analysis alongside the indicator for a holistic approach to trading.
Backtesting and Optimization:
Conduct thorough backtests on historical data to evaluate performance.
Optimize parameters based on backtest results to enhance accuracy and reliability.
Real-Time Application:
Apply the optimized settings to live charts and monitor real-time signals.
Execute trades based on confirmed signals while considering risk management principles.
LIMITATIONS
Market Conditions: The indicator might produce false signals in highly volatile or sideways-trending markets due to increased noise and lack of clear direction 🌪️.
Complementary Analysis: Traders should use this indicator in conjunction with other analytical tools to validate signals and reduce the likelihood of false positives.
Asset-Specific Performance: Effectiveness can vary across different assets and timeframes; therefore, testing on diverse instruments is recommended.
NOTES
Data Requirements: Ensure adequate historical data availability for accurate calculations and reliable signal generation.
Demo Testing: Thoroughly test the indicator on demo accounts before deploying it in live trading environments to understand its behavior under various market scenarios.
Parameter Customization: Regularly review and adjust parameters based on evolving market conditions and personal trading objectives.
Aesthetic RSI [AlchimistOfCrypto]🌌 Aesthetic RSI – Unveiling the Fractal Forces of Markets 🌌
Category: Momentum Indicators 📈
"The RSI oscillator, formalized through an advanced mathematical prism, reveals the underlying fractal structures of price movements. This indicator draws inspiration from quantum principles of divergence-convergence where the probability of a return to equilibrium increases proportionally to the distance from the median point. Our implementation employs sophisticated algorithmic smoothing to filter out the stochastic noise inherent in financial markets, allowing visualization of the true momentum forces according to thermodynamic entropy principles applied to trading systems."
📊 Professional Trading Application
The Aesthetic RSI is a visually stunning and mathematically refined take on the classic Relative Strength Index. With customizable settings, advanced smoothing, and eight unique visual palettes, it empowers traders to detect momentum shifts and divergences with unparalleled clarity.
⚙️ Indicator Configuration
- Length 📏
The core parameter (default: 20) that determines the calculation period.
- Lower values (8-14): Increase sensitivity for short-term trading.
- Higher values (21-34): Provide stronger signals for position trading.
- OverBought/OverSold Thresholds 🎯
Customizable boundaries (default: 75/25) to identify extreme market conditions.
- Calibrate based on asset volatility: Higher volatility assets may need wider thresholds (80/20) to reduce false signals.
- Style 🎨
Eight meticulously crafted visual palettes optimized for pattern recognition:
- Miami Vice (default): High-contrast cyan/magenta scheme for spotting divergences.
- Cyberpunk: Yellow/purple combo to highlight momentum shifts.
- Classic: Traditional green/red for conventional analysis.
- High Contrast: Maximum visual separation for traders with visual impairments.
- Specialized palettes (Forest, Ocean, Fire, Monochrome): Tailored for diverse market conditions.
- Mode Selection 🔄
- Full: Displays a complete gradient spectrum across the RSI range, emphasizing momentum transitions between 35-65.
- OverZone: Focuses on actionable extreme zones, reducing noise in ranging markets.
🚀 How to Use
1. Adjust Length ⏰: Set the period based on your trading style (short-term or long-term).
2. Fine-Tune Thresholds 🎚️: Customize overbought/oversold levels to match the asset’s volatility.
3. Select a Palette 🌈: Choose a visual style that enhances your pattern recognition.
4. Choose Mode 🔍: Use "Full" for detailed momentum analysis or "OverZone" for extreme zone focus.
5. Spot Divergences ✅: Look for price-RSI divergences to anticipate reversals.
6. Trade with Precision 🛡️: Combine with other indicators for high-probability setups.
📅 Release Notes (April 2025)
Aesthetic RSI blends quantum-inspired mathematics with artistic visualization, redefining momentum analysis. Stay tuned for future enhancements! ✨
🏷️ Tags
#Trading #TechnicalAnalysis #RSI #Momentum #Divergence #MultiTimeframe #TradingStrategy #RiskManagement #Forex #Stocks #Crypto #Bitcoin #AlgoTrading #DayTrading #SwingTrading #TheAlchimist #QuantumTrading #VisualTrading #PatternRecognition
EMA Crossover Strategy with Trailing Stop and AlertsPowerful EMA Crossover Strategy with Dynamic Trailing Stop and Real-Time Alerts
This strategy combines the simplicity and effectiveness of EMA crossovers with a dynamic trailing stop-loss mechanism for robust risk management.
**Key Features:**
* **EMA Crossover Signals:** Identifies potential trend changes using customizable short and long period Exponential Moving Averages.
* **Trailing Stop-Loss:** Automatically adjusts the stop-loss level as the price moves favorably, helping to protect profits and limit downside risk. The trailing stop percentage is fully adjustable.
* **Visual Buy/Sell Signals:** Clear buy (green upward label) and sell (red downward label) signals are plotted directly on the price chart.
* **Customizable Inputs:** Easily adjust the lengths of the short and long EMAs, as well as the trailing stop percentage, to optimize the strategy for different assets and timeframes.
* **Real-Time Alerts:** Receive instant alerts for buy and sell signals, ensuring you don't miss potential trading opportunities.
**How to Use:**
1. Add the strategy to your TradingView chart.
2. Customize the "Short EMA Length," "Long EMA Length," and "Trailing Stop Percentage" in the strategy's settings.
3. Enable alerts in TradingView to receive notifications when buy or sell signals are generated.
This strategy is intended to provide automated trading signals based on EMA crossovers with built-in risk management. Remember to backtest thoroughly on your chosen instruments and timeframes before using it for live trading.
#EMA
#Crossover
#TrailingStop
#Strategy
#TradingView
#TechnicalAnalysis
#Alerts
#TradingStrategy
Exponential Trend [AlgoAlpha]OVERVIEW
This script plots an adaptive exponential trend system that initiates from a dynamic anchor and accelerates based on time and direction. Unlike standard moving averages or trailing stops, the trend line here doesn't follow price directly—it expands exponentially from a pivot determined by a modified Supertrend logic. The result is a non-linear trend curve that starts at a specific price level and accelerates outward, allowing traders to visually assess trend strength, persistence, and early-stage reversal points through both base and volatility-adjusted extensions.
CONCEPTS
This indicator builds on the idea that trend-following tools often need dynamic, non-static expansion to reflect real market behavior. It uses a simplified Supertrend mechanism to define directional context and anchor levels, then applies an exponential growth function to simulate trend acceleration over time. The exponential growth is unidirectional and resets only when the direction flips, preserving trend memory. This method helps avoid whipsaws and adds time-weighted confirmation to trends. A volatility buffer—derived from ATR and modifiable by a width multiplier—adds a second layer to indicate zones of risk around the main trend path.
FEATURES
Exponential Trend Logic : Once a directional anchor is set, the base trend line accelerates using an exponential formula tied to elapsed bars, making the trend stronger the longer it persists.
Volatility-Adjusted Extension : A secondary band is plotted above or below the base trend line, widened by ATR to visualize volatility zones, act as soft stop regions or as a better entry point (Dynamic Support/Resistance).
Color-Coded Visualization : Clear green/red base and extension lines with shaded fills indicate trend direction and confidence levels.
Signal Markers & Alerts : Triangle markers indicate confirmed trend reversals. Built-in alerts notify users of bullish or bearish direction changes in real-time.
USAGE
Use this script to identify strong trends early, visually measure their momentum over time, and determine safe areas for entries or exits. Start by adjusting the *Exponential Rate* to control how quickly the trend expands—the higher the rate, the more aggressive the curve. The *Initial Distance* sets how far the anchor band is placed from price initially, helping filter out noise. Increase the *Width Multiplier* to widen the volatility zone for more conservative entries or exits. When the price crosses above or below the base line, a new trend is assumed and the exponential projection restarts from the new anchor. The base trend and its extension both shift over time, but only reset on a confirmed reversal. This makes the tool especially useful for momentum continuation setups or trailing stop logic in trending markets.
Multi-Factor Reversal AnalyzerMulti-Factor Reversal Analyzer – Quantitative Reversal Signal System
OVERVIEW
Multi-Factor Reversal Analyzer is a comprehensive technical analysis toolkit designed to detect market tops and bottoms with high precision. It combines trend momentum analysis, price action behavior, wave oscillation structure, and volatility breakout potential into one unified indicator.
This indicator is not a random mix of tools — each module is carefully selected for a specific purpose. When combined, they form a multi-dimensional view of the market, merging trend analysis, momentum divergence, and volatility compression to produce high-confidence signals.
Why Combine These Modules?
Module Combination Ideas & How to Use Them
Factor A: Trend Detector + Gold Zone
Concept:
• The Trend Detector (light yellow histogram) evaluates market strength:
• Histogram trending downward or staying below 50 → bearish conditions;
• Trending upward or staying above 50 → bullish conditions.
• The Gold Zone identifies areas of volatility compression — typically a prelude to explosive market moves.
Practical Application:
• When the Gold Zone appears and the Trend Detector is bearish → likely downside move;
• When the Gold Zone appears and the Trend Detector is bullish → likely upside breakout.
• Note: The Gold Zone does not mean the bottom is in. It is not a buy signal on its own — always combine it with other modules for directional bias.
Factor B: PAI + Wave Trend
Concept:
• PAI (Price Action Index) is a custom oscillator that combines price momentum with volatility dispersion, displaying strength zones:
• Green area → bullish dominance;
• Red area → bearish pressure.
• Wave Trend offers smoothed crossover signals via the main and signal lines.
Practical Application:
• When PAI is in the green zone and Wave Trend makes a bullish crossover → potential reversal to the upside;
• When PAI is in the red zone and Wave Trend shows a bearish crossover → potential start of a downtrend.
Factor C: Trend Detector + PAI
Concept:
• Combines directional trend strength with price action strength to confirm setups via confluence.
Practical Application:
• Trend Detector histogram bottoms out + PAI enters the green zone → high chance of upward reversal;
• Histogram tops out + PAI in the red zone → increased likelihood of downside continuation.
Multi-Factor Confluence (Advanced Use)
• When Trend Detector, PAI, and Wave Trend all align in the same direction (bullish or bearish), the directional signal becomes significantly more reliable.
• This setup is especially useful for trend-following or swing trade entries.
KEY FEATURES
1. Multi-Layer Reversal Logic
• Combines trend scoring, oscillator divergence, and volatility squeezes for triangulated reversal detection.
• Helps traders distinguish between trend pullbacks and true reversals.
2. Advanced Divergence Detection
• Detects both regular and hidden divergences using pivot-based confirmation logic.
• Customizable lookback ranges and pivot sensitivity provide flexible tuning for different market styles.
3. Gold Zone Volatility Compression
• Highlights pre-breakout zones using custom oscillation models (RSI, harmonic, Karobein, etc.).
• Improves anticipation of breakout opportunities following low-volatility compressions.
4. Trend Direction Context
• PAI and Trend Score components provide top-down insight into prevailing bias.
• Built-in “Straddle Area” highlights consolidation zones; breakouts from this area often signal new trend phases.
5. Flexible Visualization
• Color-coded trend bars, reversal markers, normalized oscillator plots, and trend strength labels.
• Designed for both visual discretionary traders and data-driven system developers.
USAGE GUIDELINES
1. Applicable Markets
• Suitable for stocks, crypto, futures, and forex
• Supports reversal, mean-reversion, and breakout trading styles
2. Recommended Timeframes
• Short-term traders: 5m / 15m / 1H — use Wave Trend divergence + Gold Zone
• Swing traders: 4H / Daily — rely on Price Action Index and Trend Detector
• Macro trend context: use PAI HTF mode for higher timeframe overlays
3. Reversal Strategy Flow
• Watch for divergence (WT/PAI) + Gold Zone compression
• Confirm with Trend Score weakening or flipping
• Use Straddle Area breakout for final trigger
• Optional: enable bar coloring or labels for visual reinforcement
• The indicator performs optimally when used in conjunction with a harmonic pattern recognition tool
4. Additional Note on the Gold Zone
The “Gold Zone” does not directly indicate a market bottom. Since it is displayed at the bottom of the chart, it may be misunderstood as a bullish signal. In reality, the Gold Zone represents a compression of price momentum and volatility, suggesting that a significant directional move is about to occur. The direction of that move—upward or downward—should be determined by analyzing the histogram:
• If histogram momentum is weakening, the Gold Zone may precede a downward move.
• If histogram momentum is strengthening, it may signal an upcoming rebound or rally.
Treat the Gold Zone as a warning of impending volatility, and always combine it with trend indicators for accurate directional judgment.
RISK DISCLAIMER
• This indicator calculates trend direction based on historical data and cannot guarantee future market performance. When using this indicator for trading, always combine it with other technical analysis tools, fundamental analysis, and personal trading experience for comprehensive decision-making.
• Market conditions are uncertain, and trend signals may result in false positives or lag. Traders should avoid over-reliance on indicator signals and implement stop-loss strategies and risk management techniques to reduce potential losses.
• Leverage trading carries high risks and may result in rapid capital loss. If using this indicator in leveraged markets (such as futures, forex, or cryptocurrency derivatives), exercise caution, manage risks properly, and set reasonable stop-loss/take-profit levels to protect funds.
• All trading decisions are the sole responsibility of the trader. The developer is not liable for any trading losses. This indicator is for technical analysis reference only and does not constitute investment advice.
• Before live trading, it is recommended to use a demo account for testing to fully understand how to use the indicator and apply proper risk management strategies.
CHANGELOG
v1.0: Initial release featuring integrated Price Action Index, Trend Strength Scoring, Wave Trend Oscillator, Gold Zone Compression Detection, and dual-type divergence recognition. Supports higher timeframe (HTF) synchronization, visual signal markers, and diversified parameter configurations.
Auto Support Resistance Channels [TradingFinder] Top/Down Signal🔵 Introduction
In technical analysis, a price channel is one of the most widely used tools for identifying and tracking price trends. A price channel consists of two parallel trendlines, typically drawn from swing highs (resistance) and swing lows (support). These lines define dynamic support and resistance zones and provide a clear framework for interpreting price fluctuations.
Drawing a channel on a price chart allows the analyst to more precisely identify entry points, exit levels, take-profit zones, and stop-loss areas based on how the price behaves within the boundaries of the channel.
Price channels in technical analysis are generally categorized into three types: upward channels with a positive slope, downward channels with a negative slope, and horizontal (range-bound) channels with near-zero slope. Each type offers unique insights into market behavior depending on the price structure and prevailing trend.
Structurally, channels can be formed using either minor or major pivot points. A major channel typically reflects a stronger, more reliable structure that appears on higher timeframes, whereas a minor channel often captures short-term fluctuations or corrective movements within a larger trend.
For instance, a major downward channel may indicate sustained selling pressure across the market, while a minor upward channel could represent a temporary pullback within a broader bearish trend.
The validity of a price channel depends on several factors, including the number of price touches on the channel lines, the symmetry and parallelism of the trendlines, the duration of price movement within the channel, and price behavior around the median line.
When a price channel is broken, it is generally expected that the price will move in the breakout direction by at least the width of the channel. This makes price channels especially useful in breakout analysis.
In the following sections, we will explore the different types of price channels, how to draw them accurately, the structural differences between minor and major channels, and key trade interpretations when price interacts with channel boundaries.
Up Channel :
Down Channel :
🔵 How to Use
A price channel is a practical tool in technical analysis for identifying areas of support, resistance, trend direction, and potential breakout zones. The structure consists of two parallel trendlines within which price fluctuates.
Traders use the relative position of price within the channel to make informed trading decisions. The two primary strategies include range-based trades (buying low, selling high) and breakout trades (entering when price exits the channel).
🟣 Up Channel
In an upward channel, price moves within a positively sloped range. The lower trendline acts as dynamic support, while the upper trendline serves as dynamic resistance. A common strategy involves buying near the lower support and taking profit or selling near the upper resistance.
If price breaks below the lower trendline with strong volume or a decisive candle, it can signal a potential trend reversal. Channels constructed from major pivots generally reflect dominant uptrends, while those based on minor pivots are often corrective structures within a broader bearish movement.
🟣 Down Channel
In a downward channel, price moves between two negatively sloped lines. The upper trendline functions as resistance, and the lower trendline as support. Ideal entry for short trades occurs near the upper boundary, especially when confirmed by bearish price action or a resistance level.
Exit targets are typically located near the lower support. If the upper boundary is broken to the upside, it may be an early sign of a bullish trend reversal. Like upward channels, a major down channel represents broader selling pressure, while a minor one may indicate a brief retracement in a bullish move.
🟣 Range Channel
A horizontal or range-bound channel is characterized by price oscillating between two nearly flat lines. This type of channel typically appears during sideways markets or periods of consolidation.
Traders often buy near the lower boundary and sell near the upper boundary to take advantage of contained volatility. However, fake breakouts are more frequent in range-bound structures, so it is important to wait for confirmation through candlestick signals and volume. A confirmed breakout beyond the channel boundaries can justify entering a trade in the direction of the breakout.
🔵 Settings
Pivot Period :This parameter defines how sensitive the channel detection is. A higher value causes the algorithm to identify major pivot points, resulting in broader and longer-term channels. Lower values focus on minor pivots and create tighter, short-term channels.
🔔 Alerts
Alert Configuration :
Enable or disable the full alert system
Set a custom alert name
Choose the alert frequency: every time, once per bar, or on bar close
Define the time zone for alert timestamps (e.g., UTC)
Channel Alert Types :
Each channel type (Major/Minor, Internal/External, Up/Down) supports two alert types :
Break Alert : Triggered when price breaks above or below the channel boundaries
React Alert : Triggered when price touches and reacts (bounces) off the channel boundary
🎨 Display Settings
For each of the eight channel types, you can customize:
Visibility : show or hide the channel
Auto-delete previous channels when new ones are drawn
Style : line color, thickness, type (solid, dashed, dotted), extension (right only, both sides)
🔵 Conclusion
The price channel is a foundational structure in technical analysis that enables traders to analyze price movement, identify dynamic support and resistance zones, and locate potential entry and exit points with greater precision.
When constructed properly using minor or major pivots, a price channel offers a consistent and intuitive framework for interpreting market behavior—often simpler and more visually clear than many other technical tools.
Understanding the differences between upward, downward, and range-bound channels—as well as recognizing the distinctions between minor and major structures—is critical for selecting the right trading strategy. Upward channels tend to generate buying opportunities, downward channels prioritize short setups, and horizontal channels provide setups for both mean-reversion and breakout trades.
Ultimately, the reliability of a price channel depends on various factors such as the number of touchpoints, the duration of the channel, the parallelism of the lines, and how the price reacts to the median line.
By taking these factors into account, an experienced analyst can effectively use price channels as a powerful tool for trend forecasting and precise trade execution. Although conceptually simple, successful application of price channels requires practice, pattern recognition, and the ability to filter out market noise.
DEMA Trend Oscillator Strategy📌 Overview
The DEMA Trend Oscillator Strategy is a dynamic trend-following approach based on the Normalized DEMA Oscillator SD.
It adapts in real-time to market volatility with the goal of improving entry accuracy and optimizing risk management.
⚠️ This strategy is provided for educational and research purposes only.
Past performance does not guarantee future results.
🎯 Strategy Objectives
The main goal of this strategy is to respond quickly to sudden price movements and trend reversals,
by combining momentum-based signals with volatility filters.
It is designed to be user-friendly for traders of all experience levels.
✨ Key Features
Normalized DEMA Oscillator: A momentum indicator that normalizes DEMA values on a 0–100 scale, allowing intuitive identification of trend strength
Two-Bar Confirmation Filter: Requires two consecutive bullish or bearish candles to reduce noise and enhance entry reliability
ATR x2 Trailing Stop: In addition to fixed stop-loss levels, a trailing stop based on 2× ATR is used to maximize profits during strong trends
📊 Trading Rules
Long Entry:
Normalized DEMA > 55 (strong upward momentum)
Candle low is above the upper SD band
Two consecutive bullish candles appear
Short Entry:
Normalized DEMA < 45 (downward momentum)
Candle high is below the lower SD band
Two consecutive bearish candles appear
Exit Conditions:
Take-profit at a risk-reward ratio of 1.5
Stop-loss triggered if price breaks below (long) or above (short) the SD band
Trailing stop activated based on 2× ATR to secure and extend profits
💰 Risk Management Parameters
Symbol & Timeframe: Any (AUDUSD 5M example)
Account size (virtual): $3000
Commission: 0.4PIPS(0.0004)
Slippage: 2 pips
Risk per trade: 5%
Number of trades (backtest):534
All parameters can be adjusted based on broker specifications and individual trading profiles.
⚙️ Trading Parameters & Considerations
Indicator: Normalized DEMA Oscillator SD
Parameter settings:
DEMA Period (len_dema): 40
Base Length: 20
Long Threshold: 55
Short Threshold: 45
Risk-Reward Ratio: 1.5
ATR Multiplier for Trailing Stop: 2.0
🖼 Visual Support
The chart displays the following visual elements:
Upper and lower SD bands (±2 standard deviations)
Entry signals shown as directional arrows
🔧 Strategy Improvements & Uniqueness
This strategy is inspired by “Normalized DEMA Oscillator SD” by QuantEdgeB,
but introduces enhancements such as a two-bar confirmation filter and an ATR-based trailing stop.
Compared to conventional trend-following strategies, it offers superior noise filtering and profit optimization.
✅ Summary
The DEMA Trend Oscillator Strategy is a responsive and practical trend-following method
that combines momentum detection with adaptive risk management.
Its visual clarity and logical structure make it a powerful and repeatable tool
for traders seeking consistent performance in trending markets.
⚠️ Always apply appropriate risk management. This strategy is based on historical data and does not guarantee future results.
RSI Forecast [Titans_Invest]RSI Forecast
Introducing one of the most impressive RSI indicators ever created – arguably the best on TradingView, and potentially the best in the world.
RSI Forecast is a visionary evolution of the classic RSI, merging powerful customization with groundbreaking predictive capabilities. While preserving the core principles of traditional RSI, it takes analysis to the next level by allowing users to anticipate potential future RSI movements.
Real-Time RSI Forecasting:
For the first time ever, an RSI indicator integrates linear regression using the least squares method to accurately forecast the future behavior of the RSI. This innovation empowers traders to stay one step ahead of the market with forward-looking insight.
Highly Customizable:
Easily adapt the indicator to your personal trading style. Fine-tune a variety of parameters to generate signals perfectly aligned with your strategy.
Innovative, Unique, and Powerful:
This is the world’s first RSI Forecast to apply this predictive approach using least squares linear regression. A truly elite-level tool designed for traders who want a real edge in the market.
⯁ SCIENTIFIC BASIS LINEAR REGRESSION
Linear Regression is a fundamental method of statistics and machine learning, used to model the relationship between a dependent variable y and one or more independent variables 𝑥.
The general formula for a simple linear regression is given by:
y = β₀ + β₁x + ε
Where:
y = is the predicted variable (e.g. future value of RSI)
x = is the explanatory variable (e.g. time or bar index)
β0 = is the intercept (value of 𝑦 when 𝑥 = 0)
𝛽1 = is the slope of the line (rate of change)
ε = is the random error term
The goal is to estimate the coefficients 𝛽0 and 𝛽1 so as to minimize the sum of the squared errors — the so-called Random Error Method Least Squares.
⯁ LEAST SQUARES ESTIMATION
To minimize the error between predicted and observed values, we use the following formulas:
β₁ = /
β₀ = ȳ - β₁x̄
Where:
∑ = sum
x̄ = mean of x
ȳ = mean of y
x_i, y_i = individual values of the variables.
Where:
x_i and y_i are the means of the independent and dependent variables, respectively.
i ranges from 1 to n, the number of observations.
These equations guarantee the best linear unbiased estimator, according to the Gauss-Markov theorem, assuming homoscedasticity and linearity.
⯁ LINEAR REGRESSION IN MACHINE LEARNING
Linear regression is one of the cornerstones of supervised learning. Its simplicity and ability to generate accurate quantitative predictions make it essential in AI systems, predictive algorithms, time series analysis, and automated trading strategies.
By applying this model to the RSI, you are literally putting artificial intelligence at the heart of a classic indicator, bringing a new dimension to technical analysis.
⯁ VISUAL INTERPRETATION
Imagine an RSI time series like this:
Time →
RSI →
The regression line will smooth these values and extend them n periods into the future, creating a predicted trajectory based on the historical moment. This line becomes the predicted RSI, which can be crossed with the actual RSI to generate more intelligent signals.
⯁ SUMMARY OF SCIENTIFIC CONCEPTS USED
Linear Regression Models the relationship between variables using a straight line.
Least Squares Minimizes the sum of squared errors between prediction and reality.
Time Series Forecasting Estimates future values based on historical data.
Supervised Learning Trains models to predict outputs from known inputs.
Statistical Smoothing Reduces noise and reveals underlying trends.
⯁ WHY THIS INDICATOR IS REVOLUTIONARY
Scientifically-based: Based on statistical theory and mathematical inference.
Unprecedented: First public RSI with least squares predictive modeling.
Intelligent: Built with machine learning logic.
Practical: Generates forward-thinking signals.
Customizable: Flexible for any trading strategy.
⯁ CONCLUSION
By combining RSI with linear regression, this indicator allows a trader to predict market momentum, not just follow it.
RSI Forecast is not just an indicator — it is a scientific breakthrough in technical analysis technology.
⯁ Example of simple linear regression, which has one independent variable:
⯁ In linear regression, observations ( red ) are considered to be the result of random deviations ( green ) from an underlying relationship ( blue ) between a dependent variable ( y ) and an independent variable ( x ).
⯁ Visualizing heteroscedasticity in a scatterplot against 100 random fitted values using Matlab:
⯁ The data sets in the Anscombe's quartet are designed to have approximately the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but are graphically very different. This illustrates the pitfalls of relying solely on a fitted model to understand the relationship between variables.
⯁ The result of fitting a set of data points with a quadratic function:
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🥇 This is the world’s first RSI indicator with: Linear Regression for Forecasting 🥇_______________________________________________________________________
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🔮 Linear Regression: PineScript Technical Parameters 🔮
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Forecast Types:
• Flat: Assumes prices will remain the same.
• Linreg: Makes a 'Linear Regression' forecast for n periods.
Technical Information:
ta.linreg (built-in function)
Linear regression curve. A line that best fits the specified prices over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where intercept and slope are the values calculated using the least squares method on the source series.
Syntax:
• Function: ta.linreg()
Parameters:
• source: Source price series.
• length: Number of bars (period).
• offset: Offset.
• return: Linear regression curve.
This function has been cleverly applied to the RSI, making it capable of projecting future values based on past statistical trends.
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⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
• Overbought: When the RSI is above 70, indicating that the asset may be overbought.
• Oversold: When the RSI is below 30, indicating that the asset may be oversold.
• Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
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⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 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 Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
📈 RSI Forecast:
🔮 RSI (Crossover) MA Forecast
🔮 RSI (Crossunder) MA Forecast
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND or OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 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 Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
📉 RSI Forecast:
🔮 RSI (Crossover) MA Forecast
🔮 RSI (Crossunder) MA Forecast
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
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⯁ UNIQUE FEATURES
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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
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
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📜 SCRIPT : RSI Forecast
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy!
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o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
Dskyz (DAFE) MAtrix with ATR-Powered Precision Dskyz (DAFE) MAtrix with ATR-Powered Precision
This cutting‐edge futures trading strategy built to thrive in rapidly changing market conditions. Developed for high-frequency futures trading on instruments such as the CME Mini MNQ, this strategy leverages a matrix of sophisticated moving averages combined with ATR-based filters to pinpoint high-probability entries and exits. Its unique combination of adaptable technical indicators and multi-timeframe trend filtering sets it apart from standard strategies, providing enhanced precision and dynamic responsiveness.
imgur.com
Core Functional Components
1. Advanced Moving Averages
A distinguishing feature of the DAFE strategy is its robust, multi-choice moving averages (MAs). Clients can choose from a wide array of MAs—each with specific strengths—in order to fine-tune their trading signals. The code includes user-defined functions for the following MAs:
imgur.com
Hull Moving Average (HMA):
The hma(src, len) function calculates the HMA by using weighted moving averages (WMAs) to reduce lag considerably while smoothing price data. This function computes an intermediate WMA of half the specified length, then a full-length WMA, and finally applies a further WMA over the square root of the length. This design allows for rapid adaptation to price changes without the typical delays of traditional moving averages.
Triple Exponential Moving Average (TEMA):
Implemented via tema(src, len), TEMA uses three consecutive exponential moving averages (EMAs) to effectively cancel out lag and capture price momentum. The final formula—3 * (ema1 - ema2) + ema3—produces a highly responsive indicator that filters out short-term noise.
Double Exponential Moving Average (DEMA):
Through the dema(src, len) function, DEMA calculates an EMA and then a second EMA on top of it. Its simplified formula of 2 * ema1 - ema2 provides a smoother curve than a single EMA while maintaining enhanced responsiveness.
Volume Weighted Moving Average (VWMA):
With vwma(src, len), this MA accounts for trading volume by weighting the price, thereby offering a more contextual picture of market activity. This is crucial when volume spikes indicate significant moves.
Zero Lag EMA (ZLEMA):
The zlema(src, len) function applies a correction to reduce the inherent lag found in EMAs. By subtracting a calculated lag (based on half the moving average window), ZLEMA is exceptionally attuned to recent price movements.
Arnaud Legoux Moving Average (ALMA):
The alma(src, len, offset, sigma) function introduces ALMA—a type of moving average designed to be less affected by outliers. With parameters for offset and sigma, it allows customization of the degree to which the MA reacts to market noise.
Kaufman Adaptive Moving Average (KAMA):
The custom kama(src, len) function is noteworthy for its adaptive nature. It computes an efficiency ratio by comparing price change against volatility, then dynamically adjusts its smoothing constant. This results in an MA that quickly responds during trending periods while remaining smoothed during consolidation.
Each of these functions—integrated into the strategy—is selectable by the trader (via the fastMAType and slowMAType inputs). This flexibility permits the tailored application of the MA most suited to current market dynamics and individual risk management preferences.
2. ATR-Based Filters and Risk Controls
ATR Calculation and Volatility Filter:
The strategy computes the Average True Range (ATR) over a user-defined period (atrPeriod). ATR is then used to derive both:
Volatility Assessment: Expressed as a ratio of ATR to closing price, ensuring that trades are taken only when volatility remains within a safe, predefined threshold (volatilityThreshold).
ATR-Based Entry Filters: Implemented as atrFilterLong and atrFilterShort, these conditions ensure that for long entries the price is sufficiently above the slow MA and vice versa for shorts. This acts as an additional confirmation filter.
Dynamic Exit Management:
The exit logic employs a dual approach:
Fixed Stop and Profit Target: Stops and targets are set at multiples of ATR (fixedStopMultiplier and profitTargetATRMult), helping manage risk in volatile markets.
Trailing Stop Adjustments: A trailing stop is calculated using the ATR multiplied by a user-defined offset (trailOffset), which captures additional profits as the trade moves favorably while protecting against reversals.
3. Multi-Timeframe Trend Filtering
The strategy enhances its signal reliability by leveraging a secondary, higher timeframe analysis:
15-Minute Trend Analysis:
By retrieving 15-minute moving averages (fastMA15m and slowMA15m) via request.security, the strategy determines the broader market trend. This secondary filter (enabled or disabled through useTrendFilter) ensures that entries are aligned with the prevailing market direction, thereby reducing the incidence of false signals.
4. Signal and Execution Logic
Combined MA Alignment:
The entry conditions are based primarily on the alignment of the fast and slow MAs. A long condition is triggered when the current price is above both MAs and the fast MA is above the slow MA—complemented by the ATR filter and volume conditions. The reverse applies for a short condition.
Volume and Time Window Validation:
Trades are permitted only if the current volume exceeds a minimum (minVolume) and the current hour falls within the predefined trading window (tradingStartHour to tradingEndHour). An additional volume spike check (comparing current volume to a moving average of past volumes) further filters for optimal market conditions.
Comprehensive Order Execution:
The strategy utilizes flexible order execution functions that allow pyramiding (up to 10 positions), ensuring that it can scale into positions as favorable conditions persist. The use of both market entries and automated exits (with profit targets, stop-losses, and trailing stops) ensures that risk is managed at every step.
5. Integrated Dashboard and Metrics
For transparency and real-time analysis, the strategy includes:
On-Chart Visualizations:
Both fast and slow MAs are plotted on the chart, making it easy to see the market’s technical foundation.
Dynamic Metrics Dashboard:
A built-in table displays crucial performance statistics—including current profit/loss, equity, ATR (both raw and as a percentage), and the percentage gap between the moving averages. These metrics offer immediate insight into the health and performance of the strategy.
Input Parameters: Detailed Breakdown
Every input is meticulously designed to offer granular control:
Fast & Slow Lengths:
Determine the window size for the fast and slow moving averages. Smaller values yield more sensitivity, while larger values provide a smoother, delayed response.
Fast/Slow MA Types:
Choose the type of moving average for fast and slow signals. The versatility—from basic SMA and EMA to more complex ones like HMA, TEMA, ZLEMA, ALMA, and KAMA—allows customization to fit different market scenarios.
ATR Parameters:
atrPeriod and atrMultiplier shape the volatility assessment, directly affecting entry filters and risk management through stop-loss and profit target levels.
Trend and Volume Filters:
Inputs such as useTrendFilter, minVolume, and the volume spike condition help confirm that a trade occurs in active, trending markets rather than during periods of low liquidity or market noise.
Trading Hours:
Restricting trade execution to specific hours (tradingStartHour and tradingEndHour) helps avoid illiquid or choppy markets outside of prime trading sessions.
Exit Strategies:
Parameters like trailOffset, profitTargetATRMult, and fixedStopMultiplier provide multiple layers of risk management and profit protection by tailoring how exits are generated relative to current market conditions.
Pyramiding and Fixed Trade Quantity:
The strategy supports multiple entries within a trend (up to 10 positions) and sets a predefined trade quantity (fixedQuantity) to maintain consistent exposure and risk per trade.
Dashboard Controls:
The resetDashboard input allows for on-the-fly resetting of performance metrics, keeping the strategy’s performance dashboard accurate and up-to-date.
Why This Strategy is Truly Exceptional
Multi-Faceted Adaptability:
The ability to switch seamlessly between various moving average types—each suited to particular market conditions—enables the strategy to adapt dynamically. This is a testament to the high level of coding sophistication and market insight infused within the system.
Robust Risk Management:
The integration of ATR-based stops, profit targets, and trailing stops ensures that every trade is executed with well-defined risk parameters. The system is designed to mitigate unexpected market swings while optimizing profit capture.
Comprehensive Market Filtering:
By combining moving average crossovers with volume analysis, volatility thresholds, and multi-timeframe trend filters, the strategy only enters trades under the most favorable conditions. This multi-layered filtering reduces noise and enhances signal quality.
-Final Thoughts-
The Dskyz Adaptive Futures Elite (DAFE) MAtrix with ATR-Powered Precision strategy is not just another trading algorithm—it is a multi-dimensional, fully customizable system built on advanced technical principles and sophisticated risk management techniques. Every function and input parameter has been carefully engineered to provide traders with a system that is both powerful and transparent.
For clients seeking a state-of-the-art trading solution that adapts dynamically to market conditions while maintaining strict discipline in risk management, this strategy truly stands in a class of its own.
****Please show support if you enjoyed this strategy. I'll have more coming out in the near future!!
-Dskyz
Caution
DAFE is experimental, not a profit guarantee. Futures trading risks significant losses due to leverage. Backtest, simulate, and monitor actively before live use. All trading decisions are your responsibility.
Leavitt Convolution ProbabilityTechnical Analysis of Markets with Leavitt Market Projections and Associated Convolution Probability
The aim of this study is to present an innovative approach to market analysis based on the research "Leavitt Market Projections." This technical tool combines one indicator and a probability function to enhance the accuracy and speed of market forecasts.
Key Features
Advanced Indicators : the script includes the Convolution line and a probability oscillator, designed to anticipate market changes. These indicators provide timely signals and offer a clear view of price dynamics.
Convolution Probability Function : The Convolution Probability (CP) is a key element of the script. A significant increase in this probability often precedes a market decline, while a decrease in probability can signal a bullish move. The Convolution Probability Function:
At each bar, i, the linear regression routine finds the two parameters for the straight line: y=mix+bi.
Standard deviations can be calculated from the sequence of slopes, {mi}, and intercepts, {bi}.
Each standard deviation has a corresponding probability.
Their adjusted product is the Convolution Probability, CP. The construction of the Convolution Probability is straightforward. The adjusted product is the probability of one times 1− the probability of the other.
Customizable Settings : Users can define oversold and overbought levels, as well as set an offset for the linear regression calculation. These options allow for tailoring the script to individual trading strategies and market conditions.
Statistical Analysis : Each analyzed bar generates regression parameters that allow for the calculation of standard deviations and associated probabilities, providing an in-depth view of market dynamics.
The results from applying this technical tool show increased accuracy and speed in market forecasts. The combination of Convolution indicator and the probability function enables the identification of turning points and the anticipation of market changes.
Additional information:
Leavitt, in his study, considers the SPY chart.
When the Convolution Probability (CP) is high, it indicates that the probability P1 (related to the slope) is high, and conversely, when CP is low, P1 is low and P2 is high.
For the calculation of probability, an approximate formula of the Cumulative Distribution Function (CDF) has been used, which is given by: CDF(x)=21(1+erf(σ2x−μ)) where μ is the mean and σ is the standard deviation.
For the calculation of probability, the formula used in this script is: 0.5 * (1 + (math.sign(zSlope) * math.sqrt(1 - math.exp(-0.5 * zSlope * zSlope))))
Conclusions
This study presents the approach to market analysis based on the research "Leavitt Market Projections." The script combines Convolution indicator and a Probability function to provide more precise trading signals. The results demonstrate greater accuracy and speed in market forecasts, making this technical tool a valuable asset for market participants.
Trend Targets [AlgoAlpha]OVERVIEW
This script combines a smoothed trend-following model with dynamic price rejection logic and ATR-based target projection to give traders a complete visual framework for trading trend continuations. It overlays on price and automatically detects potential trend shifts, confirms rejections near dynamic support/resistance, and displays calculated stop-loss and take-profit levels to support structured risk-reward management. Unlike traditional indicators that only show trend direction or signal entries, this tool brings together a unique mix of signal validation, volatility-aware positioning, and layered profit-taking to guide decision-making with more context.
CONCEPTS
The core trend logic is built on a custom Supertrend that uses an ATR-based band structure with long smoothing chains—first through a WMA, then an EMA—allowing the trend line to respond to major shifts while ignoring noise. A key addition is the use of rejection logic: the script looks for consolidation candles that "hug" the smoothed trend line and counts how many consecutive bars reject from it. This behavior often precedes significant moves. A user-defined threshold filters out weak tests and highlights only meaningful rejections.
FEATURES
Trend Detection : Automatically identifies trend direction using a smoothed Supertrend (WMA + EMA), with shape markers on trend shifts and color-coded bars for clarity.
Rejection Signals : Detects price rejections at the trend line after a user-defined number of consolidation bars; plots ▲/▼ icons to highlight strong continuation setups.
Target Projection : On trend confirmation, plots entry, stop-loss (ATR-based), and three dynamic take-profit levels based on customizable multiples.
Dynamic Updates : All levels (entry, SL, TP1–TP3) auto-adjust based on volatility and are labeled in real time on the chart.
Customization : Users can tweak trend parameters, rejection confirmation count, SL/TP ratios, smoothing lengths, and appearance settings.
Alerts : Built-in alerts for trend changes, rejection events, and when TP1, TP2, or TP3 are reached.
Chart Overlay : Plots directly on price chart with minimal clutter and clearly labeled levels for easy trading.
USAGE
Start by tuning the Supertrend factor and ATR period to fit your asset and timeframe—higher values will catch bigger swings, lower values catch faster moves. The confirmation count should match how tightly you want to filter rejection behavior—higher values make signals rarer but stronger. When the trend shifts, the indicator colors the bars and line accordingly, and if enabled, plots the full entry-TP-SL structure. Rejection markers appear only after enough qualifying bars confirm price pressure at the trend line. This is especially useful for continuation plays where price retests the trend but fails to break it. All calculations are based on volatility (ATR), so targets naturally adjust with market conditions. Add alerts to get notified of important signals even when away from the chart.
Half Causal EstimatorOverview
The Half Causal Estimator is a specialized filtering method that provides responsive averages of market variables (volume, true range, or price change) with significantly reduced time delay compared to traditional moving averages. It employs a hybrid approach that leverages both historical data and time-of-day patterns to create a timely representation of market activity while maintaining smooth output.
Core Concept
Traditional moving averages suffer from time lag, which can delay signals and reduce their effectiveness for real-time decision making. The Half Causal Estimator addresses this limitation by using a non-causal filtering method that incorporates recent historical data (the causal component) alongside expected future behavior based on time-of-day patterns (the non-causal component).
This dual approach allows the filter to respond more quickly to changing market conditions while maintaining smoothness. The name "Half Causal" refers to this hybrid methodology—half of the data window comes from actual historical observations, while the other half is derived from time-of-day patterns observed over multiple days. By incorporating these "future" values from past patterns, the estimator can reduce the inherent lag present in traditional moving averages.
How It Works
The indicator operates through several coordinated steps. First, it stores and organizes market data by specific times of day (minutes/hours). Then it builds a profile of typical behavior for each time period. For calculations, it creates a filtering window where half consists of recent actual data and half consists of expected future values based on historical time-of-day patterns. Finally, it applies a kernel-based smoothing function to weight the values in this composite window.
This approach is particularly effective because market variables like volume, true range, and price changes tend to follow recognizable intraday patterns (they are positive values without DC components). By leveraging these patterns, the indicator doesn't try to predict future values in the traditional sense, but rather incorporates the average historical behavior at those future times into the current estimate.
The benefit of using this "average future data" approach is that it counteracts the lag inherent in traditional moving averages. In a standard moving average, recent price action is underweighted because older data points hold equal influence. By incorporating time-of-day averages for future periods, the Half Causal Estimator essentially shifts the center of the filter window closer to the current bar, resulting in more timely outputs while maintaining smoothing benefits.
Understanding Kernel Smoothing
At the heart of the Half Causal Estimator is kernel smoothing, a statistical technique that creates weighted averages where points closer to the center receive higher weights. This approach offers several advantages over simple moving averages. Unlike simple moving averages that weight all points equally, kernel smoothing applies a mathematically defined weight distribution. The weighting function helps minimize the impact of outliers and random fluctuations. Additionally, by adjusting the kernel width parameter, users can fine-tune the balance between responsiveness and smoothness.
The indicator supports three kernel types. The Gaussian kernel uses a bell-shaped distribution that weights central points heavily while still considering distant points. The Epanechnikov kernel employs a parabolic function that provides efficient noise reduction with a finite support range. The Triangular kernel applies a linear weighting that decreases uniformly from center to edges. These kernel functions provide the mathematical foundation for how the filter processes the combined window of past and "future" data points.
Applicable Data Sources
The indicator can be applied to three different data sources: volume (the trading volume of the security), true range (expressed as a percentage, measuring volatility), and change (the absolute percentage change from one closing price to the next).
Each of these variables shares the characteristic of being consistently positive and exhibiting cyclical intraday patterns, making them ideal candidates for this filtering approach.
Practical Applications
The Half Causal Estimator excels in scenarios where timely information is crucial. It helps in identifying volume climaxes or diminishing volume trends earlier than conventional indicators. It can detect changes in volatility patterns with reduced lag. The indicator is also useful for recognizing shifts in price momentum before they become obvious in price action, and providing smoother data for algorithmic trading systems that require reduced noise without sacrificing timeliness.
When volatility or volume spikes occur, conventional moving averages typically lag behind, potentially causing missed opportunities or delayed responses. The Half Causal Estimator produces signals that align more closely with actual market turns.
Technical Implementation
The implementation of the Half Causal Estimator involves several technical components working together. Data collection and organization is the first step—the indicator maintains a data structure that organizes market data by specific times of day. This creates a historical record of how volume, true range, or price change typically behaves at each minute/hour of the trading day.
For each calculation, the indicator constructs a composite window consisting of recent actual data points from the current session (the causal half) and historical averages for upcoming time periods from previous sessions (the non-causal half). The selected kernel function is then applied to this composite window, creating a weighted average where points closer to the center receive higher weights according to the mathematical properties of the chosen kernel. Finally, the kernel weights are normalized to ensure the output maintains proper scaling regardless of the kernel type or width parameter.
This framework enables the indicator to leverage the predictable time-of-day components in market data without trying to predict specific future values. Instead, it uses average historical patterns to reduce lag while maintaining the statistical benefits of smoothing techniques.
Configuration Options
The indicator provides several customization options. The data period setting determines the number of days of observations to store (0 uses all available data). Filter length controls the number of historical data points for the filter (total window size is length × 2 - 1). Filter width adjusts the width of the kernel function. Users can also select between Gaussian, Epanechnikov, and Triangular kernel functions, and customize visual settings such as colors and line width.
These parameters allow for fine-tuning the balance between responsiveness and smoothness based on individual trading preferences and the specific characteristics of the traded instrument.
Limitations
The indicator requires minute-based intraday timeframes, securities with volume data (when using volume as the source), and sufficient historical data to establish time-of-day patterns.
Conclusion
The Half Causal Estimator represents an innovative approach to technical analysis that addresses one of the fundamental limitations of traditional indicators: time lag. By incorporating time-of-day patterns into its calculations, it provides a more timely representation of market variables while maintaining the noise-reduction benefits of smoothing. This makes it a valuable tool for traders who need to make decisions based on real-time information about volume, volatility, or price changes.
Multi Oscillator OB/OS Signals v3 - Scope TestIndicator Description: Multi Oscillator OB/OS Signals
Purpose:
The "Multi Oscillator OB/OS Signals" indicator is a TradingView tool designed to help traders identify potential market extremes and momentum shifts by monitoring four popular oscillators simultaneously: RSI, Stochastic RSI, CCI, and MACD. Instead of displaying these oscillators in separate panes, this indicator plots distinct visual symbols directly onto the main price chart whenever specific predefined conditions (typically related to overbought/oversold levels or line crossovers) are met for each oscillator. This provides a consolidated view of potential signals from these different technical tools.
How It Works:
The indicator calculates the values for each of the four oscillators based on user-defined settings (like length periods and price sources) and then checks for specific signal conditions on every bar:
Relative Strength Index (RSI):
It monitors the standard RSI value.
When the RSI crosses above the user-defined Overbought (OB) level (e.g., 70), it plots an "Overbought" symbol (like a downward triangle) above that price bar.
When the RSI crosses below the user-defined Oversold (OS) level (e.g., 30), it plots an "Oversold" symbol (like an upward triangle) below that price bar.
Stochastic RSI:
This works similarly to RSI but is based on the Stochastic calculation applied to the RSI value itself (specifically, the %K line of the Stoch RSI).
When the Stoch RSI's %K line crosses above its Overbought level (e.g., 80), it plots its designated OB symbol (like a downward arrow) above the bar.
When the %K line crosses below its Oversold level (e.g., 20), it plots its OS symbol (like an upward arrow) below the bar.
Commodity Channel Index (CCI):
It tracks the CCI value.
When the CCI crosses above its Overbought level (e.g., +100), it plots its OB symbol (like a square) above the bar.
When the CCI crosses below its Oversold level (e.g., -100), it plots its OS symbol (like a square) below the bar.
Moving Average Convergence Divergence (MACD):
Unlike the others, MACD signals here are not based on fixed OB/OS levels.
It identifies when the main MACD line crosses above its Signal line. This is considered a bullish crossover and is indicated by a specific symbol (like an upward label) plotted below the price bar.
It also identifies when the MACD line crosses below its Signal line. This is a bearish crossover, indicated by a different symbol (like a downward label) plotted above the price bar.
Visualization:
All these signals appear as small, distinct shapes directly on the price chart at the bar where the condition occurred. The shapes, their colors, and their position (above or below the bar) are predefined for each signal type to allow for quick visual identification. Note: In the current version of the underlying code, the size of these shapes is fixed (e.g., tiny) and not user-adjustable via the settings.
Configuration:
Users can access the indicator's settings to customize:
The calculation parameters (Length periods, smoothing, price source) for each individual oscillator (RSI, Stoch RSI, CCI, MACD).
The specific Overbought and Oversold threshold levels for RSI, Stoch RSI, and CCI.
The colors associated with each type of signal (OB, OS, Bullish Cross, Bearish Cross).
(Limitation Note: While settings exist to toggle the visibility of signals for each oscillator individually, due to a technical workaround in the current code, these toggles may not actively prevent the shapes from plotting if the underlying condition is met.)
Alerts:
The indicator itself does not automatically generate pop-up alerts. However, it creates the necessary "Alert Conditions" within TradingView's alert system. This means users can manually set up alerts for any of the specific signals generated by the indicator (e.g., "RSI Overbought Enter," "MACD Bullish Crossover"). When creating an alert, the user selects this indicator, chooses the desired condition from the list provided by the script, and configures the alert actions.
Intended Use:
This indicator aims to provide traders with convenient visual cues for potential over-extension in price (via OB/OS signals) or shifts in momentum (via MACD crossovers) based on multiple standard oscillators. These signals are often used as potential indicators for:
Identifying areas where a trend might be exhausted and prone to a pullback or reversal.
Confirming signals generated by other analysis methods or trading strategies.
Noting shifts in short-term momentum.
Disclaimer: As with any technical indicator, the signals generated should not be taken as direct buy or sell recommendations. They are best used in conjunction with other forms of analysis (price action, trend analysis, volume, fundamental analysis, etc.) and within the framework of a well-defined trading plan that includes risk management. Market conditions can change, and indicator signals can sometimes be false or misleading.
TriTrend Nexus[BullByte]TriTrend Nexus is a comprehensive market analysis tool that consolidates three well-established signals into a single, easy-to-read interface. It is designed to help traders quickly assess the market’s current condition and make more informed decisions about potential trend shifts.
Key Features and Functionality
Composite Signal System
Multi-Faceted Approach :
The indicator combines insights from three distinct market signals into one composite score. This approach provides a more holistic view of market conditions compared to relying on a single indicator.
Clear Classification :
Based on the composite score, TriTrend Nexus categorizes the market into:
Strong Signals : When all three underlying conditions are met, indicating a robust and established trend.
Early Signals : When two out of the three conditions are met, offering an early hint of a potential trend.
Neutral/Choppy : When conditions are ambiguous or conflicting, suggesting a lack of clear market direction.
Trend Qualifiers :
In addition to the composite score, the indicator subtly refines its signal by noting whether a trend is “Rising” or “Fading.” This further aids traders in understanding the momentum behind the signal.
Dynamic Signal Identification
Timely Alerts :
By analyzing the composite data in real time, the indicator quickly identifies when market conditions shift, offering early warning signals that help traders stay ahead of the market.
Adaptive Analysis :
The built-in signal assessment continuously monitors market changes. Whether the market is in the early stages of a move or firmly committed to a trend, TriTrend Nexus adapts its messaging to reflect the evolving conditions.
User-Friendly Dashboard
Integrated Display :
A customizable dashboard provides an at-a-glance summary of key metrics. Users can choose between a detailed view for comprehensive insights or a compact version for a streamlined experience.
Key Metrics Displayed :
Primary Signal : The overall market status, such as “Bullish Strong” or “Bearish Early.”
Composite Nexus Score : A numerical value representing the strength of the current market conditions.
Supporting Data : Essential values that help explain the current signal without overwhelming the trader.
Easy Interpretation :
The dashboard is designed with clarity in mind. Clear labeling and a consistent layout ensure that even traders new to composite indicators can quickly interpret the displayed information.
Visual Clarity and Aesthetic
Color-Coded Signals :
The indicator uses a vibrant color scheme to highlight market conditions:
Bright Green : Signifies a strong bullish trend.
Light Green : Indicates an emerging bullish trend.
Red : Represents a strong bearish trend.
Light Red/Pink : Denotes an early bearish signal.
Gray : Used when market conditions are neutral or choppy.
Graphical Enhancements :
The plotted oscillator visually reinforces the signal classifications with dynamic color transitions. Horizontal markers provide reference points to help traders easily compare the current readings against standard levels.
Customization Options
Adjustable Settings :
Traders can personalize the indicator by modifying input settings such as sensitivity thresholds and period lengths. This flexibility allows the tool to adapt to different market environments and trading styles.
Dashboard Flexibility :
The option to toggle between a full dashboard and a shorter version means that both novice and experienced traders can configure the display to best suit their needs. A more detailed dashboard offers extensive insights, while the compact mode provides a minimalist view for those who prefer simplicity.
Tailored User Experience :
With multiple adjustable parameters, users can fine-tune the indicator to respond precisely to their preferred timeframes and market conditions. This adaptability makes TriTrend Nexus a versatile tool for various trading strategies.
Benefits for Traders
Quick and Informed Decision-Making :
With a single glance at the dashboard and visual cues from the oscillator, traders can quickly gauge whether the market is poised for a strong move, is in the early stages of a trend, or is too volatile for clear signals. This helps in planning timely entries and exits.
Enhanced Market Insight :
By integrating multiple perspectives into one coherent score, the indicator filters out market noise and highlights the prevailing trend more reliably. This can be particularly useful during periods of market uncertainty.
Reduced Analysis Time:
The combination of clear, color-coded signals and an intuitive dashboard reduces the time spent analyzing various individual indicators, allowing traders to focus more on strategy execution.
Customization for Diverse Strategies :
The ability to adjust various input parameters and the dashboard layout ensures that traders can tailor the tool to fit their unique analysis style and market conditions, making it a versatile addition to any trading toolkit.
User-Friendly Interface :
Even for those who are not technically inclined, the clear visual design and straightforward signal descriptions make it easy to understand the current market situation without needing to interpret complex data.
