Awesome Oscillator (AO) with Signals [AIBitcoinTrend]👽 Multi-Scale Awesome Oscillator (AO) with Signals (AIBitcoinTrend)
The Multi-Scale Awesome Oscillator transforms the traditional Awesome Oscillator (AO) by integrating multi-scale wavelet filtering, enhancing its ability to detect momentum shifts while maintaining responsiveness across different market conditions.
Unlike conventional AO calculations, this advanced version refines trend structures using high-frequency, medium-frequency, and low-frequency wavelet components, providing traders with superior clarity and adaptability.
Additionally, it features real-time divergence detection and an ATR-based dynamic trailing stop, making it a powerful tool for momentum analysis, reversals, and breakout strategies.
👽 What Makes the Multi-Scale AO – Wavelet-Enhanced Momentum Unique?
Unlike traditional AO indicators, this enhanced version leverages wavelet-based decomposition and volatility-adjusted normalization, ensuring improved signal consistency across various timeframes and assets.
✅ Wavelet Smoothing – Multi-Scale Extraction – Captures short-term fluctuations while preserving broader trend structures.
✅ Frequency-Based Detail Weights – Separates high, medium, and low-frequency components to reduce noise and improve trend clarity.
✅ Real-Time Divergence Detection – Identifies bullish and bearish divergences for early trend reversals.
✅ Crossovers & ATR-Based Trailing Stops – Implements intelligent trade management with adaptive stop-loss levels.
👽 The Math Behind the Indicator
👾 Wavelet-Based AO Smoothing
The indicator applies multi-scale wavelet decomposition to extract high-frequency, medium-frequency, and low-frequency trend components, ensuring an optimal balance between reactivity and smoothness.
sma1 = ta.sma(signal, waveletPeriod1)
sma2 = ta.sma(signal, waveletPeriod2)
sma3 = ta.sma(signal, waveletPeriod3)
detail1 = signal - sma1 // High-frequency detail
detail2 = sma1 - sma2 // Intermediate detail
detail3 = sma2 - sma3 // Low-frequency detail
advancedAO = weightDetail1 * detail1 + weightDetail2 * detail2 + weightDetail3 * detail3
Why It Works:
Short-Term Smoothing: Captures rapid fluctuations while minimizing noise.
Medium-Term Smoothing: Balances short-term and long-term trends.
Long-Term Smoothing: Enhances trend stability and reduces false signals.
👾 Z-Score Normalization
To ensure consistency across different markets, the Awesome Oscillator is normalized using a Z-score transformation, making overbought and oversold levels stable across all assets.
normFactor = ta.stdev(advancedAO, normPeriod)
normalizedAO = advancedAO / nz(normFactor, 1)
Why It Works:
Standardizes AO values for comparison across assets.
Enhances signal reliability, preventing misleading spikes.
👽 How Traders Can Use This Indicator
👾 Divergence Trading Strategy
Bullish Divergence
Price makes a lower low, while AO forms a higher low.
A buy signal is confirmed when AO starts rising.
Bearish Divergence
Price makes a higher high, while AO forms a lower high.
A sell signal is confirmed when AO starts declining.
👾 Buy & Sell Signals with Trailing Stop
Bullish Setup:
✅AO crosses above the bullish trigger level → Buy Signal.
✅Trailing stop placed at Low - (ATR × Multiplier).
✅Exit if price crosses below the stop.
Bearish Setup:
✅AO crosses below the bearish trigger level → Sell Signal.
✅Trailing stop placed at High + (ATR × Multiplier).
✅Exit if price crosses above the stop.
👽 Why It’s Useful for Traders
Wavelet-Enhanced Filtering – Retains essential trend details while eliminating excessive noise.
Multi-Scale Momentum Analysis – Separates different trend frequencies for enhanced clarity.
Real-Time Divergence Alerts – Identifies early reversal signals for better entries and exits.
ATR-Based Risk Management – Ensures stops dynamically adapt to market conditions.
Works Across Markets & Timeframes – Suitable for stocks, forex, crypto, and futures trading.
👽 Indicator Settings
AO Short Period – Defines the short-term moving average for AO calculation.
AO Long Period – Defines the long-term moving average for AO smoothing.
Wavelet Smoothing – Adjusts multi-scale decomposition for different market conditions.
Divergence Detection – Enables or disables real-time divergence analysis. Normalization Period – Sets the lookback period for standard deviation-based AO normalization.
Cross Signals Sensitivity – Controls crossover signal strength for buy/sell signals.
ATR Trailing Stop Multiplier – Adjusts the sensitivity of the trailing stop.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
M-oscillator
Price / 200 SMA Ratio (Pr)Price / 200 SMA Ratio (Pr) Indicator
The Price / 200 SMA Ratio (Pr) indicator is designed to help traders analyze the relationship between the current price and the 200-period Simple Moving Average (SMA). By calculating the ratio of the close price to the 200 SMA, the indicator provides a visual representation of how the price compares to the long-term trend, giving traders a clear view of potential overbought or oversold conditions.
How It Works:
Ratio Calculation:
The core of this indicator lies in the ratio between the current close price and the 200-period Simple Moving Average (SMA). The formula is straightforward:
Ratio = Close Price / 200 SMA
This ratio indicates whether the current price is above or below the long-term trend (the 200 SMA). A ratio greater than 1 means the price is above the 200 SMA, while a ratio below 1 suggests the price is below the 200 SMA.
Color-Coded Ratio Representation:
The ratio is displayed as a line on the chart with a color that changes dynamically based on the value of the ratio. The color-coding system helps quickly identify key levels:
Black: When the ratio is greater than 5, the price is significantly above the 200 SMA, indicating a highly overbought condition.
Red: When the ratio is greater than 3.5, it signals that the price is significantly above the long-term average but not in extreme territory.
Blue: When the ratio is less than 1, the price is below the 200 SMA, indicating that the market may be in an oversold condition.
Purple: When the ratio is below 0.7, it suggests an extremely oversold market, well below the long-term average.
Green: For values in between, the ratio is considered to be in a more neutral range, showing a balanced market position.
Horizontal Reference Lines:
To make the interpretation of the ratio easier, the indicator includes several reference lines plotted at key ratio levels. These lines help traders visualize specific price zones, giving them clear boundaries for potential trading decisions:
5 Zone (Black line): Marks an extremely high price level, indicating a highly overbought condition.
3.5 Zone (Red line): Represents the upper price zone, where prices are significantly higher than the 200 SMA.
2 Zone (Purple line): This line marks the mid-range of the ratio, providing a visual representation of the transition between overbought and oversold conditions.
1 Zone (Orange line): The 1.0 line is where the price equals the 200 SMA, indicating a balanced market. Prices above 1.0 are considered above average, and prices below 1.0 are below average.
0.7 Zone (Blue line): Represents a very low price level, suggesting an extremely oversold market.
Extra Low Zone (Green line): This line marks an even lower price level, indicating severe oversold conditions.
Background Coloring:
In addition to the ratio line and reference lines, the background color of the chart changes dynamically to provide additional context to the trader:
Red Background: When the ratio is greater than 3.5, the background becomes red, signaling an overbought market condition.
Blue Background: When the ratio is less than 1, the background turns blue, indicating a potential oversold market.
Black Background: If the ratio exceeds 5, the background will be black, signifying an extreme overbought condition.
Green Background: If the ratio drops below 0.7, the background turns green, highlighting an extremely oversold market.
Candle Coloring:
The indicator also changes the color of the individual price bars (candles) based on the ratio value:
Black Candles: When the ratio is greater than 5 or less than 0.7, the price bars are black to emphasize extreme conditions in the market.
White Candles: For all other values, the candles are white, representing a neutral market condition.
What This Indicator Tells You:
Overbought Conditions: When the ratio is significantly above 1 (especially greater than 3.5 or 5), it indicates that the price is far above the 200 SMA, suggesting that the market may be overbought and could experience a correction.
Oversold Conditions: When the ratio is significantly below 1 (especially below 0.7 or 0.5), it suggests that the price is far below the 200 SMA, indicating that the market may be oversold and could be due for a bounce.
Trend and Momentum: The ratio provides insight into the overall trend. If the ratio is consistently above 1, it means the price is generally in an uptrend, and if it’s below 1, it indicates a downtrend.
Why Use This Indicator?
The Price / 200 SMA Ratio indicator is a valuable tool for traders who want to gain insights into the strength or weakness of the price relative to the long-term trend (200 SMA). The color-coding system provides an easy-to-read visual cue, and the reference lines allow traders to identify key price levels where potential reversal or continuation could occur. It helps to spot areas of overbought or oversold conditions, making it ideal for traders looking to enter or exit positions based on extreme price movements.
By combining this indicator with other technical analysis tools, traders can enhance their strategy and make more informed decisions in the market.
Clustering Volatility (ATR-ADR-ChaikinVol) [Sam SDF-Solutions]The Clustering Volatility indicator is designed to evaluate market volatility by combining three widely used measures: Average True Range (ATR), Average Daily Range (ADR), and the Chaikin Oscillator.
Each indicator is normalized using one of the available methods (MinMax, Rank, or Z-score) to create a unified metric called the Score. This Score is further smoothed with an Exponential Moving Average (EMA) to reduce noise and provide a clearer view of market conditions.
Key Features:
Multi-Indicator Integration: Combines ATR, ADR, and the Chaikin Oscillator into a single Score that reflects overall market volatility.
Flexible Normalization: (Supports three normalization methods)
MinMax: Scales values between the observed minimum and maximum.
Rank: Normalizes based on the relative rank within a moving window.
Z-score: Standardizes values using mean and standard deviation.
Dynamic Window Selection: Offers an automatic window selection option based on a specified lookback period, or a fixed window size can be used.
Customizable Weights: Allows the user to assign individual weights to ATR, ADR, and the Chaikin Oscillator. Optionally, weights can be normalized to sum to 1.
Score Smoothing: Applies an EMA to the computed Score to smooth out short-term fluctuations and reduce market noise.
Cluster Visualization: Divides the smoothed Score into a number of clusters, each represented by a distinct color. These colors can be applied to the price bars (if enabled) for an immediate visual indication of the current volatility regime.
How It Works:
Input & Window Setup: Users set parameters for indicator periods, normalization methods, weights, and window size. The indicator can automatically determine the analysis window based on the number of lookback days.
Calculation of Metrics: The indicator computes the ATR, ADR (as the average of bar ranges), and the Chaikin Oscillator (based on the difference between short and long EMAs of the Accumulation/Distribution line).
Normalization & Scoring: Each indicator’s value is normalized and then weighted to form a raw Score. This raw Score is scaled to a range using statistics from the chosen window.
Smoothing & Clustering: The raw Score is smoothed using an EMA. The resulting smoothed Score is then multiplied by the number of clusters to assign a cluster index, which is used to choose a color for visual signals.
Visualization: The smoothed Score is plotted on the chart with a color that changes based on its value (e.g., lime for low, red for high, yellow for intermediate values). Optionally, the price bars are colored according to the assigned cluster.
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This indicator is ideal for traders seeking a quick and clear assessment of market volatility. By integrating multiple volatility measures into one comprehensive Score, it simplifies analysis and aids in making more informed trading decisions.
For more detailed instructions, please refer to the guide here:
Clustering & Divergences (RSI-Stoch-CCI) [Sam SDF-Solutions]The Clustering & Divergences (RSI-Stoch-CCI) indicator is a comprehensive technical analysis tool that consolidates three popular oscillators—Relative Strength Index (RSI), Stochastic, and Commodity Channel Index (CCI)—into one unified metric called the Score. This Score offers traders an aggregated view of market conditions, allowing them to quickly identify whether the market is oversold, balanced, or overbought.
Functionality:
Oscillator Clustering: The indicator calculates the values of RSI, Stochastic, and CCI using user-defined periods. These oscillator values are then normalized using one of three available methods: MinMax, Z-Score, or Z-Bins.
Score Calculation: Each normalized oscillator value is multiplied by its respective weight (which the user can adjust), and the weighted values are summed to generate an overall Score. This Score serves as a single, interpretable metric representing the combined oscillator behavior.
Market Clustering: The indicator performs clustering on the Score over a configurable window. By dividing the Score range into a set number of clusters (also configurable), the tool visually represents the market’s state. Each cluster is assigned a unique color so that traders can quickly see if the market is trending toward oversold, balanced, or overbought conditions.
Divergence Detection: The script automatically identifies both Regular and Hidden divergences between the price action and the Score. By using pivot detection on both price and Score data, the indicator marks potential reversal signals on the chart with labels and connecting lines. This helps in pinpointing moments when the price and the underlying oscillator dynamics diverge.
Customization Options: Users have full control over the indicator’s behavior. They can adjust:
The periods for each oscillator (RSI, Stochastic, CCI).
The weights applied to each oscillator in the Score calculation.
The normalization method and its manual boundaries.
The number of clusters and whether to invert the cluster order.
Parameters for divergence detection (such as pivot sensitivity and the minimum/maximum bar distance between pivots).
Visual Enhancements:
Depending on the user’s preference, either the Score or the Cluster Index (derived from the clustering process) is plotted on the chart. Additionally, the script changes the color of the price bars based on the identified cluster, providing an at-a-glance visual cue of the current market regime.
Logic & Methodology:
Input Parameters: The script starts by accepting user inputs for clustering settings, oscillator periods, weights, divergence detection, and manual boundary definitions for normalization.
Oscillator Calculation & Normalization: It computes RSI, Stochastic, and CCI values from the price data. These values are then normalized using either the MinMax method (scaling between a lower and upper band) or the Z-Score method (standardizing based on mean and standard deviation), or using Z-Bins for an alternative scaling approach.
Score Computation: Each normalized oscillator is multiplied by its corresponding weight. The sum of these products results in the overall Score that represents the combined oscillator behavior.
Clustering Algorithm: The Score is evaluated over a moving window to determine its minimum and maximum values. Using these values, the script calculates a cluster index that divides the Score into a predefined number of clusters. An option to invert the cluster calculation is provided to adjust the interpretation of the clustering.
Divergence Analysis: The indicator employs pivot detection (using left and right bar parameters) on both the price and the Score. It then compares recent pivot values to detect regular and hidden divergences. When a divergence is found, the script plots labels and optional connecting lines to highlight these key moments on the chart.
Plotting: Finally, based on the user’s selection, the indicator plots either the Score or the Cluster Index. It also overlays manual boundary lines (for the chosen normalization method) and adjusts the bar colors according to the cluster to provide clear visual feedback on market conditions.
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By integrating multiple oscillator signals into one cohesive tool, the Clustering & Divergences (RSI-Stoch-CCI) indicator helps traders minimize subjective analysis. Its dynamic clustering and automated divergence detection provide a streamlined method for assessing market conditions and potentially enhancing the accuracy of trading decisions.
For further details on using this indicator, please refer to the guide available at:
Custom TABI Model with LayersCustom Top and Bottom Indicator (TABI) (Is a Trend Adaptive Blow-Off Indicator) -
User Guide & Description
Introduction
The TABI (Trend Adaptive Blow-Off Indicator) is a refined, multi-layered RSI tool designed to enhance trend analysis, detect momentum shifts, and highlight overbought/oversold conditions with a more nuanced, color-coded approach. This indicator is useful for traders seeking to identify key reversal points, confirm trend strength, and filter trade setups more effectively than traditional RSI.
By incorporating volume-based confirmation and divergence detection, TABI aims to reduce false signals and improve trade timing.
How It Works
TABI builds on the Relative Strength Index (RSI) by introducing:
A smoothed RSI calculation for better trend readability.
11 color-coded RSI levels, allowing traders to visually distinguish weak, neutral, and extreme conditions.
Volume-based confirmation to detect high-conviction moves.
Bearish & Bullish Divergence Detection, inspired by Market Cipher methods, to spot potential reversals early.
Overbought & Oversold alerts, with optional candlestick color changes to highlight trade signals.
Key Features
✅ Color-Coded RSI for Better Readability
The RSI is divided into multi-layered color zones:
🔵 Light Blue: Extremely oversold
🟢 Lime Green: Mild oversold, potential trend reversal
🟡 Yellow & Orange: Neutral, momentum consolidation
🟠 Dark Orange: Caution, overbought conditions developing
🔴 Red: Extreme overbought, possible exhaustion
✅ Divergence Detection
Bearish Divergence: Price makes higher highs, RSI makes lower highs → Potential top signal
Bullish Divergence: Price makes lower lows, RSI makes higher lows → Potential bottom signal
✅ Volume Confirmation Filter
Requires a 50% above-average volume spike for strong buy/sell signals, reducing false breakouts.
✅ Dynamic Labels & Alerts
🚨 Blow-Off Top Warning: If RSI is overbought + volume spikes + divergence detected
🟢 Oversold Bottom Alert: If RSI is oversold + bullish divergence
Candlestick color changes when extreme conditions are met.
How to Use
📌 Entry & Exit Signals
Buy Consideration:
RSI enters Green Zone (oversold)
Bullish divergence detected
Volume confirms the move
Sell Consideration:
RSI enters Red Zone (overbought)
Bearish divergence detected
Volume confirms exhaustion
📌 Trend Confirmation
Use the yellow/orange levels to confirm strong trends before entering counter-trend trades.
📌 Filtering Trade Noise
The RSI smoothing helps reduce false whipsaws, making it easier to read true momentum shifts.
Customization Options
🔧 User-Defined RSI Thresholds
Adjust the overbought/oversold levels to match your trading style.
🔧 Divergence Sensitivity
Modify the lookback period to fine-tune divergence detection accuracy.
🔧 Volume Thresholds
Set custom volume multipliers to control confirmation requirements.
Why This is Unique
🔹 Unlike traditional RSI, TABI visually maps RSI zones into layered gradients, making it easy to spot momentum shifts.
🔹 The multi-layered color scheme adds an intuitive, heatmap-like effect to RSI, helping traders quickly gauge conditions.
🔹 Incorporates CCF-inspired divergence detection and volume filtering, making signals more robust.
🔹 Dynamic labeling system ensures clarity without cluttering the chart.
Alerts & Notifications
🔔 TradingView Alerts Included
🚨 Blow-Off Top Detected → RSI overbought + volume spike + bearish divergence.
🟢 Oversold Bottom Detected → RSI oversold + bullish divergence.
Set alerts to receive notifications without watching the charts 24/7.
Final Thoughts
TABI is designed to simplify RSI analysis, provide better trade signals, and improve decision-making. Whether you're day trading, swing trading, or long-term investing, this tool helps you navigate market conditions with confidence.
🔥 Use it to detect high-probability reversals, confirm trends, and improve trade entries/exits! 🚀
AI Adaptive Oscillator [PhenLabs]📊 Algorithmic Adaptive Oscillator
Version: PineScript™ v6
📌 Description
The AI Adaptive Oscillator is a sophisticated technical indicator that employs ensemble learning and adaptive weighting techniques to analyze market conditions. This innovative oscillator combines multiple traditional technical indicators through an AI-driven approach that continuously evaluates and adjusts component weights based on historical performance. By integrating statistical modeling with machine learning principles, the indicator adapts to changing market dynamics, providing traders with a responsive and reliable tool for market analysis.
🚀 Points of Innovation:
Ensemble learning framework with adaptive component weighting
Performance-based scoring system using directional accuracy
Dynamic volatility-adjusted smoothing mechanism
Intelligent signal filtering with cooldown and magnitude requirements
Signal confidence levels based on multi-factor analysis
🔧 Core Components
Ensemble Framework : Combines up to five technical indicators with performance-weighted integration
Adaptive Weighting : Continuous performance evaluation with automated weight adjustment
Volatility-Based Smoothing : Adapts sensitivity based on current market volatility
Pattern Recognition : Identifies potential reversal patterns with signal qualification criteria
Dynamic Visualization : Professional color schemes with gradient intensity representation
Signal Confidence : Three-tiered confidence assessment for trading signals
🔥 Key Features
The indicator provides comprehensive market analysis through:
Multi-Component Ensemble : Integrates RSI, CCI, Stochastic, MACD, and Volume-weighted momentum
Performance Scoring : Evaluates each component based on directional prediction accuracy
Adaptive Smoothing : Automatically adjusts based on market volatility
Pattern Detection : Identifies potential reversal patterns in overbought/oversold conditions
Signal Filtering : Prevents excessive signals through cooldown periods and minimum change requirements
Confidence Assessment : Displays signal strength through intuitive confidence indicators (average, above average, excellent)
🎨 Visualization
Gradient-Filled Oscillator : Color intensity reflects strength of market movement
Clear Signal Markers : Distinct bullish and bearish pattern signals with confidence indicators
Range Visualization : Clean representation of oscillator values from -6 to 6
Zero Line : Clear demarcation between bullish and bearish territory
Customizable Colors : Color schemes that can be adjusted to match your chart style
Confidence Symbols : Intuitive display of signal confidence (no symbol, +, or ++) alongside direction markers
📖 Usage Guidelines
⚙️ Settings Guide
Color Settings
Bullish Color
Default: #2b62fa (Blue)
This setting controls the color representation for bullish movements in the oscillator. The color appears when the oscillator value is positive (above zero), with intensity indicating the strength of the bullish momentum. A brighter shade indicates stronger bullish pressure.
Bearish Color
Default: #ce9851 (Amber)
This setting determines the color representation for bearish movements in the oscillator. The color appears when the oscillator value is negative (below zero), with intensity reflecting the strength of the bearish momentum. A more saturated shade indicates stronger bearish pressure.
Signal Settings
Signal Cooldown (bars)
Default: 10
Range: 1-50
This parameter sets the minimum number of bars that must pass before a new signal of the same type can be generated. Higher values reduce signal frequency and help prevent overtrading during choppy market conditions. Lower values increase signal sensitivity but may generate more false positives.
Min Change For New Signal
Default: 1.5
Range: 0.5-3.0
This setting defines the minimum required change in oscillator value between consecutive signals of the same type. It ensures that new signals represent meaningful changes in market conditions rather than minor fluctuations. Higher values produce fewer but potentially higher-quality signals, while lower values increase signal frequency.
AI Core Settings
Base Length
Default: 14
Minimum: 2
This fundamental setting determines the primary calculation period for all technical components in the ensemble (RSI, CCI, Stochastic, etc.). It represents the lookback window for each component’s base calculation. Shorter periods create a more responsive but potentially noisier oscillator, while longer periods produce smoother signals with potential lag.
Adaptive Speed
Default: 0.1
Range: 0.01-0.3
Controls how quickly the oscillator adapts to new market conditions through its volatility-adjusted smoothing mechanism. Higher values make the oscillator more responsive to recent price action but potentially more erratic. Lower values create smoother transitions but may lag during rapid market changes. This parameter directly influences the indicator’s adaptiveness to market volatility.
Learning Lookback Period
Default: 150
Minimum: 10
Determines the historical data range used to evaluate each ensemble component’s performance and calculate adaptive weights. This setting controls how far back the AI “learns” from past performance to optimize current signals. Longer periods provide more stable weight distribution but may be slower to adapt to regime changes. Shorter periods adapt more quickly but may overreact to recent anomalies.
Ensemble Size
Default: 5
Range: 2-5
Specifies how many technical components to include in the ensemble calculation.
Understanding The Interaction Between Settings
Base Length and Learning Lookback : The base length determines the reactivity of individual components, while the lookback period determines how their weights are adjusted. These should be balanced according to your timeframe - shorter timeframes benefit from shorter base lengths, while the lookback should generally be 10-15 times the base length for optimal learning.
Adaptive Speed and Signal Cooldown : These settings control sensitivity from different angles. Increasing adaptive speed makes the oscillator more responsive, while reducing signal cooldown increases signal frequency. For conservative trading, keep adaptive speed low and cooldown high; for aggressive trading, do the opposite.
Ensemble Size and Min Change : Larger ensembles provide more stable signals, allowing for a lower minimum change threshold. Smaller ensembles might benefit from a higher threshold to filter out noise.
Understanding Signal Confidence Levels
The indicator provides three distinct confidence levels for both bullish and bearish signals:
Average Confidence (▲ or ▼) : Basic signal that meets the minimum pattern and filtering criteria. These signals indicate potential reversals but with moderate confidence in the prediction. Consider using these as initial alerts that may require additional confirmation.
Above Average Confidence (▲+ or ▼+) : Higher reliability signal with stronger underlying metrics. These signals demonstrate greater consensus among the ensemble components and/or stronger historical performance. They offer increased probability of successful reversals and can be traded with less additional confirmation.
Excellent Confidence (▲++ or ▼++) : Highest quality signals with exceptional underlying metrics. These signals show strong agreement across oscillator components, excellent historical performance, and optimal signal strength. These represent the indicator’s highest conviction trade opportunities and can be prioritized in your trading decisions.
Confidence assessment is calculated through a multi-factor analysis including:
Historical performance of ensemble components
Degree of agreement between different oscillator components
Relative strength of the signal compared to historical thresholds
✅ Best Use Cases:
Identify potential market reversals through oscillator extremes
Filter trade signals based on AI-evaluated component weights
Monitor changing market conditions through oscillator direction and intensity
Confirm trade signals from other indicators with adaptive ensemble validation
Detect early momentum shifts through pattern recognition
Prioritize trading opportunities based on signal confidence levels
Adjust position sizing according to signal confidence (larger for ++ signals, smaller for standard signals)
⚠️ Limitations
Requires sufficient historical data for accurate performance scoring
Ensemble weights may lag during dramatic market condition changes
Higher ensemble sizes require more computational resources
Performance evaluation quality depends on the learning lookback period length
Even high confidence signals should be considered within broader market context
💡 What Makes This Unique
Adaptive Intelligence : Continuously adjusts component weights based on actual performance
Ensemble Methodology : Combines strength of multiple indicators while minimizing individual weaknesses
Volatility-Adjusted Smoothing : Provides appropriate sensitivity across different market conditions
Performance-Based Learning : Utilizes historical accuracy to improve future predictions
Intelligent Signal Filtering : Reduces noise and false signals through sophisticated filtering criteria
Multi-Level Confidence Assessment : Delivers nuanced signal quality information for optimized trading decisions
🔬 How It Works
The indicator processes market data through five main components:
Ensemble Component Calculation :
Normalizes traditional indicators to consistent scale
Includes RSI, CCI, Stochastic, MACD, and volume components
Adapts based on the selected ensemble size
Performance Evaluation :
Analyzes directional accuracy of each component
Calculates continuous performance scores
Determines adaptive component weights
Oscillator Integration :
Combines weighted components into unified oscillator
Applies volatility-based adaptive smoothing
Scales final values to -6 to 6 range
Signal Generation :
Detects potential reversal patterns
Applies cooldown and magnitude filters
Generates clear visual markers for qualified signals
Confidence Assessment :
Evaluates component agreement, historical accuracy, and signal strength
Classifies signals into three confidence tiers (average, above average, excellent)
Displays intuitive confidence indicators (no symbol, +, ++) alongside direction markers
💡 Note:
The AI Adaptive Oscillator performs optimally when used with appropriate timeframe selection and complementary indicators. Its adaptive nature makes it particularly valuable during changing market conditions, where traditional fixed-weight indicators often lose effectiveness. The ensemble approach provides a more robust analysis by leveraging the collective intelligence of multiple technical methodologies. Pay special attention to the signal confidence indicators to optimize your trading decisions - excellent (++) signals often represent the most reliable trade opportunities.
Binance BTC Backwardation / ContangoThis indicator calculates difference between price of Binance BTCUSDT, and Binance BTCUSDT.P.
If the difference is negative, then it is backwardation.
If the difference is positive, then it is contango.
SMA Trend Filter Oscillator (Adaptive)The "SMA Trend Filter Oscillator (Adaptive)" indicator is a technical analysis tool that helps traders determine the direction and strength of a trend based on an adaptive Simple Moving Average (SMA). The oscillator calculates the difference between the closing price and the SMA value, allowing for the visualization of price deviation from the average and the assessment of current market dynamics.
Key Features of the Indicator:
Adaptation to Time Frame: The indicator automatically adjusts the SMA length based on the current time frame, making it versatile for use across different time intervals. For example:
Monthly Time Frame: SMA with a length of 50.
Weekly Time Frame: SMA with a length of 40.
Daily Time Frame: SMA with a length of 20.
Hourly Time Frame: SMA with a length of 10.
Intraday Time Frames: SMA with a length of 5 (for time frames up to 15 minutes) or 7 (for others).
SMA-Based Oscillator: The oscillator is calculated as the difference between the closing price and the SMA value. This allows:
Bullish Trend Identification: When the oscillator is above zero (price is above SMA).
Bearish Trend Identification: When the oscillator is below zero (price is below SMA).
Visualization: The oscillator is displayed as a histogram, where:
Green Color indicates a bullish trend.
Red Color indicates a bearish trend.
The Zero Line (Gray) serves as a reference for trend reversal.
How to Use the Indicator:
Trend Identification: If the oscillator is above zero and colored green, it signals a bullish trend. If it is below zero and colored red, it indicates a bearish trend.
Trend Strength: The larger the oscillator value (in either direction), the stronger the trend. Small oscillator values (close to zero) may indicate sideways movement or weak trend.
Entry and Exit Points:
Buy: When the oscillator crosses the zero line from below to above (transition from red to green).
Sell: When the oscillator crosses the zero line from above to below (transition from green to red).
Signal Filtering: Use the indicator in combination with other technical analysis tools (e.g., RSI, MACD, or support/resistance levels) to confirm signals.
Advantages of the Indicator:
Adaptability: Automatic adjustment of SMA length to the current time frame makes it versatile.
Simplicity: Intuitive histogram visualization allows for quick assessment of market conditions.
Flexibility: Can be used on any market (stocks, forex, cryptocurrencies) and time frame.
Limitations:
Lag: Like any SMA-based indicator, it can lag due to the use of average values.
False Signals: In sideways markets (flat), the indicator may generate false signals.
Risk Management:
Always set stop-losses and take-profits to minimize losses.
Test the indicator on historical data before using it on a live account.
The "SMA Trend Filter Oscillator (Adaptive)" is a powerful tool for traders seeking to quickly evaluate trends and their strength. Its adaptability and simplicity make it suitable for both novice and experienced traders.
Индикатор "SMA Trend Filter Oscillator (Adaptive)" — это инструмент технического анализа, который помогает трейдерам определять направление тренда и его силу на основе адаптивной скользящей средней (SMA). Осциллятор рассчитывает разницу между ценой закрытия и значением SMA, что позволяет визуализировать отклонение цены от среднего значения и оценивать текущую рыночную динамику.
Основные особенности индикатора:
Адаптация к таймфрейму
Индикатор автоматически подстраивает длину SMA в зависимости от текущего таймфрейма, что делает его универсальным для использования на различных временных интервалах. Например:
Месячный таймфрейм (Monthly): SMA с длиной 50.
Недельный таймфрейм (Weekly): SMA с длиной 40.
Дневной таймфрейм (Daily): SMA с длиной 20.
Часовой таймфрейм (Hourly): SMA с длиной 10.
Внутридневные таймфреймы (Intraday): SMA с длиной 5 (для таймфреймов до 15 минут) или 7 (для остальных).
Осциллятор на основе SMA
Осциллятор рассчитывается как разница между ценой закрытия и значением SMA. Это позволяет:
Определять бычий тренд, когда осциллятор выше нуля (цена выше SMA).
Определять медвежий тренд, когда осциллятор ниже нуля (цена ниже SMA).
Визуализация
Осциллятор отображается в виде гистограммы, где:
Зелёный цвет указывает на бычий тренд.
Красный цвет указывает на медвежий тренд.
Линия нуля (серая) служит ориентиром для определения смены тренда.
Как использовать индикатор:
Определение тренда
Если осциллятор находится выше нуля и окрашен в зелёный цвет, это сигнализирует о бычьем тренде.
Если осциллятор находится ниже нуля и окрашен в красный цвет, это указывает на медвежий тренд.
Сила тренда
Чем больше значение осциллятора (в положительную или отрицательную сторону), тем сильнее тренд.
Небольшие значения осциллятора (близкие к нулю) могут указывать на боковое движение или слабость тренда.
Точки входа и выхода
Покупка (Buy): Когда осциллятор пересекает нулевую линию снизу вверх (переход из красной зоны в зелёную).
Продажа (Sell): Когда осциллятор пересекает нулевую линию сверху вниз (переход из зелёной зоны в красную).
Фильтрация сигналов
Используйте индикатор в сочетании с другими инструментами технического анализа (например, RSI, MACD или уровнями поддержки/сопротивления) для подтверждения сигналов.
Преимущества индикатора:
Адаптивность: Автоматическая настройка длины SMA под текущий таймфрейм делает индикатор универсальным.
Простота: Интуитивно понятная визуализация в виде гистограммы позволяет быстро оценить рыночную ситуацию.
Гибкость: Может использоваться на любых рынках (акции, форекс, криптовалюты) и таймфреймах.
Ограничения:
Запаздывание: Как и любой индикатор на основе SMA, он может запаздывать из-за использования средних значений.
Ложные сигналы: В условиях бокового движения (флэта) индикатор может генерировать ложные сигналы.
Управление рисками: Всегда устанавливайте стоп-лоссы и тейк-профиты, чтобы минимизировать потери.
Тестирование: Перед использованием на реальном счёте протестируйте индикатор на исторических данных.
Индикатор "SMA Trend Filter Oscillator (Adaptive)" — это мощный инструмент для трейдеров, которые хотят быстро оценить тренд и его силу. Его адаптивность и простота делают его подходящим как для начинающих, так и для опытных трейдеров
Enhanced ROC - Savitzky–Golay [AIBitcoinTrend]👽 Adaptive ROC - Savitzky–Golay (AIBitcoinTrend)
The Adaptive ROC - Savitzky–Golay redefines traditional Rate of Change (ROC) analysis by integrating Savitzky–Golay smoothing with volatility-adaptive normalization, allowing it to dynamically adjust across different market conditions. Unlike the standard ROC, which reacts rigidly to price changes, this advanced version refines trend signals while maintaining responsiveness to volatility.
Additionally, this indicator features real-time divergence detection and an ATR-based trailing stop system, equipping traders with a powerful toolset for momentum analysis, reversals, and trend-following strategies.
👽 What Makes the Adaptive ROC - Savitzky–Golay Unique?
Unlike conventional ROC indicators, this enhanced version leverages volatility-adjusted scaling and Z-score normalization to improve signal consistency across different timeframes and assets.
✅ Savitzky–Golay Smoothing – Reduces noise while preserving trend structure for clearer signals.
✅ Volatility-Adaptive Normalization – Ensures that overbought and oversold thresholds remain consistent across different markets.
✅ Real-Time Divergence Detection – Identifies early bullish and bearish divergence signals for potential reversals.
✅ Crossovers & ATR-Based Trailing Stops – Implements intelligent trade management with dynamic stop levels.
👽 The Math Behind the Indicator
👾 Savitzky–Golay Smoothing
The indicator applies a Savitzky–Golay filter to the raw ROC data, creating a smoother curve while preserving key inflection points. This technique prevents excessive lag while maintaining the integrity of price movements.
sg_roc = (roc_raw + 3*roc_raw + 5*roc_raw + 7*roc_raw + 5*roc_raw + 3*roc_raw + roc_raw ) / 25
👾 Volatility-Adaptive Scaling
By dynamically adjusting the smoothed ROC using standard deviation, the indicator ensures that momentum readings remain relative to the market’s current volatility.
volatility = ta.stdev(close, rocLength)
dynamicFactor = 1 / (1 + volatility / 100)
advanced_sg_roc = sg_roc * dynamicFactor
👾 Z-Score Normalization
To maintain a stable Overbought/Oversold structure across different markets, the ROC is normalized using a Z-score transformation, ensuring its values remain statistically relevant.
rocMean = ta.wma(advanced_sg_roc, lenZ)
rocStdev = ta.stdev(advanced_sg_roc, lenZ)
zRoc = (advanced_sg_roc - rocMean) / rocStdev
👽 How Traders Can Use This Indicator
👾 Divergence Trading Strategy
Bullish Divergence Setup:
Price makes a lower low, while the ROC forms a higher low.
A buy signal is confirmed when the ROC starts rising.
Bearish Divergence Setup:
Price makes a higher high, while the ROC forms a lower high.
A sell signal is confirmed when the ROC starts declining.
👾 Buy & Sell Signals with Trailing Stop
Bullish Setup:
✅ ROC crosses above the bullish trigger level → Buy Signal.
✅ A bullish trailing stop is placed at Low - (ATR × Multiplier).
✅ Exit if price crosses below the stop.
Bearish Setup:
✅ ROC crosses below the bearish trigger level → Sell Signal.
✅ A bearish trailing stop is placed at High + (ATR × Multiplier).
✅ Exit if price crosses above the stop.
👽 Why It’s Useful for Traders
Savitzky–Golay Filtering – Retains essential trend details while eliminating excessive noise.
Volatility-Adjusted Normalization – Makes overbought/oversold levels universally reliable across markets.
Real-Time Divergence Alerts – Identifies early reversal signals for optimal entries and exits.
ATR-Based Risk Management – Ensures stops dynamically adapt to market conditions.
Works Across Markets & Timeframes - Suitable for stocks, forex, crypto, and futures trading.
👽 Indicator Settings
ROC Period – Defines the number of bars used for ROC calculation.
Smoothing Strength – Adjusts the degree of Savitzky–Golay filtering.
Volatility Scaling – Enables or disables the adaptive volatility factor.
Enable Divergence Analysis – Turns on real-time divergence detection.
Lookback Period – Specifies the pivot detection period for divergences.
Enable Crosses Signals – Activates trade signals based on ROC crossovers.
ATR Multiplier – Controls the sensitivity of the trailing stop.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
Volatility-Enhanced Williams %R [AIBitcoinTrend]👽 Volatility-Enhanced Williams %R (AIBitcoinTrend)
The Volatility-Enhanced Williams %R takes the classic Williams %R oscillator to the next level by incorporating volatility-adaptive smoothing, making it significantly more responsive to market dynamics. Unlike the traditional version, which uses a fixed calculation method, this indicator dynamically adjusts its smoothing factor based on market volatility, helping traders capture trends more effectively while filtering out noise.
Additionally, the indicator includes real-time divergence detection and an ATR-based trailing stop system, providing traders with enhanced risk management tools and early reversal signals.
👽 What Makes the Volatility-Enhanced Williams %R Unique?
Unlike the standard Williams %R, which applies a simple lookback-based formula, this version integrates adaptive smoothing and volatility-based filtering to refine its signals and reduce false breakouts.
✅ Volatility-Adaptive Smoothing – Adjusts dynamically based on standard deviation, enhancing signal accuracy.
✅ Real-Time Divergence Detection – Identifies bullish and bearish divergences for early trend reversal signals.
✅ Crossovers & Trailing Stops – Implements Williams %R crossovers with ATR-based trailing stops for intelligent trade management.
👽 The Math Behind the Indicator
👾 Volatility-Adaptive Smoothing
The indicator smooths the Williams %R calculation by applying an adaptive filtering mechanism, which adjusts its responsiveness based on market conditions. This helps to eliminate whipsaws and makes trend-following strategies more reliable.
The smoothing function is defined as:
clamp(x, lo, hi) => math.min(math.max(x, lo), hi)
adaptive(src, prev, len, divisor, minAlpha, maxAlpha) =>
vol = ta.stdev(src, len)
alpha = clamp(vol / divisor, minAlpha, maxAlpha)
prev + alpha * (src - prev)
Where:
Volatility Factor (vol) measures price dispersion using standard deviation.
Adaptive Alpha (alpha) dynamically adjusts smoothing strength.
Clamped Output ensures that the smoothing factor remains within a stable range.
👽 How Traders Can Use This Indicator
👾 Divergence Trading Strategy
Bullish Divergence Setup:
Price makes a lower low, while Williams %R forms a higher low.
Buy signal is confirmed when Williams %R reverses upward.
Bearish Divergence Setup:
Price makes a higher high, while Williams %R forms a lower high.
Sell signal is confirmed when Williams %R reverses downward.
👾 Trailing Stop & Signal-Based Trading
Bullish Setup:
✅ Williams %R crosses above trigger level → Buy signal.
✅ A bullish trailing stop is placed at Low - (ATR × Multiplier).
✅ Exit if price crosses below the stop.
Bearish Setup:
✅ Williams %R crosses below trigger level → Sell signal.
✅ A bearish trailing stop is placed at High + (ATR × Multiplier).
✅ Exit if price crosses above the stop.
👽 Why It’s Useful for Traders
Adaptive Filtering Mechanism – Avoids excessive noise while maintaining responsiveness.
Real-Time Divergence Alerts – Helps traders anticipate market reversals before they occur.
ATR-Based Risk Management – Stops dynamically adjust based on market volatility.
Multi-Market Compatibility – Works effectively across stocks, forex, crypto, and futures.
👽 Indicator Settings
Smoothing Factor – Controls how aggressively the indicator adapts to volatility.
Enable Divergence Analysis – Activates real-time divergence detection.
Lookback Period – Defines the number of bars for detecting pivot points.
Enable Crosses Signals – Turns on Williams %R crossover-based trade signals.
ATR Multiplier – Adjusts trailing stop sensitivity.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
Normalised Price Crossover - MACD but TickersEver noticed two different tickers are correlated yet have different lags? Ever find one ticker moves first and when the other finally goes to catch up, the first one has already reversed?
So I thought to myself, would be wicked if I took the faster one and made it into a 'Signal Line' and the slow one and made it into a 'Slow Line' almost like a MACD if you will.
So that's what I did, I took the price charts of the tickers and I normalised the price data so they could actually cross, plotted it and sat back to see it generate signals, lo and behold!
Pretty neat, though I'd advise to use spreads and such for the different tickers to really feel the power of the indicator, works well when you use formulas that model actual mechanisms instead of arbitrary price data of different assets as correlation =/= causation.
Enjoy.
Relative Vigor Index (RVI) with EMD [AIBitcoinTrend]👽 Adaptive Relative Vigor Index with EMD & Signals (AIBitcoinTrend)
The Adaptive Relative Vigor Index (RVI) with Empirical Mode Decomposition (EMD) is an enhanced version of the traditional RVI, designed to improve signal clarity and responsiveness to market conditions. By integrating EMD smoothing and adaptive volatility-based trailing stops.
👽 What Makes the Adaptive RVI with EMD Unique?
Unlike the standard RVI, which often lags in volatile markets, this version refines price momentum detection by applying Empirical Mode Decomposition (EMD), effectively filtering out noise. Additionally, it features ATR-based trailing stops for precise trade execution.
Key Features:
EMD-Enhanced RVI – Filters out short-term noise, improving signal accuracy.
Crossover & Crossunder Signals – Generates trade signals based on RVI trends.
ATR-Based Trailing Stop – Adjusts dynamically based on volatility for optimal risk management.
👽 The Math Behind the Indicator
👾 RVI Calculation with EMD Smoothing
The Relative Vigor Index (RVI) measures trend strength by comparing the relationship between closing and opening prices, relative to the high-low range. Traditional RVI uses fixed smoothing, whereas this version applies Empirical Mode Decomposition (EMD) to extract dominant price cycles and improve trend clarity.
How It Works:
The RVI is initially calculated using a weighted moving average (WMA) over a specified period.
EMD refines the RVI signal by removing high-frequency noise, creating a smoothed RVI component.
This results in a more stable and reliable trend indicator.
👽 How Traders Can Use This Indicator
👾 Trailing Stop & Signal-Based Trading
Bullish Setup:
✅ RVI crosses above EMD → Buy signal.
✅ A bullish trailing stop is placed at low - ATR × Multiplier.
✅ Exit if price crosses below the stop.
Bearish Setup:
✅ RVI crosses below EMD → Sell signal.
✅ A bearish trailing stop is placed at high + ATR × Multiplier.
✅ Exit if price crosses above the stop.
👾 Detecting Overbought & Oversold Areas
This indicator helps traders identify potential reversal zones by highlighting overbought and oversold conditions.
Overbought Zone: When RVI moves above 0.4, the market may be overextended, signaling a potential reversal downward.
Oversold Zone: When RVI moves below -0.4, the market may be undervalued, suggesting a possible upward reversal.
Using these levels, traders can confirm entry and exit points alongside divergence signals for higher probability trades.
👽 Why It’s Useful for Traders
EMD-Based Signal Enhancement: Filters out noise, refining momentum signals.
Adaptive ATR-Based Risk Management: Automatically adjusts stop-loss levels to market conditions.
Works Across Multiple Markets & Timeframes: Effective for stocks, forex, crypto, and futures trading.
👽 Indicator Settings
RVI Length – Defines the period for calculating the Relative Vigor Index.
EMD Period – Controls the level of EMD smoothing applied.
Final Smoothing – Adjusts the degree of additional signal filtering.
Lookback Period – Determines how many bars are used for detecting pivot points.
Enable Trailing Stop – Activates dynamic ATR-based trailing stops.
ATR Multiplier – Adjusts the stop-loss sensitivity.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
Pure CocaPure Coca - Trend & Mean Reversion Indicator
Overview
The Pure Coca indicator is a trend and mean reversion analysis tool designed for identifying dynamic shifts in market behavior. By leveraging Z-score calculations, this indicator captures both trend-following and mean-reverting periods, making it useful for a wide range of trading strategies.
What It Does
📉 Detects Overbought & Oversold Conditions using a Z-score framework.
🎯 Identifies Trend vs. Mean Reversion Phases by analyzing the deviation of price from its historical average.
📊 Customizable Moving Averages (EMA, SMA, VWMA, etc.) for smoothing Z-score calculations.
🔄 Adaptable to Any Timeframe – Default settings are optimized for 2D charts but can be adjusted to suit different market conditions.
How It Works
Computes a Z-score of price movements, normalized over a lookback period.
Plots upper and lower boundaries to visualize extreme price movements.
Dynamic Midlines adjust entry and exit conditions based on market shifts.
Background & Bar Coloring help traders quickly identify trading opportunities.
Key Features & Inputs
✔ Lookback Period: Adjustable period for calculating Z-score.
✔ Custom MA Smoothing: Choose from EMA, SMA, WMA, VWAP, and more.
✔ Z-Score Thresholds: Set upper and lower bounds to define overbought/oversold conditions.
✔ Trend vs. Mean Reversion Mode: Enables traders to spot momentum shifts in real-time.
✔ Bar Coloring & Background Highlights: Enhances visual clarity for decision-making.
How to Use It
Trend Trading: Enter when the Z-score crosses key levels (upper/lower boundary).
Mean Reversion: Look for reversals when price returns to the midline.
Custom Optimization: Adjust lookback periods and MA types based on market conditions.
Why It's Unique
✅ Combines Trend & Mean Reversion Analysis in one indicator.
✅ Flexible Z-score settings & MA choices for enhanced adaptability.
✅ Clear visual representation of market extremes.
Final Notes
This indicator is best suited for discretionary traders, quantitative analysts, and systematic traders looking for data-driven market insights. As with any trading tool, use in conjunction with other analysis methods for optimal results.
Market Participation Index [PhenLabs]📊 Market Participation Index
Version: PineScript™ v6
📌 Description
Market Participation Index is a well-evolved statistical oscillator that constantly learns to develop by adapting to changing market behavior through the intricate mathematical modeling process. MPI combines different statistical approaches and Bayes’ probability theory of analysis to provide extensive insight into market participation and building momentum. MPI combines diverse statistical thinking principles of physics and information and marries them for subtle changes to occur in markets, levels to become influential as important price targets, and pattern divergences to unveil before it is visible by analytical methods in an old-fashioned methodology.
🚀 Points of Innovation:
Automatic market condition detection system with intelligent preset selection
Multi-statistical approach combining classical and advanced metrics
Fractal-based divergence system with quality scoring
Adaptive threshold calculation using statistical properties of current market
🚨 Important🚨
The ‘Auto’ mode intelligently selects the optimal preset based on real-time market conditions, if the visualization does not appear to the best of your liking then select the option in parenthesis next to the auto mode on the label in the oscillator in the settings panel.
🔧 Core Components
Statistical Foundation: Multiple statistical measures combined with weighted approach
Market Condition Analysis: Real-time detection of market states (trending, ranging, volatile)
Change Point Detection: Bayesian analysis for finding significant market structure shifts
Divergence System: Fractal-based pattern detection with quality assessment
Adaptive Visualization: Dynamic color schemes with context-appropriate settings
🔥 Key Features
The indicator provides comprehensive market analysis through:
Multi-statistical Oscillator: Combines Z-score, MAD, and fractal dimensions
Advanced Statistical Components: Includes skewness, kurtosis, and entropy analysis
Auto-preset System: Automatically selects optimal settings for current conditions
Fractal Divergence Analysis: Detects and grades quality of divergence patterns
Adaptive Thresholds: Dynamically adjusts overbought/oversold levels
🎨 Visualization
Color-coded Oscillator: Gradient-filled oscillator line showing intensity
Divergence Markings: Clear visualization of bullish and bearish divergences
Threshold Lines: Dynamic or fixed overbought/oversold levels
Preset Information: On-chart display of current market conditions
Multiple Color Schemes: Modern, Classic, Monochrome, and Neon themes
Classic
Modern
Monochrome
Neon
📖 Usage Guidelines
The indicator offers several customization options:
Market Condition Settings:
Preset Mode: Choose between Auto-detection or specific market condition presets
Color Theme: Select visual theme matching your chart style
Divergence Labels: Choose whether or not you’d like to see the divergence
✅ Best Use Cases:
Identify potential market reversals through statistical divergences
Detect changes in market structure before price confirmation
Filter trades based on current market condition (trending vs. ranging)
Find optimal entry and exit points using adaptive thresholds
Monitor shifts in market participation and momentum
⚠️ Limitations
Requires sufficient historical data for accurate statistical analysis
Auto-detection may lag during rapid market condition changes
Advanced statistical calculations have higher computational requirements
Manual preset selection may be required in certain transitional markets
💡 What Makes This Unique
Statistical Depth: Goes beyond traditional indicators with advanced statistical measures
Adaptive Intelligence: Automatically adjusts to current market conditions
Bayesian Analysis: Identifies statistically significant change points in market structure
Multi-factor Approach: Combines multiple statistical dimensions for confirmation
Fractal Divergence System: More robust than traditional divergence detection methods
🔬 How It Works
The indicator processes market data through four main components:
Market Condition Analysis:
Evaluates trend strength, volatility, and price patterns
Automatically selects optimal preset parameters
Adapts sensitivity based on current conditions
Statistical Oscillator:
Combines multiple statistical measures with weights
Normalizes values to consistent scale
Applies adaptive smoothing
Advanced Statistical Analysis:
Calculates higher-order statistical moments
Applies information-theoretic measures
Detects distribution anomalies
Divergence Detection:
Uses fractal theory to identify pivot points
Detects and scores divergence quality
Filters signals based on current market phase
💡 Note:
The Market Participation Index performs optimally when used across multiple timeframes for confirmation. Its statistical foundation makes it particularly valuable during market transitions and periods of changing volatility, where traditional indicators often fail to provide clear signals.
Bollinger Bands MTF & Kalman Filter | Flux Charts📈 Multi-Timeframe Kalman Filtered Bollinger Bands Indicator
Introducing our MTF Kalman Filtered Bollinger Bands – a powerful multi-timeframe Bollinger Bands (BB) indicator enhanced with Kalman filtering for superior smoothing and trend analysis. This indicator dynamically adapts Bollinger Bands across multiple timeframes while incorporating volume-based gradient transparency to highlight significant price movements. This indicator is better optimized for lower timeframes.
❓ How to Interpret the Bands & Volume Gradient:
Our indicator combines Lower Timeframe (LTF) and Higher Timeframe (HTF) Bollinger Bands to provide a comprehensive trend analysis. It applies Kalman filtering to the LTF bands, ensuring smoother, noise-reduced signals. The color gradient and relative volume-based transparency offer deeper insights into price strength.
🔹 LTF Bollinger Bands: Shorter-period bands filtered with a Kalman smoothing algorithm, reducing lag and noise.
🔹 HTF Bollinger Bands: Traditional Bollinger Bands plotted on a higher timeframe, offering macro trend analysis.
🔹 Volume Gradient Transparency: The bands adjust their opacity based on relative buy/sell volume, allowing traders to assess momentum strength.
📌 How Does It Work?
1️⃣ Multi-Timeframe Bollinger Bands Calculation
The LTF BB uses Kalman filtering for a smoother price representation, helping to reduce false signals.
The HTF BB is EMA-smoothed for improved trend clarity.
2️⃣ Adaptive Gradient Transparency
The opacity of the fill color between the bands is determined by relative buy/sell volume.
Higher buy volume = stronger bullish signal (greener bands).
Higher sell volume = stronger bearish signal (redder bands).
3️⃣ Dynamic Trend Signals & Breakouts
Buy Signal: When price breaks below the HTF lower band and LTF bands start rising.
Sell Signal: When price breaks above the HTF upper band and LTF bands start falling.
⚙️ Settings & Customization:
🛠 LTF and HTF Bollinger Bands Settings:
Multiplier: The multiplier applied to the BB to determine the upper and lower bands
Length: Define the number of bars determines the BB calculations.
Custom Timeframe Selection: Choose from predefined options (e.g., 5m, 15m, 1H, 4H, etc).
🎨 Gradient & Transparency Settings:
Bullish/Bearish Color Options: Customize colors for uptrend and downtrend conditions.
Max & Min Opacity: Adjust the transparency levels based on volume intensity.
Solid vs. Gradient Mode: Choose between a gradient fill or a solid color mode for clarity.
📌 Recommended Settings for Optimal Use:
1️⃣ Timeframe Selection (LTF -> HTF):
1 min -> 5 min
2 min -> 5 min
3 min -> 15 min
5 min -> 15 min
15 min -> 1 hr
1 hr -> 4 hr
4 hr -> 1 day
2️⃣ Multiplier: Use 2.0 for LTF and 2.25 for HTF
3️⃣Length: Use a length of 20 - 30 bars
🚀 Why Use This Indicator?
✅ Multi-Timeframe Bollinger Bands with Kalman Filtering – Ideal for traders looking for reduced lag and clearer trend signals.
✅ Volume-Based Transparency – See momentum shifts instantly with adaptive opacity.
✅ Dynamic Buy & Sell Signals – Alerts based on price action + volume trends.
✅ Customizable for Any Strategy – Adjust colors, timeframes, and filtering options for personalized trading.
TEMA OBOS Strategy PakunTEMA OBOS Strategy
Overview
This strategy combines a trend-following approach using the Triple Exponential Moving Average (TEMA) with Overbought/Oversold (OBOS) indicator filtering.
By utilizing TEMA crossovers to determine trend direction and OBOS as a filter, it aims to improve entry precision.
This strategy can be applied to markets such as Forex, Stocks, and Crypto, and is particularly designed for mid-term timeframes (5-minute to 1-hour charts).
Strategy Objectives
Identify trend direction using TEMA
Use OBOS to filter out overbought/oversold conditions
Implement ATR-based dynamic risk management
Key Features
1. Trend Analysis Using TEMA
Uses crossover of short-term EMA (ema3) and long-term EMA (ema4) to determine entries.
ema4 acts as the primary trend filter.
2. Overbought/Oversold (OBOS) Filtering
Long Entry Condition: up > down (bullish trend confirmed)
Short Entry Condition: up < down (bearish trend confirmed)
Reduces unnecessary trades by filtering extreme market conditions.
3. ATR-Based Take Profit (TP) & Stop Loss (SL)
Adjustable ATR multiplier for TP/SL
Default settings:
TP = ATR × 5
SL = ATR × 2
Fully customizable risk parameters.
4. Customizable Parameters
TEMA Length (for trend calculation)
OBOS Length (for overbought/oversold detection)
Take Profit Multiplier
Stop Loss Multiplier
EMA Display (Enable/Disable TEMA lines)
Bar Color Change (Enable/Disable candle coloring)
Trading Rules
Long Entry (Buy Entry)
ema3 crosses above ema4 (Golden Cross)
OBOS indicator confirms up > down (bullish trend)
Execute a buy position
Short Entry (Sell Entry)
ema3 crosses below ema4 (Death Cross)
OBOS indicator confirms up < down (bearish trend)
Execute a sell position
Take Profit (TP)
Entry Price + (ATR × TP Multiplier) (Default: 5)
Stop Loss (SL)
Entry Price - (ATR × SL Multiplier) (Default: 2)
TP/SL settings are fully customizable to fine-tune risk management.
Risk Management Parameters
This strategy emphasizes proper position sizing and risk control to balance risk and return.
Trading Parameters & Considerations
Initial Account Balance: $7,000 (adjustable)
Base Currency: USD
Order Size: 10,000 USD
Pyramiding: 1
Trading Fees: $0.94 per trade
Long Position Margin: 50%
Short Position Margin: 50%
Total Trades (M5 Timeframe): 128
Deep Test Results (2024/11/01 - 2025/02/24)BTCUSD-5M
Total P&L：+1638.20USD
Max equity drawdown：694.78USD
Total trades：128
Profitable trades:44.53
Profit factor:1.45
These settings aim to protect capital while maintaining a balanced risk-reward approach.
Visual Support
TEMA Lines (Three EMAs)
Trend direction is indicated by color changes (Blue/Orange)
ema3 (short-term) and ema4 (long-term) crossover signals potential entries
OBOS Histogram
Green → Strong buying pressure
Red → Strong selling pressure
Blue → Possible trend reversal
Entry & Exit Markers
Blue Arrow → Long Entry Signal
Red Arrow → Short Entry Signal
Take Profit / Stop Loss levels displayed
Strategy Improvements & Uniqueness
This strategy is based on indicators developed by "l_lonthoff" and "jdmonto0", but has been significantly optimized for better entry accuracy, visual clarity, and risk management.
Enhanced Trend Identification with TEMA
Detects early trend reversals using ema3 & ema4 crossover
Reduces market noise for a smoother trend-following approach
Improved OBOS Filtering
Prevents excessive trading
Reduces unnecessary risk exposure
Dynamic Risk Management with ATR-Based TP/SL
Not a fixed value → TP/SL adjusts to market volatility
Fully customizable ATR multiplier settings
(Default: TP = ATR × 5, SL = ATR × 2)
Summary
The TEMA + OBOS Strategy is a simple yet powerful trading method that integrates trend analysis and oscillators.
TEMA for trend identification
OBOS for noise reduction & overbought/oversold filtering
ATR-based TP/SL settings for dynamic risk management
Before using this strategy, ensure thorough backtesting and demo trading to fine-tune parameters according to your trading style.
Adaptive RSI with Real-Time Divergence [AIBitcoinTrend]👽 Adaptive RSI Trailing Stop (AIBitcoinTrend)
The Adaptive RSI Trailing Stop is an indicator that integrates Gaussian-weighted RSI calculations with real-time divergence detection and a dynamic ATR-based trailing stop. This advanced approach allows traders to monitor momentum shifts, identify divergences early, and manage risk with adaptive trailing stop levels that adjust to price action.
👽 What Makes the Adaptive RSI with Signals and Trailing Stop Unique?
Unlike traditional RSI indicators, this version applies a Gaussian-weighted smoothing algorithm, making it more responsive to price action while reducing noise. Additionally, the trailing stop feature dynamically adjusts based on volatility and trend conditions, allowing traders to:
Detects real-time divergences (bullish/bearish) with a smart pivot-based system.
Filter noise with Gaussian weighting, ensuring smoother RSI transitions.
Utilize crossover-based trailing stop activation, for systematic trade management.
👽 The Math Behind the Indicator
👾 Gaussian Weighted RSI Calculation
Traditional RSI calculations rely on simple averages of gains and losses. Instead, this indicator weights recent price changes using a Gaussian distribution, prioritizing more relevant data points while maintaining smooth transitions.
Key Features:
Exponential decay ensures recent price changes are weighted more heavily.
Reduces short-term noise while maintaining responsiveness.
👾 Real-Time Divergence Detection
The indicator detects bullish and bearish divergences using pivot points on RSI compared to price action.
👾 Dynamic ATR-Based Trailing Stop
Bullish Trailing Stop: Activates when RSI crosses above 20 and dynamically adjusts based on low - ATR multiplier.
Bearish Trailing Stop: Activates when RSI crosses below 80 and adjusts based on high + ATR multiplier
This allows traders to:
Lock in profits systematically by adjusting stop-losses dynamically.
Stay in trades longer while maintaining adaptive risk management.
👽 How It Adapts to Market Movements
✔️ Gaussian Filtering ensures smooth RSI transitions while preventing excessive lag.
✔️ Real-Time Divergence Alerts provide early trade signals based on price-RSI discrepancies.
✔️ ATR Trailing Stop dynamically expands or contracts based on market volatility.
✔️ Crossover-Based Activation enables the stop-loss system only when RSI confirms a momentum shift.
👽 How Traders Can Use This Indicator
👾 Divergence Trading
Traders can use real-time divergence detection to anticipate reversals before they happen.
Bullish Divergence Setup:
Look for RSI making a higher low, while price makes a lower low.
Enter long when RSI confirms upward momentum.
Bearish Divergence Setup:
Look for RSI making a lower high, while price makes a higher high.
Enter short when RSI confirms downward momentum.
👾 Trailing Stop Signals
Bullish Signal and Trailing Stop Activation:
When RSI crosses above 20, a trailing stop is placed using low - ATR multiplier.
If price crosses below the stop, it exits the trade and removes the stop.
Bearish Signal and Trailing Stop Activation:
When RSI crosses below 80, a trailing stop is placed using high + ATR multiplier.
If price crosses above the stop, it exits the trade and removes the stop.
This makes trend-following strategies more efficient, while ensuring proper risk management.
👽 Why It’s Useful for Traders
✔️ Dynamic and Adaptive: Adjusts to changing market conditions automatically.
✔️ Noise Reduction: Gaussian-weighted RSI reduces short-term price distortions.
✔️ Comprehensive Strategy Tool: Combines momentum detection, divergence analysis, and automated risk management into a single indicator.
✔️ Works Across Markets & Timeframes: Suitable for stocks, forex, crypto, and futures trading.
👽 Indicator Settings
RSI Length: Defines the lookback period for RSI smoothing.
Gaussian Sigma: Controls how much weight is given to recent data points.
Enable Signal Line: Option to display an RSI-based moving average.
Divergence Lookback: Configures how far back pivot points are detected.
Crossover/crossunder values for signals: Set the crossover/crossunder values that triggers signals.
ATR Multiplier: Adjusts trailing stop sensitivity to market volatility.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
Weighted Relative Strength Index [SeerQuant]Weighted Relative Strength Index (WRSI)
The Weighted Relative Strength Index (WRSI) is an advanced momentum oscillator that enhances the traditional RSI by incorporating customizable weighting methods and moving average smoothing. With dynamic threshold logic, color-coded visuals, and optional candle coloring, the WRSI provides traders with a versatile tool for identifying trends, overbought/oversold conditions, and momentum shifts.
⚙️ How It Works
1. Weighted Momentum Calculation
The indicator calculates price changes (delta) and applies a user-defined weighting method (e.g., Volume, Momentum, Volatility, or Reversion Factor) to emphasize specific market dynamics.
2. Custom Moving Average Integration
Weighted upward and downward price movements are smoothed using a selectable moving average type (e.g., SMA, EMA, TEMA, etc.), producing a weighted RSI that blends momentum and trend data.
3. Smoothed RSI Output
An additional moving average is applied to the weighted RSI for a smoothed version, offering a clearer view of momentum trends.
4. Threshold Logic
Bullish (Uptrend): WRSI exceeds the upper neutral zone boundary (50 + Neutral Zone).
Bearish (Downtrend): WRSI falls below the lower neutral zone boundary (50 - Neutral Zone).
Neutral: WRSI remains within the neutral zone.
Extreme overbought (90+) and oversold (20-) levels are marked with X’s for quick identification.
5. Dynamic Visual Representation
A color-coded line reflects the WRSI, adjusting hues based on trend direction.
Gradient fills highlight overbought/oversold zones and neutral areas.
Optional candle coloring ties price action to WRSI or smoothed RSI values.
A histogram-style fill between the WRSI and midline enhances trend strength visibility.
✨ Customizable Settings
Calculation Settings:
Calculation Source: Select the price source (default: close).
Calculation Length: Set the lookback period for RSI calculation (default: 14).
Moving Average Type: Choose from SMA, EMA, RMA, WMA, VWMA, LSMA, HMA, ALMA, DEMA, or TEMA (default: RMA).
Moving Average Length: Adjust the smoothing period for the weighted RSI (default: 8).
Neutral Zone Range: Define the width of the neutral zone around the midline (default: 5).
RSI Weighting Method:
Volume: Weights by trading volume.
Momentum: Weights by absolute price momentum.
Volatility: Weights by standard deviation.
Reversion Factor: Weights inversely to variance for mean-reversion emphasis (default: Momentum).
Style Settings:
Colour Choice: Pick from predefined schemes: Default, Modern, Cool, or Monochrome (default: Default).
Use Custom Colors?: Toggle to use custom bull, bear, and neutral colors (default: false).
Bull/Bear/Neutral Colors: Set custom colors when enabled (default: green/red/gray).
Candle Color Mode: Color candles based on WRSI or smoothed RSI (default: RSI).
Color Candles?: Enable/disable candle coloring (default: false).
🚀 Features and Benefits
Weighted Momentum Analysis: Enhances RSI with dynamic weighting for deeper market insights.
Flexible Smoothing: Multiple MA types and adjustable lengths adapt to various trading styles.
Visual Intuition: Color-coded outputs, gradient fills, and optional candle coloring simplify trend analysis.
Customizable Thresholds: Neutral zone and extreme levels cater to individual strategies.
Overbought/Oversold Signals: Clear markers for extreme conditions improve decision-making.
📜 Disclaimer
This indicator is for educational purposes only and does not constitute financial advice. Past performance does not guarantee future results. Always consult a licensed financial advisor before making trading decisions. Use at your own risk.
AntoQQE - HistogramThis script displays a QQE-based momentum histogram, derived from the RSI line’s deviation around a neutral 50 level. It uses a smoothed RSI, monitors volatility with a dynamically adjusted multiplier, and then plots a color-coded histogram that helps traders see when the RSI is entering strong bullish or bearish territory:
• Smoothed RSI Calculation
The script calculates RSI for a user-defined period and then smooths it with an EMA. This reduces noise in the indicator’s readings.
• Dynamic Average Range (DAR)
The script computes volatility by taking the absolute change of the smoothed RSI, applying two EMAs, and multiplying by a QQE factor. This produces a band around the RSI that adapts to changes in market volatility.
• Histogram Centering and Thresholds
Rather than plotting the RSI itself, the script subtracts 50 from the RSI to center it around zero. Columns are plotted for each bar:
Blue when momentum is significantly above zero (over a threshold value).
Red when momentum is significantly below zero (under a negative threshold).
Gray when momentum is within a neutral range.
• Usage
By observing when columns turn blue or red—and how far they extend above or below zero—traders can quickly gauge the market’s momentum. The horizontal threshold lines (dashed by default) provide clear breakout levels for bullish or bearish conditions, which can help confirm entries or exits based on shifting market sentiment. It is best paired with the AntoQQE - Bars indicator for better chart visualization.
AntoQQE - BarsThis script is a variation on the QQE (Quantitative Qualitative Estimation) concept applied to RSI. It calculates a smoothed RSI line, then determines a “Dynamic Average Range” around that line. By tracking the RSI’s movement relative to these upper (shortBand) and lower (longBand) levels, it determines when price momentum shifts enough to suggest a possible trend flip. The script plots color-coded candles based on these momentum conditions:
• RSI Calculation and Smoothing
An RSI value is obtained over a specified period, then smoothed by an EMA. This smoothed RSI serves as the core measure of momentum.
• Dynamic Average Range (DAR)
The script computes the volatility of the smoothed RSI using two EMAs of its bar-to-bar movements. It multiplies this volatility factor by a QQE multiplier to create upper and lower bands that adapt to changes in RSI volatility.
• Trend Flips
When the smoothed RSI crosses above or below its previous band level (shortBand or longBand), the script interprets this as a shift in momentum and sets a trend state accordingly (long or short).
• Candle Coloring
Finally, the script colors each candle according to how far the smoothed RSI is from a neutral baseline of 50:
Candles turn green when the RSI is sufficiently above 50, suggesting bullish momentum.
Candles turn red when the RSI is sufficiently below 50, indicating bearish momentum.
Candles turn orange when they are near the 50 level, reflecting a more neutral or transitional phase.
Traders can use these colored candles to quickly see when the RSI’s momentum has moved into overbought/oversold zones—or is shifting between bullish and bearish conditions—without needing to consult a separate oscillator window. The adaptive nature of the band calculations can help in spotting significant shifts in market sentiment and volatility.
Pearson OscillatorThe Pearson Oscillator is a custom TradingView indicator that leverages statistical correlation analysis to gauge the trend strength of a given price series. By calculating the Pearson correlation coefficient between time (as an index) and price over a user-defined period, the indicator provides traders with an insight into how strongly the market is trending or oscillating.
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Key Features
- User-Defined Parameters:
– Set the calculation length, price source, and smoothing period.
– Adjust upper and lower threshold levels to suit your trading strategy.
– Customize color settings for increasing, decreasing, and neutral conditions.
- Dynamic Trend Analysis:
– Computes the Pearson correlation coefficient to measure the relationship between time and price.
– Applies a simple moving average to smooth out fluctuations in the coefficient, offering a more stable reading.
- Visual Representation:
– Plots the smoothed Pearson coefficient as a continuous line.
– Displays a histogram showing the variation (first derivative) of the coefficient to highlight changes in trend strength.
– Draws horizontal reference lines at the specified upper and lower thresholds as well as at the zero level for quick visual assessment.
- Alerts and Dynamic Labeling:
– Automatically triggers alerts when the smoothed Pearson coefficient crosses the predefined threshold levels, so you never miss a potential market turning point.
– Generates a dynamic label on the last bar that displays important statistical information, including:
- The current Pearson coefficient (rounded to three decimals).
- A classification of correlation strength (e.g., STRONG, MEDIUM, WEAK, NEUTRAL) based on the absolute value of the coefficient.
- The trend direction (Upward, Downward, or Stable).
- The delta of the coefficient, offering insight into how quickly the trend is evolving.
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How It Works
1. Calculation of the Pearson Coefficient:
- A custom function iterates over a specified number of price bars, summing time indices, price values, and their squared and cross-products.
- Using the Pearson correlation formula, it computes a coefficient that ranges between -1 and 1—values close to ±1 indicate a strong trend or linear relationship, while values near 0 suggest a weak or non-existent trend.
2. Smoothing Process:
- The raw Pearson coefficient is then smoothed using a simple moving average (SMA) to reduce noise and provide a clearer view of the underlying trend.
3. Delta (Variation) Computation:
- The script calculates the change (delta) between the current smoothed coefficient and its value on the previous bar.
- This derivative is plotted as a histogram, signaling the speed at which the correlation (and thus the trend) is changing.
4. Visual and Alert Mechanisms:
- The smoothed coefficient and its delta are plotted with colors that dynamically update to reflect increasing or decreasing trends.
- Horizontal lines set at user-defined thresholds help to quickly identify overbought or oversold (or extreme correlation) scenarios.
- Alerts are defined to notify you when the smoothed coefficient crosses these key levels, ensuring timely trade decisions.
5. Dynamic Label:
- At the last bar, a dynamic label is created displaying the current Pearson value, its strength, the direction of the trend, and the delta.
- This quick snapshot helps traders assess the market condition at a glance without diving into detailed analysis.
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Why Use the Pearson Oscillator?
This indicator is particularly useful for traders who need a quantitative measure of trend strength that goes beyond traditional moving averages. By integrating statistical correlation directly into market analysis, the Pearson Oscillator helps you:
- Identify periods of strong trending behavior or potential reversals.
- Enhance your risk management through early alerts.
- Visualize the rate of change in market sentiment, enabling more informed entry and exit decisions.
Whether you are a technical analyst or a systematic trader, this indicator provides a robust tool to complement your existing trading toolkit.
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The Pearson Oscillator merges statistical insights with technical charting, creating an intuitive yet powerful tool for market analysis. With its adjustable parameters, visual cues, dynamic labeling, and automated alerts, it assists traders in monitoring and responding to evolving market conditions efficiently. This makes it a valuable addition to any TradingView chart, particularly for those looking to quantify the strength and evolution of market trends.
Feel free to adapt the parameters and visual settings to best align the indicator with your trading strategy. Happy trading!
Astro: Moon SizeThe Astro: Moon Size indicator, built using AstroLib , calculates the distance and visualizes the apparent size of the Moon based on astronomical positioning. This script is tailored for the 1D timeframe and provides insights into lunar perigees (closest approach) and apogees (farthest distance), making it useful for astrologically-informed trading strategies.
New Astro Indicators Feature:
By setting the Julian Date to X number of days in the future, and offsetting the plot by X number of bars accordingly, it is now possible to visualize future projections of TradingView indicators that reference the AstroLib . This feature has been long requested and is far overdue, so thank you to everyone who pushed for this feature release. Enjoy, time travelers from the future!!
Key Features:
Moon Size Calculation: Uses Julian Date (J2000) conversion and AstroLib functions to determine the Moon's apparent distance.
Future Projection: Displays the Moon's distance from 28 up to 500 days ahead, with color gradients indicating proximity/size.
Pivot Identification: Marks local maxima (apogees) and minima (perigees) with labeled date stamps for easy reference.
Dynamic Labeling: Adapts label positioning and size based on the Moon's current trend and relative size.
Usage Notes:
⚠️ Timeframe Restriction: For now, the script only functions on the 1D timeframe and will prompt an error otherwise.
⚠️ Asset Restriction: This script is meant to be loaded on charts for assets that trade 24/7, like BTCUSD historical index.
Trend Detector [victhoreb]Trend Detector is a streamlined indicator that uses the Pearson correlation coefficient between the average price and time to determine market trends. It measures how closely price movement follows the progression of time over a user-defined period, providing a clear gauge of trend direction on a scale from -1 to 1.
How It Works:
The indicator calculates the correlation between price and time. A positive correlation means that as time advances, the price generally rises—signaling an uptrend. Conversely, a negative correlation indicates that the price tends to fall over time, highlighting a downtrend.
With its simple yet effective approach, Trend Detector offers traders an immediate visual and quantitative insight into prevailing market trends.
