Machine Learning | Adaptive Trend Signals [Bitwardex]⚙️🧠Machine Learning | Adaptive Trend Signals
🔷Overview
Machine Learning | Adaptive Trend Signals is a Pine Script™ v6 indicator designed to visualize market trends and generate signals through a combination of volatility clustering, Gaussian smoothing, and adaptive trend calculations. Built as an overlay indicator, it integrates advanced techniques inspired by machine learning concepts, such as K-Means clustering, to adapt to changing market conditions. The script is highly customizable, includes a backtesting module, and supports alert conditions, making it suitable for traders exploring trend-based strategies and developers studying volatility-driven indicator design.
🔷Functionality
The indicator performs the following core functions:
• Volatility Clustering: Uses K-Means clustering to categorize market volatility into high, medium, and low states, adjusting trend sensitivity accordingly.
• Trend Calculation: Computes adaptive trend lines (SmartTrend) based on volatility-adjusted standard deviation, smoothed RSI, and ADX filters.
• Signal Generation: Identifies potential buy and sell points through trend line crossovers and directional confirmation.
• Backtesting Module: Tracks trade outcomes based on the SmartTrend3 value, displaying win rate and total trades.
• Visualization: Plots trend lines with gradient colors and optional signal markers (bullish 🐮 and bearish 🐻).
• Alerts: Provides configurable alerts for trend shifts and volatility state changes.
🔷Technical Methodology
Volatility Clustering with K-Means
The indicator employs a K-Means clustering algorithm to classify market volatility, measured via the Average True Range (ATR), into three distinct clusters:
• Data Collection: Gathers ATR values over a user-defined training period (default: 100 bars).
• Centroid Initialization: Sets initial centroids at the highest, lowest, and midpoint ATR values within the training period.
• Iterative Clustering: Assigns ATR data points to the nearest centroid, recalculates centroid means, and repeats until convergence.
• Dynamic Adjustment: Assigns a volatility state (high, medium, or low) based on the closest centroid, adjusting the trend factor (e.g., tighter for high volatility, wider for low volatility).
This approach allows the indicator to adapt its sensitivity to varying market conditions, providing a data-driven foundation for trend calculations.
🔷Gaussian Smoothing
To enhance signal clarity and reduce noise, the indicator applies Gaussian kernel smoothing to:
• RSI: Smooths the Relative Strength Index (calculated from OHLC4) to filter short-term fluctuations.
• SmartTrend: Smooths the primary trend line for a more stable output.
The Gaussian kernel uses a sigma value derived from the user-defined smoothing length, ensuring mathematically consistent noise reduction.
🔷SmartTrend Calculation
The pineSmartTrend function is the core of the indicator, producing three trend lines:
• SmartTrend: The primary trend line, calculated using a volatility-adjusted standard deviation, smoothed RSI, and ADX conditions.
• SmartTrend2: A secondary trend line with a wider factor (base factor * 1.382) for signal confirmation.
SmartTrend3: The average of SmartTrend and SmartTrend2, used for plotting and backtesting.
Key components of the calculation include:
• Dynamic Standard Deviation: Scales based on ATR relative to its 50-period smoothed average, with multipliers (1.0 to 1.4) applied according to volatility thresholds.
• RSI and ADX Filters: Requires RSI > 50 for bullish trends or < 50 for bearish trends, alongside ADX > 15 and rising to confirm trend strength.
Volatility-Adjusted Bands: Constructs upper and lower bands around price action, adjusted by the volatility cluster’s dynamic factor.
🔷Signal Generation
The generate_signals function generates signals as follows:
• Buy Signal: Triggered when SmartTrend crosses above SmartTrend2 and the price is above SmartTrend, with directional confirmation.
• Sell Signal: Triggered when SmartTrend crosses below SmartTrend2 and the price is below SmartTrend, with directional confirmation.
Directional Logic: Tracks trend direction to filter out conflicting signals, ensuring alignment with the broader market context.
Signals are visualized as small circles with bullish (🐮) or bearish (🐻) emojis, with an option to toggle visibility.
🔷Backtesting
The get_backtest function evaluates signal outcomes using the SmartTrend3 value (rather than closing prices) to align with the trend-based methodology.
It tracks:
• Total Trades: Counts completed long and short trades.
• Win Rate: Calculates the percentage of trades where SmartTrend3 moves favorably (higher for longs, lower for shorts).
Position Management: Closes opposite positions before opening new ones, simulating a single-position trading system.
Results are displayed in a table at the top-right of the chart, showing win rate and total trades. Note that backtest results reflect the indicator’s internal logic and should not be interpreted as predictive of real-world performance.
🔷Visualization and Alerts
• Trend Lines: SmartTrend3 is plotted with gradient colors reflecting trend direction and volatility cluster, accompanied by a secondary line for visual clarity.
• Signal Markers: Optional buy/sell signals are plotted as small circles with customizable colors.
• Alerts: Supports alerts for:
• Bullish and bearish trend shifts (confirmed on bar close).
Transitions to high, medium, or low volatility states.
🔷Input Parameters
• ATR Length (default: 14): Period for ATR calculation, used in volatility clustering.
• Period (default: 21): Common period for RSI, ADX, and standard deviation calculations.
• Base SmartTrend Factor (default: 2.0): Base multiplier for volatility-adjusted bands.
• SmartTrend Smoothing Length (default: 10): Length for Gaussian smoothing of the trend line.
• Show Buy/Sell Signals? (default: true): Enables/disables signal markers.
• Bullish/Bearish Color: Customizable colors for trend lines and signals.
🔷Usage Instructions
• Apply to Chart: Add the indicator to any TradingView chart.
• Configure Inputs: Adjust parameters to align with your trading style or market conditions (e.g., shorter ATR length for faster markets).
• Interpret Output:
• Trend Lines: Use SmartTrend3’s direction and color to gauge market bias.
• Signals: Monitor bullish (🐮) and bearish (🐻) markers for potential entry/exit points.
• Backtest Table: Review win rate and total trades to understand the indicator’s behavior in historical data.
• Set Alerts: Configure alerts for trend shifts or volatility changes to support manual or automated trading workflows.
• Combine with Analysis: Use the indicator alongside other tools or market context, as it is designed to complement, not replace, comprehensive analysis.
🔷Technical Notes
• Data Requirements: Requires at least 100 bars for accurate volatility clustering. Ensure sufficient historical data is loaded.
• Market Suitability: The indicator is designed for trend detection and may perform differently in ranging or volatile markets due to its reliance on RSI and ADX filters.
• Backtesting Scope: The backtest module uses SmartTrend3 values, which may differ from price-based outcomes. Results are for informational purposes only.
• Computational Intensity: The K-Means clustering and Gaussian smoothing may increase processing time on lower timeframes or with large datasets.
🔷For Developers
The script is modular, well-commented, encouraging reuse and modification with proper attribution.
Key functions include:
• gaussianSmooth: Applies Gaussian kernel smoothing to any data series.
• pineSmartTrend: Computes adaptive trend lines with volatility and momentum filters.
• getDynamicFactor: Adjusts trend sensitivity based on volatility clusters.
• get_backtest: Evaluates signal performance using SmartTrend3.
Developers can extend these functions for custom indicators or strategies, leveraging the volatility clustering and smoothing methodologies. The K-Means implementation is particularly useful for adaptive volatility analysis.
🔷Limitations
• The indicator is not predictive and should be used as part of a broader trading strategy.
• Performance varies by market, timeframe, and parameter settings, requiring user experimentation.
• Backtest results are based on historical data and internal logic, not real-world trading conditions.
• Volatility clustering assumes sufficient historical data; incomplete data may affect accuracy.
🔷Acknowledgments
Developed by Bitwardex, inspired by machine learning concepts and adaptive trading methodologies. Community feedback is welcome via TradingView’s platform.
🔷 Risk Disclaimer
Trading involves significant risks, and most traders may incur losses. Bitwardex AI Algo is provided for informational and educational purposes only and does not constitute financial advice or a recommendation to buy or sell any financial instrument . The signals, metrics, and features are tools for analysis and do not guarantee profits or specific outcomes. Past performance is not indicative of future results. Always conduct your own due diligence and consult a financial advisor before making trading decisions.
Adaptive
Adaptable Relative Momentum Index [ParadoxAlgo]The Adaptable Relative Momentum Index (RMI) by ParadoxAlgo is an advanced momentum-based indicator that builds upon the well-known RSI (Relative Strength Index) concept by introducing a customizable momentum length. This indicator measures price momentum over a specified number of periods and applies a Rolling Moving Average (RMA) to both the positive and negative price changes. The result is a versatile tool that can help traders gauge the strength of a trend, pinpoint overbought/oversold levels, and potentially identify breakout opportunities.
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Smart Configuration Feature
What sets this version of the RMI apart is ParadoxAlgo’s exclusive “Smart Configuration” functionality. Instead of manually adjusting parameters, traders can simply select their Asset Class (e.g., Stocks, Forex, Futures/Indices, Crypto, Commodities) and Trading Style (e.g., Scalping, Day Trading, Swing Trading, Short-Term Investing, Long-Term Investing). Based on these selections, the indicator automatically optimizes its core parameters:
• Length – The period over which the price changes are smoothed.
• Momentum Length – The number of bars used to calculate the price change.
By automating this process, users save time on tedious trial-and-error adjustments, ensuring that the RMI’s settings are tailored to the characteristics of specific markets and personal trading horizons.
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Key Features & Benefits
1. Momentum-Based Insights
• Uses RMA to smooth price movements, helping identify shifts in market momentum more clearly than a basic RSI.
• Enhanced adaptability for a wide range of asset classes and time horizons.
2. Simple Yet Powerful Configuration
• Smart Configuration automatically sets optimal parameter values for each combination of asset class and trading style.
• Eliminates guesswork and manual recalibration when switching between markets or timeframes.
3. Overbought & Oversold Visualization
• Integrated highlight zones mark potential overbought and oversold extremes (default at 80 and 20).
• Optional breakout highlighting draws attention to times when the indicator crosses these key thresholds, helping spot possible entry or exit signals.
4. Intuitive Design & Ease of Use
• Clean plotting and color-coded signal lines make it easy to interpret bullish or bearish shifts in momentum.
• Straightforward dropdown menus keep the interface user-friendly, even for novice traders.
⸻
Practical Applications
• Early Trend Detection: Spot emerging trends when the RMI transitions from oversold to higher levels or vice versa.
• Breakout Confirmation: Confirm potential breakout trades by tracking overbought/oversold breakouts alongside other technical signals.
• Support/Resistance Confluence: Combine RMI signals with horizontal support/resistance levels to reinforce trade decisions.
• Trade Timing: Quickly gauge when momentum could be shifting, helping you time entries and exits more effectively.
⸻
Disclaimer
As with any technical indicator, the Adaptable Relative Momentum Index should be used as part of a broader trading strategy that includes risk management, fundamental analysis, and other forms of technical confirmation. Past performance does not guarantee future results.
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Enjoy using the Adaptable RMI and experience a more streamlined, flexible approach to momentum analysis. Feel free to explore different asset classes and trading styles to discover which configurations resonate best with your unique trading preferences.
Log Regression Channel [UAlgo]The "Log Regression Channel " channel is useful for analyzing price trends and volatility in a financial instrument over a specified period. By using logarithmic scaling, this indicator can more effectively handle the wide range of price movements seen in many financial markets, making it particularly valuable for assets with exponential growth characteristics.
The indicator plots the central regression line along with upper and lower deviation bands, providing a visual representation of potential support and resistance levels.
🔶 Key Features
Logarithmic Regression Line: The central line represents the logarithmic regression, which fits the price data over the specified length using a logarithmic scale. This helps in identifying the overall trend direction.
Deviation Bands: The upper and lower bands are plotted at a specified multiple of the standard deviation from the regression line, highlighting areas of potential overbought and oversold conditions.
Customizable Parameters: Users can adjust the length of the regression, the deviation multiplier, the color of the labels, and the size of the text labels to suit their preferences.
R-Squared Display: The R-squared value, which measures the goodness of fit of the regression model, is displayed on the chart. This helps traders assess the reliability of the regression line.
🔶 Calculations
The indicator performs several key calculations to plot the logarithmic regression channel:
Logarithmic Transformation: The prices and time indices are transformed using the natural logarithm to handle exponential growth in price data.
Regression Coefficients: The slope and intercept of the regression line are calculated using the least squares method on the transformed data.
Predicted Values: The regression equation is used to calculate predicted values for each data point.
Standard Deviation: The standard deviation of the residuals (differences between actual and predicted values) is computed to determine the width of the deviation bands.
Deviation Bands: Upper and lower bands are plotted at a specified multiple of the standard deviation above and below the regression line.
R-Squared Value: The R-squared value is calculated to measure how well the regression line fits the data. This value is displayed on the chart to inform the user of the model's reliability.
🔶 Disclaimer
The "Log Regression Channel " indicator is provided for educational and informational purposes only.
It is not intended as investment advice or a recommendation to buy or sell any financial instrument. Trading financial instruments involves substantial risk and may not be suitable for all investors.
Past performance is not indicative of future results. Users should conduct their own research.
Ehlers Adaptive Trend Indicator [Alpha Extract]Ehlers Adaptive Trend Indicator
The Ehlers Adaptive Trend Indicator combines Ehlers' advanced digital signal processing techniques with dynamic volatility bands to identify robust trend conditions and potential reversals. This powerful tool helps traders visualize trend strength, adaptive support/resistance levels, and momentum shifts across various market conditions.
🔶 CALCULATION
The indicator employs a sophisticated adaptive algorithm that responds to changing market conditions:
• Ehlers Filter : Calculates a weighted average based on momentum differences to create an adaptive trend baseline.
• Dynamic Bands : Volatility-adjusted bands that expand and contract based on recent price action.
• Trend Level : A dynamic support/resistance level that adapts to the current trend direction.
• Smoothed Volatility : Market volatility measured and smoothed to provide reliable band width.
Formula:
• Ehlers Basis = Weighted average of price, with weights determined by momentum differences
• Volatility = Standard deviation of price over Ehlers Length period
• Smoothed Volatility = EMA of volatility over Smoothing Length
• Upper Band = Ehlers Basis + Smoothed Volatility × Sensitivity
• Lower Band = Ehlers Basis - Smoothed Volatility × Sensitivity
• Trend Level = Adaptive support in uptrends, resistance in downtrends
🔶 DETAILS
Visual Features :
• Ehlers Basis Line (Yellow): The core adaptive trend reference that serves as the primary trend indicator.
• Trend Level Line (Dynamic Color): Changes between green (bullish) and red (bearish) based on the current trend state.
• Fill Areas : Transparent green fill during bullish trends and transparent red fill during bearish trends for clear visual identification.
• Bar Coloring : Optional price bar coloring that reflects the current trend direction for enhanced visualization.
Interpretation :
• **Bullish Signal**: Price crosses above the upper band, triggering a trend change with the Trend Level becoming dynamic support.
• **Bearish Signal**: Price drops below the lower band, confirming a trend change with the Trend Level becoming dynamic resistance.
• **Trend Continuation**: Trend Level rises in bullish markets and falls in bearish markets, providing adaptive trailing support/resistance.
🔶 EXAMPLES
The chart demonstrates:
• Bullish Trend Identification : When price breaks above the upper band, the indicator shifts to bullish mode with green trend level and fill.
• Bearish Trend Identification : When price falls below the lower band, the indicator shifts to bearish mode with red trend level and fill.
• Trend Persistence : Trend Level adapts to market movement, rising during uptrends to provide dynamic support and falling during downtrends to act as resistance.
Example Snapshots :
• During a strong uptrend, the Trend Level continuously adjusts upward, keeping traders in the trend while filtering out minor retracements.
• During trend reversals, clear color changes and Trend Level shifts provide early warning of potential direction changes.
🔶 SETTINGS
Customization Options :
• Ehlers Length (p1) (Default: 30): Controls the primary adaptive calculation period, balancing responsiveness with stability.
• Momentum Length (p2) (Default: 25): Determines the lag for momentum calculations used in the adaptive weighting.
• Smoothing Length (Default: 10): Adjusts the volatility smoothing period—higher values provide more stable bands.
• Sensitivity (Default: 1.0): Multiplier for band width—higher values increase distance between bands, lower values tighten them.
• Visual Settings : Customizable colors for bullish and bearish trends, basis line, and optional bar coloring.
The Ehlers Adaptive Trend Indicator combines John Ehlers' digital signal processing expertise with modern volatility analysis to create a robust trend-following system that adapts to changing market conditions, helping traders stay on the right side of the market.
Ehlers Adaptive RSIThe Ehlers Adaptive RSI improves on the traditional RSI by dynamically adjusting its period based on market conditions.
Problem with the Classic RSI:
The traditional Relative Strength Index (RSI) uses a fixed period (e.g., 14), making it slow to react in volatile markets and too sensitive in stable conditions.
How the Adaptive RSI Solves This:
Instead of a fixed period, this version automatically adapts based on market volatility using a combination of ATR (Average True Range) and EMA (Exponential Moving Average).
Key Benefits:
More Responsive – Quickly adapts to market shifts, reducing lag.
Less Noise – Filters out unnecessary fluctuations in stable trends.
Self-Adjusting – No need to manually change RSI settings for different market conditions.
Better Signal Accuracy – Helps detect real trend reversals without false alarms.
This script is for informational and educational purposes only. It does not constitute financial advice, and past performance does not guarantee future results. Use it at your own risk.
Hull Moving Average Adaptive RSI (Ehlers)Hull Moving Average Adaptive RSI (Ehlers)
The Hull Moving Average Adaptive RSI (Ehlers) is an enhanced trend-following indicator designed to provide a smooth and responsive view of price movement while incorporating an additional momentum-based analysis using the Adaptive RSI.
Principle and Advantages of the Hull Moving Average:
- The Hull Moving Average (HMA) is known for its ability to track price action with minimal lag while maintaining a smooth curve.
- Unlike traditional moving averages, the HMA significantly reduces noise and responds faster to market trends, making it highly effective for detecting trend direction and changes.
- It achieves this by applying a weighted moving average calculation that emphasizes recent price movements while smoothing out fluctuations.
Why the Adaptive RSI Was Added:
- The core HMA line remains the foundation of the indicator, but an additional analysis using the Adaptive RSI has been integrated to provide more meaningful insights into momentum shifts.
- The Adaptive RSI is a modified version of the traditional Relative Strength Index that dynamically adjusts its sensitivity based on market volatility.
- By incorporating the Adaptive RSI, the HMA visually represents whether momentum is strengthening or weakening, offering a complementary layer of analysis.
How the Adaptive RSI Influences the Indicator:
- High Adaptive RSI (above 65): The market may be overbought, or bullish momentum could be fading. The HMA turns shades of red, signaling a possible exhaustion phase or potential reversals.
- Neutral Adaptive RSI (around 50): The market is in a balanced state, meaning neither buyers nor sellers are in clear control. The HMA takes on grayish tones to indicate this consolidation.
- Low Adaptive RSI (below 35): The market may be oversold, or bearish momentum could be weakening. The HMA shifts to shades of blue, highlighting potential recovery zones or trend slowdowns.
Why This Combination is Powerful:
- While the HMA excels in tracking trends and reducing lag, it does not provide information about momentum strength on its own.
- The Adaptive RSI bridges this gap by adding a clear visual layer that helps traders assess whether a trend is likely to continue, consolidate, or reverse.
- This makes the indicator particularly useful for spotting trend exhaustion and confirming momentum shifts in real-time.
Best Use Cases:
- Works effectively on timeframes from 1 hour (1H) to 1 day (1D), making it suitable for swing trading and position trading.
- Particularly useful for trading indices (SPY), stocks, forex, and cryptocurrencies, where momentum shifts are frequent.
- Helps identify not just trend direction but also whether that trend is gaining or losing strength.
Recommended Complementary Indicators:
- Adaptive Trend Finder: Helps identify the dominant long-term trend.
- Williams Fractals Ultimate: Provides key reversal points to validate trend shifts.
- RVOL (Relative Volume): Confirms significant moves based on volume strength.
This enhanced HMA with Adaptive RSI provides a powerful, intuitive visual tool that makes trend analysis and momentum interpretation more effective and efficient.
This indicator is for educational and informational purposes only. It should not be considered financial advice or a guarantee of performance. Always conduct your own research and use proper risk management when trading. Past performance does not guarantee future results.
Adaptive Trend FinderAdaptive Trend Finder - The Ultimate Trend Detection Tool
Introducing Adaptive Trend Finder, the next evolution of trend analysis on TradingView. This powerful indicator is an enhanced and refined version of Adaptive Trend Finder (Log), designed to offer even greater flexibility, accuracy, and ease of use.
What’s New?
Unlike the previous version, Adaptive Trend Finder allows users to fully configure and adjust settings directly within the indicator menu, eliminating the need to modify chart settings manually. A major improvement is that users no longer need to adjust the chart's logarithmic scale manually in the chart settings; this can now be done directly within the indicator options, ensuring a smoother and more efficient experience. This makes it easier to switch between linear and logarithmic scaling without disrupting the analysis. This provides a seamless user experience where traders can instantly adapt the indicator to their needs without extra steps.
One of the most significant improvements is the complete code overhaul, which now enables simultaneous visualization of both long-term and short-term trend channels without needing to add the indicator twice. This not only improves workflow efficiency but also enhances chart readability by allowing traders to monitor multiple trend perspectives at once.
The interface has been entirely redesigned for a more intuitive user experience. Menus are now clearer, better structured, and offer more customization options, making it easier than ever to fine-tune the indicator to fit any trading strategy.
Key Features & Benefits
Automatic Trend Period Selection: The indicator dynamically identifies and applies the strongest trend period, ensuring optimal trend detection with no manual adjustments required. By analyzing historical price correlations, it selects the most statistically relevant trend duration automatically.
Dual Channel Display: Traders can view both long-term and short-term trend channels simultaneously, offering a broader perspective of market movements. This feature eliminates the need to apply the indicator twice, reducing screen clutter and improving efficiency.
Fully Adjustable Settings: Users can customize trend detection parameters directly within the indicator settings. No more switching chart settings – everything is accessible in one place.
Trend Strength & Confidence Metrics: The indicator calculates and displays a confidence score for each detected trend using Pearson correlation values. This helps traders gauge the reliability of a given trend before making decisions.
Midline & Channel Transparency Options: Users can fine-tune the visibility of trend channels, adjusting transparency levels to fit their personal charting style without overwhelming the price chart.
Annualized Return Calculation: For daily and weekly timeframes, the indicator provides an estimate of the trend’s performance over a year, helping traders evaluate potential long-term profitability.
Logarithmic Adjustment Support: Adaptive Trend Finder is compatible with both logarithmic and linear charts. Traders who analyze assets like cryptocurrencies, where log scaling is common, can enable this feature to refine trend calculations.
Intuitive & User-Friendly Interface: The updated menu structure is designed for ease of use, allowing quick and efficient modifications to settings, reducing the learning curve for new users.
Why is this the Best Trend Indicator?
Adaptive Trend Finder stands out as one of the most advanced trend analysis tools available on TradingView. Unlike conventional trend indicators, which rely on fixed parameters or lagging signals, Adaptive Trend Finder dynamically adjusts its settings based on real-time market conditions. By combining automatic trend detection, dual-channel visualization, real-time performance metrics, and an intuitive user interface, this indicator offers an unparalleled edge in trend identification and trading decision-making.
Traders no longer have to rely on guesswork or manually tweak settings to identify trends. Adaptive Trend Finder does the heavy lifting, ensuring that users are always working with the strongest and most reliable trends. The ability to simultaneously display both short-term and long-term trends allows for a more comprehensive market overview, making it ideal for scalpers, swing traders, and long-term investors alike.
With its state-of-the-art algorithms, fully customizable interface, and professional-grade accuracy, Adaptive Trend Finder is undoubtedly one of the most powerful trend indicators available.
Try it today and experience the future of trend analysis.
This indicator is a technical analysis tool designed to assist traders in identifying trends. It does not guarantee future performance or profitability. Users should conduct their own research and apply proper risk management before making trading decisions.
// Created by Julien Eche - @Julien_Eche
Supertrend with RSI FilterThis indicator is an enhanced version of the classic Supertrend, incorporating an RSI (Relative Strength Index) filter to refine trend signals. Here is a detailed explanation of its functionality and key advantages over the traditional Supertrend.
1. Indicator Functionality
The indicator uses ATR (Average True Range) to calculate the Supertrend line, just like the classic version. However, it introduces an additional condition based on RSI to strengthen or weaken the Supertrend color based on market momentum.
2. Interpretation of Colors
The indicator displays the Supertrend line with dynamic colors based on trend direction and RSI strength:
- Uptrend (Supertrend in buy mode):
- Dark green (Teal): RSI above the defined threshold (default 50) → Strong bullish confirmation.
- Light gray: RSI below the threshold → Indicates a weaker uptrend or lack of confirmation.
- Downtrend (Supertrend in sell mode):
- Dark red: RSI below the threshold → Strong bearish confirmation.
- Light gray: RSI above the threshold → Indicates a weaker downtrend or lack of confirmation.
The opacity of the color dynamically adjusts based on how far RSI is from its threshold. The greater the difference, the more vivid the color, signaling a stronger trend.
3. Key Advantages Over the Classic Supertrend
- Filters out false signals: The RSI integration helps reduce false signals by only validating trends when RSI aligns with the Supertrend direction.
- Weakens uncertain signals: When RSI is close to its threshold, the color becomes more transparent, alerting traders to a less reliable trend.
- Classic mode available: The 'Use Classic Supertrend' option allows switching to a standard Supertrend display (fixed red/green) without the RSI effect.
4. Customizable Parameters
- ATR Length & ATR Factor: Define the sensitivity of the Supertrend.
- RSI Period & RSI Threshold: Allow refining the RSI filter based on market volatility.
- Classic mode: Enables/disables the RSI filtering to revert to the original Supertrend.
This indicator is especially valuable for traders looking to refine their trend signals based on market momentum measured by RSI.
This indicator is for informational purposes only and should not be considered financial advice. Trading involves risks, and past performance does not guarantee future results. Always conduct your own analysis before making any trading decisions.
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.
Adaptive Supply and Demand [EdgeTerminal]Adaptive Supply and Demand is a dynamic supply and demand indicator with a few unique twists. It considers volume pressure, volatility-based adjustments and multi-time frame momentum for confidence scoring (multi-step confirmation) to generate dynamic lines that adjust based on the market and also to generate dynamic support/resistance levels for the supply and demand lines.
The dynamic support and resistance lines shown gives you a better situational awareness of the current state of the market and add more context to why the market is moving into a certain direction.
> Trading Scenarios
When the confidence score is over 80%, strong volume pressure in trend direction (up or down), volatility is low and momentum is aligned across timeframes, there is an indication of a strong upward or downward trend.
When the supply and demand line crossover, the confidence score is over 75% and the volume pressure is shifting, this can be an indicator of trend reversal. Use tight initial stops, scale into position as trend develops, monitor the volume pressure for continuation and wait for confidence confirmation.
When the confiance score is below 60%, the volume pressure is choppy, volatility is high, you want to avoid trading or reduce position size, wait for confidence improvements, use support and resistance for entries/exits and use tighter stops due to market conditions. This is an indication of a ranging market.
Another scenario is when there is a sudden volume pressure increase, and a raising confidence score, the volatility is expanding and the bar momentum is aligning the volatility direction. This can indicate a breakout scenario.
> How it Works
1. Volume Pressure Analysis
Volume Pressure Analysis is a key component that measures the true buying and selling force in the market. Here's a detailed breakdown. The idea is to standardize volume to prevent large spikes from skewing results.
The indicator employs an adaptive volume normalization technique to detect genuine buying and selling pressure.
It takes current volume and divides it by average volume.
If normVol > 1: Current volume is above average
If normVol < 1: Current volume is below average
An example if this would be If current volume is 1500 and average is 1000, normVol = 1.5 (50% above average)
Another component of the volume pressure analysis is the Price Change Calculation sub-module. The purpose of this is to measure price movement relative to recent average.
It works by subtracting the average price from the current price. If the value is positive, price is average and if negative, price is below average.
Finally, the volume pressure is calculated to combine volume and price for true pressure reading.
2. Savitzky-Golay Filtering
SG filtering implements advanced signal smoothing while preserving important trend features. It uses weighted moving average approximation, preserves higher moments of data and reduces noise while maintaining signal integrity.
This results in smoother signal lines, reduced false crossovers and better trend identification. Traditional moving averages tend to lag and smooth out important features. Additionally, simple moving averages can miss critical turning points and regular smoothing can delay signal generation.
SG filtering preserves higher moments such as peaks, valleys and trends, reduces noise while maintaining signal sharpness.
It works by creating a symmetric weighting scheme. This way center points get the highest weights while edge points get the lowest weight.
3. Parkinson's Volatility
Parkinson's Volatility is an advanced volatility measurement formula using high-low range data. It uses high-low range for volatility calculation, incorporates logarithmic returns and annualized the volatility measure.
This results in more accurate volatility measurement, better risk assessment and dynamic signal sensitivity.
4. Multi-timeframe Momentum
This combines signals from each module for each timeframe to calculate momentum across three timeframes. It also applies weighted importance to each timeframe and generates a composite momentum signal.
This results in a more comprehensive trend analysis, reduced timeframe bias and better trend confirmation.
> Indicator Settings
Short-term Period:
Lower values makes it more sensitive, meaning it will generate more signals. Higher values makes it less sensitive, resulting in fewer signals. We recommend a 5 to 15 range for day trading, and 10 to 20 for swing trading
Medium-term Period:
Lower values result in faster trend confirmation and higher values show slower and more reliable confirmation. We recommend a range of 15-25 for day trading and 20-30 for swing trading.
Long-term Period:
Lower values makes it more responsive to trend changes and higher values are better for major trend identification. We recommend a range of 40-60 for day trading and 50-100 for swing trading.
Volume Analysis Window:
Lower values result in more sensitivity to volume changes and higher values result in smoother volume analysis. The optimal range is 15-25 for most trading styles.
Confidence Threshold:
Lower values generate more signals but quality decreases. Higher values generate fewer signals but accuracy increases.The optimal range is 0.65-0.8 for most trading conditions.
PDF-MA Supertrend [BackQuant]PDF-MA Supertrend
The PDF-MA Supertrend combines the innovative Probability Density Function (PDF) smoothing with the widely popular Supertrend methodology, creating a robust tool for identifying trends and generating actionable trading signals. This indicator is designed to provide precise entries and exits by dynamically adapting to market volatility while visualizing long and short opportunities directly on the chart.
Core Feature: PDF Smoothing
At the foundation of this indicator is the PDF smoothing technique, which applies a Probability Density Function to calculate a smoothed moving average. This method allows the indicator to assign adaptive weights to data points, making it responsive to market changes without overreacting to short-term volatility.
Key parameters include:
Variance: Controls the spread of the PDF weighting. A smaller variance results in sharper responses, while a larger variance smooths out the curve.
Mean: Shifts the PDF’s center, allowing traders to tweak how weights are distributed around the data points.
Smoothing Method: Offers the choice between EMA (Exponential Moving Average) and SMA (Simple Moving Average) for blending the PDF-smoothed data with traditional moving average methods.
By combining these parameters, the PDF smoothing creates a moving average that effectively captures underlying trends.
Supertrend: Adaptive Trend and Volatility Tracking
The Supertrend is a well-known volatility-based indicator that dynamically adjusts to market conditions using the ATR (Average True Range). In this script, the PDF-smoothed moving average acts as the price input, making the Supertrend calculation more adaptive and precise.
Key Supertrend Features:
ATR Period: Determines the lookback period for calculating market volatility.
Factor: Multiplies the ATR to set the distance between the Supertrend and the price. A higher factor creates wider bands, filtering out smaller price movements, while a lower factor captures tighter trends.
Dynamic Direction: The Supertrend flips its direction based on price interactions with the calculated upper and lower bands:
Uptrend : When the price is above the Supertrend, the direction turns bullish.
Downtrend : When the price is below the Supertrend, the direction turns bearish.
This combination of PDF smoothing and Supertrend calculation ensures that trends are detected with greater accuracy, while volatility filters out market noise.
Long and Short Signal Generation
The PDF-MA Supertrend generates actionable trading signals by detecting transitions in the trend direction:
Long Signal (𝕃): Triggered when the trend transitions from bearish to bullish. This is visually represented with a green triangle below the price bars.
Short Signal (𝕊): Triggered when the trend transitions from bullish to bearish. This is marked with a red triangle above the price bars.
These signals provide traders with clear entry and exit points, ensuring they can capitalize on emerging trends while avoiding false signals.
Customizable Visualization Options
The indicator offers a range of visualization settings to help traders interpret the data with ease:
Show Supertrend: Option to toggle the visibility of the Supertrend line.
Candle Coloring: Automatically colors candlesticks based on the trend direction:
Green for long trends.
Red for short trends.
Long and Short Signals (𝕃 + 𝕊): Displays long (𝕃) and short (𝕊) signals directly on the chart for quick identification of trade opportunities.
Line Color Customization: Allows users to customize the colors for long and short trends.
Alert Conditions
To ensure traders never miss an opportunity, the PDF-MA Supertrend includes built-in alerts for trend changes:
Long Signal Alert: Notifies when a bullish trend is identified.
Short Signal Alert: Notifies when a bearish trend is identified.
These alerts can be configured for real-time notifications via SMS, email, or push notifications, making it easier to stay updated on market movements.
Suggested Parameter Adjustments
The indicator’s effectiveness can be fine-tuned using the following guidelines:
Variance:
For low-volatility assets (e.g., indices): Use a smaller variance (1.0–1.5) for smoother trends.
For high-volatility assets (e.g., cryptocurrencies): Use a larger variance (1.5–2.0) to better capture rapid price changes.
ATR Factor:
A higher factor (e.g., 2.0) is better suited for long-term trend-following strategies.
A lower factor (e.g., 1.5) captures shorter-term trends.
Smoothing Period:
Shorter periods provide more reactive signals but may increase noise.
Longer periods offer stability and better alignment with significant trends.
Experimentation is encouraged to find the optimal settings for specific assets and trading strategies.
Trading Applications
The PDF-MA Supertrend is a versatile indicator suited to a variety of trading approaches:
Trend Following : Use the Supertrend line and signals to follow market trends and ride sustained price movements.
Reversal Trading : Spot potential trend reversals as the Supertrend flips direction.
Volatility Analysis : Adjust the ATR factor to filter out minor price fluctuations or capture sharp movements.
Final Thoughts
The PDF-MA Supertrend combines the precision of Probability Density Function smoothing with the adaptability of the Supertrend methodology, offering traders a powerful tool for identifying trends and volatility. With its customizable parameters, actionable signals, and built-in alerts, this indicator is an excellent choice for traders seeking a robust and reliable system for trend detection and entry/exit timing.
As always, backtesting and incorporating this indicator into a broader strategy are recommended for optimal results.
PDF MA For Loop [BackQuant]PDF MA For Loop
Introducing the PDF MA For Loop, an innovative trading indicator that combines Probability Density Function (PDF) smoothing with a dynamic for-loop scoring mechanism. This advanced tool provides traders with precise trend-following signals, helping to identify long and short opportunities with improved clarity and adaptability to market conditions.
If you would like to check out the stand alone PDF Moving Average:
Core Concept: Probability Density Function (PDF) Smoothing
The PDF smoothing method is a unique approach that applies adaptive weights to price data based on a Probability Density Function. This ensures that recent data points receive appropriate emphasis while maintaining a smooth transition across the data set. The result is a moving average that is not only smoother but also more responsive to market changes.
Key parameters in PDF smoothing:
Variance : Controls the spread of the PDF, where a higher value results in broader smoothing and a lower value makes the moving average more sensitive.
Mean : Centers the PDF around a specific value, influencing the weighting and responsiveness of the smoothing process.
By combining PDF smoothing with traditional moving averages (EMA or SMA), the indicator creates a hybrid signal that balances responsiveness and reliability.
For-Loop Scoring Mechanism
At the heart of this indicator is the for-loop scoring mechanism, which evaluates the smoothed PDF moving average over a defined range of historical data points. This process assigns a score to the current market condition based on whether the PDF moving average is greater than or less than previous values.
Long Signal: A long signal is generated when the score exceeds the Long Threshold (default set at 40), indicating upward momentum.
Short Signal: A short signal is triggered when the score crosses below the Short Threshold (default set at -10), suggesting potential downward momentum.
This dynamic scoring system ensures that the indicator remains adaptive, capturing trends and shifts in market sentiment effectively.
Customization Options
The PDF MA For Loop includes a variety of customizable settings to fit different trading styles and strategies:
Calculation Settings
Price Source : Select the input price for the calculation (default is the close price).
Smoothing Method : Choose between EMA or SMA for the additional smoothing layer, providing flexibility to adapt to market conditions.
Smoothing Period : Adjust the lookback period for the smoothing function, with shorter periods providing more sensitivity and longer periods offering greater stability.
Variance & Mean : Fine-tune the PDF function parameters to control the weighting of the smoothing process.
Signal Settings
Thresholds : Customize the upper and lower thresholds to define the sensitivity of the long and short signals.
For Loop Range : Set the range of historical data points analyzed by the for-loop, influencing the depth of the scoring mechanism.
UI Settings
Signal Line Width: Adjust the thickness of the plotted signal line for better visibility.
Candle Coloring: Enable or disable the coloring of candlesticks based on trend direction (green for long, red for short, gray for neutral).
Background Coloring: Add background shading to highlight long and short signals for an enhanced visual experience.
Alerts and Automation
The indicator includes built-in alert conditions to notify traders of important market events:
Long Signal Alert: Notifies when the score exceeds the upper threshold, indicating a bullish trend.
Short Signal Alert: Notifies when the score crosses below the lower threshold, signaling a bearish trend.
These alerts can be configured for real-time notifications, allowing traders to respond quickly to market changes without constant chart monitoring.
Trading Applications
The PDF MA For Loop is versatile and can be applied across various trading strategies and market conditions:
Trend Following: The PDF smoothing method combined with for-loop scoring makes this indicator particularly effective for identifying and following trends.
Reversal Trading: By observing the thresholds and score, traders can anticipate potential reversals when the trend shifts from long to short (or vice versa).
Risk Management: The dynamic thresholds and scoring provide clear signals, allowing traders to enter and exit trades with greater confidence and precision.
Final Thoughts
The PDF MA For Loopis merges advanced mathematical concepts with practical trading tools. By leveraging Probability Density Function smoothing and a dynamic for-loop scoring system, it provides traders with clear, actionable signals while adapting to market conditions.
Whether you’re looking for an edge in trend-following strategies or seeking precision in identifying reversals, this indicator offers the flexibility and power to enhance your trading decisions
As always, backtesting and integrating the PDF MA For Loop into a comprehensive trading strategy is recommended for optimal performance, as no single indicator should be used in isolation.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Hybrid Adaptive Double Exponential Smoothing🙏🏻 This is HADES (Hybrid Adaptive Double Exponential Smoothing) : fully data-driven & adaptive exponential smoothing method, that gains all the necessary info directly from data in the most natural way and needs no subjective parameters & no optimizations. It gets applied to data itself -> to fit residuals & one-point forecast errors, all at O(1) algo complexity. I designed it for streaming high-frequency univariate time series data, such as medical sensor readings, orderbook data, tick charts, requests generated by a backend, etc.
The HADES method is:
fit & forecast = a + b * (1 / alpha + T - 1)
T = 0 provides in-sample fit for the current datum, and T + n provides forecast for n datapoints.
y = input time series
a = y, if no previous data exists
b = 0, if no previous data exists
otherwise:
a = alpha * y + (1 - alpha) * a
b = alpha * (a - a ) + (1 - alpha) * b
alpha = 1 / sqrt(len * 4)
len = min(ceil(exp(1 / sig)), available data)
sig = sqrt(Absolute net change in y / Sum of absolute changes in y)
For the start datapoint when both numerator and denominator are zeros, we define 0 / 0 = 1
...
The same set of operations gets applied to the data first, then to resulting fit absolute residuals to build prediction interval, and finally to absolute forecasting errors (from one-point ahead forecast) to build forecasting interval:
prediction interval = data fit +- resoduals fit * k
forecasting interval = data opf +- errors fit * k
where k = multiplier regulating intervals width, and opf = one-point forecasts calculated at each time t
...
How-to:
0) Apply to your data where it makes sense, eg. tick data;
1) Use power transform to compensate for multiplicative behavior in case it's there;
2) If you have complete data or only the data you need, like the full history of adjusted close prices: go to the next step; otherwise, guided by your goal & analysis, adjust the 'start index' setting so the calculations will start from this point;
3) Use prediction interval to detect significant deviations from the process core & make decisions according to your strategy;
4) Use one-point forecast for nowcasting;
5) Use forecasting intervals to ~ understand where the next datapoints will emerge, given the data-generating process will stay the same & lack structural breaks.
I advise k = 1 or 1.5 or 4 depending on your goal, but 1 is the most natural one.
...
Why exponential smoothing at all? Why the double one? Why adaptive? Why not Holt's method?
1) It's O(1) algo complexity & recursive nature allows it to be applied in an online fashion to high-frequency streaming data; otherwise, it makes more sense to use other methods;
2) Double exponential smoothing ensures we are taking trends into account; also, in order to model more complex time series patterns such as seasonality, we need detrended data, and this method can be used to do it;
3) The goal of adaptivity is to eliminate the window size question, in cases where it doesn't make sense to use cumulative moving typical value;
4) Holt's method creates a certain interaction between level and trend components, so its results lack symmetry and similarity with other non-recursive methods such as quantile regression or linear regression. Instead, I decided to base my work on the original double exponential smoothing method published by Rob Brown in 1956, here's the original source , it's really hard to find it online. This cool dude is considered the one who've dropped exponential smoothing to open access for the first time🤘🏻
R&D; log & explanations
If you wanna read this, you gotta know, you're taking a great responsability for this long journey, and it gonna be one hell of a trip hehe
Machine learning, apprentissage automatique, машинное обучение, digital signal processing, statistical learning, data mining, deep learning, etc., etc., etc.: all these are just artificial categories created by the local population of this wonderful world, but what really separates entities globally in the Universe is solution complexity / algorithmic complexity.
In order to get the game a lil better, it's gonna be useful to read the HTES script description first. Secondly, let me guide you through the whole R&D; process.
To discover (not to invent) the fundamental universal principle of what exponential smoothing really IS, it required the review of the whole concept, understanding that many things don't add up and don't make much sense in currently available mainstream info, and building it all from the beginning while avoiding these very basic logical & implementation flaws.
Given a complete time t, and yet, always growing time series population that can't be logically separated into subpopulations, the very first question is, 'What amount of data do we need to utilize at time t?'. Two answers: 1 and all. You can't really gain much info from 1 datum, so go for the second answer: we need the whole dataset.
So, given the sequential & incremental nature of time series, the very first and basic thing we can do on the whole dataset is to calculate a cumulative , such as cumulative moving mean or cumulative moving median.
Now we need to extend this logic to exponential smoothing, which doesn't use dataset length info directly, but all cool it can be done via a formula that quantifies the relationship between alpha (smoothing parameter) and length. The popular formulas used in mainstream are:
alpha = 1 / length
alpha = 2 / (length + 1)
The funny part starts when you realize that Cumulative Exponential Moving Averages with these 2 alpha formulas Exactly match Cumulative Moving Average and Cumulative (Linearly) Weighted Moving Average, and the same logic goes on:
alpha = 3 / (length + 1.5) , matches Cumulative Weighted Moving Average with quadratic weights, and
alpha = 4 / (length + 2) , matches Cumulative Weighted Moving Average with cubic weghts, and so on...
It all just cries in your shoulder that we need to discover another, native length->alpha formula that leverages the recursive nature of exponential smoothing, because otherwise, it doesn't make sense to use it at all, since the usual CMA and CMWA can be computed incrementally at O(1) algo complexity just as exponential smoothing.
From now on I will not mention 'cumulative' or 'linearly weighted / weighted' anymore, it's gonna be implied all the time unless stated otherwise.
What we can do is to approach the thing logically and model the response with a little help from synthetic data, a sine wave would suffice. Then we can think of relationships: Based on algo complexity from lower to higher, we have this sequence: exponential smoothing @ O(1) -> parametric statistics (mean) @ O(n) -> non-parametric statistics (50th percentile / median) @ O(n log n). Based on Initial response from slow to fast: mean -> median Based on convergence with the real expected value from slow to fast: mean (infinitely approaches it) -> median (gets it quite fast).
Based on these inputs, we need to discover such a length->alpha formula so the resulting fit will have the slowest initial response out of all 3, and have the slowest convergence with expected value out of all 3. In order to do it, we need to have some non-linear transformer in our formula (like a square root) and a couple of factors to modify the response the way we need. I ended up with this formula to meet all our requirements:
alpha = sqrt(1 / length * 2) / 2
which simplifies to:
alpha = 1 / sqrt(len * 8)
^^ as you can see on the screenshot; where the red line is median, the blue line is the mean, and the purple line is exponential smoothing with the formulas you've just seen, we've met all the requirements.
Now we just have to do the same procedure to discover the length->alpha formula but for double exponential smoothing, which models trends as well, not just level as in single exponential smoothing. For this comparison, we need to use linear regression and quantile regression instead of the mean and median.
Quantile regression requires a non-closed form solution to be solved that you can't really implement in Pine Script, but that's ok, so I made the tests using Python & sklearn:
paste.pics
^^ on this screenshot, you can see the same relationship as on the previous screenshot, but now between the responses of quantile regression & linear regression.
I followed the same logic as before for designing alpha for double exponential smoothing (also considered the initial overshoots, but that's a little detail), and ended up with this formula:
alpha = sqrt(1 / length) / 2
which simplifies to:
alpha = 1 / sqrt(len * 4)
Btw, given the pattern you see in the resulting formulas for single and double exponential smoothing, if you ever want to do triple (not Holt & Winters) exponential smoothing, you'll need len * 2 , and just len * 1 for quadruple exponential smoothing. I hope that based on this sequence, you see the hint that Maybe 4 rounds is enough.
Now since we've dealt with the length->alpha formula, we can deal with the adaptivity part.
Logically, it doesn't make sense to use a slower-than-O(1) method to generate input for an O(1) method, so it must be something universal and minimalistic: something that will help us measure consistency in our data, yet something far away from statistics and close enough to topology.
There's one perfect entity that can help us, this is fractal efficiency. The way I define fractal efficiency can be checked at the very beginning of the post, what matters is that I add a square root to the formula that is not typically added.
As explained in the description of my metric QSFS , one of the reasons for SQRT-transformed values of fractal efficiency applied in moving window mode is because they start to closely resemble normal distribution, yet with support of (0, 1). Data with this interesting property (normally distributed yet with finite support) can be modeled with the beta distribution.
Another reason is, in infinitely expanding window mode, fractal efficiency of every time series that exhibits randomness tends to infinitely approach zero, sqrt-transform kind of partially neutralizes this effect.
Yet another reason is, the square root might better reflect the dimensional inefficiency or degree of fractal complexity, since it could balance the influence of extreme deviations from the net paths.
And finally, fractals exhibit power-law scaling -> measures like length, area, or volume scale in a non-linear way. Adding a square root acknowledges this intrinsic property, while connecting our metric with the nature of fractals.
---
I suspect that, given analogies and connections with other topics in geometry, topology, fractals and most importantly positive test results of the metric, it might be that the sqrt transform is the fundamental part of fractal efficiency that should be applied by default.
Now the last part of the ballet is to convert our fractal efficiency to length value. The part about inverse proportionality is obvious: high fractal efficiency aka high consistency -> lower window size, to utilize only the last data that contain brand new information that seems to be highly reliable since we have consistency in the first place.
The non-obvious part is now we need to neutralize the side effect created by previous sqrt transform: our length values are too low, and exponentiation is the perfect candidate to fix it since translating fractal efficiency into window sizes requires something non-linear to reflect the fractal dynamics. More importantly, using exp() was the last piece that let the metric shine, any other transformations & formulas alike I've tried always had some weird results on certain data.
That exp() in the len formula was the last piece that made it all work both on synthetic and on real data.
^^ a standalone script calculating optimal dynamic window size
Omg, THAT took time to write. Comment and/or text me if you need
...
"Versace Pip-Boy, I'm a young gun coming up with no bankroll" 👻
∞
Adaptive Supertrend with Dynamic Optimization [EdgeTerminal]The Enhanced Adaptive Supertrend represents a significant evolution of the traditional Supertrend indicator, incorporating advanced mathematical optimization, dynamic volatility adjustment, intelligent signal filtering, reduced noise and false positives.
Key Features
Dynamic volatility-adjusted bands
Self-optimizing multiplier
Intelligent signal filtering system
Cooldown period to prevent signal clustering
Clear buy/sell signals with optimal positioning
Smooth trend visualization
RSI and MACD integration for confirmation
Performance-based optimization
Dynamic Band Calculation
Dynamic Band Calculation automatically adapts to market volatility, generates wider bands in volatile periods, reducing false signals. It also generates tighter bands in stable periods, capturing smaller moves and smooth transitions between different volatility regimes.
RSI Integration
The RSI and MACD play multiple crucial roles in the Adaptive Supertrend.
It first helps with momentum factor calculation. This dynamically adjusts band width based on momentum conditions. When the RSI is oversold, bands widen by 20% to prevent false signals during strong downtrends and provide more room for price movements in extreme conditions.
When the RSI is overbought, brands tighten by 20% and they become more sensitive to potential reversals to help catch trend changes earlier.
This reduces false signals in strong trends, helps detect potential reversals earlier than the usual, create adaptive band width based on market conditions and finally, better protection against whipsaws.
MACD Integration
The MACD in this supertrend indicator serves as a trend confirmation tool. The idea is to use MACD crossovers to confirm trend changes to reduce false trend change signals and enhance the signal quality.
For this to become a signal, MACD crossovers must align with price movement to help filter out weak or false signals, which acts as an additional layer of trend confirmation.
Additionally, MACD line position relative to signal line indicates trend strength, helps maintain positions in strong trends and assists in early detection of trend weakening.
Momentum Integration
Momentum Integration prevents false signals in extreme conditions, It adjusts dynamic bands based on market momentum, improves trend confirmation in strong moves and reduces whipsaws during consolidations.
Improved signals
There are a few systems to generate better signals, allowing for generally faster signals compared to original supertrend, such as:
Enforced cooldown period between signals
Prevents signal clustering
Clearer entry/exit points
Reduced false signals during choppy markets
Performance Optimization
This script implements a Sharpe ratio-inspired optimization algorithm to balance returns against risk, penalize large drawdowns, adapt parameters in real-time and improve risk-adjusted performance
Parameter Settings
ATR Period: 10 (default) - adjust based on timeframe
Initial Multiplier: 3.0 (default) - will self-optimize
Optimization Period: 50 (default) - longer periods for more stability
Smoothing Period: 3 (default) - adjust for signal smoothness
Best Practices
Use on multiple timeframes for confirmation
Allow the optimization process to run for at least 50 bars
Monitor the adaptive multiplier for trend strength indication
Consider RSI and MACD alignment for stronger signals
Moment-Based Adaptive DetectionMBAD (Moment-Based Adaptive Detection) : a method applicable to a wide range of purposes, like outlier or novelty detection, that requires building a sensible interval/set of thresholds. Unlike other methods that are static and rely on optimizations that inevitably lead to underfitting/overfitting, it dynamically adapts to your data distribution without any optimizations, MLE, or stuff, and provides a set of data-driven adaptive thresholds, based on closed-form solution with O(n) algo complexity.
1.5 years ago, when I was still living in Versailles at my friend's house not knowing what was gonna happen in my life tomorrow, I made a damn right decision not to give up on one idea and to actually R&D it and see what’s up. It allowed me to create this one.
The Method Explained
I’ve been wandering about z-values, why exactly 6 sigmas, why 95%? Who decided that? Why would you supersede your opinion on data? Based on what? Your ego?
Then I consciously noticed a couple of things:
1) In control theory & anomaly detection, the popular threshold is 3 sigmas (yet nobody can firmly say why xD). If your data is Laplace, 3 sigmas is not enough; you’re gonna catch too many values, so it needs a higher sigma.
2) Yet strangely, the normal distribution has kurtosis of 3, and 6 for Laplace.
3) Kurtosis is a standardized moment, a moment scaled by stdev, so it means "X amount of something measured in stdevs."
4) You generate synthetic data, you check on real data (market data in my case, I am a quant after all), and you see on both that:
lower extension = mean - standard deviation * kurtosis ≈ data minimum
upper extension = mean + standard deviation * kurtosis ≈ data maximum
Why not simply use max/min?
- Lower info gain: We're not using all info available in all data points to estimate max/min; we just pick the current higher and lower values. Lol, it’s the same as dropping exponential smoothing with alpha = 0 on stationary data & calling it a day.
You can’t update the estimates of min and max when new data arrives containing info about the matter. All you can do is just extend min and max horizontally, so you're not using new info arriving inside new data.
- Mixing order and non-order statistics is a bad idea; we're losing integrity and coherence. That's why I don't like the Hurst exponent btw (and yes, I came up with better metrics of my own).
- Max & min are not even true order statistics, unlike a percentile (finding which requires sorting, which requires multiple passes over your data). To find min or max, you just need to do one traversal over your data. Then with or without any weighting, 100th percentile will equal max. So unlike a weighted percentile, you can’t do weighted max. Then while you can always check max and min of a geometric shape, now try to calculate the 56th percentile of a pentagram hehe.
TL;DR max & min are rather topological characteristics of data, just as the difference between starting and ending points. Not much to do with statistics.
Now the second part of the ballet is to work with data asymmetry:
1) Skewness is also scaled by stdev -> so it must represent a shift from the data midrange measured in stdevs -> given asymmetric data, we can include this info in our models. Unlike kurtosis, skewness has a sign, so we add it to both thresholds:
lower extension = mean - standard deviation * kurtosis + standard deviation * skewness
upper extension = mean + standard deviation * kurtosis + standard deviation * skewness
2) Now our method will work with skewed data as well, omg, ain’t it cool?
3) Hold up, but what about 5th and 6th moments (hyperskewness & hyperkurtosis)? They should represent something meaningful as well.
4) Perhaps if extensions represent current estimated extremums, what goes beyond? Limits, beyond which we expect data not to be able to pass given the current underlying process generating the data?
When you extend this logic to higher-order moments, i.e., hyperskewness & hyperkurtosis (5th and 6th moments), they measure asymmetry and shape of distribution tails, not its core as previous moments -> makes no sense to mix 4th and 3rd moments (skewness and kurtosis) with 5th & 6th, so we get:
lower limit = mean - standard deviation * hyperkurtosis + standard deviation * hyperskewness
upper limit = mean + standard deviation * hyperkurtosis + standard deviation * hyperskewness
While extensions model your data’s natural extremums based on current info residing in the data without relying on order statistics, limits model your data's maximum possible and minimum possible values based on current info residing in your data. If a new data point trespasses limits, it means that a significant change in the data-generating process has happened, for sure, not probably—a confirmed structural break.
And finally we use time and volume weighting to include order & process intensity information in our model.
I can't stress it enough: despite the popularity of these non-weighted methods applied in mainstream open-access time series modeling, it doesn’t make ANY sense to use non-weighted calculations on time series data . Time = sequence, it matters. If you reverse your time series horizontally, your means, percentiles, whatever, will stay the same. Basically, your calculations will give the same results on different data. When you do it, you disregard the order of data that does have order naturally. Does it make any sense to you? It also concerns regressions applied on time series as well, because even despite the slope being opposite on your reversed data, the centroid (through which your regression line always comes through) will be the same. It also might concern Fourier (yes, you can do weighted Fourier) and even MA and AR models—might, because I ain’t researched it extensively yet.
I still can’t believe it’s nowhere online in open access. No chance I’m the first one who got it. It’s literally in front of everyone’s eyes for centuries—why no one tells about it?
How to use
That’s easy: can be applied to any, even non-stationary and/or heteroscedastic time series to automatically detect novelties, outliers, anomalies, structural breaks, etc. In terms of quant trading, you can try using extensions for mean reversion trades and limits for emergency exits, for example. The market-making application is kinda obvious as well.
The only parameter the model has is length, and it should NOT be optimized but picked consciously based on the process/system you’re applying it to and based on the task. However, this part is not about sharing info & an open-access instrument with the world. This is about using dem instruments to do actual business, and we can’t talk about it.
∞
Machine Learning RSI [BackQuant]Machine Learning RSI
The Machine Learning RSI is a cutting-edge trading indicator that combines the power of Relative Strength Index (RSI) with Machine Learning (ML) clustering techniques to dynamically determine overbought and oversold thresholds. This advanced indicator adapts to market conditions in real-time, offering traders a robust tool for identifying optimal entry and exit points with increased precision.
Core Concept: Relative Strength Index (RSI)
The RSI is a well-known momentum oscillator that measures the speed and change of price movements, oscillating between 0 and 100. Typically, RSI values above 70 are considered overbought, and values below 30 are considered oversold. However, static thresholds may not be effective in all market conditions.
This script enhances the RSI by integrating a dynamic thresholding system powered by Machine Learning clustering, allowing it to adapt thresholds based on historical RSI behavior and market context.
Machine Learning Clustering for Dynamic Thresholds
The Machine Learning (ML) component uses clustering to calculate dynamic thresholds for overbought and oversold levels. Instead of relying on fixed RSI levels, this indicator clusters historical RSI values into three groups using a percentile-based initialization and iterative optimization:
Cluster 1: Represents lower RSI values (typically associated with oversold conditions).
Cluster 2: Represents mid-range RSI values.
Cluster 3: Represents higher RSI values (typically associated with overbought conditions).
Dynamic thresholds are determined as follows:
Long Threshold: The upper centroid value of Cluster 3.
Short Threshold: The lower centroid value of Cluster 1.
This approach ensures that the indicator adapts to the current market regime, providing more accurate signals in volatile or trending conditions.
Smoothing Options for RSI
To further enhance the effectiveness of the RSI, this script allows traders to apply various smoothing methods to the RSI calculation, including:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Hull Moving Average (HMA)
Linear Regression (LINREG)
Double Exponential Moving Average (DEMA)
Triple Exponential Moving Average (TEMA)
Adaptive Linear Moving Average (ALMA)
T3 Moving Average
Traders can select their preferred smoothing method and adjust the smoothing period to suit their trading style and market conditions. The option to smooth the RSI reduces noise and makes the indicator more reliable for detecting trends and reversals.
Long and Short Signals
The indicator generates long and short signals based on the relationship between the RSI value and the dynamic thresholds:
Long Signals: Triggered when the RSI crosses above the long threshold, signaling bullish momentum.
Short Signals: Triggered when the RSI falls below the short threshold, signaling bearish momentum.
These signals are dynamically adjusted to reflect real-time market conditions, making them more robust than static RSI signals.
Visualization and Clustering Insights
The Machine Learning RSI provides an intuitive and visually rich interface, including:
RSI Line: Plotted in real-time, color-coded based on its position relative to the dynamic thresholds (green for long, red for short, gray for neutral).
Dynamic Threshold Lines: The script plots the long and short thresholds calculated by the ML clustering process, providing a clear visual reference for overbought and oversold levels.
Cluster Plots: Each RSI cluster is displayed with distinct colors (green, orange, and red) to give traders insights into how RSI values are grouped and how the dynamic thresholds are derived.
Customization Options
The Machine Learning RSI is highly customizable, allowing traders to tailor the indicator to their preferences:
RSI Settings : Adjust the RSI length, source price, and smoothing method to match your trading strategy.
Threshold Settings : Define the range and step size for clustering thresholds, allowing you to fine-tune the clustering process.
Optimization Settings : Control the performance memory, maximum clustering steps, and maximum data points for ML calculations to ensure optimal performance.
UI Settings : Customize the appearance of the RSI plot, dynamic thresholds, and cluster plots. Traders can also enable or disable candle coloring based on trend direction.
Alerts and Automation
To assist traders in staying on top of market movements, the script includes alert conditions for key events:
Long Signal: When the RSI crosses above the long threshold.
Short Signal: When the RSI crosses below the short threshold.
These alerts can be configured to notify traders in real-time, enabling timely decisions without constant chart monitoring.
Trading Applications
The Machine Learning RSI is versatile and can be applied to various trading strategies, including:
Trend Following: By dynamically adjusting thresholds, this indicator is effective in identifying and following trends in real-time.
Reversal Trading: The ML clustering process helps identify extreme RSI levels, offering reliable signals for reversals.
Range-Bound Trading: The dynamic thresholds adapt to market conditions, making the indicator suitable for trading in sideways markets where static thresholds often fail.
Final Thoughts
The Machine Learning RSI represents a significant advancement in RSI-based trading indicators. By integrating Machine Learning clustering techniques, this script overcomes the limitations of static thresholds, providing dynamic, adaptive signals that respond to market conditions in real-time. With its robust visualization, customizable settings, and alert capabilities, this indicator is a powerful tool for traders seeking to enhance their momentum analysis and improve decision-making.
As always, thorough backtesting and integration into a broader trading strategy are recommended to maximize the effectiveness!
Entropy-Based Adaptive SuperTrendOverview:
Introducing the Entropy-Based Adaptive SuperTrend – a groundbreaking trading indicator designed to adapt dynamically to market conditions using market entropy. This enhanced SuperTrend indicator adjusts its sensitivity according to the level of chaos (or order) in price movements, providing more stable signals during volatile periods and more responsive signals when the market becomes orderly.
Key Features:
Entropy-Adaptive Mechanism: By incorporating an entropy measure, this indicator estimates the degree of unpredictability in the market. During high entropy periods (more chaotic), signals are made less sensitive, while during low entropy periods, the indicator reacts more quickly to price changes.
Adaptive ATR Multiplier: Unlike traditional SuperTrend indicators that use a fixed ATR multiplier, this version calculates a dynamic ATR multiplier based on the entropy score, ensuring more flexibility and adaptability in setting stop levels.
Visual Clarity: The indicator is overlayed on the price chart with customizable visual elements. The bullish and bearish trends are color-coded for ease of use, and optional entry signals ("L" for long and "S" for short) are plotted to clearly mark potential entry opportunities.
Alerts for Key Opportunities : Never miss an opportunity with built-in alerts for buy and sell signals. Traders can easily configure these alerts to be notified instantly when market conditions trigger a new trend.
How It Works:
Entropy Calculation: The entropy of the price data is calculated over a user-defined period, giving an indication of the degree of randomness in the price movements. The result is then smoothed to reduce noise and create a meaningful trend indication.
Dynamic ATR Adjustment: The ATR (Average True Range) multiplier, which controls the distance of the trailing stop, is adjusted based on the entropy score. This allows the SuperTrend line to widen in chaotic times, reducing false signals, while tightening in orderly times, allowing quicker trend captures.
Parameters Explained:
Entropy Settings: Control the sensitivity of entropy calculations, including the look-back period, number of bins for price distribution, and smoothing length.
Adaptive Settings: Adjust how the indicator adapts to different levels of entropy, including the adaptation period and the filtering weight.
SuperTrend Settings : Customize the ATR period and the dynamic multiplier range to fine-tune the trailing stops for your trading style.
Visual Settings: Choose your preferred colors for bullish and bearish trends, and decide if you want the entry labels displayed directly on the chart.
Use Cases:
Swing Traders can utilize the indicator to capture trend reversals while filtering out the noise during high entropy periods.
Intraday Traders can adapt the settings for shorter time frames to benefit from dynamic adjustments that reduce overtrading and false signals.
Risk Management: The entropy-based adaptive feature provides an edge in risk management by reducing sensitivity during times of increased chaos, thus helping to limit unnecessary trades.
How to Use It:
Look for entry labels ("L" for long, "S" for short) to identify potential opportunities.
Use the color-coded trendlines to determine market bias: greenish hue for bullish trends, reddish hue for bearish trends.
Customize the input settings to align with your preferred market timeframe and risk profile.
Alerts & Notifications:
Built-in alerts notify you of significant trend changes. Simply enable these alerts to receive updates when a new long or short opportunity is detected, helping you stay ahead without needing to watch the screen constantly.
Customization Tips:
Longer Timeframes : Increase the Entropy Period to better capture macro trends in high timeframe charts.
Higher Volatility Markets: Increase the ATR Max Multiplier to ensure stops are set farther away during high entropy.
Lower Volatility Markets: Use a lower ATR Base Multiplier and tighter entropy thresholds to capture rapid price movements.
Final Thoughts:
The Entropy-Based Adaptive SuperTrend indicator merges traditional trend-following logic with an adaptive mechanism driven by market entropy, aiming to address the challenges of whipsaws and false signals common in conventional SuperTrend setups. This indicator offers an intelligent and flexible way to track market trends, suitable for both beginners and experienced trade
PDF Smoothed Moving Average [BackQuant]PDF Smoothed Moving Average
Introducing BackQuant’s PDF Smoothed Moving Average (PDF-MA) — an innovative trading indicator that applies Probability Density Function (PDF) weighting to moving averages, creating a unique, trend-following tool that offers adaptive smoothing to price movements. This advanced indicator gives traders an edge by blending PDF-weighted values with conventional moving averages, helping to capture trend shifts with enhanced clarity.
Core Concept: Probability Density Function (PDF) Smoothing
The Probability Density Function (PDF) provides a mathematical approach to applying adaptive weighting to data points based on a specified variance and mean. In the PDF-MA indicator, the PDF function is used to weight price data, adding a layer of probabilistic smoothing that enhances the detection of trend strength while reducing noise.
The PDF weights are controlled by two key parameters:
Variance: Determines the spread of the weights, where higher values spread out the weighting effect, providing broader smoothing.
Mean : Centers the weights around a particular price value, influencing the trend’s directionality and sensitivity.
These PDF weights are applied to each price point over the chosen period, creating an adaptive and smooth moving average that more closely reflects the underlying price trend.
Blending PDF with Standard Moving Averages
To further improve the PDF-MA, this indicator combines the PDF-weighted average with a traditional moving average, selected by the user as either an Exponential Moving Average (EMA) or Simple Moving Average (SMA). This blended approach leverages the strengths of each method: the responsiveness of PDF smoothing and the robustness of conventional moving averages.
Smoothing Method: Traders can choose between EMA and SMA for the additional moving average layer. The EMA is more responsive to recent prices, while the SMA provides a consistent average across the selected period.
Smoothing Period: Controls the length of the lookback period, affecting how sensitive the average is to price changes.
The result is a PDF-MA that provides a reliable trend line, reflecting both the PDF weighting and traditional moving average values, ideal for use in trend-following and momentum-based strategies.
Trend Detection and Candle Coloring
The PDF-MA includes a built-in trend detection feature that dynamically colors candles based on the direction of the smoothed moving average:
Uptrend: When the PDF-MA value is increasing, the trend is considered bullish, and candles are colored green, indicating potential buying conditions.
Downtrend: When the PDF-MA value is decreasing, the trend is considered bearish, and candles are colored red, signaling potential selling or shorting conditions.
These color-coded candles provide a quick visual reference for the trend direction, helping traders make real-time decisions based on the current market trend.
Customization and Visualization Options
This indicator offers a range of customization options, allowing traders to tailor it to their specific preferences and trading environment:
Price Source : Choose the price data for calculation, with options like close, open, high, low, or HLC3.
Variance and Mean : Fine-tune the PDF weighting parameters to control the indicator’s sensitivity and responsiveness to price data.
Smoothing Method : Select either EMA or SMA to customize the conventional moving average layer used in conjunction with the PDF.
Smoothing Period : Set the lookback period for the moving average, with a longer period providing more stability and a shorter period offering greater sensitivity.
Candle Coloring : Enable or disable candle coloring based on trend direction, providing additional clarity in identifying bullish and bearish phases.
Trading Applications
The PDF Smoothed Moving Average can be applied across various trading strategies and timeframes:
Trend Following : By smoothing price data with PDF weighting, this indicator helps traders identify long-term trends while filtering out short-term noise.
Reversal Trading : The PDF-MA’s trend coloring feature can help pinpoint potential reversal points by showing shifts in the trend direction, allowing traders to enter or exit positions at optimal moments.
Swing Trading : The PDF-MA provides a clear trend line that swing traders can use to capture intermediate price moves, following the trend direction until it shifts.
Final Thoughts
The PDF Smoothed Moving Average is a highly adaptable indicator that combines probabilistic smoothing with traditional moving averages, providing a nuanced view of market trends. By integrating PDF-based weighting with the flexibility of EMA or SMA smoothing, this indicator offers traders an advanced tool for trend analysis that adapts to changing market conditions with reduced lag and increased accuracy.
Whether you’re trading trends, reversals, or swings, the PDF-MA offers valuable insights into the direction and strength of price movements, making it a versatile addition to any trading strategy.
Savitzky Golay Median Filtered RSI [BackQuant]Savitzky Golay Median Filtered RSI
Introducing BackQuant's Savitzky Golay Median Filtered RSI, a cutting-edge indicator that enhances the classic Relative Strength Index (RSI) by applying both a Savitzky-Golay filter and a median filter to provide smoother and more reliable signals. This advanced approach helps reduce noise and captures true momentum trends with greater precision. Let’s break down how the indicator works, the features it offers, and how it can improve your trading strategy.
Core Concept: Relative Strength Index (RSI)
The Relative Strength Index (RSI) is a widely used momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100, with levels above 70 typically indicating overbought conditions and levels below 30 indicating oversold conditions. However, the standard RSI can sometimes generate noisy signals, especially in volatile markets, making it challenging to identify reliable entry and exit points.
To improve upon the traditional RSI, this indicator introduces two powerful filters: the Savitzky-Golay filter and a median filter.
Savitzky-Golay Filter: Smoothing with Precision
The Savitzky-Golay filter is a digital filtering technique used to smooth data while preserving important features, such as peaks and trends. Unlike simple moving averages that can distort important price data, the Savitzky-Golay filter uses polynomial regression to fit the data, providing a more accurate and less lagging result.
In this script, the Savitzky-Golay filter is applied to the RSI values to smooth out short-term fluctuations and provide a more reliable signal. By using a window size of 5 and a polynomial degree of 2, the filter effectively reduces noise without compromising the integrity of the underlying price movements.
Median Filter: Reducing Outliers
After applying the Savitzky-Golay filter, the median filter is applied to the smoothed RSI values. The median filter is particularly effective at removing short-lived outliers, further enhancing the accuracy of the RSI by reducing the impact of sudden and temporary price spikes or drops. This combination of filters creates an ultra-smooth RSI that is better suited for detecting true market trends.
Long and Short Signals
The Savitzky Golay Median Filtered RSI generates long and short signals based on user-defined threshold levels:
Long Signals: A long signal is triggered when the filtered RSI exceeds the Long Threshold (default set at 176). This indicates that momentum is shifting upward, and it may present a good buying opportunity.
Short Signals: A short signal is generated when the filtered RSI falls below the Short Threshold (default set at 162). This suggests that momentum is weakening, potentially signaling a selling opportunity or exit from a long position.
These threshold levels can be adjusted to suit different market conditions and timeframes, allowing traders to fine-tune the sensitivity of the indicator.
Customization and Visualization Options
The Savitzky Golay Median Filtered RSI comes with several customization options, enabling traders to tailor the indicator to their specific needs:
Calculation Source: Select the price source for the RSI calculation (default is OHLC4, but it can be changed to close, open, high, or low prices).
RSI Period: Adjust the lookback period for the RSI calculation (default is 14).
Median Filter Length: Control the length of the median filter applied to the smoothed RSI, affecting how much noise is removed from the signal.
Threshold Levels: Customize the long and short thresholds to define the sensitivity for generating buy and sell signals.
UI Settings: Choose whether to display the RSI and thresholds on the chart, color the bars according to trend direction, and adjust the line width and colors used for long and short signals.
Visual Feedback: Color-Coded Signals and Thresholds
To make the signals easier to interpret, the indicator offers visual feedback by coloring the price bars and the RSI plot according to the current market trend:
Green Bars indicate long signals when momentum is bullish.
Red Bars indicate short signals when momentum is bearish.
Gray Bars indicate neutral or undecided conditions when no clear signal is present.
In addition, the Long and Short Thresholds can be plotted directly on the chart to provide a clear reference for when signals are triggered, allowing traders to visually gauge the strength of the RSI relative to its thresholds.
Alerts for Automation
For traders who prefer automated notifications, the Savitzky Golay Median Filtered RSI includes built-in alert conditions for long and short signals. You can configure these alerts to notify you when a buy or sell condition is met, ensuring you never miss a trading opportunity.
Trading Applications
This indicator is versatile and can be used in a variety of trading strategies:
Trend Following: The combination of Savitzky-Golay and median filtering makes this RSI particularly useful for identifying strong trends without being misled by short-term noise. Traders can use the long and short signals to enter trades in the direction of the prevailing trend.
Reversal Trading: By adjusting the threshold levels, traders can use this indicator to spot potential reversals. When the RSI moves from overbought to oversold levels (or vice versa), it may signal a shift in market direction.
Swing Trading: The smoothed RSI provides a clear signal for short to medium-term price movements, making it an excellent tool for swing traders looking to capitalize on momentum shifts.
Risk Management: The filtered RSI can be used as part of a broader risk management strategy, helping traders avoid false signals and stay in trades only when the momentum is strong.
Final Thoughts
The Savitzky Golay Median Filtered RSI takes the classic RSI to the next level by applying advanced smoothing techniques that reduce noise and improve signal reliability. Whether you’re a trend follower, swing trader, or reversal trader, this indicator provides a more refined approach to momentum analysis, helping you make better-informed trading decisions.
As with all indicators, it is important to backtest thoroughly and incorporate sound risk management strategies when using the Savitzky Golay Median Filtered RSI in your trading system.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Adaptive Gaussian MA For Loop [BackQuant]Adaptive Gaussian MA For Loop
PLEASE Read the following carefully before applying this indicator to your trading system. Knowing the core logic behind the tools you're using allows you to integrate them into your strategy with confidence and precision.
Introducing BackQuant's Adaptive Gaussian Moving Average For Loop (AGMA FL) — a sophisticated trading indicator that merges the Gaussian Moving Average (GMA) with adaptive volatility to provide dynamic trend analysis. This unique indicator further enhances its effectiveness by utilizing a for-loop scoring mechanism to detect potential shifts in market direction. Let's dive into the components, the rationale behind them, and how this indicator can be practically applied to your trading strategies.
Understanding the Gaussian Moving Average (GMA)
The Gaussian Moving Average (GMA) is a smoothed moving average that applies Gaussian weighting to price data. Gaussian weighting gives more significance to data points near the center of the lookback window, making the GMA particularly effective at reducing noise while maintaining sensitivity to changes in price direction. In contrast to simpler moving averages like the SMA or EMA, GMA provides a more refined smoothing function, which can help traders follow the true trend in volatile markets.
In this script, the GMA is calculated over a defined Calculation Period (default 14), applying a Gaussian filter to smooth out market fluctuations and provide a clearer view of underlying trends.
Adaptive Volatility: A Dynamic Edge
The Adaptive feature in this indicator gives it the ability to adjust its sensitivity based on current market volatility. If the Adaptive option is enabled, the GMA uses a standard deviation-based volatility measure (with a default period of 20) to dynamically adjust the width of the Gaussian filter, allowing the GMA to react faster in volatile markets and more slowly in calm conditions. This dynamic nature ensures that the GMA stays relevant across different market environments.
When the Adaptive setting is disabled, the script defaults to a constant standard deviation value (default 1.0), providing a more stable but less responsive smoothing function.
Why Use Adaptive Gaussian Moving Average?
The Gaussian Moving Average already provides smoother results than standard moving averages, but by adding an adaptive component, the indicator becomes even more responsive to real-time price changes. In fast-moving or highly volatile markets, this adaptation allows traders to react quicker to emerging trends. Conversely, in quieter markets, it reduces over-sensitivity to minor fluctuations, thus lowering the risk of false signals.
For-Loop Scoring Mechanism
The heart of this indicator lies in its for-loop scoring system, which evaluates the smoothed price data (the GMA) over a specified range, comparing it to previous values. This scoring system assigns a numerical value based on whether the current GMA is higher or lower than previous values, creating a trend score.
Long Signals: These are generated when the for-loop score surpasses the Long Threshold (default set at 40), signaling that the GMA is gaining upward momentum, potentially identifying a favorable buying opportunity.
Short Signals: These are triggered when the score crosses below the Short Threshold (default set at -10), indicating that the market may be losing strength and that a selling or shorting opportunity could be emerging.
Thresholds & Customization Options
This indicator offers a high degree of flexibility, allowing you to fine-tune the settings according to your trading style and risk preferences:
Calculation Period: Adjust the lookback period for the Gaussian filter, affecting how smooth or responsive the indicator is to price changes.
Adaptive Mode: Toggle the adaptive feature on or off, allowing the GMA to dynamically adjust based on market volatility or remain consistent with a fixed standard deviation.
Volatility Settings: Control the standard deviation period for adaptive mode, fine-tuning how quickly the GMA responds to shifts in volatility.
For-Loop Settings: Modify the start and end points for the for-loop score calculation, adjusting the depth of analysis for trend signals.
Thresholds for Signals: Set custom long and short thresholds to determine when buy or sell signals should be generated.
Visualization Options: Choose to color bars based on trend direction, plot signal lines, or adjust the background color to reflect current market sentiment visually.
Trading Applications
The Adaptive Gaussian MA For Loop can be applied to a variety of trading styles and markets. Here are some key ways you can use this indicator in practice:
Trend Following: The combination of Gaussian smoothing and adaptive volatility helps traders stay on top of market trends, identifying significant momentum shifts while filtering out noise. The for-loop scoring system enhances this by providing a numerical representation of trend strength, making it easier to spot when a new trend is emerging or when an existing one is gaining strength.
Mean Reversion: For traders looking to capitalize on short-term market corrections, the adaptive nature of this indicator makes it easier to identify when price action is deviating too far from its smoothed trend, allowing for strategic entries and exits based on overbought or oversold conditions.
Swing Trading: With its ability to capture medium-term price movements while avoiding the noise of short-term fluctuations, this indicator is well-suited for swing traders who aim to profit from market reversals or short-to-mid-term trends.
Volatility Management: The adaptive feature allows the indicator to adjust dynamically in volatile markets, ensuring that it remains responsive in times of increased uncertainty while avoiding unnecessary noise in calmer periods. This makes it an effective tool for traders who want to manage risk by staying in tune with changing market conditions.
Final Thoughts
The Adaptive Gaussian MA For Loop is a powerful and flexible indicator that merges the elegance of Gaussian smoothing with the adaptability of volatility-based adjustments. By incorporating a for-loop scoring mechanism, this indicator provides traders with a comprehensive view of market trends and potential trade opportunities.
It’s important to test the settings on historical data and adapt them to your specific trading style, timeframe, and market conditions. As with any technical tool, the AGMA For Loop should be used in conjunction with other indicators and solid risk management practices for the best results.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Two Pole Butterworth For Loop [BackQuant]Two Pole Butterworth For Loop
PLEASE read the following carefully, as understanding the underlying concepts and logic behind the indicator is key to incorporating it into your trading system in a sound and methodical manner.
Introducing BackQuant's Two Pole Butterworth For Loop (2P BW FL) — an advanced indicator that fuses the power of the Two Pole Butterworth filter with a dynamic for-loop scoring mechanism. This unique approach is designed to extract actionable trading signals by smoothing out price data and then analyzing it using a comparative scoring method. Let's delve into how this indicator works, why it was created, and how it can be used in various trading scenarios.
Understanding the Two Pole Butterworth Filter
The Butterworth filter is a signal processing tool known for its smooth response and minimal distortion. It's often used in electronic and communication systems to filter out unwanted noise. In trading, the Butterworth filter can be applied to price data to smooth out the volatility, providing traders with a clearer view of underlying trends without the whipsaws often associated with market noise.
The Two Pole Butterworth variant further enhances this effect by applying the filter with two poles, effectively creating a sharper transition between the passband and stopband. In simple terms, this allows the filter to follow the price action more closely, reacting to changes while maintaining smoothness.
In this script, the Two Pole Butterworth filter is applied to the Calculation Source (default is set to the closing price), creating a smoothed price series that serves as the foundation for further analysis.
Why Use a Two Pole Butterworth Filter?
The Two Pole Butterworth filter is chosen for its ability to reduce lag while maintaining a smooth output. This makes it an ideal choice for traders who want to capture trends without being misled by short-term volatility or market noise. By filtering the price data, the Two Pole Butterworth enables traders to focus on the broader market movements and avoid false signals.
The For-Loop Scoring Mechanism
In addition to the Butterworth filter, this script uses a for-loop scoring system to evaluate the smoothed price data. The for-loop compares the current value of the filtered price (referred to as "subject") to previous values over a defined range (set by the start and end input). The score is calculated based on whether the subject is higher or lower than the previous points, and the cumulative score is used to determine the strength of the trend.
Long and Short Signal Logic
Long Signals: A long signal is triggered when the score surpasses the Long Threshold (default set at 40). This suggests that the price has built sufficient upward momentum, indicating a potential buying opportunity.
Short Signals: A short signal is triggered when the score crosses under the Short Threshold (default set at -10). This indicates weakening price action or a potential downtrend, signaling a possible selling or shorting opportunity.
By utilizing this scoring system, the indicator identifies moments when the price momentum is shifting, helping traders enter positions at opportune times.
Customization and Visualization Options
One of the strengths of this indicator is its flexibility. Traders can customize various settings to fit their personal trading style or adapt it to different markets and timeframes:
Calculation Periods: Adjust the lookback period for the Butterworth filter, allowing for shorter or longer smoothing depending on the desired sensitivity.
Threshold Levels: Set the long and short thresholds to define when signals should be triggered, giving you control over the balance between sensitivity and specificity.
Signal Line Width and Colors: Customize the visual presentation of the indicator on the chart, including the width of the signal line and the colors used for long and short conditions.
Candlestick and Background Colors: If desired, the indicator can color the candlesticks or the background according to the detected trend, offering additional clarity at a glance.
Trading Applications
This Two Pole Butterworth For Loop indicator is versatile and can be adapted to various market conditions and trading strategies. Here are a few use cases where this indicator shines:
Trend Following: The Butterworth filter smooths the price data, making it easier to follow trends and identify when they are gaining or losing strength. The for-loop scoring system enhances this by providing a clear indication of how strong the current trend is compared to recent history.
Mean Reversion: For traders looking to identify potential reversals, the indicator’s ability to compare the filtered price to previous values over a range of periods allows it to spot moments when the trend may be losing steam, potentially signaling a reversal.
Swing Trading: The combination of smoothing and scoring allows swing traders to capture short to medium-term price movements by filtering out the noise and focusing on significant shifts in momentum.
Risk Management: By providing clear long and short signals, this indicator helps traders manage their risk by offering well-defined entry and exit points. The smooth nature of the Butterworth filter also reduces the risk of getting caught in false signals due to market noise.
Final Thoughts
The Two Pole Butterworth For Loop indicator offers traders a powerful combination of smoothing and scoring to detect meaningful trends and shifts in price momentum. Whether you are a trend follower, swing trader, or someone looking to refine your entry and exit points, this indicator provides the tools to make more informed trading decisions.
As always, it's essential to backtest the indicator on historical data and tailor the settings to your specific trading style and market. While the Butterworth filter helps reduce noise and smooth trends, no indicator can predict the future with absolute certainty, so it should be used in conjunction with other tools and sound risk management practices.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Kalman PSaR [BackQuant]Kalman PSaR
Overview and Innovation
The Kalman PSaR combines the well-known Parabolic SAR (PSaR) with the advanced smoothing capabilities of the Kalman Filter . This innovative tool aims to enhance the traditional PSaR by integrating Kalman filtering, which reduces noise and improves trend detection. The Kalman PSaR adapts dynamically to price movements, making it a highly effective indicator for spotting trend shifts while minimizing the impact of false signals caused by market volatility.
Please Find the Basic Kalman Here:
Kalman Filter Dynamics
The Kalman Filter is a powerful algorithm for estimating the true value of a system amidst noisy data. In the Kalman PSaR, this filter is applied to the high, low, and closing prices, resulting in a smoother and more accurate representation of price action. The filter’s parameters—process noise and measurement noise—are customizable, allowing traders to fine-tune the sensitivity of the indicator to market conditions. By reducing the impact of noise, the Kalman-filtered PSaR offers clearer signals for identifying trend reversals and continuations.
Enhanced PSaR Calculation
The traditional Parabolic SAR is a popular trend-following indicator that highlights potential entry and exit points based on price acceleration. In the Kalman PSaR, this calculation is enhanced by the Kalman-filtered prices, providing a smoother and more reliable signal. The indicator continuously updates based on the acceleration factor and max step values, while the Kalman filter ensures that sudden price spikes or market noise do not trigger false signals.
Min Step and Max Step: These settings control the sensitivity of the PSaR. The Min Step sets the initial acceleration factor, while the Max Step limits how fast the PSaR adapts to price changes, helping traders fine-tune the indicator’s responsiveness.
Optional Smoothing Techniques To further enhance the signal clarity, the Kalman PSaR includes an optional smoothing feature. Traders can choose from various smoothing methods, such as SMA, Hull, EMA, WMA, TEMA, and more, to reduce short-term fluctuations and emphasize the underlying trend. The smoothing period is customizable, allowing traders to adjust the indicator’s behavior according to their preferred trading style and timeframe.
Color-Coded Candle Painting The Kalman PSaR features color-coded candles that change according to the trend direction. When the price is above the PSaR, candles are painted green to indicate a long trend, and when the price is below the PSaR, candles are painted red to signal a short trend. This visual representation makes it easy to interpret market sentiment at a glance, improving decision-making speed during fast-moving markets.
Key Features and Customization
Kalman Filter Customization: The process noise and measurement noise parameters allow traders to adjust how aggressively the filter adapts to price changes, making it suitable for both volatile and stable markets.
Smoothing Options: A variety of moving average types, such as SMA, Hull, EMA, and more, can be applied to smooth the PSaR values, ensuring that the signal remains clear even in choppy markets.
Dynamic Trend Detection: The Kalman PSaR dynamically updates based on price movements, helping traders spot trend reversals early while filtering out false signals caused by short-term volatility.
Bar Coloring and PSaR Plotting: Traders can choose to color candles based on trend direction or plot the PSaR directly on the chart for additional visual clarity.
Practical Applications
Trend-Following Strategies: The Kalman PSaR excels in trend-following strategies by providing timely signals of trend changes. The dynamic nature of the indicator allows traders to capture significant price movements while avoiding market noise.
Reversal Identification: The indicator’s ability to filter out noise and provide smoother signals makes it ideal for identifying reversals in volatile markets.
Risk Management: By plotting clear stop levels based on the PSaR, traders can use this indicator to effectively manage risk, placing stop-loss orders at key points based on the trend direction.
Conclusion
The Kalman PSaR is a fusion of the classic Parabolic SAR and the Kalman filter, offering enhanced trend detection with reduced noise. Its customizable filtering and smoothing options, combined with dynamic trend-following capabilities, make it a versatile tool for traders seeking to improve their timing and signal accuracy. The adaptive nature of the Kalman filter, combined with the robust PSaR logic, helps traders stay on the right side of the market and manage risk more effectively.
DEMA Adaptive DMI [BackQuant]DEMA Adaptive DMI
PLEASE Read the following, knowing what an indicator does at its core before adding it into a system is pivotal. The core concepts can allow you to include it in a logical and sound manner.
Conceptual Foundation and Innovation
The DEMA Adaptive DMI blends the Double Exponential Moving Average (DEMA) with the Directional Movement Index (DMI) to offer a unique approach to trend-following. By applying DEMA to the high and low prices, this indicator refines the traditional DMI calculation, enhancing its responsiveness to price changes. This results in a more adaptive and timely measure of market trends and momentum, providing traders with a more refined tool for capturing directional movements in the market.
Technical Composition and Calculation
At its core, the DEMA Adaptive DMI calculates the DEMA for both the high and low prices over a user-defined period. This dual application of DEMA serves to smooth out price fluctuations while retaining sensitivity to market movements. The DMI is then derived from the changes in these DEMA values, producing a set of plus and minus directional indicators that reflect the prevailing trend. Additionally, an Average Directional Index (ADX) is computed to measure the strength of the trend, with the entire process being dynamically adjusted based on the DEMA calculations.
DEMA Application:
The DEMA is applied to both high and low prices to reduce lag and provide a smoother representation of price action.
Directional Movement Calculation: The DMI is calculated using the smoothed price changes, resulting in plus and minus indicators that accurately reflect market trends.
ADX Calculation:
The ADX is computed to quantify the strength of the trend, offering traders insight into whether the market is trending strongly or is in a phase of consolidation.
Features and User Inputs The DEMA Adaptive DMI offers a range of customizable options to suit different trading styles and market conditions:
DEMA Calculation Period: Users can set the period for the DEMA calculation, allowing for adjustments based on the desired sensitivity.
DMI Length: The length of the DMI calculation can be adjusted, providing flexibility in how trends are measured.
ADX Smoothing Period: The smoothing period for the ADX can be customized to fine-tune the trend strength measurement.
Divergence Detection: Optional divergence detection features allow traders to spot potential reversals based on the DMI and price action.
Visualization options include static high and low levels to mark extreme DMI thresholds, the ability to color bars according to trend direction, and background hues to highlight overbought and oversold conditions.
Practical Applications
The DEMA Adaptive DMI is particularly effective in markets where trend strength and direction are crucial for successful trading. Traders can leverage this indicator to:
Identify Trend Reversals:
Detect potential trend reversals by monitoring the DMI and ADX in conjunction with divergence signals.
Trend Confirmation:
Use the DEMA-based DMI to confirm the strength and direction of a trend, aiding in the timing of entries and exits.
Strategic Positioning:
The indicator's responsiveness allows traders to position themselves effectively in fast-moving markets, reducing the risk of late entries or exits.
Advantages and Strategic Value
By integrating the DEMA with the DMI, this indicator provides a more adaptive and timely measure of market trends. The reduced lag from the DEMA ensures that traders receive signals that are closely aligned with current market conditions, while the dynamic DMI calculation offers a more accurate representation of trend direction and strength. This makes the DEMA Adaptive DMI a valuable tool for traders looking to enhance their trend-following strategies with a focus on precision and adaptability.
Summary and Usage Tips
The DEMA Adaptive DMI is a sophisticated trend-following indicator that combines the benefits of DEMA and DMI into a single, powerful tool. Traders are encouraged to incorporate this indicator into their trading systems for a more nuanced and responsive approach to trend detection and confirmation. Whether used for identifying trend reversals, confirming trend strength, or strategically positioning in the market, the DEMA Adaptive DMI offers a versatile and reliable solution for trend-following strategies.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Efficiency Weighted OrderFlow [AlgoAlpha]Introducing the Efficiency Weighted Orderflow Indicator by AlgoAlpha! 📈✨
Elevate your trading game with our cutting-edge Efficiency Weighted Orderflow Indicator, designed to provide clear insights into market trends and potential reversals. This tool is perfect for traders seeking to understand the underlying market dynamics through efficiency-weighted volume calculations.
🌟 Key Features 🌟
✨ Smooth OrderFlow Calculation : Option to smooth order flow data for more consistent signals.
🔧 Customizable Parameters : Adjust the Order Flow Period and HMA Smoothing Length to fit your trading strategy.
🔍 Visual Clarity : Easily distinguish between bullish and bearish trends with customizable colors.
📊 Standard Deviation Normalization : Keeps order flow values normalized for better comparison across different market conditions.
🔔 Trend Reversal Alerts : Stay ahead with built-in alert conditions for significant order flow changes.
🚀 Quick Guide to Using the Efficiency Weighted Orderflow Indicator
🛠 Add the Indicator: Search for "Efficiency Weighted Orderflow " in TradingView's Indicators & Strategies. Customize settings like smoothing and order flow period to fit your trading style.
📊 Market Analysis: Watch for trend reversal alerts to capture trading opportunities by studying the behaviour of the indicator.
🔔 Alerts: Enable notifications for significant order flow changes to stay updated on market trends.
🔍 How It Works
The Efficiency Weighted Orderflow Indicator starts by calculating the efficiency of price movements using the absolute difference between the close and open prices, divided by volume. The order flow is then computed by summing these efficiency-weighted volumes over a specified period, with an option to apply Hull Moving Average (HMA) smoothing for enhanced signal stability. To ensure robust comparison, the order flow is normalized using standard deviation. The indicator plots these values as columns, with distinct colors representing bullish and bearish trends. Customizable parameters for period length and smoothing allow traders to tailor the indicator to their strategies. Additionally, visual cues and alert conditions for trend reversals and significant order flow changes keep traders informed and ready to act. This indicator improves on the Orderflow aspect of our Standardized Orderflow indicator. The Efficiency Weighted Orderflow is less susceptible to noise and is also quicker at detecting trend changes.
