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Self-Adaptive RSI with Fractal Dimension and Entropy Scaling
Self-Adaptive RSI with Fractal Dimension and Entropy Scaling
This advanced oscillator is a refined version of the RSI that integrates multi-timeframe analysis, fractal scaling, and entropy to create an adaptive, highly responsive indicator. The script leverages a range of techniques to dynamically adjust to market conditions and enhance sensitivity to trend and volatility. Here’s a breakdown of the core features:
Base and Fixed Adaptive Lengths:
A base length (input by the user) seeds the initial length for calculations. The script then calculates a fixed adaptive length as a multiplier of this base, providing consistency across different calculations.
Multi-Timeframe RSI Calculation:
The script calculates RSI across multiple timeframes (5 minutes to daily) and aggregates these values using a weighted average based on the Golden Ratio. This multi-timeframe RSI accounts for both short-term and long-term trends, making it more robust and responsive to shifts in market direction.
Enhanced RSI Using Adaptive Volume Weighting:
Price differences are smoothed and adjusted incorporating volume-based weights, allowing the RSI to adapt to changes in trading volume. This volume impact factor enhances trend detection accuracy.
Adaptive Zero-Lag RSI with Golden Ratio Smoothing:
To eliminate lag, the multi-timeframe RSI is smoothed using a zero-lag EMA based on a Golden Ratio length, adding precision to the RSI’s responsiveness while minimizing delay.
Fractal Dimension Scaling:
The oscillator is scaled to expand its range using fractal dimensions, capturing market complexity and adjusting for periods of high or low volatility. This scaling enhances sensitivity to price fluctuations.
Entropy-Based Trend Sensitivity and Volatility Compression:
The final RSI incorporates entropy scaling, achieved through a trend factor derived from a linear regression. This factor adjusts the RSI output based on market volatility and directional strength, compressing the indicator during stable periods and expanding it in high-volatility conditions.
Overbought and Oversold Thresholds Using Statistical Percentiles:
Rather than fixed thresholds, the overbought and oversold levels are set dynamically using percentile ranks (99th and 1st percentiles) over a long period, making them adaptive and reflective of historical price extremes.
This self-adaptive RSI, combining multi-timeframe weighting, fractal scaling, and entropy, provides a nuanced view of market trends and momentum. It dynamically adjusts to market volatility and structure, offering a sophisticated tool for traders seeking adaptive trend analysis and reliable entry/exit signals.
This advanced oscillator is a refined version of the RSI that integrates multi-timeframe analysis, fractal scaling, and entropy to create an adaptive, highly responsive indicator. The script leverages a range of techniques to dynamically adjust to market conditions and enhance sensitivity to trend and volatility. Here’s a breakdown of the core features:
Base and Fixed Adaptive Lengths:
A base length (input by the user) seeds the initial length for calculations. The script then calculates a fixed adaptive length as a multiplier of this base, providing consistency across different calculations.
Multi-Timeframe RSI Calculation:
The script calculates RSI across multiple timeframes (5 minutes to daily) and aggregates these values using a weighted average based on the Golden Ratio. This multi-timeframe RSI accounts for both short-term and long-term trends, making it more robust and responsive to shifts in market direction.
Enhanced RSI Using Adaptive Volume Weighting:
Price differences are smoothed and adjusted incorporating volume-based weights, allowing the RSI to adapt to changes in trading volume. This volume impact factor enhances trend detection accuracy.
Adaptive Zero-Lag RSI with Golden Ratio Smoothing:
To eliminate lag, the multi-timeframe RSI is smoothed using a zero-lag EMA based on a Golden Ratio length, adding precision to the RSI’s responsiveness while minimizing delay.
Fractal Dimension Scaling:
The oscillator is scaled to expand its range using fractal dimensions, capturing market complexity and adjusting for periods of high or low volatility. This scaling enhances sensitivity to price fluctuations.
Entropy-Based Trend Sensitivity and Volatility Compression:
The final RSI incorporates entropy scaling, achieved through a trend factor derived from a linear regression. This factor adjusts the RSI output based on market volatility and directional strength, compressing the indicator during stable periods and expanding it in high-volatility conditions.
Overbought and Oversold Thresholds Using Statistical Percentiles:
Rather than fixed thresholds, the overbought and oversold levels are set dynamically using percentile ranks (99th and 1st percentiles) over a long period, making them adaptive and reflective of historical price extremes.
This self-adaptive RSI, combining multi-timeframe weighting, fractal scaling, and entropy, provides a nuanced view of market trends and momentum. It dynamically adjusts to market volatility and structure, offering a sophisticated tool for traders seeking adaptive trend analysis and reliable entry/exit signals.
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