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EMD

Relative Vigor Index (RVI) with EMD [AIBitcoinTrend]👽 Adaptive Relative Vigor Index with EMD & Signals (AIBitcoinTrend) The Adaptive Relative Vigor Index (RVI) with Empirical Mode Decomposition (EMD) is an enhanced version of the traditional RVI, designed to improve signal clarity and responsiveness to market conditions. By integrating EMD smoothing and adaptive volatility-based trailing stops. 👽 What Makes the Adaptive RVI with EMD Unique? Unlike the standard RVI, which often lags in volatile markets, this version refines price momentum detection by applying Empirical Mode Decomposition (EMD), effectively filtering out noise. Additionally, it features ATR-based trailing stops for precise trade execution. Key Features: EMD-Enhanced RVI – Filters out short-term noise, improving signal accuracy. Crossover & Crossunder Signals – Generates trade signals based on RVI trends. ATR-Based Trailing Stop – Adjusts dynamically based on volatility for optimal risk management. 👽 The Math Behind the Indicator 👾 RVI Calculation with EMD Smoothing The Relative Vigor Index (RVI) measures trend strength by comparing the relationship between closing and opening prices, relative to the high-low range. Traditional RVI uses fixed smoothing, whereas this version applies Empirical Mode Decomposition (EMD) to extract dominant price cycles and improve trend clarity. How It Works: The RVI is initially calculated using a weighted moving average (WMA) over a specified period. EMD refines the RVI signal by removing high-frequency noise, creating a smoothed RVI component. This results in a more stable and reliable trend indicator. 👽 How Traders Can Use This Indicator 👾 Trailing Stop & Signal-Based Trading Bullish Setup: ✅ RVI crosses above EMD → Buy signal. ✅ A bullish trailing stop is placed at low - ATR × Multiplier. ✅ Exit if price crosses below the stop. Bearish Setup: ✅ RVI crosses below EMD → Sell signal. ✅ A bearish trailing stop is placed at high + ATR × Multiplier. ✅ Exit if price crosses above the stop. 👾 Detecting Overbought & Oversold Areas This indicator helps traders identify potential reversal zones by highlighting overbought and oversold conditions. Overbought Zone: When RVI moves above 0.4, the market may be overextended, signaling a potential reversal downward. Oversold Zone: When RVI moves below -0.4, the market may be undervalued, suggesting a possible upward reversal. Using these levels, traders can confirm entry and exit points alongside divergence signals for higher probability trades. 👽 Why It’s Useful for Traders EMD-Based Signal Enhancement: Filters out noise, refining momentum signals. Adaptive ATR-Based Risk Management: Automatically adjusts stop-loss levels to market conditions. Works Across Multiple Markets & Timeframes: Effective for stocks, forex, crypto, and futures trading. 👽 Indicator Settings RVI Length – Defines the period for calculating the Relative Vigor Index. EMD Period – Controls the level of EMD smoothing applied. Final Smoothing – Adjusts the degree of additional signal filtering. Lookback Period – Determines how many bars are used for detecting pivot points. Enable Trailing Stop – Activates dynamic ATR-based trailing stops. ATR Multiplier – Adjusts the stop-loss sensitivity. Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
Indicatore Pine Script™
di aibitcointrend
EMD Oscillator (Zeiierman)█ Overview The Empirical Mode Decomposition (EMD) Oscillator is an advanced indicator designed to analyze market trends and cycles with high precision. It breaks down complex price data into simpler parts called Intrinsic Mode Functions (IMFs), allowing traders to see underlying patterns and trends that aren’t visible with traditional indicators. The result is a dynamic oscillator that provides insights into overbought and oversold conditions, as well as trend direction and strength. This indicator is suitable for all types of traders, from beginners to advanced, looking to gain deeper insights into market behavior. █ How It Works The core of this indicator is the Empirical Mode Decomposition (EMD) process, a method typically used in signal processing and advanced scientific fields. It works by breaking down price data into various “layers,” each representing different frequencies in the market’s movement. Imagine peeling layers off an onion: each layer (or IMF) reveals a different aspect of the price action. ⚪ Data Decomposition (Sifting): The indicator “sifts” through historical price data to detect natural oscillations within it. Each oscillation (or IMF) highlights a unique rhythm in price behavior, from rapid fluctuations to broader, slower trends. ⚪ Adaptive Signal Reconstruction: The EMD Oscillator allows traders to select specific IMFs for a custom signal reconstruction. This reconstructed signal provides a composite view of market behavior, showing both short-term cycles and long-term trends based on which IMFs are included. ⚪ Normalization: To make the oscillator easy to interpret, the reconstructed signal is scaled between -1 and 1. This normalization lets traders quickly spot overbought and oversold conditions, as well as trend direction, without worrying about the raw magnitude of price changes. The indicator adapts to changing market conditions, making it effective for identifying real-time market cycles and potential turning points. █ Key Calculations: The Math Behind the EMD Oscillator The EMD Oscillator’s advanced nature lies in its high-level mathematical operations: ⚪ Intrinsic Mode Functions (IMFs) IMFs are extracted from the data and act as the building blocks of this indicator. Each IMF is a unique oscillation within the price data, similar to how a band might be divided into treble, mid, and bass frequencies. In the EMD Oscillator: Higher-Frequency IMFs: Represent short-term market “noise” and quick fluctuations. Lower-Frequency IMFs: Capture broader market trends, showing more stable and long-term patterns. ⚪ Sifting Process: The Heart of EMD The sifting process isolates each IMF by repeatedly separating and refining the data. Think of this as filtering water through finer and finer mesh sieves until only the clearest parts remain. Mathematically, it involves: Extrema Detection: Finding all peaks and troughs (local maxima and minima) in the data. Envelope Calculation: Smoothing these peaks and troughs into upper and lower envelopes using cubic spline interpolation (a method for creating smooth curves between data points). Mean Removal: Calculating the average between these envelopes and subtracting it from the data to isolate one IMF. This process repeats until the IMF criteria are met, resulting in a clean oscillation without trend influences. ⚪ Spline Interpolation The cubic spline interpolation is an advanced mathematical technique that allows smooth curves between points, which is essential for creating the upper and lower envelopes around each IMF. This interpolation solves a tridiagonal matrix (a specialized mathematical problem) to ensure that the envelopes align smoothly with the data’s natural oscillations. To give a relatable example: imagine drawing a smooth line that passes through each peak and trough of a mountain range on a map. Spline interpolation ensures that line is as smooth and close to reality as possible. Achieving this in Pine Script is technically demanding and demonstrates a high level of mathematical coding. ⚪ Amplitude Normalization To make the oscillator more readable, the final signal is scaled by its maximum amplitude. This amplitude normalization brings the oscillator into a range of -1 to 1, creating consistent signals regardless of price level or volatility. █ Comparison with Other Signal Processing Methods Unlike standard technical indicators that often rely on fixed parameters or pre-defined mathematical functions, the EMD adapts to the data itself, capturing natural cycles and irregularities in real-time. For example, if the market becomes more volatile, EMD adjusts automatically to reflect this without requiring parameter changes from the trader. In this way, it behaves more like a “smart” indicator, intuitively adapting to the market, unlike most traditional methods. EMD’s adaptive approach is akin to AI’s ability to learn from data, making it both resilient and robust in non-linear markets. This makes it a great alternative to methods that struggle in volatile environments, such as fixed-parameter oscillators or moving averages. █ How to Use Identify Market Cycles and Trends: Use the EMD Oscillator to spot market cycles that represent phases of buying or selling pressure. The smoothed version of the oscillator can help highlight broader trends, while the main oscillator reveals immediate cycles. Spot Overbought and Oversold Levels: When the oscillator approaches +1 or -1, it may indicate that the market is overbought or oversold, signaling potential entry or exit points. Confirm Divergences: If the price movement diverges from the oscillator's direction, it may indicate a potential reversal. For example, if prices make higher highs while the oscillator makes lower highs, it could be a sign of weakening trend strength. █ Settings Window Length (N): Defines the number of historical bars used for EMD analysis. A larger window captures more data but may slow down performance. Number of IMFs (M): Sets how many IMFs to extract. Higher values allow for a more detailed decomposition, isolating smaller cycles within the data. Amplitude Window (L): Controls the length of the window used for amplitude calculation, affecting the smoothness of the normalized oscillator. Extraction Range (IMF Start and End): Allows you to select which IMFs to include in the reconstructed signal. Starting with lower IMFs captures faster cycles, while ending with higher IMFs includes slower, trend-based components. Sifting Stopping Criterion (S-number): Sets how precisely each IMF should be refined. Higher values yield more accurate IMFs but take longer to compute. Max Sifting Iterations (num_siftings): Limits the number of sifting iterations for each IMF extraction, balancing between performance and accuracy. Source: The price data used for the analysis, such as close or open prices. This determines which price movements are decomposed by the indicator. ----------------- Disclaimer The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs. My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Indicatore Pine Script™
di Zeiierman
6
[DSPrated] Modified EMD for swing tradeModified Ehlers Empirical Mode Decomposition indicator for swing trade based on Butterworth 2nd order IIR filter Description This script is inspired by John Ehlers' TECHNICAL PAPERS - Truncating Indicators and Empirical Mode Decomposition. But instead of detecting trend it applies to finding swing regions. Also here is suggested canonical DSP approach for designing coefficients for Butterworth 2nd order IIR filters - bandpass and lowpass. Besides, truncated IIR filter with configurable length parameter is used. It worth mentioning, that although truncated filter is more robust than original IIR, it losses specified properties (bandpass) the more, the less is length parameter. Butterworth Bandpass Infinite Impulse Response (IIR) Filter This is the 2nd order Butterworth Bandpass Infinite Impulse Response (IIR) Filter based on the transform from the 1st order lowpass Based on the example 8.8 on p476 from book Digital Signal Processing: A Practical Approach 2nd Edition by Emmanuel C. Ifeachor (Author), Barrie W. Jervis (Author) It differs from Ehlers BandPass Filter only in the way you initialize input parameters. Here you can define cutoff periods of region of interest. For example on a timeframe, where one bar equals 1 hour you can define periods 18 and 22, which mean you'll see the swing intensity of price movement components within specified range. Parameters Source Period 1 - cutoff period of bandpass begining Period 2 - cutoff period of the end of bandpass length - IIR truncation length Concept of usage Within specified bandpass this indicator eliminates the Trend line according to Ehlers EMD. The bandpass periods is recommended to choose accordingly to personal comfortable trading style and timeframe. The trendline painted with 3 colors depending of the next modes: up tend - green cycling - black downtrend - red So the buy signal is generated when trend line in cycling mode and filtered component reaches it local minimum. And the sell signal is generated when trend line in cycling mode and filtered component reaches it local maximum. Secure long and short zones marked with color. --- // TO DO // - compare truncated and full version using signal generators // - apply zero lag filter modification fordetectig ternd and swing peroids // - implement strategy scripts // - implement somewhat "true" EMD with sevral IMFs(intrinsic mode function) // - better description? // - parameter optimization --- Please, feel free to report any issues and improvement suggestions.
Indicatore Pine Script™
di DSPrated
1