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Deca-Oscillator Ultra Suite
Deca-Oscillator Ultra Suite
The Multi-Heuristic Consensus Framework
In modern algorithmic trading, the reliance on a single technical oscillator often leads to "analysis paralysis" or, conversely, frequent false signals due to indicator lag and market noise. The Deca-Oscillator Ultra Suite addresses these inefficiencies by utilizing a Multi-Heuristic Consensus Framework. This model synthesizes ten independent mathematical oscillators into a singular, normalized composite score, providing a high-fidelity view of market momentum.
Mathematical Foundation
1. Linear Feature Normalization
To integrate disparate oscillators (e.g., CCI ranging from -300 to +300 vs. RSI ranging from 0 to 100), the engine applies a Min-Max scaling algorithm to every internal component. This ensures that every indicator contributes to the final score within a standardized 0–100 probability space.
[math]N(x) = 100 * \frac{x - min(x)}{max(x) - min(x)}[/math]*
2. Weighted Composite Calculation
The final composite score (C) is derived from a weighted arithmetic mean of the normalized inputs. The weightings allow the model to prioritize historically reliable metrics like the RSI and Fisher Transform over more volatile indicators.
[math]C = \frac{\sum_{i=1}^{10} (w_i \cdot N(o_i))}{\sum_{i=1}^{10} w_i}[/math]
3. Consensus Logic (k-Agreement)
Beyond simple crossovers, the signal engine employs a k-agreement filter. A signal is only validated if the number of primary components agreeing with the direction meets the required threshold.
[math]Consensus \ge n[/math]
The Ten Core Components
This suite does not just average values; it analyzes market state through ten distinct lenses:
RSI: Standard momentum relative to price change.
MFI: Volume-weighted momentum (Money Flow).
Stochastic: Position of current price relative to the high-low range.
CCI: Statistical deviation from the average.
Williams %R: Inverse momentum for extreme overbought/oversold detection.
ROC: Pure velocity of price change.
Bull/Bear Power: Interaction between high/low and EMA.
Awesome Oscillator: Comparison of short-term and long-term momentum.
Momentum: Simple price-distance velocity.
Fisher Transform: Transforming price distributions into Gaussian normal distributions.
Visual Architecture
High-Contrast Theme: Features Blue (Bullish) and White (Bearish) for optimal visibility on dark-themed charts.
Precision Matrix: A real-time dashboard displaying the consensus of internal components, the HTF trend state, and the total composite score.
Heuristic Divergence: Automates pivot detection to draw momentum-price divergence lines, signaling potential exhaustion before price action confirms the reversal.
Reply below with comments, critiques, or criticisms. All is welcome!
The Multi-Heuristic Consensus Framework
In modern algorithmic trading, the reliance on a single technical oscillator often leads to "analysis paralysis" or, conversely, frequent false signals due to indicator lag and market noise. The Deca-Oscillator Ultra Suite addresses these inefficiencies by utilizing a Multi-Heuristic Consensus Framework. This model synthesizes ten independent mathematical oscillators into a singular, normalized composite score, providing a high-fidelity view of market momentum.
Mathematical Foundation
1. Linear Feature Normalization
To integrate disparate oscillators (e.g., CCI ranging from -300 to +300 vs. RSI ranging from 0 to 100), the engine applies a Min-Max scaling algorithm to every internal component. This ensures that every indicator contributes to the final score within a standardized 0–100 probability space.
[math]N(x) = 100 * \frac{x - min(x)}{max(x) - min(x)}[/math]*
2. Weighted Composite Calculation
The final composite score (C) is derived from a weighted arithmetic mean of the normalized inputs. The weightings allow the model to prioritize historically reliable metrics like the RSI and Fisher Transform over more volatile indicators.
[math]C = \frac{\sum_{i=1}^{10} (w_i \cdot N(o_i))}{\sum_{i=1}^{10} w_i}[/math]
3. Consensus Logic (k-Agreement)
Beyond simple crossovers, the signal engine employs a k-agreement filter. A signal is only validated if the number of primary components agreeing with the direction meets the required threshold.
[math]Consensus \ge n[/math]
The Ten Core Components
This suite does not just average values; it analyzes market state through ten distinct lenses:
RSI: Standard momentum relative to price change.
MFI: Volume-weighted momentum (Money Flow).
Stochastic: Position of current price relative to the high-low range.
CCI: Statistical deviation from the average.
Williams %R: Inverse momentum for extreme overbought/oversold detection.
ROC: Pure velocity of price change.
Bull/Bear Power: Interaction between high/low and EMA.
Awesome Oscillator: Comparison of short-term and long-term momentum.
Momentum: Simple price-distance velocity.
Fisher Transform: Transforming price distributions into Gaussian normal distributions.
Visual Architecture
High-Contrast Theme: Features Blue (Bullish) and White (Bearish) for optimal visibility on dark-themed charts.
Precision Matrix: A real-time dashboard displaying the consensus of internal components, the HTF trend state, and the total composite score.
Heuristic Divergence: Automates pivot detection to draw momentum-price divergence lines, signaling potential exhaustion before price action confirms the reversal.
Reply below with comments, critiques, or criticisms. All is welcome!
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免責聲明
這些資訊和出版物並非旨在提供，也不構成TradingView提供或認可的任何形式的財務、投資、交易或其他類型的建議或推薦。請閱讀使用條款以了解更多資訊。