Normal-Inverse Gaussian Probability Table
This indicator implements the Normal-Inverse Gaussian (NIG) distribution to estimate the likelihood of future price based on recent market behavior.
📊 Key Features:
- Estimates the parameters (α: tail heaviness, β: skewness, δ: scale, μ: location)
of the NIG distribution using a sliding window over log returns.
- Uses a numerically approximated version of the modified Bessel function (K₁)
to calculate the NIG probability density function (PDF).
- Normalizes the total probability across all bins to ensure the values are interpretable.
- Displays a dynamic probability table showing the chance of future returns falling into each bin.
⚠️ Notes:
- This is a real-time approximation. The Bessel function and posterior inference are simplified.
- Tail probabilities and shape parameters are sensitive to the window size and input settings.
- Useful for risk analysis, option overlays, and strategy filters.
This indicator implements the Normal-Inverse Gaussian (NIG) distribution to estimate the likelihood of future price based on recent market behavior.
📊 Key Features:
- Estimates the parameters (α: tail heaviness, β: skewness, δ: scale, μ: location)
of the NIG distribution using a sliding window over log returns.
- Uses a numerically approximated version of the modified Bessel function (K₁)
to calculate the NIG probability density function (PDF).
- Normalizes the total probability across all bins to ensure the values are interpretable.
- Displays a dynamic probability table showing the chance of future returns falling into each bin.
⚠️ Notes:
- This is a real-time approximation. The Bessel function and posterior inference are simplified.
- Tail probabilities and shape parameters are sensitive to the window size and input settings.
- Useful for risk analysis, option overlays, and strategy filters.
發行說明
Fixed Probability Calculation
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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。