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Normalised Laplace Z-Score [tordne]
Normalised Laplace Z-Score [tordne]
The Normalised Laplace Z-Score is a statistical tool designed to identify extreme price movements and potential reversal points by adjusting the traditional Z-Score methodology to account for the characteristics of financial market returns. Instead of assuming normally distributed returns, this indicator uses a Laplace distribution, which is better suited for financial data that often exhibits fat tails and higher probabilities of extreme moves.
Key Features:
Laplace Z-Score Calculation: The indicator calculates a Z-Score based on returns that deviate from the median rather than the mean, which makes it more robust in handling skewed data. The spread used for the Z-Score is calculated as the average absolute deviation from the median, a key feature of Laplace distribution modeling.
Return Type Selection: Users can choose between traditional price returns or logarithmic returns. Logarithmic returns are often preferred for financial analysis as they provide a more symmetric view of gains and losses, especially useful in markets with large swings.
Normalisation: The Z-Score is normalized over a specified period (default is 180 days), ensuring that the values consistently fall within a standard range for easier interpretation. This allows traders to compare Z-Scores across different time frames and market conditions without needing to manually adjust their expectations.
How to Use:
This indicator can be used to identify overbought or oversold conditions by highlighting when price movements deviate significantly from their typical range. Traders can apply it in a variety of strategies:
Overbought/Oversold Identification: High positive values may suggest an overbought condition, while low negative values may indicate an oversold condition. These can serve as early warning signals for potential reversals.
Volatility Adjustment: By focusing on the Laplace-distributed characteristics of price returns, this indicator is more adaptive to the actual market behaviour, offering a statistically grounded method for detecting extreme conditions.
Whether you’re looking for a more robust measure of market extremes or a refined way to detect potential reversals, the Normalised Laplace Z-Score offers a sophisticated, math-based approach to help guide your trading decisions.
The Normalised Laplace Z-Score is a statistical tool designed to identify extreme price movements and potential reversal points by adjusting the traditional Z-Score methodology to account for the characteristics of financial market returns. Instead of assuming normally distributed returns, this indicator uses a Laplace distribution, which is better suited for financial data that often exhibits fat tails and higher probabilities of extreme moves.
Key Features:
Laplace Z-Score Calculation: The indicator calculates a Z-Score based on returns that deviate from the median rather than the mean, which makes it more robust in handling skewed data. The spread used for the Z-Score is calculated as the average absolute deviation from the median, a key feature of Laplace distribution modeling.
Return Type Selection: Users can choose between traditional price returns or logarithmic returns. Logarithmic returns are often preferred for financial analysis as they provide a more symmetric view of gains and losses, especially useful in markets with large swings.
Normalisation: The Z-Score is normalized over a specified period (default is 180 days), ensuring that the values consistently fall within a standard range for easier interpretation. This allows traders to compare Z-Scores across different time frames and market conditions without needing to manually adjust their expectations.
How to Use:
This indicator can be used to identify overbought or oversold conditions by highlighting when price movements deviate significantly from their typical range. Traders can apply it in a variety of strategies:
Overbought/Oversold Identification: High positive values may suggest an overbought condition, while low negative values may indicate an oversold condition. These can serve as early warning signals for potential reversals.
Volatility Adjustment: By focusing on the Laplace-distributed characteristics of price returns, this indicator is more adaptive to the actual market behaviour, offering a statistically grounded method for detecting extreme conditions.
Whether you’re looking for a more robust measure of market extremes or a refined way to detect potential reversals, the Normalised Laplace Z-Score offers a sophisticated, math-based approach to help guide your trading decisions.
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オープンソーススクリプト
TradingViewの精神に則り、この作者はスクリプトのソースコードを公開しているので、その内容を理解し検証することができます。作者に感謝です！無料でお使いいただけますが、このコードを投稿に再利用する際にはハウスルールに従うものとします。
免責事項
これらの情報および投稿は、TradingViewが提供または保証する金融、投資、取引、またはその他の種類のアドバイスや推奨を意図したものではなく、またそのようなものでもありません。詳しくは利用規約をご覧ください。