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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.
نص برمجي مفتوح المصدر
بروح TradingView الحقيقية، قام مبتكر هذا النص البرمجي بجعله مفتوح المصدر، بحيث يمكن للمتداولين مراجعة وظائفه والتحقق منها. شكرا للمؤلف! بينما يمكنك استخدامه مجانًا، تذكر أن إعادة نشر الكود يخضع لقواعد الموقع الخاصة بنا.
إخلاء المسؤولية
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نص برمجي مفتوح المصدر
بروح TradingView الحقيقية، قام مبتكر هذا النص البرمجي بجعله مفتوح المصدر، بحيث يمكن للمتداولين مراجعة وظائفه والتحقق منها. شكرا للمؤلف! بينما يمكنك استخدامه مجانًا، تذكر أن إعادة نشر الكود يخضع لقواعد الموقع الخاصة بنا.
إخلاء المسؤولية
لا يُقصد بالمعلومات والمنشورات أن تكون، أو تشكل، أي نصيحة مالية أو استثمارية أو تجارية أو أنواع أخرى من النصائح أو التوصيات المقدمة أو المعتمدة من TradingView. اقرأ المزيد في شروط الاستخدام.