OPEN-SOURCE SCRIPT
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.
Skrypt open-source
W duchu TradingView twórca tego skryptu udostępnił go jako open-source, aby traderzy mogli analizować i weryfikować jego funkcjonalność. Brawo dla autora! Możesz korzystać z niego za darmo, ale pamiętaj, że ponowna publikacja kodu podlega naszym Zasadom Społeczności.
Wyłączenie odpowiedzialności
Informacje i publikacje przygotowane przez TradingView lub jego użytkowników, prezentowane na tej stronie, nie stanowią rekomendacji ani porad handlowych, inwestycyjnych i finansowych i nie powinny być w ten sposób traktowane ani wykorzystywane. Więcej informacji na ten temat znajdziesz w naszym Regulaminie.
Skrypt open-source
W duchu TradingView twórca tego skryptu udostępnił go jako open-source, aby traderzy mogli analizować i weryfikować jego funkcjonalność. Brawo dla autora! Możesz korzystać z niego za darmo, ale pamiętaj, że ponowna publikacja kodu podlega naszym Zasadom Społeczności.
Wyłączenie odpowiedzialności
Informacje i publikacje przygotowane przez TradingView lub jego użytkowników, prezentowane na tej stronie, nie stanowią rekomendacji ani porad handlowych, inwestycyjnych i finansowych i nie powinny być w ten sposób traktowane ani wykorzystywane. Więcej informacji na ten temat znajdziesz w naszym Regulaminie.