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Returns Momentum Oscillator RMO

The Returns Momentum Oscillator (RMO) shows the difference of exponentially weighted volatility. It is used to find market tops and bottoms. Volatility comes in waves, and as the Returns often front run price action it gives directional prediction of market movement.

 

The Oscillator signal is RMSed (root mean squared) to make the distribution closer to Gaussian distribution. While the traditional RSI indicators are often stuck in overbought or oversold areas for a long time, RMSing of the signal gets rid of this. To support this behaviour a high pass filter is applied to filter out higher periods, which should not be considered.

The mean of the indicator signal is the zero line, because it is zero meaned. This avoids skewness of the underlaying indicator distribution.

 

After the signal processing steps, the indicator value is smoothed with an advanced technic, which smooths the signal with minimal lag.

 

A standard deviation band is applied to show significance levels of the indicator, which often act as reversal point. It is like the overbought and oversold levels of RSI indicator, but with statistical significance.

 

As the indicator is nearly normal distributed, due to the signal processing, it can also be used as divergence indicator.

 

 

The Input Settings:

Sample Size: Default 7, length of periods back to calculated Volatility

Smoothing Sample Size: default 8, length of periods back to smooth signal with minimal lag

Multiplier: default 2.00 Standard Deviations, Multiplier in terms of standard deviation, to show outliers/possible reversals (line colour change)

Sample Size Bands: Default 30, length of periods back to calculated SD-Bands of RMO to find outliers

High-pass Filter Sample Size: default 50, sample size of high-pass filter to reduce low-frequency noise of the signal

 

 

Remark: Indicator is provided for statistical analysis and showing probabilities only and should not be construed as financial advice.

 

 

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Vahidreza Heidar Gholami
5 (2)
MetaForecast predice y visualiza el futuro de cualquier mercado basándose en las armonías de los datos de precios. Aunque el mercado no siempre es predecible, si existe un patrón en el precio, MetaForecast puede predecir el futuro con la mayor precisión posible. En comparación con otros productos similares, MetaForecast puede generar resultados más precisos al analizar las tendencias del mercado. Parámetros de entrada Past size (Tamaño del pasado) Especifica el número de barras que MetaForecas
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Percentile of Historical Volatility and Correlation Coefficient shows if the asset is cheap or expensive based on the volatility. It is used to determine a good entry point. It has two indicators built in: Historical Volatility is ranked percentile wise and its correlation to price action which gives an indication of the direction of a possible future move. Together the both indicators can give good entries and direction.   Historical Volatility is a statistical measure of the dispersion of retu
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Florian Nuebling
This Oscillator describes the drift of an asset, as part of the geometric Brownian Motion (GBM). As a data basis the mean reverting log returns of the asset price is considered. It gives the percentile of drift directional. For instance, a value of 0.05 means a drift of 5%, based on the selected sample size. If the value is positive, drift to higher asset values is determined.  This indicator should be used in confluence with other indicators based on volatility, probability and statistics. L
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The indicator ‘Probability Range Bands’ gives a prediction of the amount, how much the asset is moving from its current price. The range bands give probabilities, that the candle close will not exceed this certain price level. It is also called the expected move for the current candle close.   This Indicator is based on statistical methods, probabilities and volatility. Asset price is assumed to follow a log-normal distribution. Therefore, log returns are used in this indicator to determine the
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