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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This z-score indicator shows the correct z-score of an asset, as it uses the normalized price data for calculation, which is the only correct way. Z-score is only applicable for normal distributed data, therefore not the actual price is considered, but the normalised returns, which were assumed to follow a normal distribution. Returns are mean reverting and assumed to follow a normal distribution, therefore z-score calculation of returns is more reliable than z-score on price, as price is NOT m
The RSI2.0 indicator uses normalized price data and signal processing steps to get a normal distributed oscillator with no skew (mean is zero). Therefore, it can give much better reads than the traditional RSI.   Areas/Levels of reversal: Overbought or oversold levels from traditional RSI have no statistical significance, therefore the standard deviation bands are implemented here, which can be used in similar way as possible reversal points.   Divergence: As the indicator is nearly normal dist
Bollinger Bands based on Returns   This indicator characterizes the price and volatility by providing a channel/band of standard deviations like the Bollinger Bands. In contrary to standard Bollinger Bands which uses price directly, this indicator uses returns due to normalization.   The standard Bollinger Bands based on price directly, were one of the first quant or statistical methods for retail traders available. The issue with these bands, standard deviations can only be calculated, if the u
Cosine-Self-Similarity: Identify Trends & Reversals What is Cosine Similarity? Cosine similarity measures how alike two sequences are by looking at the angle between them. A value of +1 means they move perfectly together, -1 means they move perfectly opposite, and 0 means there's no directional relationship. This indicator applies this concept to price returns. Is the market trending strongly, or is it choppy and reversing? Gain insight with the Cosine-Self-Similarity indicator! This powerfu
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