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These concepts are different to traditional technical analysis (TA), where most calculations are based on price and old concepts like Moving Averages or RSI oscillators. But there is no calculatable probability of a golden cross of Moving Averages. Even back testing will only show you the past. But you can calculate the probabilities of the distribution of log returns. Support and resistance or triangles have no statistical significance, but levels of the underlaying distribution of log returns gives probability of not exceeding a certain price level.
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
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
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
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
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
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
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