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Distribution Range Bands

Distribution 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 distributed. Therefore, log returns are used in this indicator to determine the distribution, assuming they follow a normal distribution. Other than traditional technical analysis (TA) like Support and resistance or triangles which have no statistical significance, this indicator uses levels of the underlaying distribution of log returns, which gives probability of not exceeding a certain price level.

 

The Input Settings:

Sample Size: Default 30, length of periods back to calculated the distribution of log returns

1st/2nd/3rd Multiplier: Multiplier of distribution levels. Per Default 1.00, 2.00, 3.00 represent the standard deviation levels, based on the distribution of returns. à “1st SD multipl.” == 1.00, means “1st band” represents the first standard-deviation to the up and downside.

 

How to use:

Per Default the bands for 1st, 2nd and 3rd standard deviations are plotted. That means there is a probability of 68.3% that the candle will close within the bands of upper and lower 1st standard deviation band, with the given input parameters. And a probability of 95.4% that the candle close will stay within the 2nd upper/lower band, and 99.7% that it will close within the 3rd upper/lower band. That means, there is only 0.3% chance, that the candle will close outside of the 3rd std-dev. Bands.


Summary (with default settings 1.00/2.00/3.00):

• probability of 68.3% that the candle close will stay inside of 1st SD bands

• probability of 95.4% that the candle close will stay inside of 2nd SD bands

• probability of 99.7% that the candle close will stay inside of 3rd SD bands



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This Oscillator describes the drift µ 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. Looking at larger timeframes mostly positive drift can be observed, as we all are also investors. Negative drift is r
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