A benchmark is a standard or point of reference, which traders can use to measure something else.
This indicator is showing how correlated pairs are performing and what is the current correlation between them.
Features: - Market performance - daily, weekly, monthly - Sigma - volatility . It will be coloured in red, if the volatility is bigger than one standard deviation. -Correlation - Positive correlation will be coloured in green if it is confirmed by the P-value, negative correlation in red. -Confidence intervals -Determination
Markets: - Metal sector - US Stock Indices - Major USD Pairs
Market performance
The indicator is plotting a table with the current performance of the particular group, for example the metal sector and all correlated Gold pairs. The table is showing the performance of the pairs based on monthly, weekly and daily bases in the same time. In this case the trader can track all pairs simultaneously and see if there are anomalies between the pairs - SMT Divergence.
For example: We know that Gold and Silver are very strong correlated pairs. In this case if Gold is going up, but Silver not, probably this move is only current manipulation and the true move is not clear. In that moment the trader can decide not to open an order or take some profit.
With the Sigma value traders also can track the current volatility of the price. The strength of the volatility is measured by the standard deviation.
-1>Sigma<1 - The asset is moving normally -2>Sigma<-1 or 2<Sigma>1 - The asset is volatile -3>Sigma<-2 or 3<Sigma>2 - The asset is very volatile
Correlation
The indicator is showing the current correlation between all pair from the table. The correlation is set to the first pair of the table. In order to make the correlation more accurate the indicator calculates the P-value and the Determination coefficient. The confidence intervals are also displayed in order to show how strong correlation should be expected.
Pearson correlation is a measure of linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations.
P-value evaluates how well your data rejects the null hypothesis, which states that there is no relationship between two compared groups. Successfully rejecting this hypothesis tells you that your results may be statistically significant. In academic research, p-value is defined as the probability of obtaining results ‘as extreme’ or ‘more extreme’, given that the null hypothesis is true — essentially, how likely it is that you would receive the results (or more dramatic results) you did assuming that there is no correlation or relationship (e.g. the thing that you’re testing) among the subjects
Coefficient of Determination is just the square of pearson’s correlation coefficient R. This is done as it is easier to explain linear regression in terms of R² than R. As R ranges from -1 to 1, R² would range from 0 to 1 — clearly explaining relationship with 0 being not related and 1 being perfectly related.
The correlation confidence interval is the range in which the population correlation is most likely to be found. The degree of certainty for which it is likely to be within that range is called the confidence level. When you collect sample data, you can not know the exact value of the correlation.
Note: For the Stock indices there is an extra calculation, showing the current market expectations - Fear and Greed Index. The calculated index could differs a bit from the original CNN Fear and Greed indicator, because they calculate the index based on Future markets. This indicator calculate the index based on the market that we trade - indices.
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