Discussing the article: "Elements of correlation analysis in MQL5: Pearson chi-square test of independence and correlation ratio"
Traditional methods of correlation assessment (of two or more financial instruments) often use candlesticks of different timeframes as a reference element.
However, the candlestick, despite the simplicity of its structure (and convenience of use) has a significant disadvantage, namely:
The Close level of any candlestick is not a fractal level, not fixed by the market, but only an intermediate level within the previously started OBJECTIVE MOVEMENT! For an ascending candlestick - it is the price movement from High to Close. For a descending candlestick - from Low to Close.
That is, if there is a shadow, the reverse movement (at the end of the candle time) does not end at all, but can quietly continue! And taking into account such a level in correlation calculations inevitably introduces inaccuracy (or even error).
Therefore, the impulse equilibrium theory uses a different structure for correlation estimation, which has strictly fixed, fractal levels.
Traditional methods of assessing correlation (of two or more financial instruments) often use candles of different timeframes as a reference.
However, the candlestick, despite the simplicity of its structure (and convenience of use) has a significant disadvantage, namely:
The Close level of any candlestick is not a fractal level, not fixed by the market, but only an intermediate level within the previously started OBJECTIVE MOVEMENT! For an ascending candlestick - it is the price movement from High to Close. For a descending candle - from Low to Close.
That is, if there is a shadow, the reverse movement (at the end of the candle time) does not end at all, but can quietly continue! And taking into account such a level in correlation calculations inevitably introduces inaccuracy (or even error).
Therefore, the impulse equilibrium theory uses a different structure for correlation estimation, which has strictly fixed, fractal levels.
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Check out the new article: Elements of correlation analysis in MQL5: Pearson chi-square test of independence and correlation ratio.
The article observes classical tools of correlation analysis. An emphasis is made on brief theoretical background, as well as on the practical implementation of the Pearson chi-square test of independence and the correlation ratio.
In this article, I would like to touch upon such an important section of mathematical statistics as correlation analysis, including the detection and evaluation of dependencies between random variables. The most popular tool in the arsenal of correlation analysis is, of course, the correlation ratio. However, calculating the correlation ratio alone is completely insufficient if we want to assess dependencies in data, especially such as stock price increments. First, the ratio only evaluates linear dependence. Second, zero values of the correlation ratio do not mean the absence of dependence if the data sample it is calculated from has a distribution different from the normal one. To answer the question of whether the data are dependent, we should define the independence criteria. We will talk about the most famous criterion - Pearson's chi-square test of independence. We will also talk about such a numerical characteristic as the correlation ratio, which helps to determine whether the dependence under study is non-linear.
Author: Evgeniy Chernish