The problem of false signals in the study of moving averages
Technical analysis is a necessary practice, often the only resource that allows the trader to operate with knowledge of the cause, with rationality, and having at their disposal a "full" awareness of the moment the market is going through. However, it is not a "hard science", and is susceptible to physiological errors. Exactly, even if the trader practiced technical analysis according to doctrine, even if they did not make any mistakes, it could lead them to defeat. The problem revolves around the concept of false signal. An indicator, maybe even multiple indicators used together, emit an entry or exit signal.. In short, an operational suggestion. Here, however, the signal proves to be fallacious, not truthful. The trader operates on the basis of a wrong indication, and their order fails. The problem of false signals involves many indicators, therefore technical analysis in general. However, it mainly involves technical analysis conducted through the study and use of moving averages. Obviously, the contextual use of multiple moving averages, chosen according to various criteria (e.g. exponentiality and speed) reduces the incidence of false signals. However, the problem persists. In the best of hypotheses at a theoretical level (making the trader's action more uncertain), in the worst in a rather concrete way and able to impact capital. Over the decades, many analysts, theorists and professional traders have tried to fill this gap, to find a technical solution. Among those who have come closest is Perry Kaufman, who not long ago invented a moving average from scratch, the Adaptive Moving Average.What is the Adaptive Moving Average
The Adaptive Moving Average is known by many names. For example, many refer to it with the expression Kaufman Moving Average, a way to honor the work of its creator. Consequently, two different acronyms are used: AMA in the first case; KAMA in the second case. Throughout this article, we will refer to the moving average in question with the canonical expression, Adaptive Moving Average, or with its acronym. What is the Adaptive Moving Average? To summarize its meaning (as well as its function) in a few words, we could define it as a moving average that takes into account the most dangerous source of false signals in a market: systemic volatility. As long as volatility is contingent, that is, caused by contingencies, by a known event capable of impacting the market in an "acute" way, the trader is quite safe from negative consequences. In fact, being aware that the market is momentarily "drugged" by a particular condition, they take more stringent measures, adopt more prudent solutions. If they feel that the market is too shaken, and is beyond their control, they may even choose to stop for a while, waiting for the waters to calm down. When volatility is systemic, the discourse is different. Volatility is systemic when it does not depend on a "fact", but on the very nature of the market. Some markets, precisely by virtue of their composition, the meanings related to their attendance and investors' habits, can be more or less volatile. At a glance, it is a concept that all traders are aware of. However, they have a hard time accurately measuring this systemic volatility, therefore squaring it and integrating it into their analysis system. Not to mention all the cases where it goes unnoticed. It is obvious that, in this context, the tendency to employ false signals is high. So, Kaufman intervened precisely for this purpose: to measure this kind of volatility and integrate it into the calculation of the moving average. Specifically, the parameter through which Kaufman proposes to calculate systemic volatility is the Efficiency Ratio, whose acronym is ER.How to calculate the Adaptive Moving Average
To calculate the Adaptive Moving Average, it is necessary, first of all, to calculate the Efficiency Ratio. After all, it is a moving average "adjusted" based on this parameter. Well, the Efficiency Ratio is calculated by comparing, given a period of time, the price variation from point A to point B, and the number of variations recorded in the same period. When there are many variations, it means that there is a very accentuated volatility, and the ER approaches zero. When the variations are few, the ER approaches one, and therefore the systemic volatility is reduced. But why do we talk about efficiency? Well, Kaufman starts from the assumption that systemically volatile markets are actually inefficient markets, within which investors "go around in circles", before overall taking the right path. This happens when the market is truly full of investors. In most cases, these are "old" markets, apparently consolidated. If a new "round" of investors enters, in fact, they do so in these markets, and not in the young ones. On paper, an excellent reasoning. Obviously, there are some exceptions. Let's think of the cryptocurrency markets, which are prey to Sunday investors. That said, once the ER is obtained, how is the Adaptive Moving Average calculated? Well, the formula is as follows. [ER x (Fast Average - Slow Average) + Slow Average] Where:- ER is the Efficiency Ratio
- The fast average is nothing more than 2/(Period of the faster moving average +1)
- The slow average is nothing more than 2/(Period of the slower moving average +1)
