Market Blindness – Part 1

Market blindness : A trader’s inability to see and adapt to changing market conditions. 

In this two part article I offer a theory as to why this occurs and how it can be overcome.

I first became aware of this phenomena nearly 10 years ago when I was teaching a beginners retail Forex trading class and again more recently when I started working with a group of experienced Forex traders. Each time I presented the group with a new system or trading approach they performed quite well until market conditions changed. Then the trading performance for the group dropped abruptly.

In order to make good trading decisions we need to know the strength and direction of any underlying trend, the amplitude, frequency and phase of any cyclic or mean reversion component and the strength of the ambient noise level commonly known as volatility. There are also a number of other things we probably want to know about but these are the main three. They need to be evaluated in terms of the typical trade duration we are anticipating.

Financial markets are very noisy; When looking at price charts, fundamental data or even information about how to trade, we are always going to be inundated with high levels of noise that can easily confuse and trick us into seeing things that aren’t there and missing things that are.

The first and probably the most important tool that we need to understand and master is our mind:

Nobel Prize winning psychologist, Daniel Kahneman popularised the concept of two distinct modes of thinking which he labelled system 1 and system 2. System 1 thinking is responsible for our intuitive gut reactions. It is fast and efficient, mostly subconscious and responsible for our powerful pattern recognition abilities. It is also where cognitive biases that can negatively affect trading decisions tend to be stored and activated. It has been conclusively proven that subconscious system1 thinking dominates most of our decision making. When we make what we believe is a conscious decision, more often than not we are just rationalising an unconscious one that has already been made by our system 1 process.

Most retail traders these days rely heavily and sometimes exclusively on data from price charts for their trading decisions. Reading price charts is predominantly done using our pattern recognition abilities. This can be a very subjective process. I have seen two people look at the same chart at the same time with each being certain that the trend was going in the opposite direction. The reason for this may simply be that both are looking at different things and labelling their observations the same way but a more likely reason is that either or both of them are being misled by randomness – the fool’s gold of trading.

Market noise seriously affects our cognition at a mental level. It has a tendency to shut down our system 2 thinking and effectively drive us into automatic, system 1, survival mode. When a situation around us becomes chaotic and confused, we instinctively switch to our fast acting, low energy autopilot in order to survive.

Fig 1 shows a daily price chart of EURUSD data. The prices from April to August 2017 show a modest but fairly steady trend. In Forex, we are often looking at trends that are of shorter duration but much stronger than this example.

 Fig 1. EURUSD real daily price chart data

In Fig 2, I have simulated a daily price chart using noise extracted from the original EURUSD data. By using a price data simulator we can accurately control the elements that make up any financial time series and compare it to real data for training and education purposes. The data in Fig 2 has no real trend but it certainly doesn’t like look that at first glance. How can this be so?

Fig 2. EURUSD simulated data consisting of pure noise that looks like a trend

In order to understand how this can happen we need to delve into a phenomena known as a random walk. This goes beyond the scope of this article but if you want to know more there is plenty of information available on the internet. Wikipedia or Investopedia may be good places to start.

The point that I am trying to illustrate is that the chart pattern we see in Fig 2, being entirely random, is untradeable whereas the chart pattern in Fig 1 is tradeable, even though the two look much alike. Of course we could trade Fig 2 profitably in hindsight just as we can trade almost anything profitably in hindsight but we could not trade it profitably going forward. The difference is that the situation depicted in the chart of Fig 1 has a certain amount of sustainability since it is a trend driven by economic considerations or at least perceptions of them whereas the chart in Fig 2 is composed of purely random moves and has no sustainability whatsoever. An expert will pick this up at a glance but a novice is likely to struggle.

A long steady sequence of random events as shown in Fig 2 doesn’t happen very often but it can. It does, however, happen frequently on shorter time frames where many Forex traders typically operate. In Fig 3, I have added a synthetic trend back into the original noise data to re-create a theoretically tradeable time series. At a quick glance all 3 charts look much the same but Fig 2 is lacking something that Fig 1 and 3 have. Can you see the difference?

Fig 3 EURUSD simulated data consisting of noise + trend

Cyclic components:

Cyclic components can be tricky to deal with. We can usually do a good enough job of analysing them using tools commonly found on most trading platforms. While these tools are well known, the way in which they are used often leaves a lot to be desired. In time, and with a lot of practice, some traders develop the ability to see these characteristics just by looking at a chart. Novices are better advised to use charting tools until they have developed a consistent level of proficiency. This however does not mean smothering your chart with a bucket load of indicators. Referring to our three charts in Figs 1, 2 & 3, it is easy to see how even a modest amount of noise or volatility can drive us into system 1 thinking mode which then makes it difficult to interpret the underlying trend and cyclic components essential to trading success.


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