Noise

Noise is everywhere.

We could even argue that trends are made out of noise. Inside massive markets, volatility can be your worst enemy: psychology again comes on stage. Every turn in the price creates uncertainty and uncertainty accumulates. Your confusion grows. It’s difficult to make correct investment decisions and timing when in confusion.

If you approach a technical analysis platform, you get confused by the quantity of tools available. Wise designers batch all similar indicators, so that you can have some categories available. More common and old indicator to reduce volatility is the moving averages family. It is impossible to build a proficient trading system using just moving averages, but they remain the basic tool to every trader. Build your set of indicators reducing their number as much as possible. Since years I use just one momentum indicator, a modified DMI, and two volatility stops.

the lrDMI in action

Doing all this, we are immersed in noise: home, streets, commuting, workplace, public places, noise everywhere. Sounds, clamors, rattles, visual rubbish, flashing lights, advice, signs of every kind, noise.

Can you manage noise? Can you hack the noise and view the layers it is composed of? This is a point of a certain relevancy. I myself have a serious problem managing this matter because I do not want to be overwhelmed by the effort of having always the right opinions. As everyone, I’m weak and confused. This is why I developed the Amodel, or the brain that analyze the S&P 500 and that powers this site, using tools that are mostly not affected by noise, pumping it’s brute force energy and teaching to “it” how to read the market. The project is now about four year old, it is surprisingly efficient and under continuous development.

The Amodel is different.  It looks at the markets in terms of correlations, dozens and dozens of markets, simultaneously, and, to be sincere, it has in mind just one idea. The basic ultimate assumption is: “markets are all correlated and the S&P 500 is a part of these correlations”. The model does not read news, does not watch tv, does not navigate social networks. Just numbers. In fact, a huge amount of numbers. Just one simple statement and a lot of data, these are the basic ingredients of the recipe. Then add code, some various thousands lines of code. Agitate. Stir. Fry.

 

 

 

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