Much like last month, some limit orders missed their entry by only a few points. This left us with only two trades in March, one a loser, one a winner. And just like the trading textbooks advise us to do, the loser was kept small, the winner big. This resulted in a net profit of $1038,- this month for the Greed and Fear model portfolio bringing the total for 2019 at +$4651,- by trading only exactly one E-mini S&P future.
- Written by Raoul Suurmeijer
- Category: Results
In 2017, a start was made with verified actual trading results, all through a difficult but still profitable 2018. Now we continue doing so for the year 2019. The result is based on trading only 1 E-mini S&P future with a (minimum) $10.000,- base amount. This is the Greed and Fear model portfolio. With the accumulated profits, the value of this portfolio stands at $17185,50 at the start of 2019.
Every now and then when I'm reading an article from the world of behavioral economics discussing a particular phenomenon, it makes you realize once again how much trading and investing is about human behavior.
First, autotrading is a great development for the trading community, both for investors as well as traders. It gets rid of all the fluff and ambiguous analysis that float around the internet, that whole crazy circus feeding the modern gold-rush.
Early visitors of this blog have seen my daily posts for years now with the so called Greed and Fear indicator and to which direction it was pointing. This was my initial attempt to expose the indicator to the public. Along the way, the performance was measured of course, to see if it was any good and useful in trading. The most honest way to measure this performance was by counting index points 'it called right' subtracted by 'index points it called wrong', as I've explained here in more detail.
But during all those year, I never explained in some more detail what the Greed and Fear indicator really was, while this is probably one of the most fascinating subjects today: a neural network! The more widely known terminology would be machine learning, artificial intelligence etc. There are subtle differences, but it all comes down to 'intelligent software'.