Predicting the market?

Trader's Mindset

Dec 13

Some traders do not like to think they have to predict the markets to be successful. But, it is a fact that your profits as a trader are a function of your ability to accurately predict the markets.

There is, also, one loophole and a benefit to viewing the process as prediction, in terms of efficient market hypothesis, versus purely statistical. As Michael Harris (from Price Action Lab) pointed out that if one takes the view that one say follows trends then it is not enough for trends to be present to be able to profit from them. They must last long enough to be detected and the profits from profiting from trends must overcome the losses incurred during the chop.

On the other hand, if one can predict or pre-select which markets are likely to trend based on predicting factors that could cause markets to trend then trend-following as a methodology does not need to be statistically or historically profitable for one to profit from trends. The trade off is the introduction of an unknown and unknowable component which is really the edge. And, of course, it is not enough to be able to predict the market but your winning predictions must profit more then your losing predictions, you must have positive expectancy.

Unfortunately, it is not enough to be able to predict markets accurately to profit from them. One must, also, be able to execute at a high level. It requires extensive experience, training, and applied focus.

As such, it is far easier to execute a trading system, and I think unless one has a strong aptitude for reading markets then either a purely systematic or perhaps even a mixed graybox approach is more likely to lead to consistency.

In reference to Michael Harris, I found this Forbes article from 2016 very interesting and insightful,



About the Author

The author is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline. You can contact the author at