Many studies have shown that when edges are published by academics or become popularized that the markets become more efficient and those edges cease working or lose at least some of their effectiveness.
A really brief story of how the market works. In the beginning, technical analysis was discovered. This initially provided advantage over the general public. This led to the popularization of technical methods which in turn led to more sophisticated traders arbitraging those edges away. Eventually, the public adopted the techniques and it became profitable to trade against the most common techniques. This led to the rise of quantitative technical analysis and trading systems. Those methods have now became ubiquitous. While popularized methods can certainly continue to work, the bar is continually raised.
There is an interesting trend towards automation. Discretionary traders have mostly been replaced by algorithms and quantitative traders. But, the next wave of automation is the potential for self-learning machine learning.
But let’s get back to the basic question: what is the source of a trader’s profits? It boils down to basically 4 scenarios:
Is the market predictable? On the one hand, there is strong evidence that markets are efficient. On the other hand, I had some success predicting the market. What is unknown is the distribution of the returns and traders who push to make outsize returns place themselves at risk of exceeding the boundaries, and that in a nutshell is why trading is difficult.
In order to recap, traders can make outsize returns over the short term. Unfortunately, it is not possible to know how profitable any method will be. There are many risks. This explains the paradox of a wealth of opportunities available for trade when a trader is in the zone and the lack of evidence for longer term success among traders. The primary advantage for the independent trader is the ability to express and take on risk when their understanding of the market is high. This is the trader’s true sword and it cuts both ways.
Curtis 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 him at firstname.lastname@example.org.
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