There is a trade-off between “specialization” and “generalization” in markets. Greater specialization is likely to result in greater profit potential at the cost of robustness. On the other hand, greater generalization is likely to result in reduced profit potential with greater robustness.
As an illustration as to why this is the case, let us imagine you have a pattern that can predict the low of day at some probability. There are obviously going to be various bullish factors that go into that algorithm. In addition to that, you will use historical probabilities to find the optimal stop and target.
If you want to trade that reversal pattern with greater leverage then you will be specializing. With greater leverage you will be using tighter stops which probably have no predictive value, implication that you are more likely to take random stop outs once you specialize.
Of course, the market can and does change but normally the change is more pronounced and more rapid at the lower, more microscopic levels, then the macroscopic levels.
What is the practical implication? Well, one lesson is to specialize but not over specialize. It is important to understand the basis for a trade and how to optimally structure it. If you want to take advantage of a general theme in the markets then an options trade, or a reduced leverage trade, may work better then a highly leveraged future trade. On the other hand, if you do find a pattern that can be traded with some precision, futures are ideal
Another method that traders attempt to use to decrease the risk of specialization is to selectively work limit orders in the book. One benefit is that giving up the spread is a primary difficulty in making futures trading work. However, this method requires a lot of skill and increases the probability of larger losses. So, I am not really convinced that it works. On the other hand, I am fairly convinced that splitting targets works well and holding a portion for runners.
Specialization implies the requirement of a certain superior intelligence. As such, a more frequent optimization schedule or training/retraining may be another way to tackle the problem.
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 email@example.com.
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