The difficulty of building your graybox is that there’s no blue print. But, looking to identify both strengths and weaknesses in your trading may be a start:
As per building the graybox, there are many possibilities:
Some of the unique challenges of building graybox systems versus traditional quantitative systems:
Discretionary traders tend to be good at predicting direction but poorer at execution. Systems tend to be better at execution but may not always recognize the best conditions to execute. Discretionary traders tend to lose more often when they try to use tiny stops: on the other hand, it also difficult to program systems that will work with tiny stops. On the other hand, the “scale up” approach to futures trading requires extremely tight risk management to be viable. And, even though stop management is difficult to make work for any trade: entry, stop and target management seems like one area where systems might be able to do a better job. One idea is a meta-strategy that can optimize the stop/target based on conditions such that you know the system will find the optimal exit strategy for any market conditions.
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 firstname.lastname@example.org.
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