Anyone can trade well when they are winning but what defines the great traders is what happens when the losses mount. The default belief is that one can’t make money trading. The discretionary trader develops an alternative belief system as they learn they can accurately predict the market. The process is empirical and reflective. Every win reinforces the belief while too many losses create doubt. While it is easy to simply say don’t lose: if anyone trades long enough then they will have losing periods.
The solid trader is always self-reflecting and analyzing what they are doing right and wrong. But, when losses mount it can be difficult to justify placing the next trade. The difficulty for overcoming these stretches is that one must stay actively engaged with the market while not becoming overly emotional. The error is to disengage completely or become emotionally charged.
Gray box systems can help in a few ways during these times. Signals can give a trader a baseline or insight into the market and that can help a trader with their read to see new things. Second, sometimes losses aren’t caused by reading the market poorly but by subtle cognitive biases and errors. As an example, I suffered a very severe losing stretch in my trading over a period while my prediction ability soared to new levels. This produced a significant cognitive dissonance. My problem was, that while I was able to predict both very small micro-movements with high accuracy and larger moves with high accuracy, I made the mistake of trying to trade this information in a way that didn’t work. Just for understanding, imagine for example being able to predict one point movements and four point movements. I came up with the idea during this time that I could trade almost risk free by using a very tight stop and simply riding my correct one point predictions to the full four points but better I’d add extra contracts on the way! The problem with my logic was that say over even a short time interval the market would have several one point movements that would tend to mean revert, i.e. go against me enough to take out my stop. As a result, I lost extensively even though I was predicting the market well. And, most every trade did go at least one point in my favor and the market did reach my larger target within a short time period, as well. The problem was compounded by adding contracts on the movements that tended to mean revert while only one specific movement would have follow-through to the next levels. The losses weren’t caused in this case by not reading the market well but were rather cognitive errors in my thinking. Obviously, it seems simple on reflection but at the time this simple mental error caused me significant problems. In fact, it is not too unlikely that going from good to great is the matter of making small changes in ones trading. The changes may be simple but non obvious or at conflict with specific belief systems: such as using a much smaller profit target and stop loss or using a very large stop loss. If I had a real-time feedback and analysis that was feeding back to me what my optimal stop and target placements would be during this time then I may have been able to adapt faster and make the critical corrections.
But back to the matter of losses, systems in general can be useful when the losses mount because one can have a defined level based on historical performance when the system can be shut off or paused. While such losses may cause one to doubt a specific system, it does not have to cause one to doubt their basic ability to trade. Of course, the discretionary trader who carefully defines and tracks their setups can do something similar– even so, there is the added value of automatic tracking and the ability to quickly know with higher confidence how specific strategies are performing.
Gray box systems offer the promise of quantitative and advanced analytical insight while allowing the already strong discretionary trader to develop their existing strengths. Highly consistent setups can be systematized and eventually automated over time, the data used for discretionary trading can be pre-screened and filtered for predictive value, and real-time insight can be used to help the trader to adapt to changing market conditions. Performance is developed and enhanced by superior technology.
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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