My goal for the past several years has been to seek synergistic solutions to enable myself and, perhaps other discretionary traders, to use their existing strengths while taking advantage of the best algorithmic and “quantitative insights”. However, only recently, have I learned that there is an actual word for this called the “quantamental” approach. It appears to be a portmanteau of “quantitative” and “fundamental” . While my approach isn’t really fundamental, the best description would be that I am a combination tape reader, opportunistic, and synthesis trader: I think it is useful in that it allows me to sharpen my focus to myself while facilitating the communication of my efforts.
My driving aim has more or less been a few factors. I have did very well with reading and predicting the market and trading in simulated accounts using my discretionary techniques. At my very best, it is difficult to imagine that I am not trading close to what may be close to maximal limits. As such, I find it difficult to imagine that psychology or anything less then very specific quantitative driven insights could help me. I would need new sources of edge to do better. On the other hand, I have not did as well in my live accounts. There may be psychological factors at work. But, in either case, I do think that having quantitative edge will increase my probability of success provided I can still use my existing strengths that caused me to become interested in markets in the first place.
As such, what are the specific ways one can integrate and start to use the quantamental approach in their discretionary trading?
I would like to emphasize one more finding. Many traders have struggled with “system down” approaches. The system down approach, as I’m calling it, is where the system generates the signal and the discretionary trader decides whether or not to trade it. I think that the reason that traders struggle with this approach is due to several factors. The first is that most systems tend to trade infrequently. This makes it more difficult for the discretionary trader to utilize any sort of strengths that derive from intimate knowledge, implicit learning, of market behavior. The second is that systems already tend to take a larger risk and have a large profit factor. The combination of infrequent trades and larger edge makes the opportunity cost higher. Also, the lower frequency of trades increases the cost of the approach. The basic gist is that it may simply be “cheaper” to automate lower frequency systems and work on building out versus improving results with discretionary insight.
The discretionary down approach is less well-known and more difficult to implement but makes a lot more sense. The discretionary trader dictates the bias and the system decides on whether or not and how to execute the bias. This approach requires insights and methods not typically discussed but that hold promise for offering significant reward. In particular this approach is more difficult to implement because it requires that the system can either (1) generate a bias at all times or at minimum frequently or (2) identify optimal entry and exit prices, and (3) in general be able to generate probabilities of market possibilities in real-time.
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 email@example.com.
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