Early in my trading career, I had I think above average success developing profitable systems. But, I didn’t have enough capital to deploy them all. As such, my interest in developing new systems waned and growth stalled. This was combined, also, with a strong discretionary capability to call markets and a strong desire to use my innate abilities.
However, recently I have started to think about the problem in a different way. If you can find a quality score or factor for your system then you tune the system to be more selective thereby taking higher quality trades.
Consider my last post, I shared how placing just ~100 trades per year with a ~2.5 R and a 52% win ratio, risking only ~1.5% per trade, you could achieve an ~100% return.
If those 100 trades come from a single system then if that system quits working one year then your return could be much different even if the system isn’t broken. I have seen that systems can go outside the historical boundaries and appear to be broken but then continue to work.
On the other hand, if say your base system made 100 trades and you increased the selectivity of it such that you only take the best 33 trades and you get the other trades from 2 other systems. Now, you have obtained two benefits. First, you have increased your trade selectivity such that you are taking higher quality trades and second you have diversified across more systems decreasing your risk that any single system fails to perform. The downside as I can see it is that you are decreasing your sample size from each system which is probably going to matter with lower win ratio systems and it requires finding good quality and ranking scores.
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.
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