A recent Google study (or read Quartz’s summary here) on AI agents showed that AI agents acted aggressively in environments where competition was encouraged but collaborated when the environment fostered collaboration. Trading is ultimately competitive. As individual traders, our primary limiting factor tends to be a lack of capital. There are two basic risks for sharing ideas. The first is that traders with more capital can come along and trade our ideas and erode the edge. The second is that our trading ideas might become popularized and the great crowd of lesser informed traders will erode our edges. Both are actual risks. On the other hand, as social animals, sharing offers the promise of immense benefits from the therapeutic to the practical aspect of getting secondary opinions. Networking cannot be ignored as a factor for success in any endeavor, including trading.
The solution to the problem of sharing is the examination of the characteristics of networks. If your network is small and mostly exclusive then the risk of sharing any trading idea is low because individual traders typically do not possess enough capital to erode an edge. In other words, small networks can foster both sharing and collaboration while minimizing the risks to an acceptable negligible factor.
I believe that finding a trading partner, someone you can share any idea with, can be immensely valuable. Of course, such a partner must be chosen carefully both because you want someone who can encourage your work or at least be a good listener and someone who can also contribute and help out. Beyond your trading partner, smaller exclusive networks offer the possibility for accelerated learning and greater collaboration. The importance of social networks for trading success should not be overlooked– and it is something we may address in the near future. But, the fastest way to turbocharge your trading today is to find a great trading partner and start collaborating!
Thoughts? Agree or disagree? Let me know.
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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