Good trading performance and past performance in general is overrated.
A long time system vendor and big time promoter has offered several systems for subscription over the years. Every single system I have ever seen this vendor offer has crashed and burned badly.
Now, this vendor has won trading competitions and even verified his real money results. But, as far as I can tell, any given one of his trading systems making money is a coin flip at best.
This post is not about that vendor who I only use as a convenient example.
No, it is not about a really rude and nasty trader on YouTube, who I think does offer some value: however, he did rename his first Collective 2 strategy to “Test” to hide the poor performance.
Absolutely the skepticism should apply to myself in equal parts. Once, on a particular product, I made 23 record winning predictions on the market and still managed to lose money.
No, this post is really not even about being aware of overconfidence in your trades and other traders.
This post is really about a clarification.
Please do not confuse my enthusiasm for trading as suggesting or encouraging that it will work for most.
In fact, I have already shared that I guess you can beat 85% of traders just averaging down into MSFT. If you want to reduce leverage, I have had the idea to take profits at 2x your realized risk. I have noticed a 2:1 reward seems to be apparent in a lot of charts I look at. It is just a guess, of course.
Again, I figure most would do better to just focus on long term investing. But, for some of us, the activity of trading is compelling enough to bring us back. As long as the drive is there, the pursuit is worthwhile.
And, I know I can create tremendous value for traders. Just, please do not confuse my enthusiasm and drive for false confidence.
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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