I am going to share an uncommon trading method to crush it. But, first I have to disclose that this method is a new idea that I have only given a few moments thought and haven’t studied or traded, yet. While it should be obviously clear to precede with caution, do be sure to read all the disclaimers. With that out of the way…
We want to start with 2 simple ideas. The first is something that 7 figure trader Mark Melnick expounds– that is the objective to obtain a very high win rate. The second is a basic observation: most technical analysis methods work (or not) because of something called serial correlation or anti serial-correlation in the underlying price series. It is the basic stuff of trend and cycle. Importantly, a high serial correlation means then there is a higher probability of up moves after up moves.
Before we go further, we need to take a brief detour for me to discuss my small portfolio of stocks. Portfolio? I know that this is not a word that most small active traders are interested in. But, stay with me because I will get to a powerful trading method that we can use. At the time of this writing, I only own 3 stocks: MSFT, TTD, and UBER (MUT). I do not know the exact weightings but it is heavily weighted toward MSFT.
Because the history for UBER is short, let us see what a 75% weighting of MSFT and 25% weighting into TTD looks like. I simply typed MSFT *.75+TTD*.25.
While a very short history, we can see that since 2017 this portfolio is returning around 80% annualized.
Now, how can we use this as active traders?
While this certainly gets into the domain of spread trading, my core idea for this method has a simpler objective. The idea is basically to find loaded dice and then to either (1) develop systems to trade those loaded dice or (2) use the loaded dice to create easy layups for discretionary trading.
For those in my Collaborative, I will be expanding on these concepts in more detail with quantitative studies in our workbook. This gets into the areas of building spreads and synthetic instruments to trade.
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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