Hi,
I once attended a Wealth Lab training taught by Thomas Vittner. There we learned that you should never optimize the entry and the exit together.
However, recently I heard some opinions which stated it can make sense for a better overall performance to optimize them at the same time.
So, I would like to ask you for your opinions on that matter.
Thank you in advance
Werner
I once attended a Wealth Lab training taught by Thomas Vittner. There we learned that you should never optimize the entry and the exit together.
However, recently I heard some opinions which stated it can make sense for a better overall performance to optimize them at the same time.
So, I would like to ask you for your opinions on that matter.
Thank you in advance
Werner
Rename
This is not a multiple choice question. This is a graduate school essay question, and the professor will give full credit for either answer depending on how you argue.
Generally speaking, for solving a linear system of simultaneous equations, you want to solve them together. Each equation imposes a requirement (or bias) that you need to meet. But with stock trading, the picture is more murky.
1) How big is your N? Do you truly have enough trades to definitively solve all parameters of your system? You should have a minimum of 5 degrees of freedom (5 d.f) for random error. (Why is that? What did your statistics professor say about estimating the standard deviation of a sample?) So if your model has 6 d.f. (i.e. 6 parameters), then you need at least 5+6=11 trades to make this minimum. Anything less than that is junk. (I hope you are not betting money on junk.) Aside: A good rule of thumb is to design your production model with as few parameters as possible so you can meet the d.f requirement (minimum N) for random error.
If you have a really big N, then optimizing the entry points and exit points separately maybe safe; otherwise, it maybe a bad idea.
2) Does the entry points and exit points depend on the same criteria? Are they using the same indicators?
If not, then it's probably best to optimize the entry and exit points separately because they belong to a different linear system. That is, you have a mixed model. And that maybe a good thing in some cases.
In contrast, if you're using the same indicators to determine entry and exit points, then I would solve their parameters together (simultaneously) because they all belong to the same linear system.
Generally speaking, for solving a linear system of simultaneous equations, you want to solve them together. Each equation imposes a requirement (or bias) that you need to meet. But with stock trading, the picture is more murky.
1) How big is your N? Do you truly have enough trades to definitively solve all parameters of your system? You should have a minimum of 5 degrees of freedom (5 d.f) for random error. (Why is that? What did your statistics professor say about estimating the standard deviation of a sample?) So if your model has 6 d.f. (i.e. 6 parameters), then you need at least 5+6=11 trades to make this minimum. Anything less than that is junk. (I hope you are not betting money on junk.) Aside: A good rule of thumb is to design your production model with as few parameters as possible so you can meet the d.f requirement (minimum N) for random error.
If you have a really big N, then optimizing the entry points and exit points separately maybe safe; otherwise, it maybe a bad idea.
2) Does the entry points and exit points depend on the same criteria? Are they using the same indicators?
If not, then it's probably best to optimize the entry and exit points separately because they belong to a different linear system. That is, you have a mixed model. And that maybe a good thing in some cases.
In contrast, if you're using the same indicators to determine entry and exit points, then I would solve their parameters together (simultaneously) because they all belong to the same linear system.
Your Response
Post
Edit Post
Login is required