Stock Market Informational Websites I Like
This is a non-exhaustive list of websites on stock market investing (and trading) I like for various reasons.
This is a non-exhaustive list of websites on stock market investing (and trading) I like for various reasons.
The intention of this post is to shed a little light on possible applications of rhizomes. I am personally still struggling to figure out what the best use cases for rhizomes might be, but here is a list including some ideas nevertheless. The basic problem I am confronted with is my own tendency to interpret rhizomes in terms of existing data structures and algorithms instead of focusing on what is genuinely new. I have recently come to the (maybe preliminary) conclusion that rhizomes are at their best when it comes to post-structuralist applications. That is, applications where up-to-date a certain data structure is silently accepted as a best way to do things.
There are at least three different ways how to implement a rhizome on existing hard- and software platforms: as an object tree, relying on a programming language pointer arithmetic, or using mathematical pairing functions.
Be rk <= (ri, rj)
a directed relation rk between the ordered pair of relata ri and rj, with rk ≠ ri
and rk ≠ rj
. We will call a relation a terminal iff ri = rj
. Furthermore, we call ri (the left-hand-sided element in the ordered pair) the normative relatum and rj (the right-hand-sided element in the ordered pair) the associative relatum.
Definition:
As I have shown in a previous post in GP entry and exit decision rules are encoded in a tree form. The decision rule tree returns a boolean value for every processed bar, which is interpreted as an entry (root node returning true
) or exit (root node returning false
) signal. Some authors suggest using a single rule tree for entry and exit signals, but I personally prefer evolving dedicated rule trees for both entry and exit rules, as I believe them to produce better signals.