Interesting vid! The ‘Theory of Everything’, and physical Unified Field Theory being only a part of it ? Wow – this guy has really got a vision! And all this uncovered by AI algorithms. Forgive me if I’m a bit doubtful about this.
Back to our ‘simple’ FX business. After having learnt a bit more about the general approach, I might see those 52% success rate in a different light now, and I wonder what would happen if we modified what the speaker called the ‘Environment’ in his slides:
a) offer Deep Q a longer data history to learn from
b) offer it a broader data universe, say all OHLC+V of all crosses of EUR, CHF, GBP, USD, CAD, AUD, NZD, JPY to learn from
c) offer it a dynamic model of pair specific trading cost
and then let it learn.
Learn for how long?
Which computing resources (CPU, RAM) provided?
Question above all: What exactly is the ‘Goal’ we’re setting up for the learning process?
Secondary question: is it wise to add additional time series, say indicator data, or would this compromise the uniqueness of the approach?
Anyway, I’m still interested in some conceptual papers, rather than conference speaks. Sorry for being so oldfashioned!
And I would be interested in some modeling details about that ‘marketcheck’ link you provided in your first post.
s.