With AlphaGo there are several things you have to consider. It is not only using deep learning it also uses monte carlo tree search, basically this algorithms is good at exploring search spaces like games. But the key factor in AlphaGo was deep learning evaluating the states.
What I want to say is that neural networks are not meant to explore but to discover patterns. It is something very different. They are very interesting because they work as our brains (discovering and fulfilling patterns), but they as bad in searching as us.
I see.. Thanks for sharing. I assume you mean evaluating game board states, finding valuations objectively and figuring out the pattern of where to move that can lead to higher probability of winning? 20 years ago I made an Othello game that use a search tree (I think configurable from 3 to 9 levels deep), assign valuations to different board positions and let the computer assume that the player would make the best move to his advantage. It turns out can beat human players easily, but that is such a small search space and valuation weights are very clear. Thus I was amazed at the computer winning with such a large game board where even for professional players, it is arguable which state is a better state. EDIT - ok I just googled monte carlo tree search, I must have accidentally implemented that search at that time, base on common sense and what I can do with the programming langauge I had at that time (Visual Basic).
I find very interesting how some trade algorithms are using deep learning to extract features form news apart from the variables you mentioned.
About the search, in case you are interested. You have the basic tree search, then you have A* (a-start) which uses an heuristic to decide which is the next node to expand (very used in path finding in games, where the heuristic is the euclidean distance to the target point). For games you use a search where in one state you maximize the heuristic (your move) and in the next one you minimize it (your opponent move) (sorry I don't remember the name). And monte carlo tree search what it does is to no evaluate some states, it just do some random moves and evaluates the final state, this way it tries to improve the exploration/exploitation of the search (and works pretty well).
I'd call that quantitative analysis, not technical analysis. The difference between them being the difference between astronomy and astrology. Technical analysis refers to classic trading strategies of visual patterns in the price charts like double top or bottom, head and shoulders, etc; these patterns and their traders suffer from massive hindsight bias. Feeding prices into computers looking for patterns is not technical analysis unless you've programmed the computer to look for said human found patterns. If the computer is actually searching for real patterns and you're testing them properly with forward tests on fresh data, you're doing quantitative analysis not technical analysis.
TA is practiced by manual traders, Quants are generally automated traders or traders doing proper statistics rather than relying on the visual patterns manual traders think they see.
Not to say that people don't, but as mentioned, they don't last. How long they do last is an even more difficult prediction problem.
I guess as long as the users' expectations are correct it can be useful in some very specific areas. Referencing the AlphaGo game last year, I was a Go player for more than a decade. But yet AlphaGo's weird move inspires new insights that break the conventional structure / thinking-framework of a Go player. From that angle, I do think that even though DL is somewhat a blackbox, humans can pick up new insights because it explores areas which are normally ridiculous to a human with 'common sense' to explore.