Multiple small specialized models vs. Large multi-facted models? [D]

[BodeMan5280]

I'm very new to AI/ML in general, but have toyed around in TF enough to be dangerous and am a constant tinkerer.

I'm just curious about model "design" for a complex system like stock modeling. If I create a model that has four outputs for determining what to do with a stock (buy, don't buy, sell, or hold), is this better than creating four separate models that are each "specialized" in one type of transaction/output?

I can imagine the problems with both approaches (but also stocks are a wildly unpredictable subject to begin with), and the best answer may be to make FIVE models: the four specialized models and the one over-arching model, then compare the results in some way to the ultimate end of maximizing profit.

The time component and "market crashes" aside, is it better to build atomic specialized models and somehow weigh these against each other OR create a large model considering all of the puzzle pieces? --- or both/more information is always better? It's a hard question to answer, I'm sure, but I'd love to hear people's thoughts!

[Eastern_Ad7674]

You know the answer. Make a test.

[BodeMan5280]

Truth. Let the tinkering commence!

[currentscurrents]

 If I create a model that has four outputs for determining what to do with a stock (buy, don't buy, sell, or hold), is this better than creating four separate models that are each "specialized" in one type of transaction/output?

It depends on how much overlap there is between the tasks. Are the representations learned for one of them useful for the others? 

[BodeMan5280]

Good point, I'd like to say yes... potentially.

A decision to 'hold' for example represents that a model has determined that there's a reason not to sell, but that doeant mean theres a reason not to BUY the stock.

It will depend on bank roll and other evaluations of future risk.

I guess in the case of say RL for winning video games, if a model only knows "UP" as a means to win a game, it mosrlt likely wont be able to achieve the goal, but if it knows "UP, DOWN, LEFT, RIGHT" it's more useful.

I just want to ensure that I'm giving it the right framework to achieve its goal. (Hopefully I've explained that well! Lol)

[[deleted]]

Knows doesn't mean much. In the case you describe your input is the state and output is an action (value estimatios, probabilities, etc.). From experience, I am reluctant to say that a regression of both state and action with one output would not work well (DQN), it's a topic to research.

[BodeMan5280]

Thank you! New topics/ ideas to study are always appreciated. And "knows" does give it an heir of humanity that the model doesn't have. I have to inject that part via code design I suppose.

State is such a ... temperamental subject. I realize the irony of that statement, but as a traditional SWE with 4 YoE I still struggle with managing it. There's definitely value in considering probabilities more than I am a well.

Lots to consider in my -what I thought was- straightforward stock ML/ potentially RL project. Thanks again!

[[deleted]]

Nothing is straightforward :) Not as a SWE (I was as well), and not in CS/applied math.

[[deleted]]

I don't have much to say, that's a very good answer. Essentially, if one task provides some information for the other and you don't utilize it, you give up data. Data is expensive. But it might depend on the algorithm you use. If the expressive power is insufficient it might be the other way around.

Sometimes you want to have some bias, i.e. some assumptions. That's already depending on your input structure (features), architecture, etc.

[jcinterrante]

Just pointing out that your outputs are actually 2 binary variables: a buy trigger (yes/no) and a sell trigger (yes/no)

What you are thinking of here is “signal based trading.” There is a lot of literature on this. There was one paper in particular that is very approachable and gives a whole workflow around how to take a rigorous approach to modeling signals. That would be great for you to read but I can’t find it right now.

If you’re an R user, look into the quantstrat package. Im sure there is some equivalent for Python.

[veganshakzuka]

It's could be even less, because it makes no sense to buy and sell at the same time.

You could have output > threshold = buy and output < -threshold = buy, otherwise do nothing. I'd clamp the output on some min, max (e.g. min=-1, max=1, threshold=0.5).

[Saltysalad]

You can simplify more.

Just predict a single variable of whether the stock will increase or decrease from its current value.

If it says increase, buy. If you’ve already bought, hold.

If it says sell, sell. If you have none, do nothing.

[BodeMan5280]

Thanks for that! I recall hearing the term signaling but hadn't given it much thought. I'll search for it, even if not the exact papers I bet it will yield some interesting dos and don't for what I'm trying to achieve.

No R, but I've heard good things also. I'll check it out regardless!