Deep learning classification for stock trading with multiple inputs and trinary state output


I want to find the best way to approach this classification problem that applies to stock trading.

Let’s say I receive trending stock alerts form 10 sources at different times during the trading session for different stock prices. I have two risk management strategies to buy a given stock that produced different gain performances and that from statistics depend on the combination of AlertSource+TimeOfDay+Price.

So depending on the performance I got with my two risk management (RM) strategies I would like the program to tell me which action I should take:

Buy with RM1=1 (if RM1 would give a better return than RM2)
Buy with RM2=2 (if RM2 would give a better return than RM1)
Don’t buy=0 (if both RM1 and RM2 would result in a loss)

So it seems like a 3-input situation with one trinary state output (0, 1, 2) that depends on past values of two parameters (PerfRM1 and PerfRM2).

Thanks for your advice!

File example:
AlertSource   TimeOfDay   Price($)   PerfRM1(%)   PerfRM2(%)   Action
1                    09:35            15.95       0.5                 0.9                 2
4                    10:08            26.32       1.2                 0.7                 1
6                    10:43            9.97        -0.2                 0.3                 2
3                    11:06           30.15        0.6                -0.1                 1
2                    12:09           18.99       -0.4               -0.6                  0
.                      .                   .               .                     .                      .

.                      .                   .               .                     .                      .

I think you may get some great responses to that question on a forum that caters more closely to that sort of topic.  Take a look at the Computer Science and Computational Science stack exchange sites. Would your question match the typical topics of one of those sites?

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