I have coded a RL environment for trading. The action space is discrete with 3 components [0,1,2]; where 0 corresponds to selling an arbitrary amount of shares; 1 corresponds to holding; and 2 corresponds to buying. I am using the immediate profit, the amount of money made by selling or spent by buying, as reward. If the amount of shares is 0, selling has the same effect as holding. Training on historic data, I have realized that the agent ends up predicting selling at all times. If I try to penalize the agent for choosing selling when the number of shares is 0 then it gets stuck in holding. What could be a suitable reward for this application?

  • $\begingroup$ On the historical data you are using in the simulation, is it possible to make a profit reliably? How are you constructing the state? I would like to see some example numbers that you are using as input to the neural network $\endgroup$ Jul 4 at 11:32


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