Reward schemes receive the
Trade taken at each time step and return a
float, corresponding to the benefit of that specific action. For example, if the action taken this step was a sell that resulted in positive profits, our
RewardScheme could return a positive number to encourage more trades like this. On the other hand, if the action was a sell that resulted in a loss, the scheme could return a negative reward to teach the agent not to make similar actions in the future.
A version of this example algorithm is implemented in
SimpleProfit, however more complex schemes can obviously be used instead.
Each reward scheme has a
get_reward method, which takes in the trade executed at each time step and returns a
float corresponding to the value of that action. As with action schemes, it is often necessary to store additional state within a reward scheme for various reasons. This state should be reset each time the reward scheme’s reset method is called, which is done automatically when the parent
TradingEnvironment is reset.
from tensortrade.rewards import SimpleProfit reward_scheme = SimpleProfit()
The simple profit scheme returns a reward of -1 for not holding a trade, 1 for holding a trade, 2 for purchasing an instrument, and a value corresponding to the (positive/negative) profit earned by a trade if an instrument was sold.