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.
Ultimately the agent creates a sequence of actions to maximize its total reward over a given time. The
RewardScheme is an abstract class that encapsulates how to tell the trading bot in
tensortrade if it’s trading positively or negatively over time. The same methods will be called each time for each step, and we can directly swap out schemes.
Properties and Setters¶
- The central exchange for the scheme.
- The exchange being used by the current trading environment. Setting the exchange causes the scheme to reset.
- Gets the reward for the RL agent.
- Returns a float corresponding to the benefit earned by the action taken this timestep.
- Resets the current state if the reward has a state.
- Optionally implementable method for resetting stateful schemes.