# Code Structure¶

The TensorTrade library is modular. The tensortrade library usually has a common setup when using components. If you wish to make a particular class a component all you need to do is subclass Component.

class Example(Component):
"""An example component to show how to subclass."""

def foo(self, arg1, arg2) -> str:
"""A method to return a string."""
raise NotImplementedError()

def bar(self, arg1, arg2, **kwargs) -> int:
"""A method to return an integer."""


From this abstract base class, more concrete and custom subclasses can be made that provide the implementation of these methods.

Example of Structure
A good example of this structure is the RewardScheme component. This component controls the reward mechanism of a TradingEnv.

The beginning of the code in RewardScheme is seen here.

from abc import abstractmethod

from tensortrade.core.component import Component
from tensortrade.core.base import TimeIndexed

class RewardScheme(Component, TimeIndexed):
"""A component to compute the reward at each step of an episode."""

registered_name = "rewards"

@abstractmethod
def reward(self, env: 'TradingEnv') -> float:
"""Computes the reward for the current step of an episode.

Parameters
----------
env : TradingEnv
The trading environment

Returns
-------
float
The computed reward.
"""
raise NotImplementedError()

def reset(self) -> None:
"""Resets the reward scheme."""
pass


As you can see above, the RewardScheme has a majority of the structural and mechanical details that guide all other representations of that type of class. When creating a new reward scheme, one needs to add further details for how information from then environment gets converted into a reward.