class_docstr: An abstract base class for reward schemes for the default environment.
      
class_docstr_tok: ["An","abstract","base","class","for","reward","schemes","for","the","default","environment","."]
docstr: Gets the reward associated with current step of the episode.
  
  Parameters
  ----------
  portfolio : `Portfolio`
      The portfolio associated with the `TensorTradeActionScheme`.
  
  Returns
  -------
  float
      The reward for the current step of the episode.
docstr_tok: ["Gets","the","reward","associated","with","current","step","of","the","episode",".","Parameters","--","--","--","--","--","portfolio",":","`","Portfolio","`","The","portfolio","associated","with","the","`","TensorTradeActionScheme","`",".","Returns","--","--","--","-","float","The","reward","for","the","current","step","of","the","episode","."]
code: def get_reward(self, portfolio) -> float:
      """Gets the reward associated with current step of the episode.
  
      Parameters
      ----------
      portfolio : `Portfolio`
          The portfolio associated with the `TensorTradeActionScheme`.
  
      Returns
      -------
      float
          The reward for the current step of the episode.
      """
      raise NotImplementedError()
  
imports: from abc import abstractmethod
  import numpy
  import pandas
  from tensortrade.env.generic import RewardScheme, TradingEnv
  from tensortrade.feed.core import Stream, DataFeed
  import math
label_desc: Use image node_id 2 for calling the TensorTradeRewardScheme obj's underlying member method code with example usage: obj.get_reward(portfolio) without return types