class_docstr: A stock trading environment for OpenAI gym
class_docstr_tok: ["A","stock","trading","environment","for","OpenAI","gym"]
code: def _sell_stock(self, index, action):
      def _do_sell_normal():
          if (
              self.state[index + 2 * self.stock_dim + 1] != True
          ):  # check if the stock is able to sell, for simlicity we just add it in techical index
              # if self.state[index + 1] > 0: # if we use price<0 to denote a stock is unable to trade in that day, the total asset calculation may be wrong for the price is unreasonable
              # Sell only if the price is > 0 (no missing data in this particular date)
              # perform sell action based on the sign of the action
              if self.state[index + self.stock_dim + 1] > 0:
                  # Sell only if current asset is > 0
                  sell_num_shares = min(
                      abs(action),
                      self.state[index + self.stock_dim + 1],
                  )
                  sell_amount = (
                      self.state[index + 1]
                      * sell_num_shares
                      * (1 - self.sell_cost_pct[index])
                  )
                  # update balance
                  self.state[0] += sell_amount
  
                  self.state[
                      index + self.stock_dim + 1
                  ] -= sell_num_shares
                  self.cost += (
                      self.state[index + 1]
                      * sell_num_shares
                      * self.sell_cost_pct[index]
                  )
                  self.trades += 1
              else:
                  sell_num_shares = 0
          else:
              sell_num_shares = 0
  
          return sell_num_shares
  
      # perform sell action based on the sign of the action
      if self.turbulence_threshold is not None:
          if self.turbulence >= self.turbulence_threshold:
              if self.state[index + 1] > 0:
                  # Sell only if the price is > 0 (no missing data in this particular date)
                  # if turbulence goes over threshold, just clear out all positions
                  if self.state[index + self.stock_dim + 1] > 0:
                      # Sell only if current asset is > 0
                      sell_num_shares = self.state[
                          index + self.stock_dim + 1
                      ]
                      sell_amount = (
                          self.state[index + 1]
                          * sell_num_shares
                          * (1 - self.sell_cost_pct[index])
                      )
                      # update balance
                      self.state[0] += sell_amount
                      self.state[index + self.stock_dim + 1] = 0
                      self.cost += (
                          self.state[index + 1]
                          * sell_num_shares
                          * self.sell_cost_pct[index]
                      )
                      self.trades += 1
                  else:
                      sell_num_shares = 0
              else:
                  sell_num_shares = 0
          else:
              sell_num_shares = _do_sell_normal()
      else:
          sell_num_shares = _do_sell_normal()
  
      return sell_num_shares
  
imports: from __future__ import annotations
  from typing import List
  import gymnasium
  import matplotlib
  import matplotlib.pyplot
  import numpy
  import pandas
  from gymnasium import spaces
  from gymnasium.utils import seeding
  from stable_baselines3.common.vec_env import DummyVecEnv
label_desc: Use image node_id 2 for calling the StockTradingEnv obj's underlying member method code with example usage: obj._sell_stock(index, action) and returns: sell_num_shares, sell_num_shares