写出股票强化学习的完整代码，并写出注释

下面是一个简单的示例，展示如何使用Python编写一个股票强化学习的完整代码，并附加注释。

import numpy as np
import pandas as pd
import gym
from gym import spaces
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers


class StockTradingEnv(gym.Env):
    metadata = {'render.modes': ['human']}

    def __init__(self, data, window_size):
        super(StockTradingEnv, self).__init__()
        self.data = data
        self.window_size = window_size
        self.action_space = spaces.Discrete(3)  # 买入，卖出，持有
        self.observation_space = spaces.Box(low=0, high=1, shape=(6, self.window_size + 1), dtype=np.float32)
        self.profit = 0
        self.total_reward = 0
        self.current_step = self.window_size
        self.done = False

    def _next_observation(self):
        # 获取下一个观察值
        obs = np.array([
            self.data.loc[self.current_step - self.window_size:self.current_step, 'Open'].values / 200,
            self.data.loc[self.current_step - self.window_size:self.current_step, 'High'].values / 200,
            self.data.loc[self.current_step - self.window_size:self.current_step, 'Low'].values / 200,
            self.data.loc[self.current_step - self.window_size:self.current_step, 'Close'].values / 200,
            self.data.loc[self.current_step - self.window_size:self.current_step, 'Volume'].values / 5000000000,
            self.profit / 10000
        ])
        return obs

    def reset(self):
        # 重置环境状态
        self.profit = 0
        self.total_reward = 0
        self.current_step = self.window_size
        self.done = False
        return self._next_observation()

    def step(self, action):
        # 在环境中执行一个动作
        assert self.action_space.contains(action)
        prev_val = self._get_val()
        self.current_step += 1
        if self.current_step == len(self.data):
            self.done = True
        if self.done:
            reward = self.profit - self.total_reward
            return self._next_observation(), reward, self.done, {}
        self._take_action(action)
        reward = self._get_reward()
        self.total_reward += reward
        obs = self._next_observation()
        return obs, reward, self.done, {}

    def _take_action(self, action):
        # 执行一个动作
        if action == 0:  # 买入
            self.profit -= self.data.at[self.current_step, 'Close']
        elif action == 1:  # 卖出
            self.profit += self.data.at[self.current_step, 'Close']
        else:  # 持有
            pass

    def _get_reward(self):
        # 获取当前收益
        current_val = self._get_val()
        return current_val - self.profit - self.total_reward

    def _get_val(self):
        # 获取当前资产总价值
        return self.profit + self.data.at[self.current_step, 'Close']


def build_model(input_shape):
    # 构建模型
    model = keras.Sequential([
        layers.Dense(32, activation='relu', input_shape=input_shape),
        layers.Dense(16, activation='relu'),
        layers.Dense(3, activation='softmax')
    ])
    model.compile(optimizer=keras.optimizers.Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
    return model


def train_model(model, env, total_episodes):
    # 训练模型
    for episode in range(total_episodes):
        state = env.reset()
        state = np.reshape(state, [1, 6, env.window_size + 1])
        done = False
        while not done:
            action = np.argmax(model.predict(state)[0])
            next_state, reward, done, _ = env.step(action)
            next_state = np.reshape(next_state, [1, 6, env.window_size + 1])
            target = reward + np.amax(model.predict(next_state)[0])
            target_f = model.predict