用akshare,gym写出股票强化学习完整代码

好的，以下是使用akshare和gym库实现股票强化学习的代码：

import akshare as ak
import gym
from gym import spaces
import numpy as np

# 获取股票数据
stock_df = ak.stock_zh_a_daily(symbol="sh600000", adjust="hfq").iloc[:100]

# 定义股票强化学习环境
class StockEnv(gym.Env):
    def __init__(self, df):
        super(StockEnv, self).__init__()
        self.df = df
        self.reward_range = (0, np.inf)
        self.action_space = spaces.Discrete(3)  # 买入、卖出、不操作
        self.observation_space = spaces.Box(low=0, high=np.inf, shape=(6,))

    def _next_observation(self):
        obs = np.array([
            self.df.loc[self.current_step, 'open'],
            self.df.loc[self.current_step, 'high'],
            self.df.loc[self.current_step, 'low'],
            self.df.loc[self.current_step, 'close'],
            self.df.loc[self.current_step, 'volume'],
            self.df.loc[self.current_step, 'amount'],
        ])
        return obs

    def reset(self):
        self.current_step = 0
        self.account_balance = 1000000  # 初始资金
        self.stock_balance = 0  # 初始股票数量
        return self._next_observation()

    def step(self, action):
        assert self.action_space.contains(action)
        prev_val = self.account_balance + self.stock_balance * self.df.loc[self.current_step, 'close']

        if action == 0:  # 买入
            buy_num = int(self.account_balance / self.df.loc[self.current_step, 'close'])
            self.stock_balance += buy_num
            self.account_balance -= buy_num * self.df.loc[self.current_step, 'close']
            reward = self.account_balance + self.stock_balance * self.df.loc[self.current_step, 'close'] - prev_val
        elif action == 1:  # 卖出
            sell_num = self.stock_balance
            self.stock_balance = 0
            self.account_balance += sell_num * self.df.loc[self.current_step, 'close']
            reward = self.account_balance + self.stock_balance * self.df.loc[self.current_step, 'close'] - prev_val
        else:  # 不操作
            reward = self.account_balance + self.stock_balance * self.df.loc[self.current_step, 'close'] - prev_val

        self.current_step += 1
        done = self.current_step == len(self.df)
        if done:
            info = {'account_balance': self.account_balance,
                    'stock_balance': self.stock_balance}
        else:
            info = {}

        return self._next_observation(), reward, done, info

# 创建股票强化学习环境
env = StockEnv(stock_df)

# 定义随机策略
def random_policy(observation):
    return np.random.choice(3)

# 运行强化学习环境
obs = env.reset()
for i in range(100):
    action = random_policy(obs)
    obs, reward, done, info = env.step(action)
    print(f"step {i}: action={action}, reward={reward}")
    if done:
        print(f"account balance: {info['account_balance']}, stock balance: {info['stock_balance']}")
        break

在这个例子中，我们使用akshare库获取了上海证券交易所股票“600000.SH”（浦发银行）的历史日线数据，并创建了一个股票强化学习环境。我们定义了一个随机策略，用于执行买入、卖出和不操作的动作。最后，我们运行强化学习环境，打印每个步骤的动作和奖励，并在完成后打印账户余额和股票余额。