用akshare,gym写出股票强化学习代码

好的，我可以为您提供一个简单的股票强化学习代码，使用akshare和gym库。以下是代码：

import gym
import akshare as ak
import numpy as np

# 获取股票数据
stock_code = 'sh600000'
stock_df = ak.stock_zh_a_hist(stock_code)

# 定义股票强化学习环境
class StockEnv(gym.Env):
    metadata = {'render.modes': ['human']}
    
    def __init__(self, data, initial_investment=20000):
        super(StockEnv, self).__init__()
        self.data = data
        self.initial_investment = initial_investment
        self.action_space = gym.spaces.Discrete(3) # 买入、卖出、不操作
        self.observation_space = gym.spaces.Box(low=0, high=1, shape=(6,))
        self.reset()
        
    def reset(self):
        self.current_step = 0
        self.balance = self.initial_investment
        self.shares = 0
        self.net_worth = self.balance + self.shares * self.data[self.current_step][3]
        return self._next_observation()
    
    def _next_observation(self):
        obs = np.array([
            self.data[self.current_step][1] / max(self.data[:, 1]), # 当前股价
            self.data[self.current_step][2] / max(self.data[:, 2]), # 最高股价
            self.data[self.current_step][3] / max(self.data[:, 3]), # 最低股价
            self.data[self.current_step][4] / max(self.data[:, 4]), # 当前成交量
            self.balance / self.net_worth, # 当前账户余额占净值的比例
            self.shares / self.net_worth # 当前持有股票价值占净值的比例
        ])
        return obs
        
    def step(self, action):
        assert self.action_space.contains(action)
        prev_net_worth = self.net_worth
        current_price = self.data[self.current_step][3]
        
        if action == 0: # 买入股票
            shares_to_buy = int(self.balance / current_price)
            self.shares += shares_to_buy
            self.balance -= shares_to_buy * current_price
            
        elif action == 1: # 卖出股票
            shares_to_sell = self.shares
            self.shares = 0
            self.balance += shares_to_sell * current_price
            
        self.current_step += 1
        self.net_worth = self.balance + self.shares * current_price
        reward = self.net_worth - prev_net_worth
        done = self.current_step == len(self.data) - 1
        
        return self._next_observation(), reward, done, {}
        
    def render(self, mode='human'):
        print(f'Step: {self.current_step}')
        print(f'Balance: {self.balance}')
        print(f'Shares: {self.shares}')
        print(f'Net Worth: {self.net_worth}')

# 定义股票交易策略
def policy(obs):
    if obs[0] > obs[1]: # 股价上涨，买入股票
        return 0
    elif obs[0] < obs[1]: # 股价下跌，卖出股票
        return 1
    else: # 股价不变，不操作
        return 2

# 训练股票强化学习模型
env = StockEnv(stock_df.values)
for episode in range(10):
    obs = env.reset()
    done = False
    while not done:
        action = policy(obs)
        obs, reward, done, info = env.step(action)
        env.render()

该代码通过akshare库获取股票数据，定义了一个股票强化学习环境，并在该环境上实现了一个简单的股票交易策略。在训练过程中，模型将根据当前观察到的股票数据来决定下一步应该采取的行动，直到达到指定的训练次数。