tushare获取股票数据，用gym进行强化学习，完整代码

以下是使用tushare获取股票数据，并使用gym进行强化学习的示例代码：

首先，安装必要的包：

!pip install gym
!pip install pandas
!pip install matplotlib
!pip install tushare

然后，导入必要的库：

import gym
import numpy as np
import pandas as pd
import tushare as ts
import matplotlib.pyplot as plt
from gym import spaces

定义环境类：

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

    def __init__(self):
        super(StockTradingEnv, self).__init__()

        # 加载股票历史数据
        self.df = ts.get_k_data('600519', start='2010-01-01', end='2021-01-01')
        self.df = self.df.sort_values('date')
        self.df = self.df.reset_index(drop=True)

        # 定义观察空间和动作空间
        self.observation_space = spaces.Box(low=0, high=1, shape=(6,))
        self.action_space = spaces.Discrete(3)

        # 初始化状态
        self.current_step = 0
        self.profit = 0

    def reset(self):
        # 重置环境
        self.current_step = 0
        self.profit = 0

        # 返回初始状态
        return np.array([
            self.df.loc[self.current_step, 'open'] / self.df.loc[self.current_step, 'close'],
            self.df.loc[self.current_step, 'high'] / self.df.loc[self.current_step, 'close'],
            self.df.loc[self.current_step, 'low'] / self.df.loc[self.current_step, 'close'],
            self.df.loc[self.current_step, 'close'] / self.df.loc[self.current_step, 'open'],
            self.df.loc[self.current_step, 'volume'] / self.df.loc[self.current_step, 'volume'],
            self.profit
        ])

    def step(self, action):
        # 执行动作并计算奖励
        if action == 0:  # 买入
            self.profit -= self.df.loc[self.current_step, 'open']
        elif action == 1:  # 卖出
            self.profit += self.df.loc[self.current_step, 'open']
        else:  # 不操作
            pass

        # 更新状态和时间步
        self.current_step += 1

        # 判断是否到达终止状态
        if self.current_step >= len(self.df):
            done = True
        else:
            done = False

        # 计算奖励
        reward = self.profit / self.df.loc[self.current_step - 1, 'close']

        # 返回下一个观察和奖励
        obs = np.array([
            self.df.loc[self.current_step, 'open'] / self.df.loc[self.current_step, 'close'],
            self.df.loc[self.current_step, 'high'] / self.df.loc[self.current_step, 'close'],
            self.df.loc[self.current_step, 'low'] / self.df.loc[self.current_step, 'close'],
            self.df.loc[self.current_step, 'close'] / self.df.loc[self.current_step, 'open'],
            self.df.loc[self.current_step, 'volume'] / self.df.loc[self.current_step, 'volume'],
            self.profit
        ])
        return obs, reward, done, {}

最后，使用训练和测试环境：

env = StockTradingEnv()

# 训练环境
obs = env.reset()
for i in range(1000):
    action = env.action_space.sample()
    obs, reward, done, _ = env.step(action)
    if done:
        obs = env.reset()

# 测试环境
obs = env.reset()
for i in range(len(env.df)):
    action = 1
    obs, reward, done, _ = env.step(action)
    if done:
        break

# 绘制收益曲线
plt.plot(env.df['date'], env.df['close'] * obs[5])
plt.show()

这是一个简单的示例，只是通过随机动作学习。您可以使用更复杂的策略和算法来训练智能体。