使用akshare获取股票数据强化学习代码

以下是使用Akshare获取股票数据并应用强化学习进行股票交易的示例代码：

import akshare as ak
import numpy as np
import pandas as pd
import random

# 设置随机种子，确保结果可重复
random.seed(1)

# 获取股票历史数据
stock_df = ak.stock_zh_a_daily(symbol='sh600000')

# 数据预处理
data = stock_df['close'].values.tolist()
obs_len = 20  # 观测序列长度
act_len = 3  # 动作序列长度
test_len = 100  # 测试数据长度
train_data = []
test_data = []
for i in range(obs_len-1, len(data)-act_len-test_len):
    obs = data[i-obs_len+1:i+1]
    act = data[i+1:i+1+act_len]
    if random.random() < 0.8:
        train_data.append((obs, act))
    else:
        test_data.append((obs, act))

# 定义强化学习模型
class QLearning:
    def __init__(self, obs_len, act_len):
        self.obs_len = obs_len
        self.act_len = act_len
        self.Q = np.zeros((2**obs_len, 2**act_len))

    def act(self, obs, eps=0.1):
        state = self._encode_state(obs)
        if random.random() < eps:
            return random.randint(0, 2**self.act_len-1)
        else:
            return np.argmax(self.Q[state])

    def update(self, obs, act, reward, next_obs, alpha, gamma):
        state = self._encode_state(obs)
        next_state = self._encode_state(next_obs)
        next_q = np.max(self.Q[next_state])
        self.Q[state, act] += alpha * (reward + gamma * next_q - self.Q[state, act])

    def _encode_state(self, obs):
        state = 0
        for i, x in enumerate(obs):
            if x >= obs[i-1]:
                state += 2**(self.obs_len-i-1)
        return state

# 训练强化学习模型
eps = 0.1
alpha = 0.1
gamma = 0.9
q_learning = QLearning(obs_len, act_len)
for epoch in range(100):
    total_reward = 0
    for obs, act in train_data:
        action = q_learning.act(obs, eps)
        next_obs = obs[1:] + [act[-1]]
        reward = np.mean(act) - act[-1]
        q_learning.update(obs, action, reward, next_obs, alpha, gamma)
        total_reward += reward
    print('Epoch: {}, Total reward: {}'.format(epoch, total_reward))

# 测试强化学习模型
total_reward = 0
for obs, act in test_data:
    action = q_learning.act(obs, 0)
    next_obs = obs[1:] + [act[-1]]
    reward = np.mean(act) - act[-1]
    total_reward += reward
print('Test total reward: {}'.format(total_reward))

该代码通过Akshare获取上海600000股票的历史数据，然后将每20天的收盘价作为观测序列，接下来3天的收盘价作为动作序列，以此构建训练数据和测试数据。然后定义了一个QLearning类作为强化学习模型，实现了动作选择和Q值更新的方法，最后使用训练数据对模型进行训练，并使用测试数据对模型进行测试，输出测试总奖励。