用akshare,gym,pytorch写股票强化学习代码

这是一个相对复杂的任务，需要结合多个库和算法来完成。这里提供一个简单的示例代码，用于说明如何使用akshare、gym和pytorch实现股票强化学习。

首先，我们需要从akshare中获取股票数据。这里以获取上证指数的历史数据为例：

import akshare as ak

stock_df = ak.stock_zh_index_daily(symbol="sh000001", start_date="20100101")

接下来，我们可以使用gym创建一个强化学习环境。这里以gym的经典环境CartPole为例：

import gym

env = gym.make('CartPole-v0')

然后，我们可以使用pytorch来构建和训练强化学习模型。这里以DQN算法为例：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import random
from collections import deque

class DQN(nn.Module):
    def __init__(self):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(4, 64)
        self.fc2 = nn.Linear(64, 2)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

class ReplayMemory(object):
    def __init__(self, capacity):
        self.capacity = capacity
        self.memory = deque(maxlen=capacity)

    def push(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def sample(self, batch_size):
        batch = random.sample(self.memory, batch_size)
        state, action, reward, next_state, done = map(torch.stack, zip(*batch))
        return state, action.squeeze(), reward.squeeze(), next_state, done.squeeze()

    def __len__(self):
        return len(self.memory)

BATCH_SIZE = 32
GAMMA = 0.99
EPS_START = 0.9
EPS_END = 0.05
EPS_DECAY = 200
TARGET_UPDATE = 10

policy_net = DQN()
target_net = DQN()
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()

optimizer = optim.RMSprop(policy_net.parameters())
memory = ReplayMemory(10000)

steps_done = 0

def select_action(state):
    global steps_done
    sample = random.random()
    eps_threshold = EPS_END + (EPS_START - EPS_END) * \
        math.exp(-1. * steps_done / EPS_DECAY)
    steps_done += 1
    if sample > eps_threshold:
        with torch.no_grad():
            return policy_net(state).max(1)[1].view(1, 1)
    else:
        return torch.tensor([[random.randrange(2)]], device=device, dtype=torch.long)

episode_durations = []

def optimize_model():
    if len(memory) < BATCH_SIZE:
        return
    state, action, reward, next_state, done = memory.sample(BATCH_SIZE)
    action_tensor = torch.tensor(action, device=device).unsqueeze(1)
    reward_tensor = torch.tensor(reward, device=device)
    done_tensor = torch.tensor(done, device=device)
    state_batch = torch.cat(state)
    next_state_batch = torch.cat(next_state)

    q_values = policy_net(state_batch).gather(1, action_tensor)
    next_q_values = target_net(next_state_batch).max(1)[0].detach()
    expected_q_values = (next_q_values * GAMMA) + reward_tensor

    loss = F.smooth_l1_loss(q_values, expected_q_values.unsqueeze(1))

    optimizer.zero_grad()
    loss.backward()
    for param in policy_net.parameters():
        param.grad.data.clamp_(-1, 1)
    optimizer.step()

for i_episode in range(100):
    state = env.reset()
    for t in range(100):
        action = select_action(state)
        next_state, reward, done, _ = env.step(action.item())
        reward = torch.tensor([reward], device=device)

        memory.push(torch.tensor([state], device=device), action, reward, torch.tensor([next_state], device=device), torch.tensor([done], device=device))

        state = next_state

        optimize_model()
        if done:
            episode_durations.append(t + 1)
            break
    if i_episode % TARGET_UPDATE == 0:
        target_net.load_state_dict(policy_net.state_dict())

这个示例代码使用DQN算法来训练一个强化学习模型，使用CartPole环境进行测试。在实际应用中，需要根据具体情况进行修改和调整。