用akshare获取股票数据,用gym和pytorch写出股票强化学习代码
时间: 2024-02-03 22:13:54 浏览: 35
首先,我们需要安装akshare库,可以使用以下命令进行安装:
```
pip install akshare
```
接着,我们可以使用以下代码获取股票数据:
```python
import akshare as ak
# 获取股票数据
stock_zh_a_spot_df = ak.stock_zh_a_spot()
```
接下来,我们需要安装gym库和pytorch库,可以使用以下命令进行安装:
```
pip install gym
pip install torch
```
接着,我们可以使用以下代码开始编写强化学习代码:
```python
import gym
import torch
from torch import nn
class StockEnv(gym.Env):
def __init__(self):
super().__init__()
self.stock_data = ak.stock_zh_a_spot()
self.action_space = gym.spaces.Discrete(2)
self.observation_space = gym.spaces.Box(
low=0,
high=1,
shape=(10, 1),
dtype=float
)
self.reset()
def reset(self):
self.current_step = 0
self.current_price = self.stock_data.iloc[self.current_step]['price']
self.profit = 0
self.portfolio = 1
self.done = False
return self._get_observation()
def step(self, action):
assert self.action_space.contains(action)
if action == 0:
self.portfolio = self.profit / self.current_price
self.profit = 0
elif action == 1:
self.profit = self.portfolio * self.current_price
self.portfolio = 0
self.current_step += 1
if self.current_step >= len(self.stock_data):
self.done = True
else:
self.current_price = self.stock_data.iloc[self.current_step]['price']
reward = self.profit
return self._get_observation(), reward, self.done, {}
def _get_observation(self):
observation = self.stock_data.iloc[self.current_step - 9:self.current_step + 1]
observation = observation['price'].to_numpy().reshape(-1, 1)
observation = observation / observation.max()
return observation
class QNet(nn.Module):
def __init__(self, input_size, output_size):
super().__init__()
self.fc1 = nn.Linear(input_size, 32)
self.fc2 = nn.Linear(32, output_size)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
env = StockEnv()
input_size = env.observation_space.shape[0]
output_size = env.action_space.n
q_net = QNet(input_size, output_size)
optimizer = torch.optim.Adam(q_net.parameters(), lr=0.001)
criterion = nn.MSELoss()
for episode in range(100):
state = env.reset()
done = False
while not done:
q_values = q_net(torch.FloatTensor(state))
action = q_values.argmax().item()
next_state, reward, done, _ = env.step(action)
target_q_values = q_values.detach().clone()
target_q_values[action] = reward + 0.95 * q_net(torch.FloatTensor(next_state)).max().item()
loss = criterion(q_values, target_q_values)
optimizer.zero_grad()
loss.backward()
optimizer.step()
state = next_state
print(f'Episode {episode}, Reward: {reward}')
```
以上代码实现了一个简单的股票强化学习代码,可以根据实际情况进行修改和优化。
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