基于Pytorch深度学习框架的bp神经网络预测股票代码

下面是一个基于Pytorch深度学习框架的BP神经网络预测股票的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# define the neural network model
class Net(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, 1)

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

# load the stock data
df = pd.read_csv('stock_data.csv')
df = df.dropna()  # remove any missing data

# prepare the input and target data
data = df['Close'].values.reshape(-1, 1).astype(np.float32)
x_train = data[:-1]
y_train = data[1:]

# normalize the input and target data
x_mean = x_train.mean()
x_std = x_train.std()
y_mean = y_train.mean()
y_std = y_train.std()
x_train = (x_train - x_mean) / x_std
y_train = (y_train - y_mean) / y_std

# convert the data to PyTorch tensors
x_train = torch.from_numpy(x_train)
y_train = torch.from_numpy(y_train)

# define the model and optimizer
net = Net(1, 10)
criterion = nn.MSELoss()
optimizer = optim.Adam(net.parameters(), lr=0.01)

# train the model
for epoch in range(1000):
    optimizer.zero_grad()
    outputs = net(x_train)
    loss = criterion(outputs, y_train)
    loss.backward()
    optimizer.step()
    if epoch % 100 == 0:
        print('Epoch {}, Loss: {:.4f}'.format(epoch, loss.item()))

# make predictions on the test data
x_test = np.array([800, 810, 820, 830, 840, 850]).astype(np.float32)
x_test = (x_test - x_mean) / x_std
x_test = torch.from_numpy(x_test)
y_test = net(x_test).detach().numpy()
y_test = y_test * y_std + y_mean

# plot the predicted and actual values
plt.plot(df['Close'].values, label='Actual')
plt.plot(np.arange(7, 13), y_test, label='Predicted')
plt.legend()
plt.show()

在这个示例中，我们首先定义了一个`Net`类来表示BP神经网络模型，然后加载股票数据，并将其拆分为输入和目标数据。我们还对数据进行了标准化，并将其转换为PyTorch张量。接下来，我们通过定义模型、损失函数和优化器来训练模型。最后，我们使用训练好的模型进行预测，并将结果可视化。

需要注意的是，在实际应用中，我们需要使用更多的股票数据和更复杂的模型来提高预测的准确性。此外，我们还需要进行交叉验证和调参等操作，以确保模型的鲁棒性和泛化能力。