k线的transformer预测的pytorch示例
时间: 2023-07-26 15:20:48 浏览: 57
以下是一个K线的Transformer预测的PyTorch示例,该示例使用了PyTorch和Transformer模型,用于预测A股市场的股票价格变化。
首先,我们需要引入必要的库:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
```
接下来,我们需要读取和预处理数据。在这个示例中,我们使用了一个包含了A股市场某只股票的K线数据的CSV文件。我们将数据划分为训练数据和测试数据,并使用MinMaxScaler对数据进行归一化处理。
```python
# 读取数据
data = pd.read_csv('K线数据.csv')
# 划分训练集和测试集
train_data = data.iloc[:200, 1:].values.astype(np.float32)
test_data = data.iloc[200:, 1:].values.astype(np.float32)
# 归一化处理
scaler = MinMaxScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)
```
接下来,我们定义一个用于生成训练数据的函数。该函数将原始的K线数据转换为模型所需的输入格式,并将数据划分成多个时间窗口。在这个示例中,我们将时间窗口的大小设置为10,即模型将使用前10个时间点的数据来预测下一个时间点的数据。
```python
def create_dataset(data, window_size):
X, y = [], []
for i in range(len(data) - window_size):
X.append(data[i:i+window_size])
y.append(data[i+window_size])
return np.array(X), np.array(y)
window_size = 10
train_X, train_y = create_dataset(train_data, window_size)
test_X, test_y = create_dataset(test_data, window_size)
# 转换为PyTorch的Tensor格式
train_X = torch.from_numpy(train_X).float()
train_y = torch.from_numpy(train_y).float()
test_X = torch.from_numpy(test_X).float()
test_y = torch.from_numpy(test_y).float()
```
接下来,我们定义一个K线Transformer模型。该模型包括一个多头自注意力层和一个全连接层。
```python
class KLineTransformer(nn.Module):
def __init__(self, input_size, output_size, window_size, num_heads, dropout):
super(KLineTransformer, self).__init__()
self.window_size = window_size
self.num_heads = num_heads
self.dropout = dropout
self.multihead_attn = nn.MultiheadAttention(input_size=input_size, num_heads=num_heads, dropout=dropout)
self.fc = nn.Linear(window_size * input_size, output_size)
def forward(self, x):
x = x.permute(1, 0, 2)
attn_output, _ = self.multihead_attn(x, x, x)
attn_output = attn_output.permute(1, 0, 2)
flatten_output = attn_output.reshape(-1, self.window_size * attn_output.size(-1))
output = self.fc(flatten_output)
return output
```
接下来,我们定义一个训练函数和一个测试函数。
```python
def train(model, train_X, train_y, optimizer, criterion):
model.train()
optimizer.zero_grad()
output = model(train_X)
loss = criterion(output, train_y)
loss.backward()
optimizer.step()
return loss.item()
def test(model, test_X, test_y, criterion):
model.eval()
with torch.no_grad():
output = model(test_X)
loss = criterion(output, test_y)
return loss.item()
```
最后,我们定义一个主函数,用于训练和测试模型。在这个示例中,我们使用了Adam优化器和均方误差损失函数。
```python
def main():
input_size = 5
output_size = 1
num_heads = 2
dropout = 0.2
learning_rate = 0.001
num_epochs = 100
model = KLineTransformer(input_size, output_size, window_size, num_heads, dropout)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.MSELoss()
for epoch in range(num_epochs):
train_loss = train(model, train_X, train_y, optimizer, criterion)
test_loss = test(model, test_X, test_y, criterion)
print(f'Epoch [{epoch+1}/{num_epochs}], Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}')
if __name__ == '__main__':
main()
```
这个示例演示了如何使用PyTorch和Transformer模型进行K线的预测。当然,这个示例仅仅是一个简单的示例,实际的应用中需要根据具体情况进行调整和优化。