Predicting stock prices is a challenging task that has attracted extensive attention from researchers and practitioners. With the advent of deep learning techniques, many models have been proposed to solve this problem. One such model is the Transformer, which achieves state-of-the-art results in many natural language processing tasks. In this blog post, we'll walk you through an example of using a PyTorch Transformer to predict stock prices for the next 5 days based on the previous 10 days.
First, let's import the necessary libraries:
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
Generate data to train the model
For this example, we'll generate some dummy stock price data:
num_days = 200
stock_prices = np.random.rand(num_days) * 100
Preprocess data
We will prepare the input and target sequences for our model:
input_seq_len = 10
output_seq_len = 5
num_samples = num_days - input_seq_len - output_seq_len + 1
src_data = torch.tensor([stock_prices[i:i+input_seq_len] for i in range(num_samples)]).unsqueeze(-1).float()
tgt_data = torch.tensor([stock_prices[i+input_seq_len:i+input_seq_len+output_seq_len] for i in range(num_samples)]).unsqueeze(-1).float()
Create a custom converter model
We'll create a custom Transformer model for stock price prediction:
class StockPriceTransformer(nn.Module):
def __init__(self, d_model, nhead, num_layers, dropout):
super(StockPriceTransformer, self).__init__()
self.input_linear = nn.Linear(1, d_model)
self.transformer = nn.Transformer(d_model, nhead, num_layers, dropout=dropout)
self.output_linear = nn.Linear(d_model, 1)
def forward(self, src, tgt):
src = self.input_linear(src)
tgt = self.input_linear(tgt)
output = self.transformer(src, tgt)
output = self.output_linear(output)
return output
d_model = 64
nhead = 4
num_layers = 2
dropout = 0.1
model = StockPriceTransformer(d_model, nhead, num_layers, dropout=dropout)
training model
We will set the training parameters, loss function and optimizer:
epochs = 100
lr = 0.001
batch_size = 16
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=lr)
Now, we will train the model using the training loop:
for epoch in range(epochs):
for i in range(0, num_samples, batch_size):
src_batch = src_data[i:i+batch_size].transpose(0, 1)
tgt_batch = tgt_data[i:i+batch_size].transpose(0, 1)
optimizer.zero_grad()
output = model(src_batch, tgt_batch[:-1])
loss = criterion(output, tgt_batch[1:])
loss.backward()
optimizer.step()
print(f"Epoch {
epoch+1}/{
epochs}, Loss: {
loss.item()}")
Predict stock prices for the next 5 days
Finally, we will use the trained model to predict the stock price for the next 5 days:
src = torch.tensor(stock_prices[-input_seq_len:]).unsqueeze(-1).unsqueeze(1).float()
tgt = torch.zeros(output_seq_len, 1, 1)
with torch.no_grad():
for i in range(output_seq_len):
prediction = model(src, tgt[:i+1])
tgt[i] = prediction[-1]
output = tgt.squeeze().tolist()
print("Next 5 days of stock prices:", output)
In this prediction loop, we use the autoregressive decoding method ( model(src, tgt[:i+1]) ) to generate the output sequence step by step, since the output at each step depends on the previous output.
in conclusion
In this blog post, we demonstrated how to use a PyTorch Transformer model to predict stock prices. We generate dummy stock price data, preprocess it, create a custom Transformer model, train the model, and predict the stock price for the next 5 days. This example can be used as a starting point for developing a more complex stock price prediction model using deep learning techniques.
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