pytorch入门（十）—— 利用PyTorch实现量化交易

线性回归预测股价

# -*- codiing: utf-8 -*-
import numpy as np
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import torch.nn.functional as F
import torch.autograd as autograd
import pandas as pd
import csv
df = pd.read_csv(r"./data/point.xlsx", header=None, skiprows=1)
df1 = df.iloc[:, 3:7].values
df2 = df.iloc[:, -1].values
xtrain_features = torch.FloatTensor(df1.astype('float').reshape(-1, 4))
xtrain_labels = torch.FloatTensor(df2)
xtrain = torch.unsqueeze(xtrain_features, dim=1)
ytrain = torch.unsqueeze(xtrain_labels, dim=1)
x, y = autograd.Variable(xtrain), autograd.Variable(ytrain)
class Net(nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = nn.Linear(n_feature, n_hidden)
self.predict = nn.Linear(n_hidden, n_output)
def forward(self, x):
x = F.relu(self.hidden(x))
x = self.predict(x)
return x
model = Net(n_feature=4, n_hidden=10, n_output=1)
loss_fn = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)
num_epochs = 100000
for epoch in range(num_epochs):
inputs = x
target = y
out = model(inputs)
loss = loss_fn(out, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch + 1) % 2000 == 0:
print('Epoch [{}/{}], loss: {:.6f}'.format(epoch + 1, num_epochs, loss.item()))

前馈神经网络预测股价

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递归神经网络预测股价

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