当前位置：首页 > news >正文

软件开发行业市场分析seo上海网站推广

news 2026/4/8 6:43:44

软件开发行业市场分析,seo上海网站推广,企业咨询顾问的工作内容,自己开发app所需的各种费用获取数据先用下面这段代码获取上证指数的历史数据，得到的csv文件数据，为后面训练模型用的 import akshare as ak import pandas as pd# 获取上证指数历史数据 df ak.stock_zh_index_daily(symbol"sh000001")# 将数据保存到本地CSV文件 df.…

获取数据

先用下面这段代码获取上证指数的历史数据，得到的csv文件数据，为后面训练模型用的

import akshare as ak
import pandas as pd# 获取上证指数历史数据
df = ak.stock_zh_index_daily(symbol="sh000001")# 将数据保存到本地CSV文件
df.to_csv("sh000001.csv", index=False, encoding="utf-8-sig")# 打印数据
print(df)

注意：运行上段代码之前，需要先用下面这个指令安装akshare包：

pip install akshare

运行完上述代码之后，会得到如下一个csv文件：

在这里插入图片描述

训练模型

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader# ------------------------
# 1. 加载数据
# ------------------------
data = pd.read_csv('sh000001.csv')  # 替换为你的文件路径
data['date'] = pd.to_datetime(data['date'])
data.set_index('date', inplace=True)# 提取收盘价数据
close_prices = data['close'].values.reshape(-1, 1)# ------------------------
# 2. 数据预处理
# ------------------------
# 归一化
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_close = scaler.fit_transform(close_prices)# 创建时间序列数据集（滑动窗口）
def create_dataset(data, window_size=60):X, y = [], []for i in range(len(data) - window_size - 7):  # 预测未来7天X.append(data[i:i+window_size])y.append(data[i+window_size:i+window_size+7])  # 输出未来7天的数据return np.array(X), np.array(y)window_size = 60  # 用过去60天的数据预测未来7天
X, y = create_dataset(scaled_close, window_size)# 划分训练集和测试集
train_size = int(len(X) * 0.8)
X_train, X_test = X[:train_size], X[train_size:]
y_train, y_test = y[:train_size], y[train_size:]# 转换为PyTorch张量
X_train = torch.FloatTensor(X_train)
y_train = torch.FloatTensor(y_train)
X_test = torch.FloatTensor(X_test)
y_test = torch.FloatTensor(y_test)# 自定义Dataset类
class TimeSeriesDataset(Dataset):def __init__(self, X, y):self.X = Xself.y = ydef __len__(self):return len(self.X)def __getitem__(self, idx):return self.X[idx], self.y[idx]# 创建DataLoader
batch_size = 64
train_dataset = TimeSeriesDataset(X_train, y_train)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)# ------------------------
# 3. 定义LSTM模型
# ------------------------
class LSTMModel(nn.Module):def __init__(self, input_size=1, hidden_size=50, output_size=7):super().__init__()self.hidden_size = hidden_sizeself.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)self.linear = nn.Linear(hidden_size, output_size)def forward(self, x):# LSTM层out, (h_n, c_n) = self.lstm(x)# 仅取最后一个时间步的隐藏状态out = self.linear(out[:, -1, :])return out# 初始化模型
model = LSTMModel(input_size=1, hidden_size=100, output_size=7)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)# ------------------------
# 4. 训练模型
# ------------------------
num_epochs = 50
model.train()for epoch in range(num_epochs):for batch_X, batch_y in train_loader:# 前向传播outputs = model(batch_X)loss = criterion(outputs, batch_y.squeeze())# 反向传播optimizer.zero_grad()loss.backward()optimizer.step()print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()*1000:.6f}')# ------------------------
# 5. 预测与评估
# ------------------------
model.eval()
with torch.no_grad():# 测试集预测test_pred = model(X_test)test_pred = test_pred.numpy()# 反归一化test_pred = scaler.inverse_transform(test_pred.reshape(-1, 1)).reshape(-1, 7)y_test_actual = scaler.inverse_transform(y_test.numpy().reshape(-1, 1)).reshape(-1, 7)# 预测未来7天（使用最新数据）last_window = scaled_close[-window_size:].reshape(1, window_size, 1)last_window_tensor = torch.FloatTensor(last_window)future_pred = model(last_window_tensor).numpy()future_pred = scaler.inverse_transform(future_pred.reshape(-1, 1)).flatten()# ------------------------
# 6. 可视化结果
# ------------------------
# 测试集预测示例（取第一条样本）
plt.figure(figsize=(12, 6))
plt.plot(y_test_actual[0], label='True Future')
plt.plot(test_pred[0], label='Predicted Future')
plt.legend()
plt.title('Test Set Prediction Example')
plt.show()# 未来7天预测
print("未来7天收盘价预测:")
for i, price in enumerate(future_pred):print(f'Day {i+1}: {price:.2f}')