基于PyTorch的GRU网络实现股票价格预测

参考：https://www.7forz.com/3319/

根据Tushare的数据，用LSTM的变体GRU试着做一个股票价格预测，参考了上述博客的代码，大多数参数经过了调整。

1. 用新晨科技（300542）的640天的收盘数据训练

2. 在沪深A股代码中随机抽取50条用于测试

3. 查看50条里面loss最大的一支股票，画出其数据与预测曲线（有明显误差但趋势大致相同）

预测的结果比预期好很多，留个坑--Midterm考完回来研究解释......

2020.10.27 更一波：

似乎把训练次数降到800可以比较好地防止过拟合？

代码：

# -*- coding: utf-8 -*-
"""main.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1W2_OxJ3JcWnvnG8wugwms-ETq0pRYX_v
"""

import numpy as np
import pandas as pd

pip install tushare

import tushare as ts

data = ts.get_k_data('300542')['close'].values
print(data.shape)

import matplotlib.pyplot as plt

data = data.astype('float32')
mx = np.max(data)
mn = np.min(data)
data = (data - mn) / (mx - mn)

input_len = 1

def generate_dataset(data, days_for_train):
    dataset_x, dataset_y = [], []
    for i in range(len(data) - days_for_train):
        cur_x = data[i:(i + days_for_train)]
        cur_y = data[i + days_for_train]
        dataset_x.append(cur_x)
        dataset_y.append(cur_y)
    return np.array(dataset_x), np.array(dataset_y)

import torch
import torch.nn as nn
import torch.optim as optim

pr