LSTM时序数据预测实践(实时股票数据)

LSTM时序数据预测实践(实时股票数据)

​ 预测结果展示（以红线分割，红线前数据参与训练，红线后数据未参与训练）:

​ 红线以后可以看到随着预测时间段的加长，预测误差会越来越大。

1. 获取最新股票数据

   import pandas_datareader.data as web # 读取实时股票数据的接口
   import datetime
   from collections import deque
   import numpy as np
   import pandas as pd
   import torch
   import torch.nn as nn
   from torch.nn import MSELoss
   from torch.optim import Adam,SGD,RMSprop
   from torch.utils.data import DataLoader,Dataset
   from sklearn.model_selection import train_test_split
   from sklearn.preprocessing import MinMaxScaler
   import matplotlib.pyplot as plt
   
   start = datetime.datetime(year=2000,month=1,day=1)
   end = datetime.datetime(year=2022,month=3,day=16)
   df = web.DataReader(name='^DJI',data_source='stooq',start=start,end=end) # 获取道琼斯工业最新股票数据
   df.sort_index(inplace=True)
   df.dropna(inplace=True)
   shift_size = -1
   df['label'] = df['Close'].shift(periods=shift_size) # 以下一日收盘价为预测目标
   df.set_index(keys='Date',inplace=True)
   df
   
   '''
   	          Open	      High	      Low	     Close	    Volume	 label(下-日收盘价)
   Date						
   2000-01-03	11501.80	11522.00	11305.70	11357.50	169680388	10997.90
   2000-01-04	11349.80	11350.10	10986.50	10997.90	178357418	11122.70
   2000-01-05	10989.40	11215.10	10938.70	11122.70	203266571	11253.30
   2000-01-06	11113.40	11313.50	11098.50	11253.30	176642517	11522.60
   2000-01-07	11247.10	11528.10	11239.90	11522.60	184926808	11572.20
   ...	...	...	...	...	...	...
   2022-03-09	32860.42	33457.28	32860.42	33286.25	507633430	33174.07
   2022-03-10	33106.77	33236.59	32819.76	33174.07	462887778	32944.19
   2022-03-11	33279.72	33515.61	32911.89	32944.19	432875928	32945.24
   2022-03-14	33000.37	33395.59	32818.16	32945.24	475399572	33544.34
   2022-03-15	32989.27	33620.84	32989.27	33544.34	466174065	
   '''
   

2. 数据拆分

   # 拆分训练集和验证集(数据较大，进行归一化处理)
   X = df.loc[df['label'].notnull(),df.columns[:-1]]
   Y = df.loc[df['label'].notnull(),df.columns[-1]]
   X_scaler = MinMaxScaler()
   Y_scaler = MinMaxScaler()
   X = X_scaler.fit_transform(X)
   Y = Y_scaler.fit_transform(np.array(Y).reshape(-1,1))
   
   train_x,test_x,train_y,test_y = train_test_split(X,Y,test_size=0.1,shuffle=False) # shuffle设置为False,不打乱时序数据顺序
   

3. 构建时序数据加载器

   class MY_Dataset(Dataset):
       
       def __init__(self,data,label,seq_len):
           self.data = data
           self.label = label
           self.seq_len = seq_len # 序列长度，就是每一次扔几天连续股票数据到模型参与训练
           self.len_ = data.shape[0]-seq_len
       
       def __getitem__(self,index):
           return torch.tensor(self.data[index:index+self.seq_len],dtype=torch.float32),torch.tensor(self.label[index:index+self.seq_len],dtype=torch.float32)
       
       def __len__(self):
           return self.len_
    
   
   
   # 形成序列数据集
   seq_len = 14 # 设置序列为连续2周时间内的股票数据
   data_train = MY_Dataset(data=train_x,label=train_y,seq_len=seq_len)
   
   # 设置批次
   batch_size = 10
   dataloader = DataLoader(dataset=data_train,batch_size=batch_size,shuffle=True,drop_last=True) # 这里shuffle可以设置为True,只要连续seq_len不被打乱就行
   

4. 模型创建

   # 创建LSTM模型
   class Lstm(nn.Module):
       
       def __init__(self,feature_size,hidden_size,num_layers):
           super(Lstm,self).__init__()
           self.lstm = nn.LSTM(input_size=feature_size,hidden_size=hidden_size,num_layers=num_layers,batch_first=True,dropout=0.02)
           
           self.linear = nn.Linear(in_features=hidden_size,out_features=1)
       
       def forward(self,x,h0,c0):
           out,(hn,cn)= self.lstm(x,(h0,c0))
           z = self.linear(out)
           return z,(hn,cn)
   
       
   
   

5. 创建损失函数和优化器

   # 实例化模型    
   feature_size = 5 # 预测特征为[Open,High,Low,Close,Volume]5个维度
   hidden_size = 100
   num_layers = 2 # 设置的双层LSTM
   lstm = Lstm(feature_size=feature_size,hidden_size=hidden_size,num_layers=num_layers)
   for i in lstm.parameters():
       if i.dim()>=2:
           nn.init.xavier_normal_(i)
    
   # 设置损失函数和优化函数
   Loss = MSELoss()
   
   opt = Adam(params=lstm.parameters(),lr=0.01)
   

6. 训练

   # 开始训练
   epochs = 50 # 先简单50轮
   h0 = torch.zeros(size=(num_layers,batch_size,hidden_size))
   c0 = torch.zeros(size=(num_layers,batch_size,hidden_size))
   res = []
   for epoch in range(epochs):
       for x,y in dataloader:
           out,(hn,cn)= lstm(x,h0,c0) ## 每批次训练的初始h0,c0考虑怎样设置合理？？？
           # h0,c0 = hn.detach(),cn.detach()
           loss_value = Loss(out,y)
           opt.zero_grad()
           loss_value.backward()
           opt.step()
       print(loss_value)
       res.append(loss_value)
   

7. 模型预测

   # 将全部数据进行预测(其中后500多天的数据未参与训练，属于纯预测)
   pred_y = []
   h0 = torch.zeros(size=(num_layers,1,hidden_size))
   c0 = torch.zeros(size=(num_layers,1,hidden_size))
   for i in X:
       out,(hn,cn) = lstm(torch.tensor(i,dtype=torch.float32).view(1,1,len(i)),h0,c0)
       h0,c0 = hn.detach(),cn.detach()
       pred_y.append(out.detach().flatten().item())
   

8. 结果展示
   8/2分进行train和test,训练结果如下：
   
   全部train，预测下一日收盘价:(写这篇文章2022-3-15，预测3-16日股票收盘价)
   
   2022-3-16：将预测结果与真实结果对比：
   真实值与预测值之间有差异，但趋势对上了。

		
Date		预测值           真实值
2022-03-10	33303.748047	33174.07
2022-03-11	33237.870103	32944.19
2022-03-14	33265.761931	32945.24
2022-03-15	33353.923764	33544.34
2022-03-16	33751.452632	34063.10