LSTM-RNN雅虎股票预测

#基于lstm-rnn的雅虎股票价格预测 import pandas as pd import numpy as np import matplotlib.pyplot as plt import tensorflow as tf #获取雅虎股票接口 from pandas_datareader import data as pdr import datetime import fix_yahoo_finance as yf #配置matplotlib画图的符号 plt.rcParams['font.sans-serif'] = ['SimHei'] #显示中文 plt.rcParams['axes.unicode_minus']=False #用来正常显示坐标中的负号 def get_data(name): try: yf.pdr_override() #获取阿里bab实时股票数据 finance = pdr.get_data_yahoo(name,start = datetime.datetime(2014,11,11),end = datetime.datetime(2018,8,24)) data = np.array(finance['Close']) #获取收盘价的数据 data = data[::1] #获取这列的所有数据 print('股票数据获取完成！！') return data except Exception: print('股票数据获取失败！！') data = get_data('BABA') #以折线图展示导入的数据 #fig =plt.figure() #fig.add_subplot(1,2,1) #plt.plot(data) #plt.show() normalize_data=(data-np.mean(data))/np.std(data) #对数据进行标准化 normalize_data=normalize_data[:,np.newaxis] #增加数据的维度，使数据维度相同 #———————————————————形成训练集————————————————————— #设置rnn网络的常量 time_step=20 #时间步 rnn_unit=10 # hidden layer units batch_size=60 # 每一批训练多少个样例 input_size=1 # 输入层数维度 output_size=1 # 输出层数维度 lr=0.0006 # 学习率 train_x,train_y=[],[] #训练集 for i in range(len(normalize_data)-time_step-1): x=normalize_data[i:i+time_step] y=normalize_data[i+1:i+time_step+1] train_x.append(x.tolist()) train_y.append(y.tolist()) #———————————————————定义神经网络变量————————————————————— X=tf.placeholder(tf.float32, [None,time_step,input_size]) #每批次输入网络的tensor Y=tf.placeholder(tf.float32, [None,time_step,output_size]) # 每批次tensor对应的标签 #输入层、输出层的权重和偏置 weights={ 'in':tf.Variable(tf.random_normal([input_size,rnn_unit])), 'out':tf.Variable(tf.random_normal([rnn_unit,1])) } biases={ 'in':tf.Variable(tf.constant(0.1,shape=[rnn_unit,])), 'out':tf.Variable(tf.constant(0.1,shape=[1,])) } #———————————————————定义lstm网络————————————————————— def lstm(batch): #参数：输入网络批次数目 w_in=weights['in'] b_in=biases['in'] input=tf.reshape(X,[-1,input_size]) #需要将tensor转为2维进行计算，计算后的结果作为 隐藏层的输入 input_rnn=tf.matmul(input,w_in)+b_in input_rnn=tf.reshape(input_rnn,[-1,time_step,rnn_unit]) #将tensor转为3维，作为 lstm cell的输入 cell=tf.nn.rnn_cell.BasicLSTMCell(rnn_unit) init_state=cell.zero_state(batch,dtype=tf.float32) output_rnn,final_states=tf.nn.dynamic_rnn(cell, input_rnn,initial_state=init_state, dtype=tf.float32) output=tf.reshape(output_rnn,[-1,rnn_unit]) #作为输出层的输入 w_out=weights['out'] b_out=biases['out'] pred=tf.matmul(output,w_out)+b_out return pred,final_states #———————————————————对模型进行训练————————————————————— def train_lstm(): global batch_size with tf.variable_scope("sec_lstm"): pred,_=lstm(batch_size) #定义损失函数 loss=tf.reduce_mean(tf.square(tf.reshape(pred,[-1])-tf.reshape(Y, [-1]))) train_op=tf.train.AdamOptimizer(lr).minimize(loss) saver=tf.train.Saver(tf.global_variables()) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(50): #We can increase the number of iterations to gain better result. step=0 start=0 end=start+batch_size while(end<len(train_x)): _,loss_=sess.run([train_op,loss],feed_dict={X:train_x[start:end],Y:train_y[start:end]}) start+=batch_size end=start+batch_size #每训练10次保存一次参数 if step%10==0: print("Number of iterations:",i," loss:",loss_) #输出训练次数，输出损失值 print("model_save",saver.save(sess,'C:/Users/lidongming1/Desktop/tensorflow_demo/tensorflow_rnn/model_save1/modle.ckpt')) #第二个参数是保存的地址，可以修改为自己本地的保存地址 #I run the code in windows 10,so use 'model_save1\\modle.ckpt' #if you run it in Linux,please use 'model_save1/modle.ckpt' step+=1 print("The train has finished") train_lstm() #对模型进行训练 #———————————————————预测模型————————————————————— def prediction(): with tf.variable_scope("sec_lstm",reuse=True): pred,_=lstm(1) #预测时只输入[1,time_step,input_size]的测试数据 saver=tf.train.Saver(tf.global_variables()) with tf.Session() as sess: #参数恢复 saver.restore(sess, 'C:/Users/lidongming1/Desktop/tensorflow_demo/tensorflow_rnn/model_save1/modle.ckpt') #第二个参数是保存的地址，可以修改为自己本地的保存地址 #I run the code in windows 10,so use 'model_save1\\modle.ckpt' #if you run it in Linux,please use 'model_save1/modle.ckpt' #取训练集最后一行为测试样本。shape = [1,time_step,input_size] prev_seq=train_x[-1] predict=[] #得到之后的100个预测结果 for i in range(100): next_seq=sess.run(pred,feed_dict={X:[prev_seq]}) predict.append(next_seq[-1]) #每次得到最后一个时间步的预测结果，与之前的数据加在一起，形成新的测试数据 prev_seq=np.vstack((prev_seq[1:],next_seq[-1])) #以折线图展示结果 plt.figure(figsize = (8,8)) #图像大小为8*8英寸 plt.title('股票走势预测图') plt.plot(list(range(len(normalize_data))), normalize_data, color='b',label = 'raw data') #这是原来股票的价格走势，用蓝色曲线表示 plt.plot(list(range(len(normalize_data), len(normalize_data) + len(predict))), predict, color='r',label = 'predict trend') #预测未来的价格走势用红色表示 plt.legend(loc = 'best') plt.xticks([])#去掉X轴刻度 plt.yticks([]) plt.show() prediction()