股票预测（LSTM）

#coding=utf-8 #限制tensorflow只能在cpu中跑 import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import pandas as pd import numpy as np import matplotlib.pyplot as plt import tensorflow as tf rnn_unit=10 #隐层神经元的个数 lstm_layers=2 #隐层层数 input_size=7 output_size=1 lr=0.0006 #学习率 #——————————————————导入数据—————————————————————— f=open('/home/stock_predict/day/000001SZ.csv') df=pd.read_csv(f) #读入股票数据 data=df.iloc[:,2:10].values #取第3-10列 #获取训练集 def get_train_data(batch_size=60,time_step=20,train_begin=0,train_end=3000): batch_index=[] data_train=data[train_begin:train_end] normalized_train_data=(data_train-np.mean(data_train,axis=0))/np.std(data_train,axis=0) #标准化 train_x,train_y=[],[] #训练集 for i in range(len(normalized_train_data)-time_step): if i % batch_size==0: batch_index.append(i) x=normalized_train_data[i:i+time_step,:7] y=normalized_train_data[i:i+time_step,7,np.newaxis] train_x.append(x.tolist()) train_y.append(y.tolist()) batch_index.append((len(normalized_train_data)-time_step)) return batch_index,train_x,train_y #获取测试集 def get_test_data(time_step=20,test_begin=3000): data_test=data[test_begin:] mean=np.mean(data_test,axis=0) std=np.std(data_test,axis=0) normalized_test_data=(data_test-mean)/std #标准化 size=(len(normalized_test_data)+time_step-1)//time_step #有size个sample test_x,test_y=[],[] for i in range(size-1): x=normalized_test_data[i*time_step:(i+1)*time_step,:7] y=normalized_test_data[i*time_step:(i+1)*time_step,7] test_x.append(x.tolist()) test_y.extend(y) test_x.append((normalized_test_data[(i+1)*time_step:,:7]).tolist()) test_y.extend((normalized_test_data[(i+1)*time_step:,7]).tolist()) return mean,std,test_x,test_y #——————————————————定义神经网络变量—————————————————— #输入层、输出层权重、偏置、dropout参数 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,])) } keep_prob = tf.placeholder(tf.float32, name='keep_prob') #——————————————————定义神经网络变量—————————————————— def lstmCell(): #basicLstm单元 basicLstm = tf.nn.rnn_cell.BasicLSTMCell(rnn_unit) # dropout drop = tf.nn.rnn_cell.DropoutWrapper(basicLstm, output_keep_prob=keep_prob) return basicLstm def lstm(X): batch_size=tf.shape(X)[0] time_step=tf.shape(X)[1] w_in=weights['in'] b_in=biases['in'] input=tf.reshape(X,[-1,input_size]) #需要将tensor转成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转成3维，作为lstm cell的输入 cell = tf.nn.rnn_cell.MultiRNNCell([lstmCell() for i in range(lstm_layers)]) init_state=cell.zero_state(batch_size,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]) w_out=weights['out'] b_out=biases['out'] pred=tf.matmul(output,w_out)+b_out return pred,final_states #————————————————训练模型———————————————————— def train_lstm(batch_size=60,time_step=20,train_begin=1000,train_end=3000): X=tf.placeholder(tf.float32, shape=[None,time_step,input_size]) Y=tf.placeholder(tf.float32, shape=[None,time_step,output_size]) batch_index,train_x,train_y=get_train_data(batch_size,time_step,train_begin,train_end) with tf.variable_scope("sec_lstm"): pred,_=lstm(X) 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(),max_to_keep=15) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(1000): #这个迭代次数，可以更改，越大预测效果会更好，但需要更长时间 for step in range(len(batch_index)-1): _,loss_=sess.run([train_op,loss],feed_dict={X:train_x[batch_index[step]:batch_index[step+1]],Y:train_y[batch_index[step]:batch_index[step+1]],keep_prob:0.5}) print("Number of iterations:",i," loss:",loss_) print("model_save: ",saver.save(sess,'model_save2/modle.ckpt')) #I run the code on windows 10,so use 'model_save2\\modle.ckpt' #if you run it on Linux,please use 'model_save2/modle.ckpt' print("The train has finished") train_lstm() #————————————————预测模型———————————————————— def prediction(time_step=20): X=tf.placeholder(tf.float32, shape=[None,time_step,input_size]) mean,std,test_x,test_y=get_test_data(time_step) print(test_x) with tf.variable_scope("sec_lstm",reuse=tf.AUTO_REUSE): pred,_=lstm(X) saver=tf.train.Saver(tf.global_variables()) with tf.Session() as sess: #参数恢复 module_file = tf.train.latest_checkpoint('model_save2') saver.restore(sess, module_file) test_predict=[] for step in range(len(test_x)-1): prob=sess.run(pred,feed_dict={X:[test_x[step]],keep_prob:1}) predict=prob.reshape((-1)) test_predict.extend(predict) test_y=np.array(test_y)*std[7]+mean[7] test_predict=np.array(test_predict)*std[7]+mean[7] print(test_predict) acc=np.average(np.abs(test_predict-test_y[:len(test_predict)])/test_y[:len(test_predict)]) #偏差程度 print("The accuracy of this predict:",acc) #以折线图表示结果 plt.figure() plt.plot(list(range(len(test_predict))), test_predict, color='b',) plt.plot(list(range(len(test_y))), test_y, color='r') plt.show() prediction()