Python与lstm实现股票数据分析系统

import tensorflow as tf import numpy as np from tensorflow.contrib import rnn import xlrd import xlwt import os import time from sklearn.preprocessing import minmax_scale def two_up(time,market): ####设置常量#### input_size = 19 time_steps = 2 hiddens = 64 layers = 1 output_size = 3 batch_size = 16 learning_rate = 0.001 #keep_prob = 0.5 decay_rate = 0.8 decay_step = 50 alpha = 1.0 scale = 1e-4 train = False count = 0 test_num = 20 fileName = "E:/Python/tushare/data/everyday/" MODEL_NAME = "dichan_LSTM" if market == "zhong": MODEL_SAVE_PATH = 'E:/Python/tushare/model/中小板/2up' if market == "chuang": MODEL_SAVE_PATH = 'E:/Python/tushare/model/创业板/2up' if market == "zhu": MODEL_SAVE_PATH = 'E:/Python/tushare/model/主板/2up' ####从Excel中读出数据#### readbook = xlrd.open_workbook(fileName + time + '/' + market + '/' + '2up_data.xlsx') table = readbook.sheet_by_name('Sheet1') writebook = xlwt.Workbook(encoding = 'ascii') wtable = writebook.add_sheet('Sheet1') row = table.nrows col = table.ncols stock_inf = [] for i in range(row-1): stock_inf.append([]) for j in range(2): stock_inf[i].append('s') for i in range(row-1): for j in range(2): stock_inf[i][j] =table.cell(i + 1,j + 1).value data_x = np.zeros((row-1,22)) data_y = np.zeros((row-1,output_size)) for i in range(row-1): for j in range(23): if j != 22: temp = table.cell(i + 1,j + 3).value if type(temp) == str: data_x[i][j] = float(temp) if type(temp) != str: data_x[i][j] = temp else: a = table.cell(i + 1,j + 3).value if a >= 5: data_y[i] = [1,0,0] elif a > 0 and a < 5: data_y[i] = [0,1,0] elif a <= 0 and a > -5: data_y[i] = [0,0,1] else: data_y[i] = [0,0,1] ####当数据不足被batch_size整除时，末尾添加#### ####数据处理，删除data_x中不要列#### data_x = np.delete(data_x,[17,18,19],1) print(data_x.shape) print(data_y.shape) ####对输入数据的每一列标注化处理#### for i in range(input_size): data_x[:,i] = minmax_scale(data_x[:,i],copy = True,feature_range = (-1,1)) ####把数据变成三维数据#### data_x = data_x.reshape((-1,time_steps,input_size)) data_y = data_y.reshape((-1,time_steps,output_size)) data_x1 = data_x data_y1 = data_y ####把数据添加为整数个batch_size,用于预测数据#### while data_x.shape[0] % batch_size != 0: data_x = np.row_stack((data_x,data_x[-1].reshape((-1,time_steps,input_size)))) data_y = np.row_stack((data_y,data_y[-1].reshape((-1,time_steps,output_size)))) ##随机打乱训练数据顺序## perm = np.random.permutation(data_x1.shape[0]) data_x1 = data_x1[perm,:,:] data_y1 = data_y1[perm,:,:] ##train_x = train_x.reshape((-1,input_size)) ##train_y = train_y.reshape((-1,output_size)) test_x = data_x1[0:batch_size*test_num,:,:] test_y = data_y1[0:batch_size*test_num,:,:] train_x = data_x1[batch_size*test_num:data_x.shape[0],:,:] train_y = data_y1[batch_size*test_num:data_x.shape[0],:,:] print(train_x.shape) print(train_y.shape) print(test_x.shape) print(test_y.shape) ####把数据分成batch_size大小#### def Next_batch_x(data,batch_size,ii): num_b = ii % (data.shape[0] // batch_size) return data[num_b * batch_size:(num_b + 1) * batch_size] def Next_batch_y(data,batch_size,ii): num_b = ii % (data.shape[0] // batch_size) return data[num_b * batch_size:(num_b + 1) * batch_size,time_steps - 1,:] ####初始化权重值#### weights = tf.Variable(tf.truncated_normal([hiddens,output_size],stddev = 0.1,name = 'w',dtype = tf.float32)) biases = tf.Variable(tf.truncated_normal([output_size],stddev = 0.001,name = 'b',dtype = tf.float32)) def Weights(n_in,n_out,name = None): return tf.Variable(tf.truncated_normal([n_in,n_out],stddev = 0.00001,name = name,dtype = tf.float32)) def Biases(n_out,name = None): return tf.Variable(tf.truncated_normal([n_out],stddev = 0.00001,name = name,dtype = tf.float32)) def Cell(x,w,b): return tf.matmul(x,w) + b ####定义网络结构#### x = tf.placeholder(tf.float32,[None,time_steps,input_size],name = 'input') y = tf.placeholder(tf.float32,[None,output_size],name = 'output') keep_prob = tf.placeholder(tf.float32) def LSTM1(x): def cell(): cell = rnn.LSTMCell(hiddens,state_is_tuple=True,reuse=tf.get_variable_scope().reuse) return rnn.DropoutWrapper(cell, output_keep_prob=keep_prob) cells = [] for i in range(layers): cells.append(cell()) lstm_cell = rnn.MultiRNNCell(cells) outputs, states = tf.nn.dynamic_rnn(lstm_cell,x,dtype=tf.float32,time_major=False) return outputs[:,-1,:] #return tf.nn.relu(tf.matmul(outputs[:,-1,:], weights) + biases) #return tf.nn.softmax(tf.matmul(tf.tanh(outputs[:,-1,:]), weights) + biases) def LSTM2(x): rnn_cell = tf.contrib.rnn.BasicLSTMCell(num_units=hiddens) outputs,final_state = tf.nn.dynamic_rnn( cell=rnn_cell, # 选择传入的cell inputs=x, # 传入的数据 initial_state=None, # 初始状态 dtype=tf.float32, # 数据类型 time_major=False, # False: (batch, time step, input); True: (time step, batch, input)，这里根据image结构选择False ) return outputs[:, -1, :] ##pred = tf.nn.softsign(y4) L=LSTM1(x) pred = tf.layers.dense(inputs=L, units=output_size) ####选择损失函数和优化器#### #pred = LSTM(x,weights,biases) global_step = tf.Variable(0,trainable = False) reg = tf.contrib.layers.apply_regularization(tf.contrib.layers.l2_regularizer(scale),tf.trainable_variables()) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = pred,labels = y)) ##cost = tf.reduce_mean(tf.square(y - pred)) + reg ##learning_rate = tf.train.exponential_decay(learning_rate,global_step,decay_step,decay_rate,staircase = True) train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost,global_step = global_step) ##train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost,global_step = global_step) ##train_op = tf.train.MomentumOptimizer(learning_rate,momentum = 0.9).minimize(cost,global_step = global_step) ##train_op = tf.train.AdadeltaOptimizer(learning_rate).minimize(cost,global_step = global_step) ##train_op = tf.train.RMSPropOptimizer(learning_rate).minimize(cost,global_step = global_step) ##accurary = tf.metrics.accuracy(labels = tf.argmax(y,axis = 1),predictions = tf.argmax(pred,axis = 1))[1] prediction = tf.equal(tf.argmax(y,1