深度有趣 | 10 股票价格预测

简介股票价格预测是一件非常唬人的事情，但如果只基于历史数据进行预测，显然完全不靠谱
股票价格是典型的时间序列数据（简称时序数据），会受到经济环境、政府政策、人为操作多种复杂因素的影响
不像气象数据那样具备明显的时间和季节性模式，例如一天之内和一年之内的气温变化等
尽管如此，以股票价格为例，介绍如何对时序数据进行预测，仍然值得一做
以下使用TensorFlow和Keras，对S&P 500股价数据进行分析和预测
数据S&P 500股价数据爬取自Google Finance API，已经进行过缺失值处理
加载库，pandas主要用于数据清洗和整理
# -*- coding: utf-8 -*-import pandas as pdimport numpy as npimport tensorflow as tfimport matplotlib.pyplot as plt%matplotlib inlinefrom sklearn.preprocessing import MinMaxScalerimport time复制代码用pandas读取csv文件为DataFrame，并用describe()查看特征的数值分布
data = pd.read_csv('data_stocks.csv')data.describe()复制代码还可以用info()查看特征的概要
data.info()复制代码数据共502列，41266行，502列分别为：
DATE：该行数据的时间戳SP500：可以理解为大盘指数其他：可以理解为500支个股的股价
查看数据的前五行
data.head()复制代码查看时间跨度
print(time.strftime('%Y-%m-%d', time.localtime(data['DATE'].max())),       time.strftime('%Y-%m-%d', time.localtime(data['DATE'].min())))复制代码绘制大盘趋势折线图
plt.plot(data['SP500'])复制代码去掉DATE一列，训练集测试集分割
data.drop('DATE', axis=1, inplace=True)data_train = data.iloc[:int(data.shape[0] * 0.8), :]data_test = data.iloc[int(data.shape[0] * 0.8):, :]print(data_train.shape, data_test.shape)复制代码数据归一化，只能使用data_train进行fit()
scaler = MinMaxScaler(feature_range=(-1, 1))scaler.fit(data_train)data_train = scaler.transform(data_train)data_test = scaler.transform(data_test)复制代码同步预测同步预测是指，使用当前时刻的500支个股股价，预测当前时刻的大盘指数，即一个回归问题，输入共500维特征，输出一维，即[None, 500] => [None, 1]
使用TensorFlow实现同步预测，主要用到多层感知机（Multi-Layer Perceptron，MLP），损失函数用均方误差（Mean Square Error，MSE）
X_train = data_train[:, 1:]y_train = data_train[:, 0]X_test = data_test[:, 1:]y_test = data_test[:, 0]input_dim = X_train.shape[1]hidden_1 = 1024hidden_2 = 512hidden_3 = 256hidden_4 = 128output_dim = 1batch_size = 256epochs = 10tf.reset_default_graph()X = tf.placeholder(shape=[None, input_dim], dtype=tf.float32)Y = tf.placeholder(shape=[None], dtype=tf.float32)W1 = tf.get_variable('W1', [input_dim, hidden_1], initializer=tf.contrib.layers.xavier_initializer(seed=1))b1 = tf.get_variable('b1', [hidden_1], initializer=tf.zeros_initializer())W2 = tf.get_variable('W2', [hidden_1, hidden_2], initializer=tf.contrib.layers.xavier_initializer(seed=1))b2 = tf.get_variable('b2', [hidden_2], initializer=tf.zeros_initializer())W3 = tf.get_variable('W3', [hidden_2, hidden_3], initializer=tf.contrib.layers.xavier_initializer(seed=1))b3 = tf.get_variable('b3', [hidden_3], initializer=tf.zeros_initializer())W4 = tf.get_variable('W4', [hidden_3, hidden_4], initializer=tf.contrib.layers.xavier_initializer(seed=1))b4 = tf.get_variable('b4', [hidden_4], initializer=tf.zeros_initializer())W5 = tf.get_variable('W5', [hidden_4, output_dim], initializer=tf.contrib.layers.xavier_initializer(seed=1))b5 = tf.get_variable('b5', [output_dim], initializer=tf.zeros_initializer())h1 = tf.nn.relu(tf.add(tf.matmul(X, W1), b1))h2 = tf.nn.relu(tf.add(tf.matmul(h1, W2), b2))h3 = tf.nn.relu(tf.add(tf.matmul(h2, W3), b3))h4 = tf.nn.relu(tf.add(tf.matmul(h3, W4), b4))out = tf.transpose(tf.add(tf.matmul(h4, W5), b5))cost = tf.reduce_mean(tf.squared_difference(out, Y))optimizer = tf.train.AdamOptimizer().minimize(cost)with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    for e in range(epochs):        shuffle_indices = np.random.permutation(np.arange(y_train.shape[0]))        X_train = X_train[shuffle_indices]        y_train = y_train[shuffle_indices]        for i in range(y_train.shape[0] // batch_size):            start = i * batch_size            batch_x = X_train[start : start + batch_size]            batch_y = y_train[start : start + batch_size]            sess.run(optimizer, feed_dict={X: batch_x, Y: batch_y})            if i % 50 == 0:                print('MSE Train:', sess.run(cost, feed_dict={X: X_train, Y: y_train}))                print('MSE Test:', sess.run(cost, feed_dict={X: X_test, Y: y_test}))                y_pred = sess.run(out, feed_dict={X: X_test})                y_pred = np.squeeze(y_pred)                plt.plot(y_test, label='test')                plt.plot(y_pred, label='pred')                plt.title('Epoch ' + str(e) + ', Batch ' + str(i))                plt.legend()                plt.show()复制代码最后测试集的loss在0.005左右，预测结果如下    

使用Keras实现同步预测，代码量会少很多，但具体实现细节不及TensorFlow灵活
from keras.layers import Input, Densefrom keras.models import ModelX_train = data_train[:, 1:]y_train = data_train[:, 0]X_test = data_test[:, 1:]y_test = data_test[:, 0]input_dim = X_train.shape[1]hidden_1 = 1024hidden_2 = 512hidden_3 = 256hidden_4 = 128output_dim = 1batch_size = 256epochs = 10X = Input(shape=[input_dim,])h = Dense(hidden_1, activation='relu')(X)h = Dense(hidden_2, activation='relu')(h)h = Dense(hidden_3, activation='relu')(h)h = Dense(hidden_4, activation='relu')(h)Y = Dense(output_dim, activation='sigmoid')(h)model = Model(X, Y)model.compile(loss='mean_squared_error', optimizer='adam')model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, shuffle=False)y_pred = model.predict(X_test)print('MSE Train:', model.evaluate(X_train, y_train, batch_size=batch_size))print('MSE Test:', model.evaluate(X_test, y_test, batch_size=batch_size))plt.plot(y_test, label='test')plt.plot(y_pred, label='pred')plt.legend()plt.show()复制代码最后测试集的loss在0.007左右，预测结果如下

异步预测异步预测是指，使用历史若干个时刻的大盘指数，预测当前时刻的大盘指数，这样才更加符合预测的定义
例如，使用前五个大盘指数，预测当前的大盘指数，每组输入包括5个step，每个step对应一个历史时刻的大盘指数，输出一维，即[None, 5, 1] => [None, 1]
使用Keras实现异步预测，主要用到循环神经网络即RNN（Recurrent Neural Network）中的LSTM（Long Short-Term Memory）
from keras.layers import Input, Dense, LSTMfrom keras.models import Modeloutput_dim = 1batch_size = 256epochs = 10seq_len = 5hidden_size = 128X_train = np.array([data_train[i : i + seq_len, 0] for i in range(data_train.shape[0] - seq_len)])[:, :, np.newaxis]y_train = np.array([data_train[i + seq_len, 0] for i in range(data_train.shape[0] - seq_len)])X_test = np.array([data_test[i : i + seq_len, 0] for i in range(data_test.shape[0] - seq_len)])[:, :, np.newaxis]y_test = np.array([data_test[i + seq_len, 0] for i in range(data_test.shape[0] - seq_len)])print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)X = Input(shape=[X_train.shape[1], X_train.shape[2],])h = LSTM(hidden_size, activation='relu')(X)Y = Dense(output_dim, activation='sigmoid')(h)model = Model(X, Y)model.compile(loss='mean_squared_error', optimizer='adam')model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, shuffle=False)y_pred = model.predict(X_test)print('MSE Train:', model.evaluate(X_train, y_train, batch_size=batch_size))print('MSE Test:', model.evaluate(X_test, y_test, batch_size=batch_size))plt.plot(y_test, label='test')plt.plot(y_pred, label='pred')plt.legend()plt.show()复制代码最后测试集的loss在0.0015左右，预测结果如下，一层LSTM的效果已经好非常多了

 当然，还有一种可能的尝试，使用历史若干个时刻的500支个股股价以及大盘指数，预测当前时刻的大盘指数，即[None, 5, 501] => [None, 1]
from keras.layers import Input, Dense, LSTMfrom keras.models import Modeloutput_dim = 1batch_size = 256epochs = 10seq_len = 5hidden_size = 128X_train = np.array([data_train[i : i + seq_len, :] for i in range(data_train.shape[0] - seq_len)])y_train = np.array([data_train[i + seq_len, 0] for i in range(data_train.shape[0] - seq_len)])X_test = np.array([data_test[i : i + seq_len, :] for i in range(data_test.shape[0] - seq_len)])y_test = np.array([data_test[i + seq_len, 0] for i in range(data_test.shape[0] - seq_len)])print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)X = Input(shape=[X_train.shape[1], X_train.shape[2],])h = LSTM(hidden_size, activation='relu')(X)Y = Dense(output_dim, activation='sigmoid')(h)model = Model(X, Y)model.compile(loss='mean_squared_error', optimizer='adam')model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, shuffle=False)y_pred = model.predict(X_test)print('MSE Train:', model.evaluate(X_train, y_train, batch_size=batch_size))print('MSE Test:', model.evaluate(X_test, y_test, batch_size=batch_size))plt.plot(y_test, label='test')plt.plot(y_pred, label='pred')plt.legend()plt.show()复制代码最后的loss在0.004左右，结果反而变差了
500支个股加上大盘指数的预测效果，还不如仅使用大盘指数
说明特征并不是越多越好，有时候反而会引入不必要的噪音
由于并未涉及到复杂的CNN或RNN，所以在CPU上运行的速度还可以

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