深度有趣 | 10 股票價格預測

簡介股票價格預測是一件非常唬人的事情，但如果只基于歷史數據進行預測，顯然完全不靠譜
股票價格是典型的時間序列數據（簡稱時序數據），會受到經濟環(huán)境、政府政策、人為操作多種復雜因素的影響
不像氣象數據那樣具備明顯的時間和季節(jié)性模式，例如一天之內和一年之內的氣溫變化等
盡管如此，以股票價格為例，介紹如何對時序數據進行預測，仍然值得一做
以下使用TensorFlow和Keras，對S&P 500股價數據進行分析和預測
數據S&P 500股價數據爬取自Google Finance API，已經進行過缺失值處理
加載庫，pandas主要用于數據清洗和整理
# -*- coding: utf-8 -*-import pandas as pdimport numpy as npimport tensorflow as tfimport matplotlib.pyplot as plt%matplotlib inlinefrom sklearn.preprocessing import MinMaxScalerimport time復制代碼用pandas讀取csv文件為DataFrame，并用describe()查看特征的數值分布
data = pd.read_csv('data_stocks.csv')data.describe()復制代碼還可以用info()查看特征的概要
data.info()復制代碼數據共502列，41266行，502列分別為：
DATE：該行數據的時間戳SP500：可以理解為大盤指數其他：可以理解為500支個股的股價
查看數據的前五行
data.head()復制代碼查看時間跨度
print(time.strftime('%Y-%m-%d', time.localtime(data['DATE'].max())),       time.strftime('%Y-%m-%d', time.localtime(data['DATE'].min())))復制代碼繪制大盤趨勢折線圖
plt.plot(data['SP500'])復制代碼去掉DATE一列，訓練集測試集分割
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)復制代碼數據歸一化，只能使用data_train進行fit()
scaler = MinMaxScaler(feature_range=(-1, 1))scaler.fit(data_train)data_train = scaler.transform(data_train)data_test = scaler.transform(data_test)復制代碼同步預測同步預測是指，使用當前時刻的500支個股股價，預測當前時刻的大盤指數，即一個回歸問題，輸入共500維特征，輸出一維，即[None, 500] => [None, 1]
使用TensorFlow實現(xiàn)同步預測，主要用到多層感知機（Multi-Layer Perceptron，MLP），損失函數用均方誤差（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()復制代碼最后測試集的loss在0.005左右，預測結果如下    

使用Keras實現(xiàn)同步預測，代碼量會少很多，但具體實現(xiàn)細節(jié)不及TensorFlow靈活
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()復制代碼最后測試集的loss在0.007左右，預測結果如下

異步預測異步預測是指，使用歷史若干個時刻的大盤指數，預測當前時刻的大盤指數，這樣才更加符合預測的定義
例如，使用前五個大盤指數，預測當前的大盤指數，每組輸入包括5個step，每個step對應一個歷史時刻的大盤指數，輸出一維，即[None, 5, 1] => [None, 1]
使用Keras實現(xiàn)異步預測，主要用到循環(huán)神經網絡即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()復制代碼最后測試集的loss在0.0015左右，預測結果如下，一層LSTM的效果已經好非常多了

 當然，還有一種可能的嘗試，使用歷史若干個時刻的500支個股股價以及大盤指數，預測當前時刻的大盤指數，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()復制代碼最后的loss在0.004左右，結果反而變差了
500支個股加上大盤指數的預測效果，還不如僅使用大盤指數
說明特征并不是越多越好，有時候反而會引入不必要的噪音
由于并未涉及到復雜的CNN或RNN，所以在CPU上運行的速度還可以

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