股票走势预测

想折腾一下股票了，整个预测的神经网络玩玩（仅参考，股票这东西说不准）

一、思路

RNN神经网络的特点在于：输出受到一个阶段的输入的影响，

　　　　　　　　　　　　x(t) -> RNN(t) -> y(t)

　　　　　　　　　　　　x(t + 1) -> RNN(t) + RNN(t + 1) -> y(t + 1)

相当于RNN在循环使用。股票这类数据量很大的情况下，如果使用卷积之类的神经网络，则每次输入量和计算量就很夸张了；RNN则可以将数据分解成很多小段，每段数据对之后的数据做预测

二、工具

　　主要有：

　　　　编程语言：python

　　　　框架：Keras+LSTM、matplotlib、sklearn、pandas

三、过程

　　1.数据

　　原始数据可以从这里获得：股票数据

　　这里简化了数据格式：时间戳+最高价格

timestamp,height
1531724100,8.730
1531724400,8.740
1531791300,8.750
1531791600,8.740
1531791900,8.720
1531792200,8.700
1531792500,8.720
1531792800,8.710
1531793100,8.690
1531793400,8.690
1531793700,8.700
1531794000,8.700
1531794300,8.690
1531794600,8.700
1531794900,8.700
1531795200,8.690
1531795500,8.700
1531795800,8.720
1531796100,8.730
1531796400,8.730
1531796700,8.730
1531797000,8.720
1531797300,8.710
1531797600,8.720
1531797900,8.710
1531798200,8.710
1531803900,8.710
1531804200,8.710
1531804500,8.710
1531804800,8.700
1531805100,8.710
1531805400,8.710
1531805700,8.700
1531806000,8.700
1531806300,8.700
1531806600,8.700
1531806900,8.700
1531807200,8.710
1531807500,8.710
1531807800,8.680
1531808100,8.690
1531808400,8.700
1531808700,8.700
1531809000,8.700
1531809300,8.720
1531809600,8.720
1531809900,8.720
1531810200,8.720
1531810500,8.720
1531810800,8.720
1531877700,8.800
1531878000,8.830
1531878300,8.850
1531878600,8.840
1531878900,8.810
1531879200,8.800
1531879500,8.800
1531879800,8.800
1531880100,8.790
1531880400,8.790
1531880700,8.810
1531881000,8.820
1531881300,8.820
1531881600,8.810
1531881900,8.800
1531882200,8.820
1531882500,8.830
1531882800,8.830
1531883100,8.820
1531883400,8.810
1531883700,8.800
1531884000,8.800
1531884300,8.800
1531884600,8.810
1531890300,8.810
1531890600,8.810
1531890900,8.800
1531891200,8.810
1531891500,8.810
1531891800,8.810
1531892100,8.800
1531892400,8.790
1531892700,8.780
1531893000,8.770
1531893300,8.770
1531893600,8.760
1531893900,8.750
1531894200,8.740
1531894500,8.740
1531894800,8.730
1531895100,8.730
1531895400,8.730
1531895700,8.720
1531896000,8.730
1531896300,8.730
1531896600,8.720
1531896900,8.710
1531897200,8.700
1531964100,8.770
1531964400,8.770
1531964700,8.780
1531965000,8.810
1531965300,8.790
1531965600,8.780
1531965900,8.780
1531966200,8.770
1531966500,8.770
1531966800,8.760
1531967100,8.770
1531967400,8.760
1531967700,8.770
1531968000,8.780
1531968300,8.780
1531968600,8.780
1531968900,8.790
1531969200,8.810
1531969500,8.790
1531969800,8.790
1531970100,8.780
1531970400,8.760
1531970700,8.760
1531971000,8.760
1531976700,8.760
1531977000,8.760
1531977300,8.750
1531977600,8.750
1531977900,8.750
1531978200,8.750
1531978500,8.750
1531978800,8.750
1531979100,8.750
1531979400,8.750
1531979700,8.750
1531980000,8.750
1531980300,8.730
1531980600,8.730
1531980900,8.730
1531981200,8.720
1531981500,8.730
1531981800,8.730
1531982100,8.730
1531982400,8.710
1531982700,8.710
1531983000,8.710
1531983300,8.710
1531983600,8.730
1532050500,8.730
1532050800,8.700
1532051100,8.690
1532051400,8.680
1532051700,8.640
1532052000,8.650
1532052300,8.650
1532052600,8.660
1532052900,8.650
1532053200,8.690
1532053500,8.690
1532053800,8.680
1532054100,8.750
1532054400,8.760
1532054700,8.730
1532055000,8.730
1532055300,8.730
1532055600,8.740
1532055900,8.730
1532056200,8.700
1532056500,8.700
1532056800,8.690
1532057100,8.690
1532057400,8.690
1532063100,8.680
1532063400,8.710
1532063700,8.730
1532064000,8.780
1532064300,8.820
1532064600,8.920
1532064900,8.960
1532065200,9.050
1532065500,9.090
1532065800,9.060
1532066100,9.050
1532066400,9.170
1532066700,9.150
1532067000,9.160
1532067300,9.200
1532067600,9.180
1532067900,9.200
1532068200,9.180
1532068500,9.140
1532068800,9.140
1532069100,9.160
1532069400,9.130
1532069700,9.130
1532070000,9.110
1532309700,9.080
1532310000,9.100
1532310300,9.090
1532310600,9.060
1532310900,9.060
1532311200,9.100
1532311500,9.100
1532311800,9.090
1532312100,9.100
1532312400,9.160
1532312700,9.130
1532313000,9.140
1532313300,9.250
1532313600,9.240
1532313900,9.240
1532314200,9.230
1532314500,9.190
1532314800,9.190
1532315100,9.180
1532315400,9.170
1532315700,9.210
1532316000,9.230
1532316300,9.220
1532316600,9.230
1532322300,9.290
1532322600,9.350
1532322900,9.370
1532323200,9.370
1532323500,9.330
1532323800,9.350
1532324100,9.340
1532324400,9.320
1532324700,9.330
1532325000,9.400
1532325300,9.390
1532325600,9.380
1532325900,9.360
1532326200,9.370
1532326500,9.370
1532326800,9.400
1532327100,9.430
1532327400,9.410
1532327700,9.420
1532328000,9.390
1532328300,9.390
1532328600,9.390
1532328900,9.450
1532329200,9.450
1532396100,9.500
1532396400,9.550
1532396700,9.550
1532397000,9.550
1532397300,9.590
1532397600,9.580
1532397900,9.580
1532398200,9.560
1532398500,9.490
1532398800,9.480
1532399100,9.470
1532399400,9.470
1532399700,9.460
1532400000,9.460

　　2.代码

#!/usr/bin/env python 
# -*- coding: utf-8 -*- 

import numpy
import matplotlib.pyplot as plt
import pandas
import math
from keras.models import Sequential
from keras.models import load_model
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error

# 分割数据集：每look_back分割一次，这段数据对其后的数据做预测
def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset) - look_back - 1):
        a = dataset[i:(i + look_back), 0]
        b = dataset[i + look_back, 0]
        dataX.append(a)
        dataY.append(b)
    return numpy.array(dataX), numpy.array(dataY)


if __name__ == '__main__':
    numpy.random.seed(7)
    dataframe = pandas.read_csv('data.cvs', usecols=[1], engine='python')
    dataset = dataframe.values
    dataset = dataset.astype('float32')

    # 将数据压缩到0~1之间
    scaler = MinMaxScaler(feature_range=(0, 1))
    dataset = scaler.fit_transform(dataset)
    # 90%的数据作为训练，剩下的做为测试
    train_size = int(len(dataset) * 0.9)
    test_size = len(dataset) - train_size
    train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :]

    # 每3个数据做一次分割，并对其后的数据做预测
    look_back = 3
    trainX, trainY = create_dataset(train, look_back)
    testX, testY = create_dataset(test, look_back)

    # 重新格式化训练和测试数据为(n,3,1)格式
    trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
    testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], 1))

    # 创建LSTM网络并训练
    model = Sequential()
    # 创建LSTM模型，输入层包含一个神经元，隐藏层包含256个神经元
    # input_shape为输入数据格式，模型第一层必须指定
    model.add(LSTM(256, input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True))
    # 增加一个128个神经元的隐藏层
    model.add(LSTM(128))
    # 输出层一个神经元
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer='adam')
    model.summary()
    # 训练网络
    model.fit(trainX, trainY, nb_epoch=500, batch_size=1, verbose=2)
    model.save('lstm-predit.h5')
    # model = load_model('lstm-predit.h5')

    # 预测
    trainPredict = model.predict(trainX)
    testPredict = model.predict(testX)

    # 预测值放大
    trainPredict = scaler.inverse_transform(trainPredict)
    trainY = scaler.inverse_transform([trainY])
    testPredict = scaler.inverse_transform(testPredict)
    testY = scaler.inverse_transform([testY])

    # 评价预测
    trainScore = math.sqrt(mean_squared_error(trainY[0],trainPredict[:,0]))
    print '训练 分数: %.2f RMSE' % (trainScore)
    testScore = math.sqrt(mean_squared_error(testY[0],testPredict[:,0]))
    print '测试 分数: %.2f RMSE' % (testScore)

    #绘制训练预测图
    trainPredictPlot = numpy.empty_like(dataset)
    trainPredictPlot[:,:] = numpy.nan
    trainPredictPlot[look_back:len(trainPredict)+look_back,:] = trainPredict

    #绘制测试预测图
    testPredictPlot = numpy.empty_like(dataset)
    testPredictPlot[:,:] = numpy.nan
    testPredictPlot[len(trainPredict)+look_back*2+1:len(dataset)-1,:] = testPredict

    plt.plot(scaler.inverse_transform(dataset))
    plt.plot(trainPredictPlot)
    plt.plot(testPredictPlot)
    plt.show()

贴一个结果图

　　训练集的预测误差：0.02 RMSE

　　测试集的预测误差：0.06 RMSE

 

这里的数据量比较少，结果也就那么回事；具体问题具体分析吧~