【阿旭机器学习实战】系列文章主要介绍机器学习的各种算法模型及其实战案例，欢迎点赞，关注共同学习交流。 注:本文模型结果不好，仅做学习参考使用，提供思路。了解数据处理思路,训练模型和预测数值的过程。 目录 1. 读取数据 2.构建回归模型 3.绘制预测结果 1. 读取数据 import numpy as np # 数学计算
import pandas as pd # 数据处理
import matplotlib.pyplot as plt
from datetime import datetime as dt
关注公众号：阿旭算法与机器学习，回复：“ML31”即可获取本文数据集、源码与项目文档，欢迎共同学习交流 print(np.shape(df))
df.head()
(611, 14)
date open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 2019-05-30 12.32 12.38 12.22 12.11 646284.62 -0.18 -1.45 12.366 12.390 12.579 747470.29 739308.42 953969.39 1 2019-05-29 12.36 12.59 12.40 12.26 666411.50 -0.09 -0.72 12.380 12.453 12.673 751584.45 738170.10 973189.95 2 2019-05-28 12.31 12.55 12.49 12.26 880703.12 0.12 0.97 12.380 12.505 12.742 719548.29 781927.80 990340.43 3 2019-05-27 12.21 12.42 12.37 11.93 1048426.00 0.02 0.16 12.394 12.505 12.824 689649.77 812117.30 1001879.10 4 2019-05-24 12.35 12.45 12.35 12.31 495526.19 0.06 0.49 12.396 12.498 12.928 637251.61 781466.47 1046943.98
股票数据的特征
date：日期 open：开盘价 high：最高价 close：收盘价 low：最低价 volume：成交量 price_change：价格变动 p_change：涨跌幅 ma5：5日均价 ma10：10日均价 ma20:20日均价 v_ma5:5日均量 v_ma10:10日均量 v_ma20:20日均量 # 将每一个数据的键值的类型从字符串转为日期
df['date'] = pd.to_datetime(df['date'])
# 将日期变为索引
df = df.set_index('date')
# 按照时间升序排列
df.sort_values(by=['date'], inplace=True, ascending=True)
df.tail()
open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 date 2019-05-24 12.35 12.45 12.35 12.31 495526.19 0.06 0.49 12.396 12.498 12.928 637251.61 781466.47 1046943.98 2019-05-27 12.21 12.42 12.37 11.93 1048426.00 0.02 0.16 12.394 12.505 12.824 689649.77 812117.30 1001879.10 2019-05-28 12.31 12.55 12.49 12.26 880703.12 0.12 0.97 12.380 12.505 12.742 719548.29 781927.80 990340.43 2019-05-29 12.36 12.59 12.40 12.26 666411.50 -0.09 -0.72 12.380 12.453 12.673 751584.45 738170.10 973189.95 2019-05-30 12.32 12.38 12.22 12.11 646284.62 -0.18 -1.45 12.366 12.390 12.579 747470.29 739308.42 953969.39
# 检测是否有缺失数据 NaNs
df.dropna(axis=0 , inplace=True)
df.isna().sum()
open 0
high 0
close 0
low 0
volume 0
price_change 0
p_change 0
ma5 0
ma10 0
ma20 0
v_ma5 0
v_ma10 0
v_ma20 0
dtype: int64
K线图绘制 Min_date = df.index.min()
Max_date = df.index.max()
print ("First date is",Min_date)
print ("Last date is",Max_date)
print (Max_date - Min_date)
First date is 2016-11-29 00:00:00
Last date is 2019-05-30 00:00:00
912 days 00:00:00
from plotly import tools
from plotly.graph_objs import *
from plotly.offline import init_notebook_mode, iplot, iplot_mpl
init_notebook_mode()
import chart_studio.plotly as py
import plotly.graph_objs as go
trace = go.Ohlc(x=df.index, open=df['open'], high=df['high'], low=df['low'], close=df['close'])
data = [trace]
iplot(data, filename='simple_ohlc')
【阿旭机器学习实战】【31】股票价格预测案例--线性回归1. 读取数据2.构建回归模型3.绘制预测结果 2.构建回归模型 from sklearn.linear_model import LinearRegression
from sklearn import preprocessing
# 创建标签数据：即预测值, 根据当前的数据预测5天以后的收盘价
num = 5 # 预测5天后的情况
df['label'] = df['close'].shift(-num) # 预测值,将5天后的收盘价当作当前样本的标签
print(df.shape)
(611, 14)
# 丢弃 'label', 'price_change', 'p_change', 不需要它们做预测
Data = df.drop(['label', 'price_change', 'p_change'],axis=1)
Data.tail()
open high close low volume ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 date 2019-05-24 12.35 12.45 12.35 12.31 495526.19 12.396 12.498 12.928 637251.61 781466.47 1046943.98 2019-05-27 12.21 12.42 12.37 11.93 1048426.00 12.394 12.505 12.824 689649.77 812117.30 1001879.10 2019-05-28 12.31 12.55 12.49 12.26 880703.12 12.380 12.505 12.742 719548.29 781927.80 990340.43 2019-05-29 12.36 12.59 12.40 12.26 666411.50 12.380 12.453 12.673 751584.45 738170.10 973189.95 2019-05-30 12.32 12.38 12.22 12.11 646284.62 12.366 12.390 12.579 747470.29 739308.42 953969.39
X = Data.values
# 去掉最后5行，因为没有Y的值
X = X[:-num]
# 将特征进行归一化
X = preprocessing.scale(X)
# 去掉标签为null的最后5行
df.dropna(inplace=True)
Target = df.label
y = Target.values
print(np.shape(X), np.shape(y))
(606, 11) (606,)
# 将数据分为训练数据和测试数据
X_train, y_train = X[0:550, :], y[0:550]
X_test, y_test = X[550:, -51:], y[550:606]
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)
(550, 11)
(550,)
(56, 11)
(56,)
lr = LinearRegression()
lr.fit(X_train, y_train)
lr.score(X_test, y_test) # 使用绝对系数 R^2 评估模型
0.04930040648385525
# 做预测 ：取最后5行数据，预测5天后的股票价格
X_Predict = X[-num:]
Forecast = lr.predict(X_Predict)
print(Forecast)
print(y[-num:])
[12.5019651 12.45069629 12.56248765 12.3172638 12.27070154]
[12.35 12.37 12.49 12.4 12.22]
# 查看模型的各个特征参数的系数值
for idx, col_name in enumerate(['open', 'high', 'close', 'low', 'volume', 'ma5', 'ma10', 'ma20', 'v_ma5', 'v_ma10', 'v_ma20']):
print("The coefficient for {} is {}".format(col_name, lr.coef_[idx]))
The coefficient for open is -0.7623399996475224
The coefficient for high is 0.8321435171405448
The coefficient for close is 0.24463705375238926
The coefficient for low is 1.091415550493547
The coefficient for volume is 0.0043807937569128675
The coefficient for ma5 is -0.30717535019465575
The coefficient for ma10 is 0.1935431079947582
The coefficient for ma20 is 0.24902077484698157
The coefficient for v_ma5 is 0.17472336466033722
The coefficient for v_ma10 is 0.08873934447969857
The coefficient for v_ma20 is -0.27910702694420775
3.绘制预测结果 # 预测 2019-05-13 到 2019-05-17 , 一共 5 天的收盘价
trange = pd.date_range('2019-05-13', periods=num, freq='d')
trange
DatetimeIndex(['2019-05-13', '2019-05-14', '2019-05-15', '2019-05-16',
'2019-05-17'],
dtype='datetime64[ns]', freq='D')
# 产生预测值dataframe
Predict_df = pd.DataFrame(Forecast, index=trange)
Predict_df.columns = ['forecast']
Predict_df
forecast 2019-05-13 12.501965 2019-05-14 12.450696 2019-05-15 12.562488 2019-05-16 12.317264 2019-05-17 12.270702
# 将预测值添加到原始dataframe
df = pd.read_csv('./000001.csv')
df['date'] = pd.to_datetime(df['date'])
df = df.set_index('date')
# 按照时间升序排列
df.sort_values(by=['date'], inplace=True, ascending=True)
df_concat = pd.concat([df, Predict_df], axis=1)
df_concat = df_concat[df_concat.index.isin(Predict_df.index)]
df_concat.tail(num)
open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 forecast 2019-05-13 12.33 12.54 12.30 12.23 741917.75 -0.38 -3.00 12.538 13.143 13.637 1107915.51 1191640.89 1211461.61 12.501965 2019-05-14 12.20 12.75 12.49 12.16 1182598.12 0.19 1.54 12.446 12.979 13.585 1129903.46 1198753.07 1237823.69 12.450696 2019-05-15 12.58 13.11 12.92 12.57 1103988.50 0.43 3.44 12.510 12.892 13.560 1155611.00 1208209.79 1254306.88 12.562488 2019-05-16 12.93 12.99 12.85 12.78 634901.44 -0.07 -0.54 12.648 12.767 13.518 971160.96 1168630.36 1209357.42 12.317264 2019-05-17 12.92 12.93 12.44 12.36 965000.88 -0.41 -3.19 12.600 12.626 13.411 925681.34 1153473.43 1138638.70 12.270702
# 画预测值和实际值
df_concat['close'].plot(color='green', linewidth=1)
df_concat['forecast'].plot(color='orange', linewidth=3)
plt.xlabel('Time')
plt.ylabel('Price')
plt.show()
【阿旭机器学习实战】【31】股票价格预测案例--线性回归1. 读取数据2.构建回归模型3.绘制预测结果 如果文章对你有帮助，感谢点赞+关注！
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