股票价格时序分析

Posted on | Edited on | In | Views:

Introduction

本文是在之前做数据分析的学习时候发现沈浩老师微信公共号分享的一篇文章,股票分析| 用Python玩玩A股股票数据分析-可视化部分, 边学python边学习了。

1
2
3
4
5
6
7
8
9
10
11
12
13
import pandas as pd
import pandas_datareader.data as dr
import datetime


start = datetime.datetime(2010,1,1)
end = datetime.date.today()

dianjian= dr.DataReader("601669.SS","yahoo",start,end)
maotai = dr.DataReader("600519.SS","yahoo",start,end)
quanjude = dr.DataReader("002186.SZ","yahoo",start,end)

type(dianjian)

pandas.core.frame.DataFrame

1
len(dianjian)

1338

1
dianjian.head()

Date Open High Low Close Volume Adj Close
2011-10-27 4.92 5.01 4.85 4.96 186583300 4.36
2011-10-28 5.04 5.18 4.92 4.97 218858400 4.37
2011-10-31 4.95 5.08 4.84 5.06 196341900 4.45
2011-11-01 5.00 5.27 4.97 5.09 284853100 4.47
2011-11-02 4.96 5.30 4.93 5.26 218048700 4.62 
1
dianjian.tail()
Date Open High Low Close Volume Adj Close
2016-12-08 7.39 7.39 7.20 7.22 71872100 7.22
2016-12-09 7.21 7.47 7.16 7.40 137389300 7.40
2016-12-12 7.32 7.33 6.95 7.16 121215100 7.16
2016-12-13 7.09 7.17 7.00 7.12 60272400 7.12
2016-12-14 7.09 7.11 6.83 6.87 82643000 6.87 
1
2
3
4
5
6
import matplotlib.pyplot as plt
%matplotlib inline
# 控制图标尺寸
%pylab inline
pylab.rcParams['figure.figsize']=(15,9)
dianjian['Adj Close'].plot(grid = True)

Populating the interactive namespace from numpy and matplotlib

<matplotlib.axes._subplots.AxesSubplot at 0x21852ac4d68>

mark

三只股票聚合在一个dataframe里面

1
2
3
4
stocks = pd.DataFrame({"dijian": dianjian["Adj Close"],
                       "maotai": maotai["Adj Close"],
                       "quanjude": quanjude["Adj Close"]})
stocks.head()
Date dijian maotai quanjude
2010-01-04 NaN 109.08326 14.571
2010-01-05 NaN 108.76288 15.051
2010-01-06 NaN 107.04219 14.881
2010-01-07 NaN 105.09082 14.419
2010-01-08 NaN 103.98699 NaN 
1
2
stocks.plot(grid=True)
<matplotlib.axes._subplots.AxesSubplot at 0x21852ac4c88>

mark

不同股票双坐标可视化

1
2
stocks.plot(secondary_y=["maotai","quanjude"],grid=True)
<matplotlib.axes._subplots.AxesSubplot at 0x21854c92978>

mark

以某一日股价为基准标准化股价

1
2
stock_return =stocks.apply(lambda x:x/x[1000])
stock_return.head()
Date dijian maotai quanjude
2010-01-04 NaN 1.015128 0.761325
2010-01-05 NaN 1.012146 0.786405
2010-01-06 NaN 0.996134 0.777522
2010-01-07 NaN 0.977974 0.753383
2010-01-08 NaN 0.967702 NaN 
1
stock_return.tail()
Date dijian maotai quanjude
2016-12-08 2.406667 3.142726 1.129108
2016-12-09 2.466667 3.143843 1.160458
2016-12-12 2.386667 3.041756 1.104028
2016-12-13 2.373333 3.098988 1.110298
2016-12-14 2.290000 3.092101 1.110298 
1
2
stock_return.plot(grid=True).axhline(y =1,color ="black",lw=2)
<matplotlib.lines.Line2D at 0x218558bc048>

mark

TBD