六、股票市场分析实战项目一

最新推荐文章于 2025-04-15 09:15:14 发布

weixin_30861797

最新推荐文章于 2025-04-15 09:15:14 发布

阅读量435

点赞数

CC 4.0 BY-SA版权

文章标签： python

原文链接：http://www.cnblogs.com/tianqizhi/p/8867122.html

本文通过Python的pandas库和可视化工具对阿里巴巴、亚马逊等公司的股价数据进行分析，包括数据获取、历史趋势分析、每日收益率统计及风险评估等内容。

数据获取

import pandas_datareader as pdr
alibaba = pdr.get_data_yahoo('BABA')
alibaba.head()

　　结果：

	Open	High	Low	Close	Adj Close	Volume
Date
2014-09-19	92.699997	99.699997	89.949997	93.889999	93.889999	271879400
2014-09-22	92.699997	92.949997	89.500000	89.889999	89.889999	66657800
2014-09-23	88.940002	90.480003	86.620003	87.169998	87.169998	39009800
2014-09-24	88.470001	90.570000	87.220001	90.570000	90.570000	32088000
2014-09-25	91.089996	91.500000	88.500000	88.919998	88.919998	28598000

alibaba.shape#(789, 6)
alibaba.tail()

　　结果：

	Open	High	Low	Close	Adj Close	Volume
Date
2017-10-30	178.429993	181.899994	177.589996	181.580002	181.580002	20219700
2017-10-31	183.570007	185.119995	181.811005	184.889999	184.889999	21256700
2017-11-01	187.880005	188.880005	183.580002	186.080002	186.080002	28594700
2017-11-02	190.990005	191.220001	183.309998	184.809998	184.809998	41239900
2017-11-03	186.509995	186.929993	182.059998	183.210007	183.210007	19621400

alibaba.describe()#汇总信息，基本信息

　　结果：

	Open	High	Low	Close	Adj Close	Volume
count	789.000000	789.000000	789.000000	789.000000	789.000000	7.890000e+02
mean	98.879004	99.977879	97.622681	98.800431	98.800431	1.681925e+07
std	29.076254	29.240037	28.743547	29.007961	29.007961	1.427472e+07
min	57.299999	58.650002	57.200001	57.389999	57.389999	3.775300e+06
25%	79.849998	80.989998	79.150002	79.889999	79.889999	1.003060e+07
50%	89.099998	90.459999	88.059998	88.900002	88.900002	1.340540e+07
75%	106.500000	107.550003	105.129997	105.980003	105.980003	1.915080e+07
max	190.990005	191.220001	183.580002	186.080002	186.080002	2.718794e+08

alibaba.info()#也是基本信息

　　结果：

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 789 entries, 2014-09-19 to 2017-11-03
Data columns (total 6 columns):
Open         789 non-null float64
High         789 non-null float64
Low          789 non-null float64
Close        789 non-null float64
Adj Close    789 non-null float64
Volume       789 non-null int64
dtypes: float64(5), int64(1)
memory usage: 43.1 KB

View Code

历史趋势分析

# 基本信息
import numpy as np
import pandas as pd
from pandas import Series, DataFrame

# 股票数据的读取
import pandas_datareader as pdr

# 可视化
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

# time
from datetime import datetime

start = datetime(2015,9,20)
alibaba = pdr.get_data_yahoo('BABA', start=start)
amazon = pdr.get_data_yahoo('AMZN', start=start)

alibaba.to_csv('../homework/BABA.csv')
amazon.to_csv('../homework/AMZN.csv')

alibaba.head()

　　结果：

	Open	High	Low	Close	Adj Close	Volume
Date
2015-09-21	65.379997	66.400002	62.959999	63.900002	63.900002	22355100
2015-09-22	62.939999	63.270000	61.580002	61.900002	61.900002	14897900
2015-09-23	61.959999	62.299999	59.680000	60.000000	60.000000	22684600
2015-09-24	59.419998	60.340000	58.209999	59.919998	59.919998	20645700
2015-09-25	60.630001	60.840000	58.919998	59.240002	59.240002	17009100

alibaba['Adj Close'].plot(legend=True)

　　结果：<matplotlib.axes._subplots.AxesSubplot at 0x11d910588>

alibaba['Volume'].plot(legend=True)

　　结果：<matplotlib.axes._subplots.AxesSubplot at 0x120ebcda0>

alibaba['Adj Close'].plot()
amazon['Adj Close'].plot()

　　结果：<matplotlib.axes._subplots.AxesSubplot at 0x120ee9a90>

alibaba.head()

　　结果：

	Open	High	Low	Close	Adj Close	Volume
Date
2015-09-21	65.379997	66.400002	62.959999	63.900002	63.900002	22355100
2015-09-22	62.939999	63.270000	61.580002	61.900002	61.900002	14897900
2015-09-23	61.959999	62.299999	59.680000	60.000000	60.000000	22684600
2015-09-24	59.419998	60.340000	58.209999	59.919998	59.919998	20645700
2015-09-25	60.630001	60.840000	58.919998	59.240002	59.240002	17009100

alibaba['high-low'] = alibaba['High'] - alibaba['Low']
alibaba.head()

　　结果：

	Open	High	Low	Close	Adj Close	Volume	high-low
Date
2015-09-21	65.379997	66.400002	62.959999	63.900002	63.900002	22355100	3.440003
2015-09-22	62.939999	63.270000	61.580002	61.900002	61.900002	14897900	1.689998
2015-09-23	61.959999	62.299999	59.680000	60.000000	60.000000	22684600	2.619999
2015-09-24	59.419998	60.340000	58.209999	59.919998	59.919998	20645700	2.130001
2015-09-25	60.630001	60.840000	58.919998	59.240002	59.240002	17009100	1.920002

alibaba['high-low'].plot()

　　结果：<matplotlib.axes._subplots.AxesSubplot at 0x120fdcf98>

# daily return  每天变化情况
alibaba['daily-return'] = alibaba['Adj Close'].pct_change() #.pct_change() 实现每天每天变化情况
alibaba['daily-return'].plot(figsize=(10,4),linestyle='--',marker='o')#figsize图大小，marker每个数据点类型

　　结果：<matplotlib.axes._subplots.AxesSubplot at 0x121504d68>

alibaba['daily-return'].plot(kind='hist')#可以看出，基本上在-0.025到.025之间，所以股票价格稳定

　　结果：<matplotlib.axes._subplots.AxesSubplot at 0x12169c630>

sns.distplot(alibaba['daily-return'].dropna(),bins=100,color='purple')

　　结果：<matplotlib.axes._subplots.AxesSubplot at 0x121b15f98>

风险分析

# 基本信息
import numpy as np
import pandas as pd
from pandas import Series, DataFrame

# 股票数据的读取
import pandas_datareader as pdr

# 可视化
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

# time
from datetime import datetime

start = datetime(2015,1,1)
company = ['AAPL','GOOG','MSFT','AMZN','FB']#5家公司
top_tech_df = pdr.get_data_yahoo(company, start=start)['Adj Close']

top_tech_df.to_csv('../homework/top5.csv')

top_tech_df.head()

　　结果：

	AAPL	AMZN	FB	GOOG	MSFT
Date
2017-11-03	172.500000	1111.599976	178.919998	1032.479980	84.139999
2017-11-02	168.110001	1094.219971	178.919998	1025.579956	84.050003
2017-11-01	166.889999	1103.680054	182.660004	1025.500000	83.180000
2017-10-31	169.039993	1105.280029	180.059998	1016.640015	83.180000
2017-10-30	166.720001	1110.849976	179.869995	1017.109985	83.889999

top_tech_dr = top_tech_df.pct_change()#股票数据每天的变化
top_tech_dr.head()

　　结果：

	AAPL	AMZN	FB	GOOG	MSFT
Date
2017-11-03	NaN	NaN	NaN	NaN	NaN
2017-11-02	-0.025449	-0.015635	0.000000	-0.006683	-0.001070
2017-11-01	-0.007257	0.008646	0.020903	-0.000078	-0.010351
2017-10-31	0.012883	0.001450	-0.014234	-0.008640	0.000000
2017-10-30	-0.013725	0.005039	-0.001055	0.000462	0.008536

top_tech_df.plot()#GOOG与AMZN曲线比较相似

　　结果：<matplotlib.axes._subplots.AxesSubplot at 0x122293fd0>

top_tech_df[['AAPL','FB','MSFT']].plot()#画出其中三列

　　结果：<matplotlib.axes._subplots.AxesSubplot at 0x1225960b8>

sns.jointplot('AMZN','GOOG', top_tech_dr, kind='scatter')#kind='scatter'表示散点图；看点是否在一条斜率为45度的直线上来观察两家股票的相关性，注意坐标大小代表的是上升或者下降

　　结果：<seaborn.axisgrid.JointGrid at 0x124dd0f98>

sns.jointplot('MSFT','FB', top_tech_dr, kind='scatter')

　　结果：<seaborn.axisgrid.JointGrid at 0x125928940>

sns.pairplot(top_tech_dr.dropna())#.dropna()表示去掉NaN

　　结果：<seaborn.axisgrid.PairGrid at 0x125fa27f0>

top_tech_dr['AAPL'].quantile(0.52)
#结果：-0.0001447090809730694

top_tech_dr['MSFT'].quantile(0.05)#有（1-%5）的信心每天的变化最大为-0.021328913487084877  （最大亏损为%2）

　　结果：-0.021328913487084877

vips = pdr.get_data_yahoo('VIPS', start=start)['Adj Close']
vips.plot()

　　结果：<matplotlib.axes._subplots.AxesSubplot at 0x127b0f7b8>

vips.pct_change().quantile(0.2)#-0.02181125767068397

转载于:https://www.cnblogs.com/tianqizhi/p/8867122.html